WO2022042287A1 - 信道状态信息的处理方法及通信装置 - Google Patents

信道状态信息的处理方法及通信装置 Download PDF

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WO2022042287A1
WO2022042287A1 PCT/CN2021/111673 CN2021111673W WO2022042287A1 WO 2022042287 A1 WO2022042287 A1 WO 2022042287A1 CN 2021111673 W CN2021111673 W CN 2021111673W WO 2022042287 A1 WO2022042287 A1 WO 2022042287A1
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cluster
transformation
csi
information
sparse
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PCT/CN2021/111673
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English (en)
French (fr)
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徐剑标
何高宁
卢建民
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华为技术有限公司
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Priority to EP21860126.8A priority Critical patent/EP4191895A4/en
Publication of WO2022042287A1 publication Critical patent/WO2022042287A1/zh
Priority to US18/174,380 priority patent/US20230318675A1/en

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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0686Hybrid systems, i.e. switching and simultaneous transmission
    • H04B7/0695Hybrid systems, i.e. switching and simultaneous transmission using beam selection
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413MIMO systems
    • H04B7/0456Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413MIMO systems
    • H04B7/0456Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
    • H04B7/0478Special codebook structures directed to feedback optimisation
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0621Feedback content
    • H04B7/0626Channel coefficients, e.g. channel state information [CSI]
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0636Feedback format
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/06Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station
    • H04B7/0613Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission
    • H04B7/0615Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal
    • H04B7/0619Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the transmitting station using simultaneous transmission of weighted versions of same signal using feedback from receiving side
    • H04B7/0658Feedback reduction
    • H04B7/0663Feedback reduction using vector or matrix manipulations
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B7/00Radio transmission systems, i.e. using radiation field
    • H04B7/02Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413MIMO systems

Definitions

  • the embodiments of the present application relate to the field of communication, and in particular, to a method and a communication device for processing channel state information.
  • the terminal equipment feeds back the channel quality to the network equipment through the channel state information CSI (Channel State Information, CSI), so that the network equipment can select an appropriate modulation and coding scheme for downlink transmission and improve the performance of data transmission.
  • CSI Channel State Information
  • the number of antennas continues to increase, and the information contained in CSI becomes more and more abundant, making the signaling overhead more and more expensive. Big.
  • the CSI feedback process in the prior art usually adopts the CSI compression technology based on Discrete Fourier transform (DFT).
  • DFT Discrete Fourier transform
  • the magnitude of the beam domain coefficients after DFT transformation will also increase, which in turn leads to an increase in the amount of feedback, resulting in a larger feedback overhead.
  • the number of feedback coefficients also referred to as beam components
  • the error of CSI feedback and the loss of precoding performance will increase.
  • the present application provides a method for processing CSI information and a communication device, which can reduce the error of CSI feedback and the loss of precoding performance while reducing channel overhead.
  • an embodiment of the present application provides a method for processing CSI information.
  • the method includes: acquiring CSI information of a channel, where the CSI information includes N-dimensional antenna domain coefficients, and N is an integer greater than 1; and based on array response characteristics, beam domain transform is performed on the CSI information to obtain first CSI transform information, the first CSI transform
  • the information includes N-dimensional beam domain coefficients; based on the cluster sparse transformation base, perform cluster sparse domain transformation on the first CSI transformation information to obtain second CSI transformation information, and the second CSI transformation information includes N-dimensional cluster sparse domain coefficients; wherein, the cluster sparse domain
  • the transform basis is determined based on channel prior statistical characteristics of the channel; the quantized values of L cluster sparse domain coefficients in the N-dimensional cluster sparse domain coefficients are sent to the second communication device, where L is a positive integer less than N.
  • the present application performs a secondary transformation on the beam domain coefficients, that is, cluster sparse transformation, so that the beam domain beam is compressed into the cluster sparse domain to achieve the purpose of dimensionality reduction, thereby reducing feedback overhead and improving the accuracy of CSI feedback. degree, thereby reducing the loss of precoding performance.
  • the channel prior statistical feature is used to indicate the statistical covariance matrix corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse transformation base is the eigenvector corresponding to the statistical covariance matrix.
  • indication information is sent to the second communication apparatus, where the indication information is used to indicate a statistical covariance matrix or an eigenvector corresponding to the statistical covariance matrix.
  • the opposite device such as the second communication device, can be configured with the same cluster sparse transformation basis as the first communication device.
  • the statistical covariance matrix is determined according to sample values of a plurality of N-dimensional beam domain coefficients.
  • the present application exemplarily proposes a method for obtaining the statistical covariance matrix.
  • the statistical covariance matrix may also be obtained by other methods, which is not limited in the present application.
  • the channel prior statistical feature is used to indicate the cluster sparse feature of multiple beam channel clusters corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse feature includes at least one of the following : The size of the beam channel cluster, the shape of the beam channel cluster, and the cluster spacing of the beam channel cluster.
  • the cluster sparse transformation basis is determined based on cluster sparse features of multiple beam channel clusters.
  • the present application exemplarily shows a method for constructing a cluster sparse transform basis according to the characteristics of the beam channel cluster.
  • performing cluster sparse domain transformation on the first CSI transformation information based on the cluster sparse transformation basis including: The beam channel cluster is subjected to cluster sparse domain transformation to obtain multiple second CSI transformation sub-information, where the second CSI transformation information includes multiple second CSI transformation sub-information.
  • the present application exemplarily shows another method of constructing a cluster sparse transformation basis according to the characteristics of the beam channel clusters. , so as to further improve the CSI feedback accuracy.
  • the cluster sparse transformation basis is obtained by solidifying and approximating the prior statistical characteristics of the channel. In this way, by configuring a fixed cluster sparse transformation base, the channel overhead between communication devices can be effectively reduced.
  • performing cluster sparse domain transformation on the first CSI transformation information based on the cluster sparse transformation basis including: performing N-dimensional beam domain coefficients in the first CSI transformation information Perform amplitude and phase separation to obtain amplitude information and phase information corresponding to each beam domain coefficient; based on the cluster sparse transform basis, perform cluster sparse domain transform on the amplitude information of each beam domain coefficient in the first CSI transform information to obtain a second CSI transform information; sending the quantized values of the L cluster sparse domain coefficients and the quantized values of the phase information corresponding to the L cluster sparse domain coefficients among the N-dimensional cluster sparse domain coefficients to the second communication device.
  • the amplitude and the phase are separately processed, that is, the cluster sparse domain transformation is performed on the amplitude coefficients, thereby further improving the accuracy of the CSI feedback.
  • sending the quantized values of the L cluster sparse domain coefficients in the N-dimensional cluster sparse domain coefficients to the second communication device includes: sending the L cluster sparse domain coefficients to the second communication device The quantized values of the cluster sparse domain coefficients and the index values corresponding to the L cluster sparse domain coefficients, where the index values are used to indicate the positions of the cluster sparse domain coefficients in the N-dimensional cluster sparse domain coefficients.
  • the first communication device feeds back the quantized value of the cluster sparse domain coefficient and the corresponding index value to the second communication device, so that the second communication device can obtain the N-dimensional cluster sparse based on the quantized value of the sparse domain coefficient and the corresponding index value Domain coefficients.
  • the method further includes: receiving a first signal sent by a second communication device; and acquiring CSI information of a channel, including: acquiring CSI information based on the first signal.
  • the first communication device may perform CSI estimation based on the first signal to obtain CSI information.
  • the L cluster sparse domain coefficients are the L coefficients with the largest modulus value among the N-dimensional cluster sparse domain coefficients.
  • the L coefficients with the largest modulus values among the N-dimensional cluster sparse domain coefficients can be selected, that is, the L cluster sparse domain beam components with the greatest intensity can be fed back, thereby effectively improving the CSI feedback accuracy.
  • the L coefficients with the largest modulus values among the N-dimensional cluster sparse domain coefficients can be selected, that is, the L cluster sparse domain beam components with the greatest intensity can be fed back, thereby effectively improving the CSI feedback accuracy.
  • an embodiment of the present application provides a method for processing CSI information.
  • the method includes: receiving quantized values of L cluster sparse domain coefficients sent by a first communication device; based on a cluster sparse transformation basis, performing an inverse cluster sparse domain transformation on the L cluster sparse domain coefficients to obtain first CSI transformation information, and the first
  • the CSI transformation information includes N-dimensional beam domain coefficients; wherein, the cluster sparse transformation basis is determined based on the channel's prior statistical characteristics of the channel, N is an integer greater than 1, and L is a positive integer less than N; based on the array response characteristics, for the first One CSI transformation information is inversely transformed in the beam domain to obtain CSI information, and the CSI information includes N-dimensional antenna domain coefficients.
  • the second communication device can obtain CSI information with higher accuracy based on the cluster sparse domain coefficient fed back by the first communication device.
  • the channel prior statistical feature is used to indicate the statistical covariance matrix corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse transformation base is the eigenvector corresponding to the statistical covariance matrix
  • the method further includes: receiving indication information sent by the first communication device, where the indication information is used to indicate a statistical covariance matrix or an eigenvector corresponding to the statistical covariance matrix.
  • the statistical covariance matrix is determined according to sample values of a plurality of N-dimensional beam domain coefficients.
  • the channel prior statistical feature is used to indicate the cluster sparse feature of multiple beam channel clusters corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse feature includes at least one of the following : The size of the beam channel cluster, the shape of the beam channel cluster, and the cluster spacing of the beam channel cluster.
  • the cluster sparse transformation basis is determined based on cluster sparse features of multiple beam channel clusters.
  • performing inverse cluster sparse domain transformation on L cluster sparse domain coefficients based on the cluster sparse transform basis including: based on the cluster sparse transform basis of each beam channel cluster, Perform cluster sparse inverse transformation on the corresponding beam channel cluster to obtain a plurality of first CSI transformation sub-information, where the first CSI transformation information includes a plurality of first CSI transformation sub-information.
  • the cluster sparse transformation basis is obtained by solidifying and approximating the prior statistical characteristics of the channel.
  • performing inverse cluster sparse domain transformation on the L cluster sparse domain coefficients based on the cluster sparse transformation basis comprising: receiving the L cluster sparse domain coefficients sent by the first communication device The quantized value of the domain amplitude coefficient and the quantized value of the phase information corresponding to the N cluster sparse domain coefficients; based on the cluster sparse transformation basis, perform the cluster sparse domain inverse transformation on the L cluster sparse domain amplitude coefficients to obtain the N-dimensional beam domain amplitude coefficients; The N-dimensional beam domain amplitude coefficients are combined with N pieces of phase information to obtain first CSI transformation information.
  • the method further includes: receiving the quantized values of the L cluster sparse domain coefficients and the index values corresponding to the L cluster sparse domain coefficients sent by the first communication device, and the index The value is used to indicate the position of the cluster sparse domain coefficients within the N-dimensional cluster sparse domain coefficients.
  • the method further includes: sending a first signal to the first communication device, for instructing the first communication device to feed back the L cluster sparse domain coefficients based on the first signal quantized value.
  • performing inverse cluster sparse domain transformation on the L cluster sparse domain coefficients based on the cluster sparse transformation basis including: acquiring second CSI transformation information, the second CSI transformation The information includes N-dimensional cluster sparse domain coefficients, and the N-dimensional cluster sparse domain coefficients are obtained by filling the L cluster sparse domain coefficients with zeros; based on the cluster sparse transformation base, perform inverse cluster sparse domain transformation on the second CSI transformation information to obtain the first A CSI transformation information.
  • the second communication device may transform the L-dimensional cluster sparse domain coefficients into N-dimensional cluster sparse domain coefficients based on zero-padding for subsequent processing.
  • the method further includes: performing single-user or multi-user precoding based on the CSI information.
  • the second communication apparatus performs user precoding based on the CSI obtained by the solution of the present application, which can effectively improve the performance of precoding.
  • an embodiment of the present application provides a communication device, the device includes an acquisition unit, a processing unit, and a transceiver unit, wherein the acquisition unit can be used to acquire channel state information CSI information of a channel, where the CSI information includes an N-dimensional antenna Domain coefficient, where N is an integer greater than 1.
  • the processing unit is configured to perform beam domain transformation on the CSI information based on the array response feature to obtain first CSI transformation information, where the first CSI transformation information includes N-dimensional beam domain coefficients.
  • the processing unit is further configured to perform cluster sparse domain transformation on the first CSI transformation information based on the cluster sparse transformation basis to obtain second CSI transformation information, where the second CSI transformation information includes N-dimensional cluster sparse domain coefficients; wherein, The cluster sparse transformation basis is determined based on channel prior statistical characteristics of the channel.
  • a transceiver unit configured to send the quantized values of the L cluster sparse domain coefficients in the N-dimensional cluster sparse domain coefficients to the second communication device, where L is a positive integer smaller than N.
  • the channel prior statistical feature is used to indicate the statistical covariance matrix corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse transformation base is the eigenvector corresponding to the statistical covariance matrix
  • the transceiver unit is further configured to send indication information to the second communication device, where the indication information is used to indicate the statistical covariance matrix or the eigenvector corresponding to the statistical covariance matrix .
  • the statistical covariance matrix is determined according to sample values of a plurality of N-dimensional beam domain coefficients.
  • the channel prior statistical feature is used to indicate the cluster sparse feature of multiple beam channel clusters corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse feature includes at least one of the following : The size of the beam channel cluster, the shape of the beam channel cluster, and the cluster spacing of the beam channel cluster.
  • the cluster sparse transformation basis is determined based on the cluster sparse features of the multiple beam channel clusters.
  • the processing unit is configured to perform cluster sparse domain transformation on the corresponding beam channel cluster based on the cluster sparse transformation basis of each beam channel cluster, to obtain a plurality of second CSI transformation sub-information, the second CSI transformation information includes a plurality of second CSI transformation sub-information.
  • the cluster sparse transformation basis is obtained by solidifying and approximating the prior statistical characteristics of the channel.
  • the processing unit is configured to perform amplitude and phase separation on the N-dimensional beam domain coefficients in the first CSI transformation information, to obtain amplitude information corresponding to each beam domain coefficient and phase information; based on the cluster sparse transformation basis, perform cluster sparse domain transformation on the amplitude information of each beam domain coefficient in the first CSI transformation information to obtain second CSI transformation information; the transceiver unit 303 is used to send N to the second communication device The quantized values of the L cluster sparse domain coefficients in the dimensional cluster sparse domain coefficients and the quantized values of the phase information corresponding to the L cluster sparse domain coefficients.
  • the transceiver unit is configured to send the quantized values of the L cluster sparse domain coefficients and the index values corresponding to the L cluster sparse domain coefficients to the second communication device, and the index The value is used to indicate the position of the cluster sparse domain coefficients within the N-dimensional cluster sparse domain coefficients.
  • the transceiver unit is configured to receive the first signal sent by the second communication device; the acquisition unit is configured to obtain the CSI information based on the first signal.
  • the L cluster sparse domain coefficients are the L coefficients with the largest modulus value among the N-dimensional cluster sparse domain coefficients.
  • an embodiment of the present application provides a communication device, including a transceiver unit and a processing unit.
  • the transceiver unit is configured to receive the quantized values of the L cluster sparse domain coefficients sent by the first communication device.
  • the processing unit is configured to perform inverse cluster sparse domain transformation on the L cluster sparse domain coefficients based on the cluster sparse transformation base to obtain first CSI transformation information, where the first CSI transformation information includes N-dimensional beam domain coefficients; wherein, the cluster sparse transformation base Determined based on the channel priori statistical characteristics of the channel, N is an integer greater than 1, and L is a positive integer less than N;
  • the processing unit 402 is further configured to perform inverse beam domain transformation on the first CSI transformation information based on the array response characteristics , to obtain CSI information, where the CSI information includes N-dimensional antenna domain coefficients.
  • the channel prior statistical feature is used to indicate the statistical covariance matrix corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse transformation base is the eigenvector corresponding to the statistical covariance matrix
  • the transceiver unit is configured to receive indication information sent by the first communication device, where the indication information is used to indicate a statistical covariance matrix or an eigenvector corresponding to the statistical covariance matrix .
  • the statistical covariance matrix is determined according to the sample values of a plurality of N-dimensional beam domain coefficients.
  • the channel prior statistical feature is used to indicate the cluster sparse feature of multiple beam channel clusters corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse feature includes at least one of the following : The size of the beam channel cluster, the shape of the beam channel cluster, and the cluster spacing of the beam channel cluster.
  • the cluster sparse transformation basis is determined based on cluster sparse features of multiple beam channel clusters.
  • the processing unit is configured to perform cluster sparse inverse transformation on the corresponding beam channel cluster based on the cluster sparse transformation basis of each beam channel cluster, to obtain a plurality of first CSI transformation sub-information, where the first CSI transformation information includes a plurality of first CSI transformation sub-information.
  • the cluster sparse transformation basis is obtained by solidifying and approximating the prior statistical characteristics of the channel.
  • the transceiver unit is configured to receive the quantized values of the L cluster sparse domain amplitude coefficients and the phase corresponding to the N cluster sparse domain coefficients sent by the first communication device The quantized value of the information; the processing unit is used to perform inverse cluster sparse domain transform on the L cluster sparse domain amplitude coefficients based on the cluster sparse transform basis to obtain the N-dimensional beam domain amplitude coefficients.
  • the processing unit is configured to perform amplitude and phase synthesis on the N-dimensional beam domain amplitude coefficient and N pieces of phase information to obtain first CSI transformation information.
  • the transceiver unit is further configured to receive the quantized values of the L cluster sparse domain coefficients and the index values corresponding to the L cluster sparse domain coefficients sent by the first communication device , the index value is used to indicate the position of the cluster sparse domain coefficients in the N-dimensional cluster sparse domain coefficients.
  • the transceiver unit is further configured to send a first signal to the first communication device, for instructing the first communication device to feed back L cluster sparse based on the first signal The quantized value of the domain coefficients.
  • the processing unit is configured to acquire second CSI transformation information, where the second CSI transformation information includes N-dimensional cluster sparse domain coefficients, and the N-dimensional cluster sparse domain coefficients are The L cluster sparse domain coefficients are obtained after zero-padded.
  • the processing unit is configured to perform inverse cluster sparse domain transformation on the second CSI transformation information based on the cluster sparse transformation basis to obtain first CSI transformation information.
  • the processing unit is further configured to perform single-user or multi-user precoding based on the CSI information.
  • an embodiment of the present application provides a communication device.
  • the communication device includes a processor and a transceiver.
  • the processor is configured to acquire channel state information CSI information of the channel, where the CSI information includes N-dimensional antenna domain coefficients, and N is an integer greater than 1.
  • the processor is further configured to perform beam domain transformation on the CSI information based on the array response feature to obtain first CSI transformation information, where the first CSI transformation information includes N-dimensional beam domain coefficients.
  • the processor is further configured to perform cluster sparse domain transformation on the first CSI transformation information based on the cluster sparse transformation base to obtain second CSI transformation information, where the second CSI transformation information includes N-dimensional cluster sparse domain coefficients; wherein the cluster sparse transformation base Determined for channel-based channel prior statistics.
  • a transceiver configured to send the quantized values of L cluster sparse domain coefficients in the N-dimensional cluster sparse domain coefficients to the second communication device, where L is a positive integer smaller than N.
  • the channel prior statistical feature is used to indicate the statistical covariance matrix corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse transformation base is the eigenvector corresponding to the statistical covariance matrix
  • the transceiver is further configured to send indication information to the second communication device, where the indication information is used to indicate the statistical covariance matrix or the eigenvector corresponding to the statistical covariance matrix .
  • the statistical covariance matrix is determined according to the sample values of a plurality of N-dimensional beam domain coefficients.
  • the channel prior statistical feature is used to indicate the cluster sparse feature of multiple beam channel clusters corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse feature includes at least one of the following : The size of the beam channel cluster, the shape of the beam channel cluster, and the cluster spacing of the beam channel cluster.
  • the cluster sparse transformation basis is determined based on cluster sparse features of multiple beam channel clusters.
  • the processor is further configured to perform cluster sparse domain transformation on the corresponding beam channel cluster based on the cluster sparse transformation basis of each beam channel cluster, to obtain a plurality of Two CSI transformation sub-information, where the second CSI transformation information includes a plurality of second CSI transformation sub-information.
  • the cluster sparse transformation basis is obtained by solidifying and approximating the prior statistical characteristics of the channel.
  • the processor is configured to perform amplitude and phase separation on the N-dimensional beam domain coefficients in the first CSI transformation information, to obtain amplitude information corresponding to each beam domain coefficient and phase information; the processor is further configured to perform cluster sparse domain transformation on the amplitude information of each beam domain coefficient in the first CSI transformation information based on the cluster sparse transformation basis, to obtain second CSI transformation information; The second communication device transmits the quantized values of the L cluster sparse domain coefficients and the quantized value of the phase information corresponding to the L cluster sparse domain coefficients among the N-dimensional cluster sparse domain coefficients.
  • the transceiver is further configured to send the quantized values of the L cluster sparse domain coefficients and the index values corresponding to the L cluster sparse domain coefficients to the second communication device,
  • the index value is used to indicate the position of the cluster sparse domain coefficients in the N-dimensional cluster sparse domain coefficients.
  • the transceiver is configured to receive the first signal sent by the second communication device; the processor is configured to acquire CSI information based on the first signal.
  • the L cluster sparse domain coefficients are the L coefficients with the largest modulus value among the N-dimensional cluster sparse domain coefficients.
  • an embodiment of the present application provides a communication device.
  • the communication device includes a processor and a transceiver; the transceiver is configured to receive the quantized values of the L cluster sparse domain coefficients sent by the first communication device; Perform cluster sparse domain inverse transform to obtain first CSI transform information, where the first CSI transform information includes N-dimensional beam domain coefficients; wherein, the cluster sparse transform base is determined based on channel prior statistical characteristics of the channel, and N is an integer greater than 1 , L is a positive integer less than N; the processor is configured to perform inverse beam domain transformation on the first CSI transformation information based on the array response feature to obtain CSI information, where the CSI information includes N-dimensional antenna domain coefficients.
  • the channel prior statistical feature is used to indicate the statistical covariance matrix corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse transformation base is the eigenvector corresponding to the statistical covariance matrix
  • the transceiver is further configured to receive indication information sent by the first communication device, where the indication information is used to indicate a statistical covariance matrix or a feature corresponding to the statistical covariance matrix vector.
  • the statistical covariance matrix is determined according to the sample values of a plurality of N-dimensional beam domain coefficients.
  • the channel prior statistical feature is used to indicate the cluster sparse feature of multiple beam channel clusters corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse feature includes at least one of the following : The size of the beam channel cluster, the shape of the beam channel cluster, and the cluster spacing of the beam channel cluster.
  • the cluster sparse transformation basis is determined based on cluster sparse features of multiple beam channel clusters.
  • the processor is further configured to perform cluster sparse inverse transformation on the corresponding beam channel clusters based on the cluster sparse transformation basis of each beam channel cluster, to obtain a plurality of A CSI transformation sub-information, the first CSI transformation information includes a plurality of first CSI transformation sub-information.
  • the cluster sparse transformation basis is obtained by solidifying and approximating the prior statistical characteristics of the channel.
  • the transceiver is configured to receive the quantized values of the L cluster sparse domain amplitude coefficients and the phase information corresponding to the N cluster sparse domain coefficients sent by the first communication device The quantized value of ; the processor is used to perform the cluster sparse domain inverse transform on the L cluster sparse domain amplitude coefficients based on the cluster sparse transform basis to obtain the N-dimensional beam domain amplitude coefficients; the processor is also used to convert the N-dimensional beam domain amplitude coefficients The coefficients and the N pieces of phase information are combined in amplitude and phase to obtain first CSI transformation information.
  • the transceiver is further configured to receive the quantized values of the L cluster sparse domain coefficients and the index values corresponding to the L cluster sparse domain coefficients sent by the first communication device , the index value is used to indicate the position of the cluster sparse domain coefficients in the N-dimensional cluster sparse domain coefficients.
  • the transceiver is configured to send a first signal to the first communication apparatus, for instructing the first communication apparatus to feed back L cluster sparse domains based on the first signal The quantized value of the coefficient.
  • the processor is further configured to acquire second CSI transformation information, where the second CSI transformation information includes N-dimensional cluster sparse domain coefficients, and the N-dimensional cluster sparse domain coefficients are: obtained by filling the L cluster sparse domain coefficients with zeros; the processor is configured to perform inverse cluster sparse domain transformation on the second CSI transformation information based on the cluster sparse transformation basis to obtain first CSI transformation information.
  • the processor is further configured to perform single-user or multi-user precoding based on the CSI information.
  • an embodiment of the present application provides a computer-readable storage medium.
  • the medium includes a computer program that, when the computer program runs on the apparatus, causes the first aspect and the method for processing CSI information of any one of the first aspect to be performed.
  • an embodiment of the present application provides a computer-readable storage medium.
  • the medium includes a computer program that, when executed on the device, causes the second aspect and the method for processing CSI information of any one of the second aspect to be performed.
  • an embodiment of the present application provides a computer program for executing the first aspect and the method for processing CSI information in any one of the first aspect.
  • an embodiment of the present application provides a computer program for executing the second aspect and the method for processing CSI information in any one of the second aspect.
  • the embodiments of the present application further provide a computer program product including executable instructions, when the computer program product is executed, the first aspect and any possible implementation of the method in the above-mentioned first aspect are made. or all steps are executed.
  • the embodiments of the present application further provide a computer program product including executable instructions, when the computer program product is executed, the above-mentioned second aspect and any possible implementation of the method part thereof or all steps are executed.
  • an embodiment of the present application provides a chip, where the chip includes at least one processing circuit and an interface.
  • the interface and the processing circuit communicate with each other through an internal connection path, and the processing circuit executes the method in the first aspect or any possible implementation manner of the first aspect to control the interface to receive or send signals.
  • an embodiment of the present application provides a chip, where the chip includes at least one processing circuit and an interface.
  • the interface and the processing circuit communicate with each other through an internal connection path, and the processing circuit executes the method in the second aspect or any possible implementation manner of the second aspect to control the interface to receive or send signals.
  • an embodiment of the present application provides a communication system, where the communication system includes a first communication device related to the first aspect and any aspect thereof, and a second communication device related to the second aspect and any one of the aspects.
  • an embodiment of the present application provides a communication device, including at least one processor configured to execute a program instruction stored in a memory, and when the program instruction is executed by the processor, the device enables the device to execute the first aspect of the embodiment of the present application and any of its possible implementations of the method shown, or causing the apparatus to perform the method shown in the second aspect of the embodiments of the present application and any of its possible implementations.
  • the memory is outside the communication device, or the memory is included in the communication device.
  • the memory and processor are integrated.
  • FIG. 1 is a schematic diagram of a communication system exemplarily shown
  • 2a is a schematic structural diagram of an exemplary network device
  • 2b is a schematic structural diagram of an exemplary terminal
  • FIG. 3 is an interactive schematic diagram of a CSI feedback method exemplarily shown
  • 4a is a schematic diagram of beam domain coefficients exemplarily shown
  • Figure 4b is a schematic diagram of beam selection exemplarily shown
  • FIG. 4c is a schematic diagram of CSI reconstruction exemplarily shown
  • FIG. 4d is a schematic diagram of exemplarily shown cluster sparse domain coefficients
  • FIG. 4e is a schematic diagram of CSI reconstruction exemplarily shown
  • FIG. 5 is a schematic flowchart of a method for processing CSI information of a terminal provided by an embodiment of the present application
  • FIG. 6 is a schematic flowchart of a method for processing CSI information on a network device end provided by an embodiment of the present application
  • FIG. 7 is an interactive schematic diagram of a method for processing CSI information on a network device side provided by an embodiment of the present application.
  • Fig. 8 is a schematic diagram of a set of window functions shown by way of example.
  • FIG. 9 is a schematic diagram of the structure of an exemplary cluster sparse transformation base
  • FIG. 10 is a schematic structural diagram of a terminal provided by an embodiment of the present application.
  • FIG. 11 is a schematic structural diagram of a network device provided by an embodiment of the present application.
  • FIG. 12 is a schematic structural diagram of a device provided by an embodiment of the present application.
  • FIG. 13 is a schematic structural diagram of a chip provided by an embodiment of the present application.
  • first and second in the description and claims of the embodiments of the present application are used to distinguish different objects, rather than to describe a specific order of the objects.
  • first target object, the second target object, etc. are used to distinguish different target objects, rather than to describe a specific order of the target objects.
  • words such as “exemplary” or “for example” are used to represent examples, illustrations or illustrations. Any embodiments or designs described in the embodiments of the present application as “exemplary” or “such as” should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as “exemplary” or “such as” is intended to present the related concepts in a specific manner.
  • multiple processing units refers to two or more processing units; multiple systems refers to two or more systems.
  • FIG. 1 it is a schematic diagram of a communication system according to an embodiment of the present application.
  • the communication system includes network equipment and terminals. It should be noted that, in practical applications, the number of network devices and terminals may be one or more. The number of network devices and terminals in the communication system shown in FIG. 1 is only an example of adaptability, which is not limited in this application. .
  • the above communication system may be used to support fourth generation (4G) access technology, such as long term evolution (LTE) access technology; or, the communication system may also support fifth generation (5G) ) access technologies, such as new radio (NR) access technologies; and future mobile communication systems.
  • 4G fourth generation
  • 5G fifth generation
  • NR new radio
  • the network equipment in FIG. 1 can be used to support terminal access, for example, it can be an evolved base station (evolutional Node B, eNB or eNodeB) in LTE; or a 5G network or a future evolved public land mobile network (public land mobile network) network, PLMN) in the base station, broadband network gateway (BNG), aggregation switch or non-3rd generation partnership project (3rd generation partnership project, 3GPP) access equipment and so on.
  • eNB evolved Node B
  • PLMN public land mobile network
  • BNG broadband network gateway
  • aggregation switch or non-3rd generation partnership project (3rd generation partnership project, 3GPP) access equipment and so on.
  • the network device in this embodiment of the present application may include various forms of base stations, such as: a macro base station, a micro base station (also referred to as a small cell), a relay station, an access point, a 5G base station or future base station, satellite, Transmission point (transmitting and receiving point, TRP), transmitting point (transmitting point, TP), mobile switching center and device-to-device (Device-to-Device, D2D), vehicle outreach (vehicle-to-everything, V2X),
  • a device that undertakes the function of a base station in a machine-to-machine (M2M) communication, etc. is not specifically limited in this embodiment of the present application.
  • M2M machine-to-machine
  • the devices that provide the wireless communication function for the terminal are collectively referred to as network devices or base stations.
  • the terminal in FIG. 1 may be a device that provides voice or data connectivity to users, for example, it may also be called a mobile station (mobile station), user equipment (user equipment, UE), subscriber unit (subscriber unit), station ( station), terminal equipment (terminal equipment, TE), etc.
  • the terminal may be a cellular phone, a personal digital assistant (PDA), a wireless modem, a handheld, a laptop computer, a cordless phone, a wireless Local loop (wireless local loop, WLL) station, tablet computer (pad), etc.
  • PDA personal digital assistant
  • WLL wireless Local loop
  • WLL wireless local loop
  • pad tablet computer
  • devices that can access the communication system, communicate with the network side of the communication system, or communicate with other objects through the communication system can be the terminals in the embodiments of the present application, for example, intelligent transportation Terminals and automobiles in the smart home, household equipment in the smart home, power meter reading instruments in the smart grid, voltage monitoring instruments, environmental monitoring instruments, video monitoring instruments in the smart security network, cash registers, etc.
  • the terminal may communicate with a network device, for example, the network device in FIG. 1 . Communication between multiple terminals is also possible. Terminals can be statically fixed or mobile.
  • Figure 2a is a schematic structural diagram of a network device.
  • Figure 2a is a schematic structural diagram of a network device.
  • the network device includes at least one processor 101 , at least one transceiver 103 and one or more antennas 105 , and may also include a memory 102 and a network interface 104 .
  • the processor 101, the memory 102, the transceiver 103 and the network interface 104 are connected, for example, via a bus.
  • the antenna 105 is connected to the transceiver 103 .
  • the network interface 104 is used to connect the network device with other communication devices through a communication link. In this embodiment of the present application, the connection may include various types of interfaces, transmission lines, or buses, which are not limited in this embodiment.
  • the processor in this embodiment of the present application may include at least one of the following types: a general-purpose central processing unit (Central Processing Unit, CPU), a digital signal processor (Digital Signal Processor, DSP), a microprocessor, Application-Specific Integrated Circuit (ASIC), Microcontroller Unit (MCU), Field Programmable Gate Array (FPGA), or an integrated circuit for implementing logic operations .
  • processor 101 may be a single-CPU processor or a multi-CPU processor. At least one processor 101 may be integrated in one chip or located on multiple different chips.
  • the memory in this embodiment of the present application may include at least one of the following types: read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (random access memory, RAM) or other types of dynamic storage devices that can store information and instructions, or electrically erasable programmable read-only memory (Electrically erasable programmable read-only memory, EEPROM).
  • ROM read-only memory
  • RAM random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EEPROM electrically erasable programmable read-only memory
  • the memory may also be compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.) , a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, without limitation.
  • CD-ROM compact disc read-only memory
  • optical disc storage including compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.
  • magnetic disk storage medium or other magnetic storage device or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, without limitation.
  • the memory 102 may exist independently and be connected to the processor 101 .
  • the memory 102 can also be integrated with the processor 101, for example, integrated in one chip.
  • the memory 102 can store program codes for implementing the technical solutions of the embodiments of the present application, and is controlled and executed by the processor 101 .
  • the processor 101 is configured to execute the computer program codes stored in the memory 102, thereby implementing the technical solutions in the embodiments of the present application.
  • the memory 102 can also be connected to the processor 101 through an interface outside the chip.
  • the transceiver 103 may be used to support the reception or transmission of radio frequency signals between the access network device and the terminal, and the transceiver 103 may be connected to the antenna 105 .
  • the transceiver 103 includes a transmitter Tx and a receiver Rx.
  • one or more antennas 105 may receive radio frequency signals
  • the receiver Rx of the transceiver 103 is configured to receive the radio frequency signals from the antennas, convert the radio frequency signals into digital baseband signals or digital intermediate frequency signals, and convert the digital
  • the baseband signal or the digital intermediate frequency signal is provided to the processor 101 so that the processor 101 performs further processing on the digital baseband signal or the digital intermediate frequency signal, such as demodulation processing and decoding processing.
  • the transmitter Tx in the transceiver 103 is also used to receive the modulated digital baseband signal or digital intermediate frequency signal from the processor 101, and convert the modulated digital baseband signal or digital intermediate frequency signal into a radio frequency signal, and pass a or multiple antennas 105 transmit the radio frequency signal.
  • the receiver Rx can selectively perform one or more stages of down-mixing processing and analog-to-digital conversion processing on the radio frequency signal to obtain a digital baseband signal or a digital intermediate frequency signal. The order of precedence is adjustable.
  • the transmitter Tx can selectively perform one or more stages of up-mixing processing and digital-to-analog conversion processing on the modulated digital baseband signal or digital intermediate frequency signal to obtain a radio frequency signal, and the up-mixing processing and digital-to-analog conversion processing
  • the order of s is adjustable.
  • Digital baseband signals and digital intermediate frequency signals can be collectively referred to as digital signals.
  • FIG. 2b is a schematic structural diagram of a terminal.
  • Figure 2b is a schematic structural diagram of a terminal.
  • the terminal includes at least one processor 201 , at least one transceiver 202 and at least one memory 203 .
  • the processor 201 , the memory 203 and the transceiver 202 are connected.
  • the terminal may further include an output device 204 , an input device 205 and one or more antennas 206 .
  • the antenna 206 is connected to the transceiver 202 , and the output device 204 and the input device 205 are connected to the processor 201 .
  • the transceiver 202, the memory 203 and the antenna 206 can implement similar functions with reference to the relevant description in FIG. 2a.
  • the processor 201 may be a baseband processor or a CPU, and the baseband processor and the CPU may be integrated or separated.
  • the processor 201 can be used to implement various functions for the terminal, for example, to process communication protocols and communication data, or to control the entire terminal device, execute software programs, and process data of software programs; or to assist in completing Computational processing tasks, such as graphic image processing or audio processing, etc.; or the processor 201 is used to implement one or more of the above functions.
  • the output device 204 communicates with the processor 201 and can display information in a variety of ways.
  • the memory 203 may exist independently and be connected to the processor 201 .
  • the memory 203 can also be integrated with the processor 201, for example, integrated in one chip.
  • the memory 203 can store program codes for implementing the technical solutions of the embodiments of the present application, and is controlled and executed by the processor 201 .
  • the processor 201 is configured to execute computer program codes stored in the memory 203, thereby implementing the technical solutions in the embodiments of the present application.
  • the memory 203 can also be connected to the processor 201 through an interface outside the chip.
  • Massive-Multi-In-Multi-Out is an important technical means for wireless communication systems to improve system capacity and spectral efficiency.
  • the effective transmission space of the channel is determined by methods such as Singular Value Decomposition (SVD) decomposition, in which there are multiple mutually orthogonal or nearly orthogonal parallel sub-channels.
  • Singular Value Decomposition Singular Value Decomposition
  • By transmitting multiple parallel sub-channels in these parallel sub-channels Separate data streams for space-division multiplexing gain that doubles the capacity.
  • a key condition for this technology to obtain space division multiplexing gain is that network equipment can obtain sufficiently accurate CSI information.
  • a typical CSI feedback technology is: the terminal will After the pre-defined CSI codebook is quantized, it is fed back to the network device through the reverse link. The network device performs direct or indirect CSI reconstruction according to the feedback value, and performs a single CSI reconstruction based on the reconstructed CSI (hereinafter referred to as the CSI reconstruction value).
  • the CSI reconstruction value User or multi-user precoding.
  • the above-mentioned CSI feedback technology will introduce reverse link signaling overhead while enabling network equipment precoding.
  • the number of antennas in Massive-MIMO technology continues to increase, and the amount of feedback increases exponentially with the number of antennas, which requires more effective CSI feedback technology.
  • FIG. 3 is an exemplary CSI feedback technology, in FIG. 3:
  • the network device sends a pilot signal to the terminal; correspondingly, the terminal receives the pilot signal sent by the network device.
  • the network device sends the pilot signal to the terminal through the air interface resource.
  • the terminal obtains a CSI estimated value.
  • the terminal performs CSI estimation in response to the received pilot signal to obtain a CSI estimation value.
  • the CSI estimation value is not the real CSI value.
  • CSI information is used herein to refer to CSI estimation. value, and the description will not be repeated below.
  • the network equipment has N antennas
  • the terminal has one antenna
  • the CSI information is an N-dimensional vector for illustration, where the N-dimensional vector includes N antenna domain coefficients, where N represents the number of antennas of the network device.
  • the present application can also be applied to a scenario where the terminal has M antennas, and correspondingly, the CSI information is an N*M dimensional matrix.
  • the terminal performs DFT transformation on the CSI information to obtain N-dimensional beam domain coefficients, which can also be understood as transform domain coefficients corresponding to N DFT beams.
  • Figure 4a is a schematic diagram of the N-dimensional beam domain coefficients, wherein the vertical axis represents the intensity, and the horizontal axis is the beam domain. Referring to Figure 4a, each coefficient in the N-dimensional beam domain coefficients corresponds to a beam component in Figure 4a, Unless otherwise specified, the beam components involved in this document all refer to beam components in the DFT domain.
  • the CSI information includes 64-dimensional beam domain coefficients, that is to say, the beam components corresponding to the 64 beam domain coefficients should be included in FIG. That is, the beam components corresponding to the 14 beam domain coefficients in the 64-dimensional beam domain coefficients.
  • the distribution of the beam domain coefficients has the characteristics of diameter clusters, and may include one or more diameter clusters. As shown in FIG. 4a, the diameter clusters may be composed of multiple beam components with large and dense beam components. , such as cluster 1 and cluster 2 in Figure 4a.
  • the terminal performs beam selection to reduce feedback overhead.
  • the terminal performs beam selection and quantization.
  • the terminal performs the process of beam selection and quantization.
  • the terminal selects the beam domain coefficients corresponding to the L strong beams for quantization, wherein the beam domain coefficients corresponding to the L strong beams are the N-dimensional beam domain coefficients. The L coefficients with the largest value.
  • the terminal performs beam selection. Referring to FIG. 4b, the corresponding 6 beam components in the dashed box (that is, the solid line beam components shown in the figure) That is, the beam domain coefficients corresponding to the selected L strong beams. That is to say, the terminal only selects 6 effective beam components with the largest energy in cluster 2 for feedback, while all beam components in cluster 1 and one beam component in cluster 2 (that is, the dotted beam component shown in the figure) ) did not respond.
  • the terminal sends L beam domain coefficients and corresponding index values to the network device; correspondingly, the network device receives the L beam domain coefficients and corresponding index values sent by the terminal.
  • the terminal sends the quantized L beam domain coefficients and corresponding index values to the network device through the feedback channel.
  • the index value is the position of the L beam domain coefficients in the N-dimensional beam domain coefficients.
  • the network device performs inverse DFT transformation.
  • the network device in response to the received quantized values of the L beam domain coefficients and their index values, performs inverse DFT transform on the L beam domain coefficients to obtain a CSI reconstruction value, which also includes the N-dimensional antenna. Domain coefficients. Specifically, the network device searches for the corresponding L DFT transform vectors (referred to as DFT transform bases in this paper) through the L index values, and then performs other processing after multiplying the L beam domain coefficients by the corresponding DFT transform bases, respectively. For example, linear merging (for specific processing details, please refer to the prior art, which will not be repeated in this paper), and the merging result is the CSI reconstruction value.
  • the network device performs user precoding.
  • the network device performs single-user or multi-user precoding based on the CSI reconstruction value.
  • the network device may transmit data and/or pilot signals based on the result of precoding.
  • Figure 3 and the following Figures 5 and 6 only show the processing flow of CSI information. It can be seen from the figures that the processing flow of CSI information is actually a closed-loop process, which can be triggered periodically or can be It is a conditional trigger, and the specific trigger can actually refer to the provisions in the existing standard, which is not limited in this application.
  • the network device can transmit data to a single user or multiple users based on the precoding result, which will not be described in detail below.
  • Figure 4c shows the CSI reconstruction value recovered on the basis of Figure 4b.
  • the network device can only recover the path cluster in Figure 4a based on the obtained CSI reconstruction value. 2.
  • the CSI information received by the network device is missing, which further causes the performance loss of precoding.
  • the present application proposes a method for processing CSI information, which can improve the feedback accuracy of CSI while saving overhead.
  • the specific implementation of the present application will be introduced below in conjunction with the above schematic diagram of the application scenario shown in FIG. 1 .
  • the first communication device is used as a terminal
  • the second communication device is used as a network device
  • the application scenario of the downlink wireless communication system is used as an example for description.
  • the technical solution in the application can also be applied to an uplink wireless communication system, that is, the first communication device is a network device, and the second communication device is a terminal, that is, the CSI information is determined by the network device and fed back to the terminal.
  • FIG. 5 shows a schematic flowchart of a method for processing CSI information of a terminal in an embodiment of the present application.
  • FIG. 5 shows a schematic flowchart of a method for processing CSI information of a terminal in an embodiment of the present application.
  • the terminal acquires CSI information of a channel, where the CSI information includes N-dimensional antenna domain coefficients.
  • the terminal performs channel estimation in response to the received pilot signal sent by the network device, so as to obtain the CSI information of the channel.
  • the CSI information includes N-dimensional antenna domain coefficients, where N is the number of antennas of the network device, which is an integer greater than 1.
  • the value of N is previously sent by the network device to the terminal through signaling.
  • the CSI information in this application is denoted as The dimension is N*1.
  • the terminal performs beam domain transformation on the CSI information based on the array response feature to obtain first CSI transformation information, where the first CSI transformation information includes N-dimensional beam domain coefficients.
  • the terminal may perform beam domain transformation on the CSI information based on the array response characteristics to obtain N-dimensional beam domain coefficients.
  • the array response feature is used to describe the array shape of the N-dimensional antenna domain coefficients.
  • the corresponding beam domain transform basis is a DFT transform basis as an example for illustration, and the DFT transform basis can be understood as a special form of the array response feature.
  • different array forms may correspond to different transform bases (ie, beam domain transform bases), for example, a coprime (coprime) array steering vector-based transform base corresponding to a coprime array, which is not limited in this application.
  • the terminal performs beam domain transform (also referred to as DFT transform) on the CSI information based on the DFT transform basis, which may be expressed as:
  • Q represents the DFT transform basis, which is specifically N*N is a matrix, Indicates the first CSI transformation information, that is, the N-dimensional beam domain coefficients after DFT transformation.
  • the terminal performs cluster sparse domain transformation on the first CSI transformation information based on the cluster sparse transformation base, to obtain second CSI transformation information, where the second CSI transformation information includes N-dimensional cluster sparse domain coefficients; wherein, the cluster sparse transformation base is channel-based The channel prior statistical characteristics are determined.
  • the terminal can perform cluster sparse domain transformation on the N-dimensional beam domain coefficients based on the cluster sparse transformation basis to obtain the N-dimensional cluster sparse domain coefficients, that is, the second CSI information, so as to further compress the N-dimensional beam domain coefficients into the cluster sparse domain , so as to achieve a more effective channel dimension reduction effect.
  • the terminal performing cluster sparse domain transformation on the N-dimensional beam domain coefficients (ie, the first CSI information) based on the cluster sparse transformation basis may be expressed as:
  • P represents the cluster sparse transformation basis, which is specifically dimensional matrix, where Indicates the second CSI transformation information, that is, the N-dimensional cluster sparse domain coefficients after cluster sparse transformation.
  • the cluster sparse transformation base is determined based on the channel's prior statistical characteristics of the channel.
  • a representation of the channel's prior statistical characteristics is the statistical covariance matrix of the channel.
  • the statistical covariance matrix of determines the corresponding cluster sparse transformation basis, and the specific method can refer to the following method 1.
  • a way of expressing the prior statistical characteristics of the channel is to use a way similar to the statistical covariance.
  • the cluster sparse feature is used as a priori statistical feature of the channel to construct the cluster sparse transformation basis, and the specific method can refer to the second method below. It should be noted that the beam channel cluster is the path cluster shown in Fig. 4a.
  • the structure of the cluster sparse transformation base is slow time-varying, that is to say, the network device and the terminal need to perform signaling interaction, so that the network device and the terminal are configured with the same cluster sparse transformation base, and
  • This method also increases the signaling overhead.
  • the terminal can perform a solid approximation on the channel priori statistical characteristics to obtain a fixed cluster sparse transformation basis, thereby further reducing the signaling overhead of the network device and the terminal.
  • the third method refer to the third method below.
  • S204 The terminal sends the quantized values of the L cluster sparse domain coefficients in the N-dimensional cluster sparse domain coefficients to the network device.
  • the terminal selects L cluster sparse domain coefficients from the acquired N-dimensional cluster sparse domain coefficients for quantization, and sends the quantized L coefficients to the network device, where L is a positive integer smaller than N.
  • L is a positive integer smaller than N.
  • the L cluster sparse domain coefficients may be the L coefficients with the largest modulus values among the N-dimensional cluster sparse domain coefficients.
  • the L cluster sparse domain coefficients can be expressed as h L , which is specifically an L-dimensional vector (L-dimensional column vector), and the quantized L coefficients are expressed as It is an L-dimensional vector.
  • the terminal further sends index information to the network device, where the index information includes index values corresponding to the L cluster sparse domain coefficients, where the index values are used to indicate the positions of the beam components corresponding to the cluster sparse domain coefficients on the cluster sparse domain.
  • the index information may be represented as n, which is specifically an L-dimensional vector, including L index values.
  • the kth element (or coefficient) n k in the vector n represents the coefficient of the beam component on the kth cluster sparse domain in the N-dimensional cluster sparse domain ordinal index in .
  • FIG. 6 is a schematic flowchart of the network device side in this embodiment of the present application. It should be noted that, unless otherwise specified, the meaning of the parameters involved in the network device side can refer to the relevant description of the terminal. No longer. Specifically, in Figure 6:
  • the network device receives the quantized values of the L cluster sparse domain coefficients sent by the terminal.
  • the network device sends a pilot signal to the terminal, where the pilot signal can be used to instruct the terminal to perform channel estimation based on the pilot signal to feed back CSI information (specifically, the L cluster sparse domain coefficients described above) .
  • the network device can receive the L-dimensional cluster sparse domain coefficient fed back by the terminal quantized value.
  • the network device may use the received L-dimensional cluster sparse domain coefficients Zero-padding is performed to convert the L-dimensional cluster sparse domain coefficients Expanded to an N-dimensional vector, the N-dimensional cluster sparse domain coefficients are obtained, which can be expressed as Exemplarily, the specific expansion method is as follows:
  • 0 N represents an N-dimensional all-zero vector
  • n k represents the kth element in n (ie, index information, see above for the concept).
  • the network device performs inverse cluster sparse domain transformation on the L cluster sparse domain coefficients based on the cluster sparse transformation base, to obtain first CSI transformation information, where the first CSI transformation information includes N-dimensional beam domain coefficients; wherein the cluster sparse transformation base is: Determined based on channel prior statistical characteristics of the channel.
  • the network device may obtain the cluster sparse transformation basis in advance.
  • the network device may receive in advance the cluster sparse transformation basis sent by the terminal.
  • the network device performs inverse cluster sparse domain transformation on the received L cluster sparse domain coefficients based on the current cluster sparse transform basis to obtain N-dimensional beam domain coefficients.
  • the network device performs zero-filling on the L cluster sparse domain coefficients in S201 to obtain an N-dimensional cluster sparse domain transformation
  • the network device performs the cluster sparse domain inverse transformation on the N-dimensional cluster sparse domain coefficients, which can be expressed as:
  • the first CSI transformation information (ie the N-dimensional beam domain coefficients) acquired by the network device and the second CSI transformation information (ie the N-dimensional cluster sparse domain coefficients) involved in the following are and There may be errors between the CSI information (that is, the N-dimensional antenna domain coefficients) and the information obtained by the terminal side.
  • the first CSI transformation information (that is, the N-dimensional beam domain coefficients) and the second CSI are still used on the network device side. Transformation information (ie, N-dimensional cluster sparse domain coefficients) and CSI information (ie, N-dimensional antenna domain coefficients) are used to represent the correspondence between the information obtained by the network device side and the information obtained by the terminal side.
  • the network device may also disregard the L-dimensional cluster sparse domain coefficients. Perform zero-padding, and in other embodiments, other equivalent methods can also be used to obtain N-dimensional beam domain coefficients Exemplarily, the network device may, based on the N ⁇ L-dimensional cluster sparse transform basis, perform the L-dimensional cluster sparse domain coefficients. Perform cluster sparse domain inverse transform to get N-dimensional beam domain coefficients Specifically, it can be expressed as:
  • p n represents the nth column vector of the cluster sparse transformation base P
  • the network device performs inverse beam domain transformation on the first CSI transformation information based on the array response feature to obtain CSI information, where the CSI information includes N-dimensional antenna domain coefficients.
  • the network device performs inverse DFT transform on the N-dimensional beam domain coefficients (that is, the first CSI transform information) based on the DFT transform basis to obtain CSI information, which may also be referred to as a CSI reconstruction value.
  • the CSI information includes N-dimensional antenna domain coefficients.
  • both the cluster sparse transformation base and the array response feature (such as the DFT transformation base) used by the network device correspond to the terminal side, that is, the terminal uses the cluster sparse transformation base P1 to perform
  • the network device side also needs to perform cluster sparse domain inverse transformation based on the cluster sparse transformation base P1.
  • the network device performs single-user or multi-user precoding based on the CSI information.
  • the precoding for a multiple-transmit single-receive channel or a single-user single-stream can be expressed as:
  • w represents the precoding weight (N-dimensional column vector)
  • x represents the single-stream data symbol
  • Zero-forcing (Zero-Forcing, ZF) precoding for multiple-transmit single-receive channels and multiple users per user single stream is as follows:
  • Nu represents the total number of space division multiplexing users
  • N represents the channel CSI reconstruction value (N-dimensional row vector) corresponding to multi-transmission and single-receive
  • H represents the N u ⁇ N-dimensional equivalent channel matrix spliced by rows of Nu row vectors
  • W represents the N ⁇ N equivalent channel matrix corresponding to the equivalent channel H N u -dimensional ZF precoding weights
  • x n represents the single-stream data symbols of the nth user
  • the network device may transmit data to a single user or multiple users based on the precoding result.
  • the network device may also transmit a pilot signal to a single user or multiple users based on the precoding result, that is, repeatedly perform S201.
  • the terminal may separate the amplitude and phase of each coefficient in the N-dimensional beam domain coefficients, and only separate the amplitude coefficients in the N-dimensional beam domain coefficients (hereinafter referred to as the N-dimensional beam domain amplitude coefficients) for cluster sparse domain transformation.
  • the terminal may compare the result obtained in S202, that is, the N-dimensional beam domain coefficients Amplitude and phase separation are performed, which can be expressed as:
  • N-dimensional modulo value vector that is, the N-dimensional beam domain amplitude coefficient described above
  • a is used as the input of formula (2), that is, the terminal performs cluster sparse domain transformation based on the N-dimensional beam domain amplitude coefficients to obtain the N-dimensional cluster sparse domain amplitude coefficients.
  • the terminal sends the quantized values of the L coefficients in the N-dimensional cluster sparse domain amplitude coefficients to the network device, the details of which can be referred to above, and are not repeated here.
  • the terminal can The quantized value of , that is, the N-dimensional phase information is sent to the network device.
  • the terminal may send phase information corresponding to M beam domain amplitude coefficients with larger beam components among the N-dimensional beam domain amplitude coefficients to the network device, where M is an integer less than N and greater than or equal to L.
  • the network device can perform inverse cluster sparse domain transformation based on the above method to obtain N-dimensional cluster sparse domain amplitude coefficients, and then the network device can convert The N-dimensional cluster sparse domain amplitude coefficients and the received quantized value of the phase information are combined in amplitude and phase to obtain the N-dimensional beam domain coefficients, and then the N-dimensional beam domain coefficients are subjected to subsequent processing.
  • the network device can perform inverse cluster sparse domain transformation based on the above method to obtain N-dimensional cluster sparse domain amplitude coefficients, and then the network device can convert The N-dimensional cluster sparse domain amplitude coefficients and the received quantized value of the phase information are combined in amplitude and phase to obtain the N-dimensional beam domain coefficients, and then the N-dimensional beam domain coefficients are subjected to subsequent processing.
  • the network device sends a pilot signal to the terminal; the terminal receives the pilot signal sent by the network device.
  • the terminal obtains the CSI estimated value.
  • the terminal performs DFT transformation.
  • the terminal performs beam domain transformation.
  • the terminal performs beam selection and quantization.
  • the terminal sends the quantized values of the L cluster sparse domain coefficients and their index values to the network device; the network device receives the quantized values of the L cluster sparse domain coefficients and their index values sent by the terminal.
  • the network device performs the inverse transformation of the cluster sparse domain.
  • the network device performs inverse DFT transformation.
  • the network device performs user precoding.
  • the network device may generate a pilot signal based on the precoding result of this time, and send it to the terminal, that is, repeat the above process to update the CSI feedback information. For specific details, reference may be made to the above description, which will not be repeated here.
  • the terminal performs the calculation on the N-dimensional antenna domain coefficient (still taking N as 64 as an example). Description) Perform DFT transformation to obtain N-dimensional beam domain coefficients, the result can still refer to Figure 4a, that is, including 14 effective beam components, wherein, the effective beam components refer to the above-mentioned larger numerical beam domain coefficients corresponding beam components. Further, in S203, the terminal performs cluster sparse domain transformation on the N-dimensional beam domain coefficients to obtain N-dimensional cluster sparse domain coefficients.
  • Figure 4d is a schematic diagram of the cluster sparse domain.
  • the N-dimensional cluster sparse domain coefficients are The corresponding effective beams are smaller in number than the effective beams corresponding to the N-dimensional beam domain coefficients, but the amount of information they contain remains unchanged. It should be noted that the energy of the beam components after the cluster sparse domain transformation shown in Figure 4d The values and quantities are only illustrative examples, and are not limited in this application. In this application, since the terminal further compresses the beam domain coefficients to reduce the dimension to the cluster sparse domain, that is, the number of effective beam components is reduced, but the amount of information remains unchanged.
  • FIG. 4e is a schematic diagram of the inverse cluster sparse domain transformation performed on the network device side and the result of inverse DFT transformation.
  • the CSI reconstruction value obtained by the network device side is closer to the CSI estimated value on the original terminal side (that is, the CSI). It can be seen that, based on the cluster sparse transformation basis, the present application further transforms the DFT-transformed CSI estimated value to further compress its beam components, thereby improving the CSI feedback accuracy while saving overhead.
  • the terminal can obtain the statistical covariance matrix R corresponding to the N-dimensional beam domain coefficients, perform eigendecomposition on the statistical covariance matrix R, obtain the eigenvector U of the statistical covariance matrix R, and use the eigenvector U as the cluster sparse transformation basis .
  • the feature vector U ie, the cluster sparse transformation basis
  • the feature vector U is an N*N-dimensional matrix, that is,
  • the statistical covariance matrix R is determined by the N S sample values of the N-dimensional beam domain coefficients, which can be exemplarily expressed as:
  • the sample value is the historical N-dimensional beam domain coefficient obtained by the terminal.
  • the terminal may send the statistical covariance matrix R or the eigenvector U to the network device. If the network device receives the statistical covariance matrix R, the network device performs eigendecomposition on the statistical covariance matrix R to obtain the statistical covariance matrix.
  • the eigenvector U of the matrix R i.e. the cluster sparse transform basis).
  • the statistical covariance matrix R and the eigenvector U are slowly time-varying, that is, they will change with the movement of the terminal. Therefore, the terminal needs to send the updated statistical covariance matrix or eigenvector U to the network device, and also The current statistical covariance matrix R or eigenvector U may be reported to the network device periodically.
  • the terminal and the network device can pre-store several groups of typical feature vectors U, that is, the cluster sparse transformation basis.
  • the terminal can still obtain the eigenvector U of the statistical covariance matrix R in the above-mentioned manner as the cluster sparse transformation basis, and match the obtained cluster sparse transformation basis with the pre-stored cluster sparse transformation basis to obtain
  • the identification information of the successfully matched cluster sparse transformation base such as the storage sequence number, is sent to the network device.
  • the network device may find the corresponding cluster sparse transformation basis based on the identification information.
  • the terminal may perform cluster sparse domain transformation on the N-dimensional beam domain coefficients based on several pre-stored groups of typical cluster sparse transform bases, and compare the transformed N-dimensional cluster sparse domain coefficients, and the terminal The identification information of the cluster sparse domain transform basis corresponding to the largest N-dimensional cluster sparse domain coefficient is sent to the network device.
  • the channel prior statistical feature is used to indicate the cluster sparse feature of multiple beam channel clusters corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse feature includes at least one of the following: the size of the beam channel cluster, the shape of the beam channel cluster, the beam channel cluster The cluster spacing of the channel clusters.
  • the cluster sparse transformation basis is determined based on cluster sparse features of multiple beam channel clusters.
  • the shape of the beam-channel cluster can be expressed as a N 0 -dimensional vector p 0 (eg, it can be a plurality of triangular beam-channel clusters on the beam domain as described in FIG.
  • the spacing between the beam-channel clusters is M, and NN 0 is divisible by M, then the cluster sparse transformation base P can be obtained by uniformly shifting p 0 at equal intervals, that is, P has Column vectors, the n-th column vector is obtained by shifting (n-1)M to p 0 in an N-dimensional length, where n is an integer greater than or equal to 1.
  • the cluster sparse transformation base described above is composed of the characteristics of multiple beam channel clusters.
  • the terminal may determine the corresponding cluster sparse transformation base according to the characteristics of each beam channel cluster, And based on the determined cluster sparse transformation basis, the cluster sparse domain transformation is performed on the corresponding beam channel clusters. For example, the terminal may determine the first cluster sparse transformation basis corresponding to the radius cluster 1 based on the shape and other characteristics of the radius cluster 1, and determine the second cluster sparse transformation basis corresponding to the radius cluster 2 based on the shape feature of the radius cluster 2.
  • the terminal performs cluster sparse domain transformation on path cluster 1 based on the first cluster sparse transformation basis, and performs cluster sparse domain transformation on path cluster 2 based on the second cluster sparse transformation basis.
  • the first cluster sparse transformation basis and The second cluster sparse transform basis is the same or different.
  • the first cluster of sparse transformation bases and the second cluster of sparse transformation bases can also be understood to be composed of vectors corresponding to multiple window functions.
  • the first cluster of sparse transformation bases and the second cluster of sparse transformation bases are based on rectangular window functions.
  • the first cluster of sparse transformation bases and the second cluster of sparse transformation bases are X*3-dimensional matrices, where X depends on the length of the window function .
  • the terminal performs the cluster sparse domain transformation on the path cluster 1 based on the first cluster sparse transform basis, and can obtain the effective beam components in the three cluster sparse domains. Similarly, the corresponding beam components can be obtained. Effective beam components on the 3-cluster sparse domain of path cluster 2.
  • the cluster sparse domain coefficient corresponding to the beam component with the highest intensity can be respectively selected from the path cluster 1 and the path cluster 2 for feedback.
  • the information fed back by the terminal also needs to feed back the number of beam channel clusters (referring to the channel clusters in the beam domain), the position information of the beam channel clusters, and the information of each beam channel cluster.
  • the cluster sparse domain transform basis corresponding to the channel cluster and the index value of the fed back cluster sparse domain coefficient so that the network device can perform the cluster sparse domain inverse transform on the corresponding beam channel cluster based on the cluster sparse transform basis of each beam channel cluster to obtain Get the N-dimensional beam domain coefficients.
  • the index value represents the serial number index of the cluster sparse domain coefficient in the cluster sparse domain coefficient corresponding to the cluster sparse domain diameter cluster to which it belongs.
  • the location information of the beam channel cluster includes at least one of the following: a center point of the beam channel cluster, a left start point of the beam channel cluster, and a right start point of the beam channel cluster.
  • p n corresponds to the nth vector of the transformation basis in the cluster
  • p n *p 1 represents the result of convolution of p n and p 1 , and then it is constructed by the iterative method of modulo normalization
  • n takes 1 ⁇
  • An example of the window function set at 4 is shown in Figure 8.
  • the terminal can perform a solid approximation on the channel prior statistical characteristics of the beam domain coefficients to construct an N*N-dimensional cluster sparse transformation basis, that is, For example, the correlation distribution pattern of a multi-period rectangular window can be used as the cluster sparse transformation basis, and the construction method is as follows:
  • the first relative bias vector consists of a 2-1 N - dimensional vector It is formed by concatenating with 2-1 N - dimensional all-zero vectors.
  • the second relative offset vector is obtained by performing a 2-1 N value cyclic shift on the first relative offset vector.
  • the third relative offset vector is obtained by performing a 2-1 N cyclic shift of the second relative offset vector.
  • the fourth relative offset vector is obtained by performing a 2-1 N value cyclic shift on the third relative offset vector.
  • the first relative bias vector consists of a 2- (n-1) N-dimensional vector And (1-2 -(n-1) )N-dimensional all-zero vector concatenation.
  • the rest of the relative bias vectors are obtained by performing a 2-1 N value cyclic shift of the first relative bias vector.
  • N*N-dimensional cluster sparse domain transform basis corresponding to FIG. 9 can be expressed as follows:
  • the terminal and the network device include corresponding hardware structures and/or software modules for executing each function.
  • the embodiments of the present application can be implemented in hardware or a combination of hardware and computer software. Whether a function is performed by hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each particular application, but such implementations should not be considered beyond the scope of this application.
  • the terminal may be divided into functional modules according to the foregoing method examples.
  • each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module.
  • the above-mentioned integrated modules can be implemented in the form of hardware, and can also be implemented in the form of software function modules. It should be noted that, the division of modules in the embodiments of the present application is schematic, and is only a logical function division, and there may be other division manners in actual implementation.
  • FIG. 10 shows a possible structure of the terminal 300 involved in the above embodiment.
  • the terminal 300 may include: an acquisition unit 301, a processing unit 302, and a transceiver unit 303, wherein the acquisition unit 301 may be used to acquire channel state information CSI information of a channel, where the CSI information includes an N-dimensional antenna Domain coefficient, where N is an integer greater than 1.
  • the processing unit 302 is configured to perform beam domain transformation on the CSI information based on the array response feature to obtain first CSI transformation information, where the first CSI transformation information includes N-dimensional beam domain coefficients.
  • the processing unit 302 is further configured to perform cluster sparse domain transformation on the first CSI transformation information based on the cluster sparse transformation basis to obtain second CSI transformation information, where the second CSI transformation information includes N-dimensional cluster sparse domain coefficients; wherein , the cluster sparse transformation basis is determined based on the channel prior statistical characteristics of the channel.
  • the transceiver unit 303 is configured to send the quantized values of L cluster sparse domain coefficients in the N-dimensional cluster sparse domain coefficients to the second communication device, where L is a positive integer smaller than N.
  • the channel prior statistical feature is used to indicate the statistical covariance matrix corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse transformation base is the eigenvector corresponding to the statistical covariance matrix
  • the transceiver unit 303 is further configured to send indication information to the second communication device, where the indication information is used to indicate the statistical covariance matrix or the eigenvector corresponding to the statistical covariance matrix.
  • the statistical covariance matrix is determined according to the sample values of a plurality of N-dimensional beam domain coefficients.
  • the channel prior statistical feature is used to indicate the cluster sparse feature of multiple beam channel clusters corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse feature includes at least one of the following: the size of the beam channel cluster, the size of the beam channel The shape of the channel cluster, the cluster spacing of the beam channel cluster.
  • the cluster sparse transformation basis is determined based on the cluster sparse characteristics of multiple beam channel clusters.
  • the processing unit 302 is configured to perform cluster sparse domain transformation on the corresponding beam channel cluster based on the cluster sparse transformation basis of each beam channel cluster, to obtain a plurality of second CSI transformation sub-information, the second
  • the CSI transform information includes a plurality of second CSI transform sub-information.
  • the cluster sparse transformation base is obtained by solidifying the prior statistical characteristics of the channel.
  • the processing unit 302 is configured to perform amplitude and phase separation on the N-dimensional beam domain coefficients in the first CSI transform information, to obtain amplitude information and phase information corresponding to each beam domain coefficient; based on the cluster sparse transform base, perform cluster sparse domain transformation on the amplitude information of each beam domain coefficient in the first CSI transformation information to obtain second CSI transformation information; the transceiver unit 303 is configured to send the N-dimensional cluster sparse domain coefficients to the second communication device.
  • the transceiver unit 303 is configured to send the quantized values of the L cluster sparse domain coefficients and the index values corresponding to the L cluster sparse domain coefficients to the second communication device, where the index value is used to indicate the cluster sparse domain The position of the coefficients in the N-dimensional cluster sparse domain coefficients.
  • the transceiver unit 303 is configured to receive the first signal sent by the second communication device; the obtaining unit 301 is configured to obtain CSI information based on the first signal.
  • the L cluster sparse domain coefficients are the L coefficients with the largest modulus value among the N-dimensional cluster sparse domain coefficients.
  • FIG. 11 shows a possible schematic structural diagram of the network device 400 involved in the above embodiment.
  • the network device 400 may include: a transceiver unit 401 and a processing unit 402.
  • the transceiver unit 401 is configured to receive the quantized values of the L cluster sparse domain coefficients sent by the first communication device.
  • the processing unit 402 is configured to perform inverse cluster sparse domain transformation on the L cluster sparse domain coefficients based on the cluster sparse transformation basis to obtain first CSI transformation information, where the first CSI transformation information includes N-dimensional beam domain coefficients; wherein, the cluster sparse transformation The basis is determined based on channel prior statistical characteristics of the channel, N is an integer greater than 1, and L is a positive integer less than N; the processing unit 402 is further configured to perform beam domain inversion on the first CSI transformation information based on the array response characteristics Transform to obtain CSI information, where the CSI information includes N-dimensional antenna domain coefficients.
  • the channel prior statistical feature is used to indicate the statistical covariance matrix corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse transformation base is the eigenvector corresponding to the statistical covariance matrix
  • the transceiver unit 401 is configured to receive indication information sent by the first communication device, where the indication information is used to indicate a statistical covariance matrix or an eigenvector corresponding to the statistical covariance matrix.
  • the statistical covariance matrix is determined according to the sample values of a plurality of N-dimensional beam domain coefficients.
  • the channel prior statistical feature is used to indicate the cluster sparse feature of multiple beam channel clusters corresponding to the N-dimensional beam domain coefficients
  • the cluster sparse feature includes at least one of the following: the size of the beam channel cluster, the size of the beam channel The shape of the channel cluster, the cluster spacing of the beam channel cluster.
  • the cluster sparse transformation basis is determined based on the cluster sparse characteristics of multiple beam channel clusters.
  • the processing unit 402 is configured to perform cluster sparse inverse transformation on the corresponding beam channel cluster based on the cluster sparse transformation basis of each beam channel cluster, to obtain a plurality of first CSI transformation sub-information, the first
  • the CSI transform information includes a plurality of first CSI transform sub-information.
  • the cluster sparse transformation base is obtained by solidifying the prior statistical characteristics of the channel.
  • the transceiver unit 401 is configured to receive the quantized values of the L cluster sparse domain amplitude coefficients and the quantized values of the phase information corresponding to the N cluster sparse domain coefficients sent by the first communication device; the processing unit 402 , which is used to perform cluster sparse domain inverse transform on L cluster sparse domain amplitude coefficients based on the cluster sparse transform basis to obtain N-dimensional beam domain amplitude coefficients.
  • the processing unit 402 is configured to perform amplitude and phase synthesis on the N-dimensional beam domain amplitude coefficient and N pieces of phase information to obtain first CSI transformation information.
  • the transceiver unit 401 is further configured to receive the quantized values of the L cluster sparse domain coefficients and the index values corresponding to the L cluster sparse domain coefficients sent by the first communication device, where the index value is used to indicate the cluster The position of the sparse domain coefficients in the N-dimensional cluster sparse domain coefficients.
  • the transceiver unit 401 is further configured to send a first signal to the first communication device, for instructing the first communication device to feed back the quantized values of the L cluster sparse domain coefficients based on the first signal.
  • the processing unit 402 is configured to obtain second CSI transformation information, where the second CSI transformation information includes N-dimensional cluster sparse domain coefficients, and the N-dimensional cluster sparse domain coefficients are obtained by complementing L cluster sparse domain coefficients obtained after zero.
  • the processing unit 402 is configured to perform inverse cluster sparse domain transformation on the second CSI transformation information based on the cluster sparse transformation basis to obtain first CSI transformation information.
  • the processing unit 402 is further configured to perform single-user or multi-user precoding based on the CSI information.
  • FIG. 12 is a schematic structural diagram of a communication apparatus provided by an embodiment of the present application.
  • the communication apparatus 500 may include: a processor 501 , a transceiver 505 , and optionally a memory 502 .
  • the transceiver 505 may be referred to as a transceiver unit, a transceiver, or a transceiver circuit, etc., for implementing a transceiver function.
  • the transceiver 505 may include a receiver and a transmitter, the receiver may be called a receiver or a receiving circuit, etc., for implementing a receiving function; the transmitter may be called a transmitter or a transmitting circuit, etc., for implementing a transmitting function.
  • the processor 501 Stored in memory 502 may be a computer program or software code or instructions 504, which may also be referred to as firmware.
  • the processor 501 can control the MAC layer and the PHY layer by running the computer program or software code or instruction 503 therein, or by calling the computer program or software code or instruction 504 stored in the memory 502, so as to realize the following aspects of the present application. Methods provided by the examples.
  • the processor 501 may be a central processing unit (central processing unit, CPU), and the memory 502 may be, for example, a read-only memory (read-only memory, ROM), or a random access memory (random access memory, RAM).
  • the processor 501 and transceiver 505 described in this application can be implemented in integrated circuits (ICs), analog ICs, radio frequency integrated circuits (RFICs), mixed-signal ICs, application specific integrated circuits (application specific integrated circuits) circuit, ASIC), printed circuit board (printed circuit board, PCB), electronic equipment, etc.
  • ICs integrated circuits
  • RFICs radio frequency integrated circuits
  • mixed-signal ICs application specific integrated circuits (application specific integrated circuits) circuit, ASIC), printed circuit board (printed circuit board, PCB), electronic equipment, etc.
  • the above-mentioned communication apparatus 500 may further include an antenna 506, and each module included in the communication apparatus 500 is only for illustration, which is not limited in this application.
  • the communication apparatus described in the above embodiments may be a terminal or a network device, but the scope of the communication apparatus described in this application is not limited thereto, and the structure of the communication apparatus may not be limited by FIG. 12 .
  • the communication apparatus may be a stand-alone device or may be part of a larger device.
  • the implementation form of the communication device may be:
  • Independent integrated circuit IC or chip, or, chip system or subsystem
  • a set of one or more ICs, optionally, the IC set may also include storage for storing data and instructions components; (3) modules that can be embedded in other devices; (4) receivers, smart terminals, wireless devices, handsets, mobile units, in-vehicle devices, cloud devices, artificial intelligence devices, etc.; (5) network devices, Sites, base stations, etc. (6) others, etc.
  • the chip shown in FIG. 13 includes a processor 601 and an interface 602 .
  • the number of processors 601 may be one or more, and the number of interfaces 602 may be multiple.
  • the chip or chip system may include memory 603 .
  • the processor 601 may be a logic circuit or a processing circuit.
  • the interface 602 can be an input-output interface or an input-output circuit.
  • the input and output interface is used to obtain the CSI information of the channel
  • the logic circuit is used to execute the method in the above method embodiment to obtain N is the cluster sparse domain coefficient according to the CSI information
  • the input The output interface is also used to output the quantized values of the L cluster sparse domain coefficients in an N-dimensional cluster sparse system.
  • the input/output interface is used to obtain the quantized values of the L cluster sparse domain coefficients
  • the logic circuit is used to execute the method in the above method embodiment to obtain the L cluster sparse domain coefficients.
  • the logic circuit can also be used to perform single-user or multi-user precoding based on the CSI information.
  • the present application also provides a communication system, including the network device and the terminal described in the foregoing embodiments.
  • Embodiments of the present application also provide a computer-readable storage medium.
  • the methods described in the above embodiments may be implemented in whole or in part by software, hardware, firmware or any combination thereof. If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium.
  • Computer-readable media can include both computer storage media and communication media and also include any medium that can transfer a computer program from one place to another.
  • a storage medium can be any available medium that can be accessed by a computer.
  • the computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to carry or carry instructions or data structures
  • the required program code is stored in the form and can be accessed by the computer.
  • any connection is properly termed a computer-readable medium.
  • coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave
  • coaxial cable, fiber optic cable , twisted pair, DSL or wireless technologies such as infrared, radio and microwave
  • Disk and disc as used herein includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.
  • the embodiments of the present application also provide a computer program product.
  • the methods described in the above embodiments may be implemented in whole or in part by software, hardware, firmware or any combination thereof. If implemented in software, it may be implemented in whole or in part in the form of a computer program product.
  • a computer program product includes one or more computer instructions. When the above-mentioned computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the above-mentioned method embodiments are generated.
  • the aforementioned computer may be a general purpose computer, a special purpose computer, a computer network, network equipment, user equipment, or other programmable devices.

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Abstract

本申请实施例提供了一种信道状态信息的处理方法及通信装置,涉及通信领域,该方法包括:获取信道的CSI信息,所述CSI信息包括N维天线域系数,所述N为大于1的整数;基于阵列响应特征,对所述CSI信息进行波束域变换,得到第一CSI变换信息,所述第一CSI变换信息包括N维波束域系数;基于簇稀疏变换基,对所述第一CSI变换信息进行簇稀疏域变换,得到第二CSI变换信息,所述第二CSI变换信息包括N维簇稀疏域系数;其中,所述簇稀疏变换基为基于所述信道的信道先验统计特征确定的;向第二通信装置发送所述N维簇稀疏域系数中的L个簇稀疏域系数的量化值,L为小于N的正整数。本申请可在降低反馈开销的同时,提升CSI反馈的精准度。

Description

信道状态信息的处理方法及通信装置
本申请要求于2020年8月25日提交中国国家知识产权局、申请号为202010865817.0、申请名称为“信道状态信息的处理方法及通信装置”的中国专利申请的优先权,其全部内容通过引用结合在本申请中。
技术领域
本申请实施例涉及通信领域,尤其涉及一种信道状态信息的处理方法及通信装置。
背景技术
无线通信系统中,终端设备通过信道状态信息CSI(Channel State Information,CSI)向网络设备反馈信道质量,以便网络设备对下行传输选择合适的调制和编码方案,提高数据传输的性能。
随着大规模多入多出(Massive-Multi-In-Multi-Out,Massive-MIMO)等技术的引入,天线数持续增加,CSI所蕴含的信息越来越丰富,使得信令开销越来越大。目前,已有技术中的CSI反馈流程通常采用基于离散傅里叶变换(Discrete Fourier transform,DFT)的CSI压缩技术,终端设备对CSI估计值进行DFT变换,并将DFT变换后的波束分量进行反馈,但是,随着天线数的增加,DFT变换后的波束域系数的量级同样会增长,进而导致反馈量增加,使得反馈开销较大。为降低反馈开销,则需要缩减反馈的系数的数量(也可以称为波束分量),但是,由于反馈的波束分量的减少,则会造成CSI反馈的误差和预编码性能损失增大。
发明内容
本申请提供一种CSI信息的处理方法及通信装置,能够在降低信道开销的同时,减小CSI反馈的误差以及预编码性能损失。
为达到上述目的,本申请采用如下技术方案:
第一方面,本申请实施例提供一种CSI信息的处理方法。该方法包括:获取信道的CSI信息,CSI信息包括N维天线域系数,N为大于1的整数;基于阵列响应特征,对CSI信息进行波束域变换,得到第一CSI变换信息,第一CSI变换信息包括N维波束域系数;基于簇稀疏变换基,对第一CSI变换信息进行簇稀疏域变换,得到第二CSI变换信息,第二CSI变换信息包括N维簇稀疏域系数;其中,簇稀疏变换基为基于信道的信道先验统计特征确定的;向第二通信装置发送N维簇稀疏域系数中的L个簇稀疏域系数的量化值,L为小于N的正整数。这样,本申请通过对波束域系数进行二次变换,即簇稀疏变换,使得波束域波束压缩到簇稀疏域上,以实现降维的目的,从而在降低反馈开销的同时,提升CSI反馈的精准度,进而减小预编码性能损失。
根据第一方面,信道先验统计特征用于指示N维波束域系数对应的统计协方差矩阵,簇稀疏变换基为统计协方差矩阵对应的特征向量。这样,装置可基于获取到的统计协方 差矩阵,以构造簇稀疏变换基。
根据第一方面,或者以上第一方面的任意一种实现方式,向第二通信装置发送指示信息,指示信息用于指示统计协方差矩阵或者统计协方差矩阵对应的特征向量。这样,可使得对端装置,例如第二通信装置可与第一通信装置配置相同的簇稀疏变换基。
根据第一方面,或者以上第一方面的任意一种实现方式,统计协方差矩阵为根据多个N维波束域系数的样本值确定的。这样,本申请示例性的提出一种统计协方差矩阵的获取方式,需要说明的是,在其他实施例中,也可以通过其它方式获取统计协方差矩阵,本申请不做限定。
根据第一方面,或者以上第一方面的任意一种实现方式,信道先验统计特征用于指示N维波束域系数对应的多个波束信道簇的簇稀疏特征,簇稀疏特征包括以下至少之一:波束信道簇的大小、波束信道簇的形状、波束信道簇的簇间距。这样,本申请示例性的示出另一种近似于协方差矩阵的信道先验统计特征的获取方式,以降低运算量。
根据第一方面,或者以上第一方面的任意一种实现方式,簇稀疏变换基为基于多个波束信道簇的簇稀疏特征确定的。这样,本申请示例性的示出一种根据波束信道簇的特征构造簇稀疏变换基的方式。
根据第一方面,或者以上第一方面的任意一种实现方式,基于簇稀疏变换基,对第一CSI变换信息进行簇稀疏域变换,包括:基于各波束信道簇的簇稀疏变换基,对对应的波束信道簇进行簇稀疏域变换,得到多个第二CSI变换子信息,第二CSI变换信息包括多个第二CSI变换子信息。这样,本申请示例性的示出另一种根据波束信道簇的特征构造簇稀疏变换基的方式,该方式是针对每个波束信道簇的特征构造变换基,并依次针对单个波束信道簇进行变换,从而进一步提升CSI反馈精度。
根据第一方面,或者以上第一方面的任意一种实现方式,簇稀疏变换基为对信道先验统计特征进行固化近似后得到的。这样,通过配置固定的簇稀疏变换基,可有效降低通信装置之间的信道开销。
根据第一方面,或者以上第一方面的任意一种实现方式,基于簇稀疏变换基,对第一CSI变换信息进行簇稀疏域变换,包括:对第一CSI变换信息中的N维波束域系数进行幅度相位分离,得到各波束域系数对应的幅度信息和相位信息;基于簇稀疏变换基,对第一CSI变换信息中的各波束域系数的幅度信息进行簇稀疏域变换,得到第二CSI变换信息;向第二通信装置发送N维簇稀疏域系数中的L个簇稀疏域系数的量化值和L个簇稀疏域系数对应的相位信息的量化值。这样,通过将波束域系数进行幅度相位分离,以对幅度和相位进行分别处理,即,对幅度系数进行簇稀疏域变换,从而进一步提升CSI 反馈的精准度。
根据第一方面,或者以上第一方面的任意一种实现方式,向第二通信装置发送N维簇稀疏域系数中的L个簇稀疏域系数的量化值,包括:向第二通信装置发送L个簇稀疏域系数的量化值以及L个簇稀疏域系数对应的索引值,索引值用于指示簇稀疏域系数在N维簇稀疏域系数中的位置。这样,第一通信装置向第二通信装置反馈簇稀疏域系数的量化值以及对应的索引值,以使第二通信装置可基于稀疏域系数的量化值以及对应的索引值,获取N维簇稀疏域系数。
根据第一方面,或者以上第一方面的任意一种实现方式,方法还包括:接收第二通信装置发送的第一信号;获取信道的CSI信息,包括:基于第一信号,获取CSI信息。这样,第一通信装置可基于第一信号,进行CSI估计,以得到CSI信息。
根据第一方面,或者以上第一方面的任意一种实现方式,L个簇稀疏域系数为N维簇稀疏域系数中模值最大的L个系数。这样,可通过选取N维簇稀疏域系数中模值最大的L个系数,也就是说,反馈L个强度最大的簇稀疏域波束分量,从而有效提高CSI反馈精准度。这样,可通过选取N维簇稀疏域系数中模值最大的L个系数,也就是说,反馈L个强度最大的簇稀疏域波束分量,从而有效提高CSI反馈精准度。
第二方面,本申请实施例提供一种CSI信息的处理方法。该方法包括:接收第一通信装置发送的L个簇稀疏域系数的量化值;基于簇稀疏变换基,对L个簇稀疏域系数进行簇稀疏域反变换,得到第一CSI变换信息,第一CSI变换信息包括N维波束域系数;其中,簇稀疏变换基为基于信道的信道先验统计特征确定的,N为大于1的整数,L为小于N的正整数;基于阵列响应特征,对第一CSI变换信息进行波束域反变换,得到CSI信息,CSI信息包括N维天线域系数。这样,第二通信装置可基于第一通信装置反馈的簇稀疏域系数,获取到精准度更高的CSI信息。
根据第二方面,信道先验统计特征用于指示N维波束域系数对应的统计协方差矩阵,簇稀疏变换基为统计协方差矩阵对应的特征向量。
根据第二方面,或者以上第二方面的任意一种实现方式,方法还包括:接收第一通信装置发送的指示信息,指示信息用于指示统计协方差矩阵或者统计协方差矩阵对应的特征向量。
根据第二方面,或者以上第二方面的任意一种实现方式,统计协方差矩阵为根据多个N维波束域系数的样本值确定的。
根据第二方面,或者以上第二方面的任意一种实现方式,信道先验统计特征用于指 示N维波束域系数对应的多个波束信道簇的簇稀疏特征,簇稀疏特征包括以下至少之一:波束信道簇的大小、波束信道簇的形状、波束信道簇的簇间距。
根据第二方面,或者以上第二方面的任意一种实现方式,簇稀疏变换基为基于多个波束信道簇的簇稀疏特征确定的。
根据第二方面,或者以上第二方面的任意一种实现方式,基于簇稀疏变换基,对L个簇稀疏域系数进行簇稀疏域反变换,包括:基于各波束信道簇的簇稀疏变换基,对对应的波束信道簇进行簇稀疏反变换,得到多个第一CSI变换子信息,第一CSI变换信息包括多个第一CSI变换子信息。
根据第二方面,或者以上第二方面的任意一种实现方式,簇稀疏变换基为对信道先验统计特征进行固化近似后得到的。
根据第二方面,或者以上第二方面的任意一种实现方式,基于簇稀疏变换基,对L个簇稀疏域系数进行簇稀疏域反变换,包括:接收第一通信装置发送的L个簇稀疏域幅度系数的量化值和N个簇稀疏域系数对应的相位信息的量化值;基于簇稀疏变换基,对L个簇稀疏域幅度系数进行簇稀疏域反变换,得到N维波束域幅度系数;将N维波束域幅度系数与N个相位信息进行幅度相位合成,得到第一CSI变换信息。
根据第二方面,或者以上第二方面的任意一种实现方式,方法还包括:接收第一通信装置发送的L个簇稀疏域系数的量化值以及L个簇稀疏域系数对应的索引值,索引值用于指示簇稀疏域系数在N维簇稀疏域系数中的位置。
根据第二方面,或者以上第二方面的任意一种实现方式,方法还包括:向第一通信装置发送第一信号,用于指示第一通信装置基于第一信号,反馈L个簇稀疏域系数的量化值。
根据第二方面,或者以上第二方面的任意一种实现方式,基于簇稀疏变换基,对L个簇稀疏域系数进行簇稀疏域反变换,包括:获取第二CSI变换信息,第二CSI变换信息包括N维簇稀疏域系数,N维簇稀疏域系数为将L个簇稀疏域系数补零后得到的;基于簇稀疏变换基,对第二CSI变换信息进行簇稀疏域反变换,得到第一CSI变换信息。这样,第二通信装置可基于补零的方式,将L维簇稀疏域系数变换为N维簇稀疏域系数,以进行后续的处理。
根据第二方面,或者以上第二方面的任意一种实现方式,方法还包括:基于CSI信息进行单用户或多用户的预编码。这样,第二通信装置基于本申请的方案获取到的CSI进行用户预编码,可有效提升预编码的性能。
第三方面,本申请实施例提供一种通信装置,该装置包括获取单元、处理单元,收发单元,其中,获取单元,可用于获取信道的信道状态信息CSI信息,所述CSI信息包括N维天线域系数,所述N为大于1的整数。处理单元,用于基于阵列响应特征,对所述CSI信息进行波束域变换,得到第一CSI变换信息,所述第一CSI变换信息包括N维波束域系数。处理单元,还用于基于簇稀疏变换基,对所述第一CSI变换信息进行簇稀疏域变换,得到第二CSI变换信息,所述第二CSI变换信息包括N维簇稀疏域系数;其中,所述簇稀疏变换基为基于所述信道的信道先验统计特征确定的。收发单元,用于向第二通信装置发送所述N维簇稀疏域系数中的L个簇稀疏域系数的量化值,L为小于N的正整数。
根据第三方面,信道先验统计特征用于指示N维波束域系数对应的统计协方差矩阵,簇稀疏变换基为统计协方差矩阵对应的特征向量。
根据第三方面,或者以上第三方面的任意一种实现方式,收发单元,还用于向第二通信装置发送指示信息,指示信息用于指示统计协方差矩阵或者统计协方差矩阵对应的特征向量。
根据第三方面,或者以上第三方面的任意一种实现方式,统计协方差矩阵为根据多个N维波束域系数的样本值确定的。
根据第三方面,或者以上第三方面的任意一种实现方式,信道先验统计特征用于指示N维波束域系数对应的多个波束信道簇的簇稀疏特征,簇稀疏特征包括以下至少之一:波束信道簇的大小、波束信道簇的形状、波束信道簇的簇间距。
根据第三方面,或者以上第三方面的任意一种实现方式,簇稀疏变换基为基于多个波束信道簇的簇稀疏特征确定的。
根据第三方面,或者以上第三方面的任意一种实现方式,处理单元,用于基于各波束信道簇的簇稀疏变换基,对对应的波束信道簇进行簇稀疏域变换,得到多个第二CSI变换子信息,第二CSI变换信息包括多个第二CSI变换子信息。
根据第三方面,或者以上第三方面的任意一种实现方式,簇稀疏变换基为对信道先验统计特征进行固化近似后得到的。
根据第三方面,或者以上第三方面的任意一种实现方式,处理单元,用于对第一CSI变换信息中的N维波束域系数进行幅度相位分离,得到各波束域系数对应的幅度信息和相位信息;基于簇稀疏变换基,对第一CSI变换信息中的各波束域系数的幅度信息进行 簇稀疏域变换,得到第二CSI变换信息;收发单元303,用于向第二通信装置发送N维簇稀疏域系数中的L个簇稀疏域系数的量化值和L个簇稀疏域系数对应的相位信息的量化值。
根据第三方面,或者以上第三方面的任意一种实现方式,收发单元,用于向第二通信装置发送L个簇稀疏域系数的量化值以及L个簇稀疏域系数对应的索引值,索引值用于指示簇稀疏域系数在N维簇稀疏域系数中的位置。
根据第三方面,或者以上第三方面的任意一种实现方式,收发单元,用于接收第二通信装置发送的第一信号;获取单元,用于基于第一信号,获取CSI信息。
根据第三方面,或者以上第三方面的任意一种实现方式,L个簇稀疏域系数为N维簇稀疏域系数中模值最大的L个系数。
第四方面,本申请实施例提供一种通信装置,包括收发单元,处理单元。其中,收发单元,用于接收第一通信装置发送的L个簇稀疏域系数的量化值。处理单元,用于基于簇稀疏变换基,对L个簇稀疏域系数进行簇稀疏域反变换,得到第一CSI变换信息,第一CSI变换信息包括N维波束域系数;其中,簇稀疏变换基为基于信道的信道先验统计特征确定的,N为大于1的整数,L为小于N的正整数;处理单元402,还用于基于阵列响应特征,对第一CSI变换信息进行波束域反变换,得到CSI信息,CSI信息包括N维天线域系数。
根据第四方面,信道先验统计特征用于指示N维波束域系数对应的统计协方差矩阵,簇稀疏变换基为统计协方差矩阵对应的特征向量。
根据第四方面,或者以上第四方面的任意一种实现方式,收发单元,用于接收第一通信装置发送的指示信息,指示信息用于指示统计协方差矩阵或者统计协方差矩阵对应的特征向量。
根据第四方面,或者以上第四方面的任意一种实现方式,统计协方差矩阵为根据多个N维波束域系数的样本值确定的。
根据第四方面,或者以上第四方面的任意一种实现方式,信道先验统计特征用于指示N维波束域系数对应的多个波束信道簇的簇稀疏特征,簇稀疏特征包括以下至少之一:波束信道簇的大小、波束信道簇的形状、波束信道簇的簇间距。
根据第四方面,或者以上第四方面的任意一种实现方式,簇稀疏变换基为基于多个波束信道簇的簇稀疏特征确定的。
根据第四方面,或者以上第四方面的任意一种实现方式,处理单元,用于基于各波束信道簇的簇稀疏变换基,对对应的波束信道簇进行簇稀疏反变换,得到多个第一CSI变换子信息,第一CSI变换信息包括多个第一CSI变换子信息。
根据第四方面,或者以上第四方面的任意一种实现方式,簇稀疏变换基为对信道先验统计特征进行固化近似后得到的。
根据第四方面,或者以上第四方面的任意一种实现方式,收发单元单元,用于接收第一通信装置发送的L个簇稀疏域幅度系数的量化值和N个簇稀疏域系数对应的相位信息的量化值;处理单元,用于基于簇稀疏变换基,对L个簇稀疏域幅度系数进行簇稀疏域反变换,得到N维波束域幅度系数。处理单元,用于将N维波束域幅度系数与N个相位信息进行幅度相位合成,得到第一CSI变换信息。
根据第四方面,或者以上第四方面的任意一种实现方式,收发单元,还用于接收第一通信装置发送的L个簇稀疏域系数的量化值以及L个簇稀疏域系数对应的索引值,索引值用于指示簇稀疏域系数在N维簇稀疏域系数中的位置。
根据第四方面,或者以上第四方面的任意一种实现方式,收发单元,还用于向第一通信装置发送第一信号,用于指示第一通信装置基于第一信号,反馈L个簇稀疏域系数的量化值。
根据第四方面,或者以上第四方面的任意一种实现方式,处理单元,用于获取第二CSI变换信息,第二CSI变换信息包括N维簇稀疏域系数,N维簇稀疏域系数为将L个簇稀疏域系数补零后得到的。处理单元,用于基于簇稀疏变换基,对第二CSI变换信息进行簇稀疏域反变换,得到第一CSI变换信息。
根据第四方面,或者以上第四方面的任意一种实现方式,处理单元,还用于基于CSI信息进行单用户或多用户的预编码。
第五方面,本申请实施例提供一种通信装置。该通信装置包括处理器和收发器。其中,处理器,用于获取信道的信道状态信息CSI信息,CSI信息包括N维天线域系数,N为大于1的整数。处理器,还用于基于阵列响应特征,对CSI信息进行波束域变换,得到第一CSI变换信息,第一CSI变换信息包括N维波束域系数。处理器,还用于基于簇稀疏变换基,对第一CSI变换信息进行簇稀疏域变换,得到第二CSI变换信息,第二CSI变换信息包括N维簇稀疏域系数;其中,簇稀疏变换基为基于信道的信道先验统计特征确定的。收发器,用于向第二通信装置发送N维簇稀疏域系数中的L个簇稀疏域系数的量化值,L为小于N的正整数。
根据第五方面,信道先验统计特征用于指示N维波束域系数对应的统计协方差矩阵,簇稀疏变换基为统计协方差矩阵对应的特征向量。
根据第五方面,或者以上第五方面的任意一种实现方式,收发器,还用于向第二通信装置发送指示信息,指示信息用于指示统计协方差矩阵或者统计协方差矩阵对应的特征向量。
根据第五方面,或者以上第五方面的任意一种实现方式,统计协方差矩阵为根据多个N维波束域系数的样本值确定的。
根据第五方面,或者以上第五方面的任意一种实现方式,信道先验统计特征用于指示N维波束域系数对应的多个波束信道簇的簇稀疏特征,簇稀疏特征包括以下至少之一:波束信道簇的大小、波束信道簇的形状、波束信道簇的簇间距。
根据第五方面,或者以上第五方面的任意一种实现方式,簇稀疏变换基为基于多个波束信道簇的簇稀疏特征确定的。
根据第五方面,或者以上第五方面的任意一种实现方式,处理器,还用于基于各波束信道簇的簇稀疏变换基,对对应的波束信道簇进行簇稀疏域变换,得到多个第二CSI变换子信息,第二CSI变换信息包括多个第二CSI变换子信息。
根据第五方面,或者以上第五方面的任意一种实现方式,簇稀疏变换基为对信道先验统计特征进行固化近似后得到。
根据第五方面,或者以上第五方面的任意一种实现方式,处理器,用于对第一CSI变换信息中的N维波束域系数进行幅度相位分离,得到各波束域系数对应的幅度信息和相位信息;处理器,还用于基于簇稀疏变换基,对第一CSI变换信息中的各波束域系数的幅度信息进行簇稀疏域变换,得到第二CSI变换信息;收发器,用于向第二通信装置发送N维簇稀疏域系数中的L个簇稀疏域系数的量化值和L个簇稀疏域系数对应的相位信息的量化值。
根据第五方面,或者以上第五方面的任意一种实现方式,收发器,还用于向第二通信装置发送L个簇稀疏域系数的量化值以及L个簇稀疏域系数对应的索引值,索引值用于指示簇稀疏域系数在N维簇稀疏域系数中的位置。
根据第五方面,或者以上第五方面的任意一种实现方式,收发器,用于接收第二通信装置发送的第一信号;处理器,用于基于第一信号,获取CSI信息。
根据第五方面,或者以上第五方面的任意一种实现方式,L个簇稀疏域系数为N维簇稀疏域系数中模值最大的L个系数。
第六方面,本申请实施例提供一种通信装置。该通信装置包括处理器和收发器;收发器,用于接收第一通信装置发送的L个簇稀疏域系数的量化值;处理器,用于基于簇稀疏变换基,对L个簇稀疏域系数进行簇稀疏域反变换,得到第一CSI变换信息,第一CSI变换信息包括N维波束域系数;其中,簇稀疏变换基为基于信道的信道先验统计特征确定的,N为大于1的整数,L为小于N的正整数;处理器,用于基于阵列响应特征,对第一CSI变换信息进行波束域反变换,得到CSI信息,CSI信息包括N维天线域系数。
根据第六方面,信道先验统计特征用于指示N维波束域系数对应的统计协方差矩阵,簇稀疏变换基为统计协方差矩阵对应的特征向量。
根据第六方面,或者以上第六方面的任意一种实现方式,收发器,还用于接收第一通信装置发送的指示信息,指示信息用于指示统计协方差矩阵或者统计协方差矩阵对应的特征向量。
根据第六方面,或者以上第六方面的任意一种实现方式,统计协方差矩阵为根据多个N维波束域系数的样本值确定的。
根据第六方面,或者以上第六方面的任意一种实现方式,信道先验统计特征用于指示N维波束域系数对应的多个波束信道簇的簇稀疏特征,簇稀疏特征包括以下至少之一:波束信道簇的大小、波束信道簇的形状、波束信道簇的簇间距。
根据第六方面,或者以上第六方面的任意一种实现方式,簇稀疏变换基为基于多个波束信道簇的簇稀疏特征确定的。
根据第六方面,或者以上第六方面的任意一种实现方式,处理器,还用于基于各波束信道簇的簇稀疏变换基,对对应的波束信道簇进行簇稀疏反变换,得到多个第一CSI变换子信息,第一CSI变换信息包括多个第一CSI变换子信息。
根据第六方面,或者以上第六方面的任意一种实现方式,簇稀疏变换基为对信道先验统计特征进行固化近似后得到的。
根据第六方面,或者以上第六方面的任意一种实现方式,收发器,用于接收第一通信装置发送的L个簇稀疏域幅度系数的量化值和N个簇稀疏域系数对应的相位信息的量化值;处理器,用于基于簇稀疏变换基,对L个簇稀疏域幅度系数进行簇稀疏域反变换, 得到N维波束域幅度系数;处理器,还用于将N维波束域幅度系数与N个相位信息进行幅度相位合成,得到第一CSI变换信息。
根据第六方面,或者以上第六方面的任意一种实现方式,收发器,还用于接收第一通信装置发送的L个簇稀疏域系数的量化值以及L个簇稀疏域系数对应的索引值,索引值用于指示簇稀疏域系数在N维簇稀疏域系数中的位置。
根据第六方面,或者以上第六方面的任意一种实现方式,收发器,用于向第一通信装置发送第一信号,用于指示第一通信装置基于第一信号,反馈L个簇稀疏域系数的量化值。
根据第六方面,或者以上第六方面的任意一种实现方式,处理器,还用于获取第二CSI变换信息,第二CSI变换信息包括N维簇稀疏域系数,N维簇稀疏域系数为将L个簇稀疏域系数补零后得到的;处理器,用于基于簇稀疏变换基,对第二CSI变换信息进行簇稀疏域反变换,得到第一CSI变换信息。
根据第六方面,或者以上第六方面的任意一种实现方式,处理器,还用于基于CSI信息进行单用户或多用户的预编码。
第七方面,本申请实施例提供一种计算机可读存储介质。该介质包括计算机程序,当计算机程序在装置上运行时,使得第一方面以及第一方面中任意一项的CSI信息的处理方法被执行。
第八方面,本申请实施例提供一种计算机可读存储介质。该介质包括计算机程序,当计算机程序在装置上运行时,使得第二方面以及第二方面中任意一项的CSI信息的处理方法被执行。
第九方面,本申请实施例提供一种计算机程序,该计算机程序用于执行第一方面以及第一方面中任意一项的CSI信息的处理方法。
第十方面,本申请实施例提供一种计算机程序,该计算机程序用于执行第二方面以及第二方面中任意一项的CSI信息的处理方法。
第十一方面,本申请实施例还提供了一种包括可执行指令的计算机程序产品,当该计算机程序产品被运行时,使得上述第一方面及其任一种可能的实现中的方法的部分或全部步骤被执行。
第十二方面,本申请实施例还提供了一种包括可执行指令的计算机程序产品,当该计算机程序产品被运行时,使得上述第二方面及其任一种可能的实现中的方法的部分或全部步骤被执行。
第十三方面,本申请实施例提供了一种芯片,该芯片包括至少一个处理电路和接口。其中,该接口、和该处理电路通过内部连接通路互相通信,该处理电路执行第一方面或第一方面的任一种可能的实现方式中的方法,以控制接口接收或发送信号。
第十四方面,本申请实施例提供了一种芯片,该芯片包括至少一个处理电路和接口。其中,该接口、和该处理电路通过内部连接通路互相通信,该处理电路执行第二方面或 第二方面的任一种可能的实现方式中的方法,以控制接口接收或发送信号。
第十五方面,本申请实施例提供一种通信系统,通信系统包括第一方面及其任一方面涉及的第一通信装置以及第二方面及其任一方面涉及的第二通信装置。
第十六方面,本申请实施例提供一种通信装置,包括至少一个处理器用于执行存储器中存储的程序指令,当该程序指令被处理器执行时,使得该装置执行本申请实施例第一方面及其任意种可能的实现所示的方法,或者使得该装置执行本申请实施例第二方面及其任意种可能的实现所示的方法。可选的,该存储器在该通信装置之外,或者该存储器包括在该通信装置之内。可选的,存储器和处理器集成在一起。
附图说明
为了更清楚地说明本申请实施例的技术方案,下面将对本申请实施例的描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本申请的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动性的前提下,还可以根据这些附图获得其他的附图。
图1是示例性示出的一种通信系统的示意图;
图2a是示例性示出的一种网络设备的结构示意图;
图2b是示例性示出的一种终端的结构示意图;
图3是示例性示出的一种CSI反馈方法的交互示意图;
图4a是示例性示出的波束域系数的示意图;
图4b是示例性示出的波束选择的示意图;
图4c是示例性示出的CSI重构的示意图;
图4d是示例性示出的簇稀疏域系数的示意图;
图4e是示例性示出的CSI重构的示意图;
图5是本申请实施例提供的终端的CSI信息的处理方法的流程示意图;
图6是本申请实施例提供的网络设备端的CSI信息的处理方法的流程示意图;
图7是本申请实施例提供的网络设备端的CSI信息的处理方法的交互示意图;
图8是示例性示出的窗函数集合示意图;
图9是示例性示出的簇稀疏变换基的构造示意图;
图10是本申请实施例提供的一种终端的结构示意图;
图11是本申请实施例提供的一种网络设备的结构示意图;
图12是本申请实施例提供的一种装置的结构示意图;
图13是本申请实施例提供的一种芯片的结构示意图。
具体实施方式
下面将结合本申请实施例中的附图,对本申请实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例是本申请一部分实施例,而不是全部的实施例。基于本申请中的实施例,本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他 实施例,都属于本申请保护的范围。
本文中术语“和/或”,仅仅是一种描述关联对象的关联关系,表示可以存在三种关系,例如,A和/或B,可以表示:单独存在A,同时存在A和B,单独存在B这三种情况。
本申请实施例的说明书和权利要求书中的术语“第一”和“第二”等是用于区别不同的对象,而不是用于描述对象的特定顺序。例如,第一目标对象和第二目标对象等是用于区别不同的目标对象,而不是用于描述目标对象的特定顺序。
在本申请实施例中,“示例性的”或者“例如”等词用于表示作例子、例证或说明。本申请实施例中被描述为“示例性的”或者“例如”的任何实施例或设计方案不应被解释为比其它实施例或设计方案更优选或更具优势。确切而言,使用“示例性的”或者“例如”等词旨在以具体方式呈现相关概念。
在本申请实施例的描述中,除非另有说明,“多个”的含义是指两个或两个以上。例如,多个处理单元是指两个或两个以上的处理单元;多个系统是指两个或两个以上的系统。
在本申请实施例的描述中,可能涉及到的数学符号及其描述如表1所示:
表1
Figure PCTCN2021111673-appb-000001
在对本申请实施例的技术方案说明之前,首先结合附图对本申请实施例的通信系统进行说明。参见图1,为本申请实施例提供的一种通信系统示意图。该通信系统中包括网络设备和终端。需要说明的是,在实际应用中,网络设备与终端的数量均可以为一个或多个,图1所示通信系统的网络设备与终端的数量仅为适应性举例,本申请对此不做限定。
上述通信系统可以用于支持第四代(fourth generation,4G)接入技术,例如长期演进(long term evolution,LTE)接入技术;或者,该通信系统也可以支持第五代(fifth generation,5G)接入技术,例如新无线(new radio,NR)接入技术;以及未来的移动通信系统。
以及,图1中的网络设备可用于支持终端接入,例如,可以是LTE中的演进型基站(evolutional Node B,eNB或eNodeB);或者5G网络或者未来演进的公共陆地移动网络(public land mobile network,PLMN)中的基站,宽带网络业务网关(broadband network gateway,BNG),汇聚交换机或非第三代合作伙伴项目(3rd generation partnership project,3GPP)接入设备等。可选的,本申请实施例中的网络设备可以包括各种形式的基站,例如:宏基站、微基站(也称为小站)、中继站、接入点、5G基站或未来的基站、卫星、传输点(transmitting and receiving point,TRP)、发射点(transmitting point,TP)、移动交换中心以及设备到设备(Device-to-Device,D2D)、车辆外联(vehicle-to-everything,V2X)、机器到机器(machine-to-machine,M2M)通信中承担基站功能的设备等,本申请实施例对此不作具体限定。为方便描述,本申请所有实施例中,为终端提供无线通信功能的装置统称为网络设备或基站。
图1中的终端可以是一种向用户提供语音或者数据连通性的设备,例如也可以称为移动台(mobile station),用户设备(user equipment,UE),用户单元(subscriber unit),站台(station),终端设备(terminal equipment,TE)等。终端可以为蜂窝电话(cellular phone),个人数字助理(personal digital assistant,PDA),无线调制解调器(modem),手持设备(handheld),膝上型电脑(laptop computer),无绳电话(cordless phone),无线本地环路(wireless local loop,WLL)台,平板电脑(pad)等。随着无线通信技术的发展,可以接入通信系统、可以与通信系统的网络侧进行通信,或者通过通信系统与其它物体进行通信的设备都可以是本申请实施例中的终端,譬如,智能交通中的终端和汽车、智能家居中的家用设备、智能电网中的电力抄表仪器、电压监测仪器、环境监测仪器、智能安全网络中的视频监控仪器、收款机等等。在本申请实施例中,终端可以与网络设备,例如图1中的网络设备进行通信。多个终端之间也可以进行通信。终端可以是静态固定的,也可以是移动的。
图2a是一种网络设备的结构示意图。在图2a中:
网络设备中包括至少一个处理器101、至少一个收发器103和一个或多个天线105,还可以包括存储器102和网络接口104。处理器101、存储器102、收发器103和网络接口104相连,例如通过总线相连。天线105与收发器103相连。网络接口104用于使得网络设备通过通信链路,与其它通信设备相连。在本申请实施例中,所述连接可包括各类接口、传输线或总线等,本实施例对此不做限定。
本申请实施例中的处理器,例如处理器101,可以包括如下至少一种类型:通用中央处理器(Central Processing Unit,CPU)、数字信号处理器(Digital Signal Processor,DSP)、微处理器、特定应用集成电路专用集成电路(Application-Specific Integrated Circuit,ASIC)、微控制器(Microcontroller Unit,MCU)、现场可编程门阵列(Field Programmable Gate Array,FPGA)、或者用于实现逻辑运算的集成电路。例如,处理器101可以是一 个单核(single-CPU)处理器或多核(multi-CPU)处理器。至少一个处理器101可以是集成在一个芯片中或位于多个不同的芯片上。
本申请实施例中的存储器,例如存储器102,可以包括如下至少一种类型:只读存储器(read-only memory,ROM)或可存储静态信息和指令的其他类型的静态存储设备,随机存取存储器(random access memory,RAM)或者可存储信息和指令的其他类型的动态存储设备,也可以是电可擦可编程只读存储器(Electrically erasable programmabler-only memory,EEPROM)。在某些场景下,存储器还可以是只读光盘(compact disc read-only memory,CD-ROM)或其他光盘存储、光碟存储(包括压缩光碟、激光碟、光碟、数字通用光碟、蓝光光碟等)、磁盘存储介质或者其他磁存储设备、或者能够用于携带或存储具有指令或数据结构形式的期望的程序代码并能够由计算机存取的任何其他介质,但不限于此。
存储器102可以是独立存在,与处理器101相连。可选的,存储器102也可以和处理器101集成在一起,例如集成在一个芯片之内。其中,存储器102能够存储执行本申请实施例的技术方案的程序代码,并由处理器101来控制执行,被执行的各类计算机程序代码也可被视为是处理器101的驱动程序。例如,处理器101用于执行存储器102中存储的计算机程序代码,从而实现本申请实施例中的技术方案。可选的,存储器102还可以在芯片之外,通过接口与处理器101相连。
收发器103可以用于支持接入网设备与终端之间射频信号的接收或者发送,收发器103可以与天线105相连。收发器103包括发射机Tx和接收机Rx。具体地,一个或多个天线105可以接收射频信号,该收发器103的接收机Rx用于从天线接收所述射频信号,并将射频信号转换为数字基带信号或数字中频信号,并将该数字基带信号或数字中频信号提供给所述处理器101,以便处理器101对该数字基带信号或数字中频信号做进一步的处理,例如解调处理和译码处理。此外,收发器103中的发射机Tx还用于从处理器101接收经过调制的数字基带信号或数字中频信号,并将该经过调制的数字基带信号或数字中频信号转换为射频信号,并通过一个或多个天线105发送所述射频信号。具体地,接收机Rx可以选择性地对射频信号进行一级或多级下混频处理和模数转换处理以得到数字基带信号或数字中频信号,所述下混频处理和模数转换处理的先后顺序是可调整的。发射机Tx可以选择性地对经过调制的数字基带信号或数字中频信号时进行一级或多级上混频处理和数模转换处理以得到射频信号,所述上混频处理和数模转换处理的先后顺序是可调整的。数字基带信号和数字中频信号可以统称为数字信号。
图2b是一种终端的结构示意图。在图2b中:
终端包括至少一个处理器201、至少一个收发器202和至少一个存储器203。处理器201、存储器203和收发器202相连。可选的,终端还可以包括输出设备204、输入设备205和一个或多个天线206。天线206与收发器202相连,输出设备204、输入设备205与处理器201相连。
收发器202、存储器203以及天线206可以参考图2a中的相关描述,实现类似功能。
处理器201可以是基带处理器,也可以是CPU,基带处理器和CPU可以集成在一起,或者分开。
处理器201可以用于为终端实现各种功能,例如用于对通信协议以及通信数据进行处理,或者用于对整个终端设备进行控制,执行软件程序,处理软件程序的数据;或者用于协助完成计算处理任务,例如对图形图像处理或者音频处理等等;或者处理器201用于实现上述功能中的一种或者多种。
输出设备204和处理器201通信,可以以多种方式来显示信息。
存储器203可以是独立存在,与处理器201相连。可选的,存储器203也可以和处理器201集成在一起,例如集成在一个芯片之内。其中,存储器203能够存储执行本申请实施例的技术方案的程序代码,并由处理器201来控制执行,被执行的各类计算机程序代码也可被视为是处理器201的驱动程序。例如,处理器201用于执行存储器203中存储的计算机程序代码,从而实现本申请实施例中的技术方案。可选的,存储器203还可以在芯片之外,通过接口与处理器201相连。
为使本领域技术人员更好地理解本申请的技术方案,下面结合图1对可能涉及到的背景技术进行简单说明。
大规模多入多出(Massive-Multi-In-Multi-Out,Massive-MIMO)是无线通信系统提升系统容量及频谱效率的重要技术手段,其基本原理是网络设备根据终端反馈的CSI信息,利用奇异值分解(Singular Value Decomposition,SVD)分解等方法确定信道的有效传输空间,在该传输空间中存在多个相互正交或接近正交的并行子信道,通过在这些并行子信道中发射多个独立的数据流以获取容量成倍增加的空分复用增益。
因此,该技术获取空分复用增益的一个关键条件是网络设备能够得到足够精确的CSI信息,为提升CSI反馈的精确度,一种典型的CSI反馈技术为:终端将获取到的CSI信息按预定义的CSI码本进行量化后,通过反向链路反馈给网络设备,网络设备根据反馈值进行直接或间接的CSI重构,并根据重构的CSI(以下简称CSI重构值)进行单用户或多用户的预编码。
但是,上述CSI反馈技术在使能网络设备预编码的同时,会引入反向链路的信令开销,一般而言,网络设备的天线数越多,CSI信息所蕴含的信息(例如空域特征)越丰富,相应的反馈开销亦越大。而随着通信技术的发展,Massive-MIMO技术中的天线数的持续增加,反馈量随天线数成倍增加,需要更有效的CSI反馈技术。
示例性的,如图3所示为示例性示出的一种CSI反馈技术,在图3中:
S101,网络设备向终端发送导频信号;相应的,终端接收网络设备发送的导频信号。
示例性的,网络设备通过空口资源向终端发送导频信号。
S102,终端获取CSI估计值。
示例性的,终端响应于接收到的导频信号,进行CSI估计,以获取CSI估计值,需要说明的是,CSI估计值非CSI真实值,为表示方便,本文中以CSI信息代指CSI估计值,下文中不再重复说明。
需要说明的是,在本申请实施例的描述中,均是以网络设备具有N个天线,终端具有一个天线,相应的,CSI信息为N维向量为例进行说明的,其中,N维向量包括N个天线域系数,N表示网络设备的天线数。在其他实施例中,本申请也可以应用于终端具有M个天线的场景中,相应的,CSI信息为N*M维矩阵。
S103,终端进行DFT变换。
示例性的,终端对CSI信息进行DFT变换,得到N维波束域系数,也可以理解为N个DFT波束对应的变换域系数。如图4a所示为N维波束域系数的示意图,其中,纵轴表示强度,横轴为波束域,参照图4a,N维波束域系数中的每个系数对应图4a中的一个波束分量,如非特殊说明,本文中涉及到的波束分量均是指DFT域上的波束分量。需要说明的是,由于波束域系数之间的数值相差较大,因此,数值较小的波束域系数对应的波束分量在图中未显示,也可以理解为,未显示的波束分量的能量较小。举例说明,CSI信息包括64维波束域系数,也就是说,图4a中应包括64个波束域系数对应的波束分量,而由于能量强度不同,图4a中仅示出能量较大的波束分量,即64维波束域系数中的14个波束域系数对应的波束分量。进一步需要说明的是,其中,波束域系数的分布具有径簇特征,可以包括一个或多个径簇,如图4a所示,径簇可以是由波束分量较大且密集的多个波束分量构成的,例如图4a中的径簇1和径簇2。
如上文所述,随着天线数量的增加,CSI反馈量也会增加,而造成反馈开销增大。因此,已有技术在反馈之前,终端进行波束选择,以降低反馈开销。
S104,终端进行波束选择和量化。
具体的,终端进行波束选择和量化的过程,示例性的,终端选择L个强波束对应的波束域系数进行量化,其中,L个强波束对应的波束域系数即为N维波束域系数中模值最大的L个系数。示例性的,如图4b所示为在图4a的基础上,终端进行波束选择后的结果,参照图4b,虚线框内对应的6个波束分量(即图中所示的实线波束分量)即为挑选出的L个强波束对应的波束域系数。也就是说,终端仅挑选了径簇2内的6个能量最大的有效波束分量进行反馈,而径簇1的所有波束分量以及径簇2内的一个波束分量(即图中显示的虚线波束分量)未反馈。
S105,终端向网络设备发送L个波束域系数及对应的索引值;相应的,网络设备接收终端发送的L个波束域系数及对应的索引值。
终端将量化后的L个波束域系数及对应的索引值通过反馈信道发送至网络设备。其中,索引值即为L个波束域系数在N维波束域系数中的位置。
S106,网络设备进行DFT反变换。
具体的,网络设备响应于接收到的L个波束域系数的量化值及其索引值,对L个波束域系数进行DFT反变换,以得到CSI重构值,CSI重构值同样包括N维天线域系数。具体的,网络设备通过L个索引值查找对应的L个DFT变换向量(在本文中称为DFT变换基),将L个波束域系数各自乘以对应的DFT变换基后,再进行其他处理,例如线性合并(具体处理细节可参照已有技术,本文不做赘述),合并结果即为CSI重构值。
S107,网络设备进行用户预编码。
具体的,网络设备基于CSI重构值,进行单用户或多用户的预编码。示例性的,网络设备可基于预编码的结果传输数据和/或导频信号。需要说明的是,图3以及下文中图5、图6仅示出了CSI信息的处理流程,由图可见,CSI信息的处理流程实际上是闭环流程,其可以是周期性触发的,也可以是条件触发的,具体触发实际可参照现有标准中的规定,本申请不做限定。而对于数据链,网络设备可基于预编码结果,向单用户或多用 户传输数据,下文中不再赘述。
如图4c所示为在图4b的基础上恢复的CSI重构值,参照图4c,由于CSI反馈量的降低,网络设备基于获取到的CSI重构值仅能恢复出图4a中的径簇2,导致网络设备接收到的CSI信息缺失,进一步造成预编码的性能损失。
本申请提出一种CSI信息的处理方式,可在节省开销的同时,提高CSI的反馈精度。下面结合上述如图1所示的应用场景示意图,介绍本申请的具体实施方案。需要说明的是,本申请实施例的描述中均是以第一通信装置为终端,第二通信装置为网络设备,即是以下行无线通信系统的应用场景为例进行说明的,实际上,本申请中的技术方案还可以应用于上行无线通信系统,即,第一通信装置为网络设备,第二通信装置为终端,也就是说,由网络设备确定CSI信息,并向终端反馈,具体实施细节与本申请中的类似,本申请不再重复说明。
结合图1,如图5所示为本申请实施例中的终端的CSI信息的处理方法的流程示意图,在图5中:
S201,终端获取信道的CSI信息,CSI信息包括N维天线域系数。
具体的,终端响应于接收到的网络设备发送的导频信号进行信道估计,以获取信道的CSI信息。其中,CSI信息中包括N维天线域系数,其中,N为网络设备的天线数量,其为大于1的整数。可选地,N值为网络设备预先通过信令发送至终端的。
示例性的,为方便表示,本申请中将CSI信息记为
Figure PCTCN2021111673-appb-000002
维度为N*1。
S202,终端基于阵列响应特征,对CSI信息进行波束域变换,得到第一CSI变换信息,第一CSI变换信息包括N维波束域系数。
具体的,在本申请中,终端可基于阵列响应特征,对CSI信息进行波束域变换,以得到N维波束域系数。其中,阵列响应特征用于描述N维天线域系数的阵列形态。
示例性的,本申请中以阵列形态为均匀线性阵列时,对应的波束域变换基为DFT变换基为例进行说明,DFT变换基可以理解为阵列响应特征的一种特殊形式,在其他实施例中,不同的阵列形态可对应不同的变换基(即波束域变换基),例如互质阵列对应的基于coprime(互质)阵列导向向量的变换基,本申请不做限定。
示例性的,终端基于DFT变换基对CSI信息进行波束域变换(也可以称为DFT变换)可以表示为:
Figure PCTCN2021111673-appb-000003
其中,Q表示DFT变换基,其具体为N*N为矩阵,
Figure PCTCN2021111673-appb-000004
表示第一CSI变换信息,即经过DFT变换后的N维波束域系数。
S203,终端基于簇稀疏变换基,对第一CSI变换信息进行簇稀疏域变换,得到第二CSI变换信息,第二CSI变换信息包括N维簇稀疏域系数;其中,簇稀疏变换基为基于信道的信道先验统计特征确定的。
具体的,终端可基于簇稀疏变换基,对N维波束域系数进行簇稀疏域变换,得到N维簇稀疏域系数,即第二CSI信息,以将N维波束域系数进一步压缩至簇稀疏域,从而实现更有效的信道降维效果。
示例性的,终端基于簇稀疏变换基对N维波束域系数(即第一CSI信息)进行簇稀疏域变换可以表示为:
Figure PCTCN2021111673-appb-000005
其中,P表示簇稀疏变换基,其具体为
Figure PCTCN2021111673-appb-000006
维矩阵,其中
Figure PCTCN2021111673-appb-000007
表示第二CSI变换信息,即经簇稀疏变换后的N维簇稀疏域系数。
具体的,在本申请中,簇稀疏变换基是基于信道的信道先验统计特征确定的,一个示例中,信道先验统计特征的一种表示方式为信道的统计协方差矩阵,终端可基于信道的统计协方差矩阵,确定对应的簇稀疏变换基,具体方式可参照下文中的方式一。
另一个示例中,信道先验统计特征的一种表示方式为采用近似于统计协方差的方式,示例性的,由于统计协方差矩阵的计算方式复杂,本申请实施例中可将波束信道簇的簇稀疏特征作为信道的先验统计特征,以构造簇稀疏变换基,具体方式可参照下文中的方式二。需要说明的是,波束信道簇即为图4a中所示的径簇。
上述两种方式中,簇稀疏变换基的构造都是具有慢时变性的,也就是说,网络设备与终端需要进行信令的交互,以使网络设备和终端配置相同的簇稀疏变换基,而该种方式同样也会增加信令开销,在又一个示例中,终端可对信道先验统计特征进行固化近似,以得到固定的簇稀疏变换基,从而进一步降低网络设备和终端的信令开销,具体方式可参照下文中的方式三。
S204,终端向网络设备发送N维簇稀疏域系数中的L个簇稀疏域系数的量化值。
具体的,终端从获取到的N维簇稀疏域系数中选择L个簇稀疏域系数进行量化,并将量化后的L个系数发送至网络设备,其中,L为小于N的正整数。需要说明的是,对簇稀疏域系数进行量化的具体方式可参照已有技术,本申请不做限定。可选地,L个簇稀疏域系数可以为N维簇稀疏域系数中模值最大的L个系数。
示例性的,为方便表示,L个簇稀疏域系数可表示为h L,其具体为L维向量(L维列向量),量化后的L个系数表示为
Figure PCTCN2021111673-appb-000008
其为L维向量。
可选地,终端还向网络设备发送索引信息,索引信息包括L个簇稀疏域系数对应的索引值,其中,索引值用于指示簇稀疏域系数对应的波束分量在簇稀疏域上的位置。示例性的,索引信息可表示为n,其具体为L维向量,包括L个索引值。其中向量n中第k个元素(或系数)n k表示第k个簇稀疏域上的波束分量在N维簇稀疏域系数
Figure PCTCN2021111673-appb-000009
中的序号索引。
结合图1,如图6所示为本申请实施例中的网络设备端的流程示意图,需要说明的是,除非特殊说明,网络设备侧所涉及到的参数的含义可参照终端的相关描述,下文中不再赘述。具体的,在图6中:
S301,网络设备接收终端发送的L个簇稀疏域系数的量化值。
具体的,网络设备向终端发送导频信号,其中,导频信号可用于指示终端基于该导频信号进行信道估计,以反馈CSI信息(具体是指上文所述的L个簇稀疏域系 数)。网络设备可接收到终端反馈的L维簇稀疏域系数
Figure PCTCN2021111673-appb-000010
的量化值。
可选地,网络设备可对接收到的L维簇稀疏域系数
Figure PCTCN2021111673-appb-000011
进行补零,以将L维簇稀疏域系数
Figure PCTCN2021111673-appb-000012
扩展为N维向量,得到N维簇稀疏域系数,可表示为
Figure PCTCN2021111673-appb-000013
示例性的,具体扩展方式如下:
Figure PCTCN2021111673-appb-000014
上式中,0 N表示N维全0向量,n k表示n(即索引信息,概念见上文)中的第k个元素。
S302,网络设备基于簇稀疏变换基,对L个簇稀疏域系数进行簇稀疏域反变换,得到第一CSI变换信息,第一CSI变换信息包括N维波束域系数;其中,簇稀疏变换基为基于信道的信道先验统计特征确定的。
具体的,网络设备可预先获取到簇稀疏变换基,一个示例中,如果簇稀疏变换基具有慢时变性,则网络设备可预先接收到终端发送的簇稀疏变换基。接着,网络设备基于当前的簇稀疏变换基,对接收到的L个簇稀疏域系数进行簇稀疏域反变换,以得到N维波束域系数。
示例性的,若网络设备对S201中对L个簇稀疏域系数进行补零,得到N维簇稀疏域变换,则网络设备对N维簇稀疏域系数进行簇稀疏域反变换可表示为:
Figure PCTCN2021111673-appb-000015
其中,
Figure PCTCN2021111673-appb-000016
表示经簇稀疏域反变换后得到的N维波束域系数,即第一CSI变换信息。需要说明的是,由于反馈误差的存在,网络设备获取到的第一CSI变换信息(即N维波束域系数)以及下文中涉及到的第二CSI变换信息(即N维簇稀疏域系数)和CSI信息(即N维天线域系数)可能和终端侧获取到的信息之间存在误差,为表示方便,在网络设备侧仍沿用第一CSI变换信息(即N维波束域系数)、第二CSI变换信息(即N维簇稀疏域系数)以及CSI信息(即N维天线域系数),以表示网络设备侧获取到的信息与终端侧获取到的信息的对应关系。
在一种可能的实现方式中,网络设备也可以不对L维簇稀疏域系数
Figure PCTCN2021111673-appb-000017
进行补零,在其他实施例中,还可以通过其它等效方式,得到N维波束域系数
Figure PCTCN2021111673-appb-000018
示例性的,网络设备可基于N×L维的簇稀疏变换基,对L维簇稀疏域系数
Figure PCTCN2021111673-appb-000019
进行簇稀疏域反变换,以得到N维波束域系数
Figure PCTCN2021111673-appb-000020
具体可表示为:
Figure PCTCN2021111673-appb-000021
其中,p n表示簇稀疏变换基P的第n个列向量,
Figure PCTCN2021111673-appb-000022
表示由L个N维列向量按列拼接得到的N×L维簇稀疏变换基。
S303,网络设备基于阵列响应特征,对第一CSI变换信息进行波束域反变换,得到CSI信息,CSI信息包括N维天线域系数。
示例性的,仍以DFT变换基为例,网络设备基于DFT变换基,对N维波束域系数(即第一CSI变换信息)进行DFT反变换,得到CSI信息,也可称为CSI重构值。其中,CSI信息包括N维天线域系数。
需要说明的是,在S202和S203中,网络设备所使用的簇稀疏变换基与阵列响应特征(例如DFT变换基)均是与终端侧对应的,也就是说,终端使用簇稀疏变换基P1进行簇稀疏域变换,则网络设备侧需同样基于簇稀疏变换基P1进行簇稀疏域反变换。
S304,网络设备基于CSI信息进行单用户或多用户的预编码。
示例性的,对多发单收信道或单用户单流的预编码可表示为:
Figure PCTCN2021111673-appb-000023
其中,w表示预编码权值(N维列向量),x表示单流数据符号,
Figure PCTCN2021111673-appb-000024
表示可以映射到N维发天线端口的预编码后符号向量。
示例性的,多发单收信道、多用户每用户单流的迫零(Zero-Forcing,ZF)预编码示例如下:
Figure PCTCN2021111673-appb-000025
其中,N u表示总的空分复用用户数,
Figure PCTCN2021111673-appb-000026
表示多发单收对应的信道CSI重构值(N维行向量),H表示N u个行向量按行拼接的N u×N维等效信道矩阵,W表示与等效信道H对应的N×N u维ZF预编码权值,x n表示第n个用户的单流数据符号,
Figure PCTCN2021111673-appb-000027
表示可以映射到N维发天线端口的多用户预编码后的符号向量。
可选地,网络设备可基于预编码结果,向单用户或多用户传输数据。可选地,网络设备还可基于预编码结果,向单用户或多用户传输导频信号,即重复执行S201。
在一种可能的实现方式中,为进一步提高CSI反馈的精准度,终端可对N维波束域系数中的各系数进行幅度相位分离,并仅对N维波束域系数中的幅度系数(以下简称N维波束域幅度系数)进行簇稀疏域变换。示例性的,终端可对S202中获取到的结果,即N维波束域系数
Figure PCTCN2021111673-appb-000028
进行幅度相位分离,可表示为:
Figure PCTCN2021111673-appb-000029
其中a表示由N维波束域系数
Figure PCTCN2021111673-appb-000030
中的各元素取模值后,组成的N维模值向量(即上文所述的N维波束域幅度系数)。
Figure PCTCN2021111673-appb-000031
表示
Figure PCTCN2021111673-appb-000032
中的第n个元素的复相角(即相位信息)。
示例性的,a作为公式(2)的输入,即,终端基于N维波束域幅度系数进行簇稀疏域变换,得到N维簇稀疏域幅度系数。
示例性的,终端将N维簇稀疏域幅度系数中的L个系数的量化值发送至网络设备,其细节可参照上文,此处不赘述。一个示例中,终端可将
Figure PCTCN2021111673-appb-000033
的量化值,即N维相位信息发送给网络设备。另一个示例中,终端可将N维波束域幅度系数中波束分量较大的M个波束域幅度系数对应的相位信息发送至网络设备,其中,M为小于N 且大于或等于L的整数。
示例性的,对于网络设备侧,网络设备接收到L个簇稀疏域幅度系数后,可基于上文中的方式进行簇稀疏域反变换,得到N维簇稀疏域幅度系数,随后,网络设备可将N维簇稀疏域幅度系数与接收到的相位信息的量化值进行幅度相位合并,以得到N维波束域系数,再对N维波束域系数进行后续的处理,细节可参照上文。
需要说明的是,上文中是分别以终端和网络设备端的流程进行说明的,实际上,终端与网络设备端构成的通信系统在执行本申请的技术方案的步骤时的流程示意图如图7所示,在图7中:
S401,网络设备向终端发送导频信号;终端接收网络设备发送的导频信号。
S402,终端获取CSI估计值。
S403,终端进行DFT变换。
S404,终端进行波束域变换。
S405,终端进行波束选择和量化。
S406,终端向网络设备发送L个簇稀疏域系数的量化值及其索引值;网络设备接收终端发送的L个簇稀疏域系数的量化值及其索引值。
S407,网络设备进行簇稀疏域反变换。
S408,网络设备进行DFT反变换。
S409,网络设备进行用户预编码。
网络设备在下一次发送导频信号时,可基于本次预编码结果生成导频信号,并发送至终端,即重复上述过程,以更新CSI反馈信息。具体细节可参照上文中,此处不再赘述。
为更好的说明本申请技术方案的技术效果,下面结合图4a~4c所示示意图进行对比说明,示例性的,在S202中,终端对N维天线域系数(仍以N为64为例进行说明)进行DFT变换,得到N维波束域系数,结果仍可参照图4a,即,包括14个有效的波束分量,其中,有效的波束分量是指上文所述的数值较大的波束域系数对应的波束分量。进一步的,在S203中,终端对N维波束域系数进行簇稀疏域变换,得到N维簇稀疏域系数,如图4d所示为簇稀疏域的示意图,参照图4d,N维簇稀疏域系数对应的有效波束较之N维波束域系数对应的有效波束数量更少,但是其所包含的信息量不变,需要说明的是,图4d中所示的簇稀疏域变换后的波束分量的能量值以及数量仅为示意性举例,本申请对此不做限定。在本申请中,由于终端对波束域系数进行进一步压缩,使其降维到簇稀疏域,即,有效波束分量数量减少,但信息量不变,因此,在同样反馈L=6个波束分量对应的系数的前提下,本申请中的终端可向网络设备反馈图4d中的6个有效波束分量对应的簇稀疏域系数。如图4e为网络设备侧进行簇稀疏域反变换,以及DFT反变换后的结果示意图,参照图4e,网络设备侧获取到的CSI重构值则更加接近原终端侧的CSI估计值(即CSI信息),由此可见,本申请基于簇稀疏变换基,对DFT变换后的CSI估计值进一步变换,使其波束分量进一步压缩,从而在节省开销的情况下,提高CSI 反馈精度。
下面对上文中提及的簇稀疏变换基的不同构造方式进行详细说明:
方式一:
具体的,终端可获取N维波束域系数对应的统计协方差矩阵R,对统计协方差矩阵R进行特征分解,得到统计协方差矩阵R的特征向量U,并将特征向量U作为簇稀疏变换基。示例性的,特征向量U(即簇稀疏变换基)为N*N维矩阵,即,
Figure PCTCN2021111673-appb-000034
在一种可能的实现方式中,统计协方差矩阵R是通过对N维波束域系数的N S个样本值确定的,示例性的,可以表示为:
Figure PCTCN2021111673-appb-000035
其中,样本值为终端获取到的历史N维波束域系数。
可选地,终端可向网络设备发送统计协方差矩阵R或特征向量U,若网络设备接收到的是统计协方差矩阵R,则网络设备对统计协方差矩阵R进行特征分解,得到统计协方差矩阵R的特征向量U(即簇稀疏变换基)。
如上文所述,统计协方差矩阵R与特征向量U具有慢时变性,即,会随着终端的移动变换,因此,终端需要向网络设备发送更新后的统计协方差矩阵或特征向量U,也可周期性地向网络设备上报当前的统计协方差矩阵R或特征向量U。
在一种可能的实现方式中,为进一步降低信令开销,终端与网络设备端可预先存储几组典型的特征向量U,即簇稀疏变换基。一个示例中,终端仍可按照上述方式,获取统计协方差矩阵R的特征向量U,以作为簇稀疏变换基,并将获取到的簇稀疏变换基与预先存储的簇稀疏变换基进行匹配,以将匹配成功的簇稀疏变换基的标识信息,例如存储序号,发送至网络设备。网络设备可基于标识信息,查找到对应的簇稀疏变换基。另一个示例中,终端可基于预先存储的几组典型的簇稀疏变换基,分别对N维波束域系数进行簇稀疏域变换,并对变换后的多个N维簇稀疏域系数进行比较,终端向网络设备发送最大的N维簇稀疏域系数对应的簇稀疏域变换基的标识信息。
方式二:
具体的,信道先验统计特征用于指示N维波束域系数对应的多个波束信道簇的簇稀疏特征,簇稀疏特征包括以下至少之一:波束信道簇的大小、波束信道簇的形状、波束信道簇的簇间距。
一种可能的实现方式中,簇稀疏变换基为基于多个波束信道簇的簇稀疏特征确定的。举例说明,假设波束信道簇的形状可以表示为N 0维向量p 0(例如其在如图4a所述的波束域上可以是多个呈三角形的波束信道簇)、波束信道簇之间的间距为M,且N-N 0能被M整除,则簇稀疏变换基P可由p 0等间距均匀移位得到,也就是说,P具有
Figure PCTCN2021111673-appb-000036
个列向量,第n个列向量通过对p 0在N维长度下移位(n-1)M得到,n为大于或等于1的整数。
上文中所述的簇稀疏变换基是由多个波束信道簇的特征构成的,在另一种可能的实现方式中,终端可针对每个波束信道簇的特征,确定对应的簇稀疏变换基,并基于确定的簇稀疏变换基,对对应的波束信道簇进行簇稀疏域变换。举例说明,终端可基于径簇1的形状等特征,确定径簇1对应的第一簇稀疏变换基,并基于径簇2的形状特征,确定径簇2对应的第二簇稀疏变换基。终端基于第一簇稀疏变换基,对径簇1进行簇稀疏域变换,以及,基于第二簇稀疏变换基,对径簇2进行簇稀疏域变换,可选地,第一簇稀疏变换基与第二簇稀疏变换基相同或不同。第一簇稀疏变换基与第二簇稀疏变换基也可以理解为是基于多个窗函数对应的向量构成的,例如,第一簇稀疏变换基和第二簇稀疏变换基均是基于矩形窗函数对应的向量、三角窗函数对应的向量以及抛物线窗函数对应的向量构成的,则第一簇稀疏变换基和第二簇稀疏变换基为X*3维的矩阵,其中,X取决于窗函数长度。相应的,以径簇1为例,终端基于第一簇稀疏变换基,对径簇1进行簇稀疏域变换后,可得到3个簇稀疏域上的有效波束分量,类似的,可得到对应于径簇2的3个簇稀疏域上的有效波束分量。终端在反馈时,可从径簇1与径簇2中分别选取强度最大的波束分量对应的簇稀疏域系数进行反馈。可选地,终端反馈的信息中除包括所述簇稀疏域系数的量化值外,还需要反馈波束信道簇(是指波束域上的信道簇)的数量、波束信道簇的位置信息、各波束信道簇对应的簇稀疏域变换基以及反馈的簇稀疏域系数的索引值,以使网络设备可基于各波束信道簇的簇稀疏变换基,对对应的波束信道簇进行簇稀疏域反变换,以得到N维波束域系数。示例性的,在本实施例中,索引值表示簇稀疏域系数在其所属的簇稀疏域径簇对应的簇稀疏域系数中的序号索引。示例性的,波束信道簇的位置信息包括以下至少之一:波束信道簇的中心点、波束信道簇的左起始点、波束信道簇的右起始点。
示例性的,构造对应的簇稀疏变换基的过程可以表示为:
Figure PCTCN2021111673-appb-000037
Figure PCTCN2021111673-appb-000038
其中,p n对应簇内变换基的第n个向量,p n*p 1表示p n与p 1做卷积后的结果,再进行模值归一化的迭代方法构造得到,n取1~4时的窗函数集合示例如图8所示。
方式三:
具体的,终端可对波束域系数的信道先验统计特征进行固化近似,以构造N*N维簇稀疏变换基,即
Figure PCTCN2021111673-appb-000039
例如,可基于多周期矩形窗的相关性分布图案作为簇稀疏变换基,构造方式如下:
1)第1个基向量为全1向量,对应全相关图案,如图9所示,也即p 1=+1 N,其中1 N表示N维全1向量。需要说明的是,图9中仅以N=8为例进行说明,本申请不做限定。
2)取n=1,生成2 -1N维全1向量
Figure PCTCN2021111673-appb-000040
则第2个基向量p 2
Figure PCTCN2021111673-appb-000041
及其反转值拼接构成,表示成式有
Figure PCTCN2021111673-appb-000042
3)取n=2,对应2 2个基向量,其中:
第1个相对偏置向量由2 -1N维向量
Figure PCTCN2021111673-appb-000043
和2 -1N维全0向量拼接构成。
第2个相对偏置向量由第1个相对偏置向量做2 -1N值的循环移位得到。
第3个相对偏置向量由第2个相对偏置向量做2 -1N值的循环移位得到。
第4个相对偏置向量由第3个相对偏置向量做2 -1N值的循环移位得到。
4)对于其余n,对应2 2个基向量,其中:
第1个相对偏置向量由2 -(n-1)N维向量
Figure PCTCN2021111673-appb-000044
和(1-2 -(n-1))N维全0向量拼接构成。
其余相对偏置向量由第1个相对偏置向量依次做2 -1N值的循环移位得到。
对上述N个基向量分别进行归一化。
示例性的,与图9所对应的N*N维簇稀疏域变换基可表示如下:
Figure PCTCN2021111673-appb-000045
上述主要从各个网元之间交互的角度对本申请实施例提供的方案进行了介绍。可以理解的是,终端和网络设备为了实现上述功能,其包含了执行各个功能相应的硬件结构和/或软件模块。本领域技术人员应该很容易意识到,结合本文中所公开的实施例描述的各示例的单元及算法步骤,本申请实施例能够以硬件或硬件和计算机软件的结合形式来实现。某个功能究竟以硬件还是计算机软件驱动硬件的方式来执行,取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能,但是这种实现不应认为超出本申请的范围。
本申请实施例可以根据上述方法示例对终端进行功能模块的划分,例如,可以对应各个功能划分各个功能模块,也可以将两个或两个以上的功能集成在一个处理模块中。上述集成的模块既可以采用硬件的形式实现,也可以采用软件功能模块的形式实现。需要说明的是,本申请实施例中对模块的划分是示意性的,仅仅为一种逻辑功能划分,实际实现时可以有另外的划分方式。
一个示例中,在采用对应各个功能划分各个功能模块的情况下,在采用对应各个功能划分各个功能模块的情况下,图10示出了上述实施例中所涉及的终端300的一种可能 的结构示意图,如图10所示,终端300可以包括:获取单元301、处理单元302,收发单元303,其中,获取单元301,可用于获取信道的信道状态信息CSI信息,所述CSI信息包括N维天线域系数,所述N为大于1的整数。处理单元302,用于基于阵列响应特征,对所述CSI信息进行波束域变换,得到第一CSI变换信息,所述第一CSI变换信息包括N维波束域系数。处理单元302,还用于基于簇稀疏变换基,对所述第一CSI变换信息进行簇稀疏域变换,得到第二CSI变换信息,所述第二CSI变换信息包括N维簇稀疏域系数;其中,所述簇稀疏变换基为基于所述信道的信道先验统计特征确定的。收发单元303,用于向第二通信装置发送所述N维簇稀疏域系数中的L个簇稀疏域系数的量化值,L为小于N的正整数。
在上述方法实施例的基础上,信道先验统计特征用于指示N维波束域系数对应的统计协方差矩阵,簇稀疏变换基为统计协方差矩阵对应的特征向量。
在上述方法实施例的基础上,收发单元303,还用于向第二通信装置发送指示信息,指示信息用于指示统计协方差矩阵或者统计协方差矩阵对应的特征向量。
在上述方法实施例的基础上,统计协方差矩阵为根据多个N维波束域系数的样本值确定的。
在上述方法实施例的基础上,信道先验统计特征用于指示N维波束域系数对应的多个波束信道簇的簇稀疏特征,簇稀疏特征包括以下至少之一:波束信道簇的大小、波束信道簇的形状、波束信道簇的簇间距。
在上述方法实施例的基础上,簇稀疏变换基为基于多个波束信道簇的簇稀疏特征确定的。
在上述方法实施例的基础上,处理单元302,用于基于各波束信道簇的簇稀疏变换基,对对应的波束信道簇进行簇稀疏域变换,得到多个第二CSI变换子信息,第二CSI变换信息包括多个第二CSI变换子信息。
在上述方法实施例的基础上,簇稀疏变换基为对信道先验统计特征进行固化近似后得到的。
在上述方法实施例的基础上,处理单元302,用于对第一CSI变换信息中的N维波束域系数进行幅度相位分离,得到各波束域系数对应的幅度信息和相位信息;基于簇稀疏变换基,对第一CSI变换信息中的各波束域系数的幅度信息进行簇稀疏域变换,得到第二CSI变换信息;收发单元303,用于向第二通信装置发送N维簇稀疏域系数中的L个簇稀疏域系数的量化值和L个簇稀疏域系数对应的相位信息的量化值。
在上述方法实施例的基础上,收发单元303,用于向第二通信装置发送L个簇稀疏域系数的量化值以及L个簇稀疏域系数对应的索引值,索引值用于指示簇稀疏域系数在N维簇稀疏域系数中的位置。
在上述方法实施例的基础上,收发单元303,用于接收第二通信装置发送的第一信号;获取单元301,用于基于第一信号,获取CSI信息。
在上述方法实施例的基础上,L个簇稀疏域系数为N维簇稀疏域系数中模值最大的L个系数。
另一个示例中,图11示出了上述实施例中所涉及的网络设备400的一种可能的结构 示意图,如图11所示,网络设备400可以包括:收发单元401,处理单元402。其中,收发单元401,用于接收第一通信装置发送的L个簇稀疏域系数的量化值。处理单元402,用于基于簇稀疏变换基,对L个簇稀疏域系数进行簇稀疏域反变换,得到第一CSI变换信息,第一CSI变换信息包括N维波束域系数;其中,簇稀疏变换基为基于信道的信道先验统计特征确定的,N为大于1的整数,L为小于N的正整数;处理单元402,还用于基于阵列响应特征,对第一CSI变换信息进行波束域反变换,得到CSI信息,CSI信息包括N维天线域系数。
在上述方法实施例的基础上,信道先验统计特征用于指示N维波束域系数对应的统计协方差矩阵,簇稀疏变换基为统计协方差矩阵对应的特征向量。
在上述方法实施例的基础上,收发单元401,用于接收第一通信装置发送的指示信息,指示信息用于指示统计协方差矩阵或者统计协方差矩阵对应的特征向量。
在上述方法实施例的基础上,统计协方差矩阵为根据多个N维波束域系数的样本值确定的。
在上述方法实施例的基础上,信道先验统计特征用于指示N维波束域系数对应的多个波束信道簇的簇稀疏特征,簇稀疏特征包括以下至少之一:波束信道簇的大小、波束信道簇的形状、波束信道簇的簇间距。
在上述方法实施例的基础上,簇稀疏变换基为基于多个波束信道簇的簇稀疏特征确定的。
在上述方法实施例的基础上,处理单元402,用于基于各波束信道簇的簇稀疏变换基,对对应的波束信道簇进行簇稀疏反变换,得到多个第一CSI变换子信息,第一CSI变换信息包括多个第一CSI变换子信息。
在上述方法实施例的基础上,簇稀疏变换基为对信道先验统计特征进行固化近似后得到的。
在上述方法实施例的基础上,收发单元401,用于接收第一通信装置发送的L个簇稀疏域幅度系数的量化值和N个簇稀疏域系数对应的相位信息的量化值;处理单元402,用于基于簇稀疏变换基,对L个簇稀疏域幅度系数进行簇稀疏域反变换,得到N维波束域幅度系数。处理单元402,用于将N维波束域幅度系数与N个相位信息进行幅度相位合成,得到第一CSI变换信息。
在上述方法实施例的基础上,收发单元401,还用于接收第一通信装置发送的L个簇稀疏域系数的量化值以及L个簇稀疏域系数对应的索引值,索引值用于指示簇稀疏域系数在N维簇稀疏域系数中的位置。
在上述方法实施例的基础上,收发单元401,还用于向第一通信装置发送第一信号,用于指示第一通信装置基于第一信号,反馈L个簇稀疏域系数的量化值。
在上述方法实施例的基础上,处理单元402,用于获取第二CSI变换信息,第二CSI变换信息包括N维簇稀疏域系数,N维簇稀疏域系数为将L个簇稀疏域系数补零后得到的。处理单元402,用于基于簇稀疏变换基,对第二CSI变换信息进行簇稀疏域反变换,得到第一CSI变换信息。
在上述方法实施例的基础上,处理单元402,还用于基于CSI信息进行单用户或多 用户的预编码。
下面介绍本申请实施例提供的一种装置。如图12所示:
图12为本申请实施例提供的一种通信装置的结构示意图。如图12所示,该通信装置500可包括:处理器501、收发器505,可选地还包括存储器502。
所述收发器505可以称为收发单元、收发机、或收发电路等,用于实现收发功能。收发器505可以包括接收器和发送器,接收器可以称为接收机或接收电路等,用于实现接收功能;发送器可以称为发送机或发送电路等,用于实现发送功能。
存储器502中可存储计算机程序或软件代码或指令504,该计算机程序或软件代码或指令504还可称为固件。处理器501可通过运行其中的计算机程序或软件代码或指令503,或通过调用存储器502中存储的计算机程序或软件代码或指令504,对MAC层和PHY层进行控制,以实现本申请下述各实施例提供的方法。其中,处理器501可以为中央处理器(central processing unit,CPU),存储器502例如可以为只读存储器(read-only memory,ROM),或为随机存取存储器(random access memory,RAM)。
本申请中描述的处理器501和收发器505可实现在集成电路(integrated circuit,IC)、模拟IC、射频集成电路(radio frequency integrated circuit,RFIC)、混合信号IC、专用集成电路(application specific integrated circuit,ASIC)、印刷电路板(printed circuit board,PCB)、电子设备等上。
上述通信装置500还可以包括天线506,该通信装置500所包括的各模块仅为示例说明,本申请不对此进行限制。
如前所述,以上实施例描述中的通信装置可以是终端或网络设备,但本申请中描述的通信装置的范围并不限于此,而且通信装置的结构可以不受图12的限制。通信装置可以是独立的设备或者可以是较大设备的一部分。例如所述通信装置的实现形式可以是:
(1)独立的集成电路IC,或芯片,或,芯片系统或子系统;(2)具有一个或多个IC的集合,可选的,该IC集合也可以包括用于存储数据,指令的存储部件;(3)可嵌入在其他设备内的模块;(4)接收机、智能终端、无线设备、手持机、移动单元、车载设备、云设备、人工智能设备等等;(5)网络设备、站点、基站等(6)其他等等。
对于通信装置的实现形式是芯片或芯片系统的情况,可参见图13所示的芯片的结构示意图。图13所示的芯片包括处理器601和接口602。其中,处理器601的数量可以是一个或多个,接口602的数量可以是多个。可选的,该芯片或芯片系统可以包括存储器603。
其中,上述方法实施例涉及的各步骤的所有相关内容均可以援引到对应功能模块的功能描述,在此不再赘述。
一种可能的实现中,该处理器601可以是逻辑电路,或处理电路。该接口602可以为输入输出接口或输入输出电路。举例来说,当该装置为第一通信装置时,输入输出接口用于获取信道的CSI信息,逻辑电路用于执行上述方法实施例中的方法根据该CSI信息得到N为簇稀疏域系数,输入输出接口还用于输出N维簇稀疏系统中的L个簇稀疏域系数的量化值。举例来说,当该装置为第二通信装置时,输入输出接口用于获取L个簇稀疏域系数的量化值,逻辑电路用于执行上述方法实施例中的方法根据L个簇稀疏域系 数得到CSI信息,逻辑电路还可以用于基于该CSI信息进行单用户或多用户的预编码。
本申请还提供了一种通信系统,包括上述实施例中所述的网络设备和终端。
本申请实施例还提供了一种计算机可读存储介质。上述实施例中描述的方法可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。如果在软件中实现,则功能可以作为一个或多个指令或代码存储在计算机可读介质上或者在计算机可读介质上传输。计算机可读介质可以包括计算机存储介质和通信介质,还可以包括任何可以将计算机程序从一个地方传送到另一个地方的介质。存储介质可以是可由计算机访问的任何可用介质。
作为一种可选的设计,计算机可读介质可以包括RAM,ROM,EEPROM,CD-ROM或其它光盘存储器,磁盘存储器或其它磁存储设备,或可用于承载的任何其它介质或以指令或数据结构的形式存储所需的程序代码,并且可由计算机访问。而且,任何连接被适当地称为计算机可读介质。例如,如果使用同轴电缆,光纤电缆,双绞线,数字用户线(DSL)或无线技术(如红外,无线电和微波)从网站,服务器或其它远程源传输软件,则同轴电缆,光纤电缆,双绞线,DSL或诸如红外,无线电和微波之类的无线技术包括在介质的定义中。如本文所使用的磁盘和光盘包括光盘(CD),激光盘,光盘,数字通用光盘(DVD),软盘和蓝光盘,其中磁盘通常以磁性方式再现数据,而光盘利用激光光学地再现数据。上述的组合也应包括在计算机可读介质的范围内。
本申请实施例还提供了一种计算机程序产品。上述实施例中描述的方法可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。如果在软件中实现,可以全部或者部分得通过计算机程序产品的形式实现。计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行上述计算机程序指令时,全部或部分地产生按照上述方法实施例中描述的流程或功能。上述计算机可以是通用计算机、专用计算机、计算机网络、网络设备、用户设备或者其它可编程装置。
上面结合附图对本申请的实施例进行了描述,但是本申请并不局限于上述的具体实施方式,上述的具体实施方式仅仅是示意性的,而不是限制性的,本领域的普通技术人员在本申请的启示下,在不脱离本申请宗旨和权利要求所保护的范围情况下,还可做出很多形式,均属于本申请的保护之内。

Claims (43)

  1. 一种信道状态信息CSI信息的处理方法,其特征在于,包括:
    获取信道的CSI信息,所述CSI信息包括N维天线域系数,所述N为大于1的整数;
    基于阵列响应特征,对所述CSI信息进行波束域变换,得到第一CSI变换信息,所述第一CSI变换信息包括N维波束域系数;
    基于簇稀疏变换基,对所述第一CSI变换信息进行簇稀疏域变换,得到第二CSI变换信息,所述第二CSI变换信息包括N维簇稀疏域系数;其中,所述簇稀疏变换基为基于所述信道的信道先验统计特征确定的;
    向第二通信装置发送所述N维簇稀疏域系数中的L个簇稀疏域系数的量化值,L为小于N的正整数。
  2. 根据权利要求1所述的方法,其特征在于,所述信道先验统计特征用于指示所述N维波束域系数对应的统计协方差矩阵,所述簇稀疏变换基为所述统计协方差矩阵对应的特征向量。
  3. 根据权利要求2所述的方法,其特征在于,所述方法还包括:
    向所述第二通信装置发送指示信息,所述指示信息用于指示所述统计协方差矩阵或者所述统计协方差矩阵对应的特征向量。
  4. 根据权利要求2或3所述的方法,其特征在于,所述统计协方差矩阵为根据多个N维波束域系数的样本值确定的。
  5. 根据权利要求1至4任一项所述的方法,其特征在于,所述信道先验统计特征用于指示所述N维波束域系数对应的多个波束信道簇的簇稀疏特征,所述簇稀疏特征包括以下至少之一:
    所述波束信道簇的大小、所述波束信道簇的形状、所述波束信道簇的簇间距。
  6. 根据权利要求5所述的方法,其特征在于,所述簇稀疏变换基为基于多个波束信道簇的簇稀疏特征确定的。
  7. 根据权利要求5或6所述的方法,其特征在于,所述基于簇稀疏变换基,对所述第一CSI变换信息进行簇稀疏域变换,包括:
    基于各波束信道簇的簇稀疏变换基,对对应的波束信道簇进行簇稀疏域变换,得到多个第二CSI变换子信息,所述第二CSI变换信息包括所述多个第二CSI变换子信息。
  8. 根据权利要求1所述的方法,其特征在于,所述簇稀疏变换基为对所述信道先验统计特征进行固化近似后得到的。
  9. 根据权利要求1至8任一项所述的方法,其特征在于,所述基于簇稀疏变换基,对所述第一CSI变换信息进行簇稀疏域变换,包括:
    对所述第一CSI变换信息中的N维波束域系数进行幅度相位分离,得到各波束域系数对应的幅度信息和相位信息;
    基于所述簇稀疏变换基,对所述第一CSI变换信息中的各波束域系数的幅度信息进行簇稀疏域变换,得到所述第二CSI变换信息;
    向所述第二通信装置发送所述N维簇稀疏域系数中的L个簇稀疏域系数的量化值和L个簇稀疏域系数对应的相位信息的量化值。
  10. 根据权利要求1至9任一项所述的方法,其特征在于,所述向第二通信装置发送所述N维簇稀疏域系数中的L个簇稀疏域系数的量化值,包括:
    向所述第二通信装置发送所述L个簇稀疏域系数的量化值以及所述L个簇稀疏域系数对应的索引值,所述索引值用于指示簇稀疏域系数在所述N维簇稀疏域系数中的位置。
  11. 根据权利要求1至10任一项所述的方法,其特征在于,所述方法还包括:
    接收所述第二通信装置发送的第一信号;
    所述获取信道的CSI信息,包括:
    基于所述第一信号,获取所述CSI信息。
  12. 根据权利要求1至11任一项所述的方法,其特征在于,所述L个簇稀疏域系数为所述N维簇稀疏域系数中模值最大的L个系数。
  13. 一种信道状态信息CSI信息的处理方法,其特征在于,包括:
    接收第一通信装置发送的L个簇稀疏域系数的量化值;
    基于簇稀疏变换基,对所述L个簇稀疏域系数进行簇稀疏域反变换,得到第一CSI变换信息,所述第一CSI变换信息包括N维波束域系数;其中,所述簇稀疏变换基为基于信道的信道先验统计特征确定的,N为大于1的整数,L为小于N的正整数;
    基于阵列响应特征,对所述第一CSI变换信息进行波束域反变换,得到CSI信息,所述CSI信息包括N维天线域系数。
  14. 根据权利要求13所述的方法,其特征在于,所述信道先验统计特征用于指示所述N维波束域系数对应的统计协方差矩阵,所述簇稀疏变换基为所述统计协方差矩阵对应的特征向量。
  15. 根据权利要求14所述的方法,其特征在于,所述方法还包括:
    接收所述第一通信装置发送的指示信息,所述指示信息用于指示所述统计协方差矩阵或者所述统计协方差矩阵对应的特征向量。
  16. 根据权利要求14所述的方法,其特征在于,所述统计协方差矩阵为根据多个N维波束域系数的样本值确定的。
  17. 根据权利要求13至16任一项所述的方法,其特征在于,所述信道先验统计特征用于指示所述N维波束域系数对应的多个波束信道簇的簇稀疏特征,所述簇稀疏特征包括以下至少之一:
    所述波束信道簇的大小、所述波束信道簇的形状、所述波束信道簇的簇间距。
  18. 根据权利要求17所述的方法,其特征在于,所述簇稀疏变换基为基于多个波束信道簇的簇稀疏特征确定的。
  19. 根据权利要求17或18所述的方法,其特征在于,所述基于簇稀疏变换基,对所述L个簇稀疏域系数进行簇稀疏域反变换,包括:
    基于各波束信道簇的簇稀疏变换基,对对应的波束信道簇进行簇稀疏反变换,得到多个第一CSI变换子信息,所述第一CSI变换信息包括所述多个第一CSI变换子信息。
  20. 根据权利要求13至19任一项所述的方法,其特征在于,所述簇稀疏变换基为对所述信道先验统计特征进行固化近似后得到的。
  21. 根据权利要求13至20任一项所述的方法,其特征在于,所述基于簇稀疏变换基,对所述L个簇稀疏域系数进行簇稀疏域反变换,包括:
    接收所述第一通信装置发送的所述L个簇稀疏域幅度系数的量化值和N个簇稀疏域系数对应的相位信息的量化值;
    基于所述簇稀疏变换基,对L个簇稀疏域幅度系数进行簇稀疏域反变换,得到N维波束域幅度系数;
    将所述N维波束域幅度系数与所述N个相位信息进行幅度相位合成,得到所述第一CSI变换信息。
  22. 根据权利要求13至21任一项所述的方法,其特征在于,所述方法还包括:
    接收所述第一通信装置发送的所述L个簇稀疏域系数的量化值以及所述L个簇稀疏域系数对应的索引值,所述索引值用于指示簇稀疏域系数在所述N维簇稀疏域系数中的位置。
  23. 根据权利要求13至22任一项所述的方法,其特征在于,所述方法还包括:
    向所述第一通信装置发送第一信号,用于指示所述第一通信装置基于所述第一信号,反馈所述L个簇稀疏域系数的量化值。
  24. 根据权利要求13至23任一项所述的方法,其特征在于,所述基于簇稀疏变换基,对所述L个簇稀疏域系数进行簇稀疏域反变换,包括:
    获取第二CSI变换信息,所述第二CSI变换信息包括N维簇稀疏域系数,所述N维簇稀疏域系数为将所述L个簇稀疏域系数补零后得到的;
    基于簇稀疏变换基,对所述第二CSI变换信息进行簇稀疏域反变换,得到所述第一CSI变换信息。
  25. 根据权利要求13至24任一项所述的方法,其特征在于,所述方法还包括:
    基于所述CSI信息进行单用户或多用户的预编码。
  26. 一种通信装置,其特征在于,包括获取单元、处理单元和收发单元;
    所述获取单元,用于获取信道的信道状态信息CSI信息,所述CSI信息包括N维天线域系数,所述N为大于1的整数;
    所述处理单元,还用于基于阵列响应特征,对所述CSI信息进行波束域变换,得到第一CSI变换信息,所述第一CSI变换信息包括N维波束域系数;
    所述处理单元,还用于基于簇稀疏变换基,对所述第一CSI变换信息进行簇稀疏域变换,得到第二CSI变换信息,所述第二CSI变换信息包括N维簇稀疏域系数;其中,所述簇稀疏变换基为基于所述信道的信道先验统计特征确定的;
    所述收发单元,用于向第二通信装置发送所述N维簇稀疏域系数中的L个簇稀疏域系数的量化值,L为小于N的正整数。
  27. 一种通信装置,其特征在于,包括处理单元和收发单元;
    所述收发单元,用于接收第一通信装置发送的L个簇稀疏域系数的量化值;
    所述处理单元,用于基于簇稀疏变换基,对所述L个簇稀疏域系数进行簇稀疏域反变换,得到第一CSI变换信息,所述第一CSI变换信息包括N维波束域系数;其中,所述簇稀疏变换基为基于信道的信道先验统计特征确定的,N为大于1的整数,L为小于N的正整数;
    所述处理单元,用于基于阵列响应特征,对所述第一CSI变换信息进行波束域反变换,得到CSI信息,所述CSI信息包括N维天线域系数。
  28. 根据权利要求26或27所述的装置,其特征在于,所述信道先验统计特征用于指示所述N维波束域系数对应的统计协方差矩阵,所述簇稀疏变换基为所述统计协方差矩阵对应的特征向量。
  29. 根据权利要求28所述的装置,其特征在于,所述统计协方差矩阵为根据多个N维波束域系数的样本值确定的。
  30. 根据权利要求26或27所述的装置,其特征在于,所述信道先验统计特征用于指 示所述N维波束域系数对应的多个波束信道簇的簇稀疏特征,所述簇稀疏特征包括以下至少之一:
    所述波束信道簇的大小、所述波束信道簇的形状、所述波束信道簇的簇间距。
  31. 根据权利要求26或27所述的装置,其特征在于,所述簇稀疏变换基为对所述信道先验统计特征进行固化近似后得到。
  32. 根据权利要求26所述的装置,其特征在于,
    所述处理单元,用于对所述第一CSI变换信息中的N维波束域系数进行幅度相位分离,得到各波束域系数对应的幅度信息和相位信息;
    所述处理单元,还用于基于所述簇稀疏变换基,对所述第一CSI变换信息中的各波束域系数的幅度信息进行簇稀疏域变换,得到所述第二CSI变换信息;
    所述收发单元,用于向所述第二通信装置发送所述N维簇稀疏域系数中的L个簇稀疏域系数的量化值和L个簇稀疏域系数对应的相位信息的量化值。
  33. 根据权利要求26所述的装置,其特征在于,
    所述收发单元,还用于向所述第二通信装置发送所述L个簇稀疏域系数的量化值以及所述L个簇稀疏域系数对应的索引值,所述索引值用于指示簇稀疏域系数在所述N维簇稀疏域系数中的位置。
  34. 根据权利要求26至33任一项所述的装置,其特征在于,所述L个簇稀疏域系数为所述N维簇稀疏域系数中模值最大的L个系数。
  35. 根据权利要求27所述的装置,其特征在于,
    所述收发单元,用于接收所述第一通信装置发送的所述L个簇稀疏域幅度系数的量化值和N个簇稀疏域系数对应的相位信息的量化值;
    所述处理单元,用于基于所述簇稀疏变换基,对L个簇稀疏域幅度系数进行簇稀疏域反变换,得到N维波束域幅度系数;
    所述处理单元,还用于将所述N维波束域幅度系数与所述N个相位信息进行幅度相位合成,得到所述第一CSI变换信息。
  36. 根据权利要求27所述的装置,其特征在于,
    所述收发单元,还用于接收所述第一通信装置发送的所述L个簇稀疏域系数的量化值以及所述L个簇稀疏域系数对应的索引值,所述索引值用于指示簇稀疏域系数在所述N维簇稀疏域系数中的位置。
  37. 根据权利要求27所述的装置,其特征在于,
    所述处理单元,还用于获取第二CSI变换信息,所述第二CSI变换信息包括N维簇 稀疏域系数,所述N维簇稀疏域系数为将所述L个簇稀疏域系数补零后得到的;
    所述处理单元,用于基于簇稀疏变换基,对所述第二CSI变换信息进行簇稀疏域反变换,得到所述第一CSI变换信息。
  38. 根据权利要求27所述的装置,其特征在于,
    所述处理单元,还用于基于所述CSI信息进行单用户或多用户的预编码。
  39. 一种通信装置,其特征在于,包括至少一个处理器用于执行存储器中存储的程序指令,所述程序指令被所述处理器执行时,使得所述装置
    执行权利要求1至12任一项所述的方法;或者
    执行权利要求13至25任一项所述的方法。
  40. 一种计算机可读存储介质,其特征在于,包括计算机程序,当其在计算机上执行时,使得
    权利要求1至12任一项所述的方法被执行;或者
    权利要求13至25任一项所述的方法被执行。
  41. 一种计算机程序,当其在计算机上运行时,使得权利要求1至12任一项所述的方法被执行;或者权利要求13至25任一项所述的方法被执行。
  42. 一种包含指令的计算机程序产品,当其在计算机上运行时,使得权利要求1至12任一项所述的方法被执行;或者权利要求13至25任一项所述的方法被执行。
  43. 一种通信系统,包括第一通信装置和第二通信装置,所述第一通信装置用于执行权利要求1至12任一项所述的方法,所述第二通信装置用于执行权利要求13至25任一项所述的方法。
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