WO2023061277A1 - 导频信号发送方法、信道估计方法、装置及设备 - Google Patents

导频信号发送方法、信道估计方法、装置及设备 Download PDF

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Publication number
WO2023061277A1
WO2023061277A1 PCT/CN2022/124029 CN2022124029W WO2023061277A1 WO 2023061277 A1 WO2023061277 A1 WO 2023061277A1 CN 2022124029 W CN2022124029 W CN 2022124029W WO 2023061277 A1 WO2023061277 A1 WO 2023061277A1
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matrix
pilot signal
communication device
channel estimation
equal
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PCT/CN2022/124029
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English (en)
French (fr)
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宋扬
孙鹏
杨昂
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维沃移动通信有限公司
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
    • H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
    • H04B7/0413—MIMO systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines

Definitions

  • the present application belongs to the technical field of communication, and in particular relates to a pilot signal sending method, a channel estimation method, device and equipment.
  • OFDM Orthogonal Frequency Division Multiplexing
  • Embodiments of the present application provide a pilot signal sending method, channel estimation method, device, and equipment, which can solve the problem of how to reduce pilot overhead.
  • a method for sending a pilot signal including:
  • the first communication device sends the first pilot signal in the first matrix
  • the first matrix is an N ⁇ M matrix
  • the i-th row and the j-th column in the first matrix represent the pilot signal sent by the i-th RE and the j-th antenna
  • i is greater than or equal to 1 and less than or equal to a positive integer of N
  • j is a positive integer greater than or equal to 1 and less than or equal to M
  • the N is the number of antennas of the first communication device
  • the M is the number of antennas transmitted on each RB
  • the M is smaller than the N
  • both N and M are integers greater than 1.
  • a method for channel estimation characterized in that it includes:
  • the second communication device receives the first pilot signal in the first matrix sent by the first communication device
  • the second communication device performs channel estimation according to the first pilot signal
  • the first matrix is an N ⁇ M matrix
  • the i-th row and the j-th column in the first matrix represent the pilot signal sent by the i-th RE and the j-th antenna
  • i is greater than or equal to 1 and less than or equal to a positive integer of N
  • j is a positive integer greater than or equal to 1 and less than or equal to M
  • the N is the number of antennas of the first communication device
  • the M is the number of antennas transmitted on each RB
  • the M is smaller than the N
  • both N and M are integers greater than 1.
  • a pilot signal sending device including:
  • a first sending module configured to send the first pilot signal in the first matrix
  • the first matrix is an N ⁇ M matrix
  • the i-th row and the j-th column in the first matrix represent the pilot signal sent by the i-th RE and the j-th antenna
  • i is greater than or equal to 1 and less than or equal to a positive integer of N
  • j is a positive integer greater than or equal to 1 and less than or equal to M
  • the N is the number of antennas of the first communication device
  • the M is the number of antennas transmitted on each RB
  • the M is smaller than the N
  • both N and M are integers greater than 1.
  • a channel estimation device including:
  • the first receiving module is configured to receive the first pilot signal in the first matrix sent by the first communication device
  • a channel estimation module configured for the second communication device to perform channel estimation according to the first pilot signal
  • the first matrix is an N ⁇ M matrix
  • the i-th row and the j-th column in the first matrix represent the pilot signal sent by the i-th RE and the j-th antenna
  • i is greater than or equal to 1 and less than or equal to a positive integer of N
  • j is a positive integer greater than or equal to 1 and less than or equal to M
  • the N is the number of antennas of the first communication device
  • the M is the number of antennas transmitted on each RB
  • the M is smaller than the N
  • both N and M are integers greater than 1.
  • a communication device including: a processor, a memory, and a program stored on the memory and operable on the processor, and when the program is executed by the processor, the first aspect is implemented. Or the steps of the method described in the second aspect.
  • a readable storage medium where a program or an instruction is stored on the readable storage medium, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect or the second aspect are implemented.
  • a computer program product is provided, the computer program product is stored in a non-transitory storage medium, and the computer program product is executed by at least one processor to implement the computer program product described in the first aspect or the second aspect. steps of the method.
  • a chip in an eighth aspect, includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions, so as to implement the first aspect or the second aspect the method described.
  • the first communication device transmits the first pilot signal in the first matrix. Since the first matrix is an N ⁇ M matrix, N is the number of antennas of the first communication device, and M is the number of antennas sent on each RB.
  • the number of REs of the pilot signal is set to be smaller than N, so as to achieve the purpose of pilot overhead compression, and reduce the pilot overhead on the basis of maintaining the channel estimation accuracy of the system.
  • FIG. 1 is a schematic diagram of a wireless communication system applicable to an embodiment of the present application
  • Fig. 2 is a flowchart of a method for sending a pilot signal in an embodiment of the present application
  • FIG. 3 is a flowchart of a channel estimation method in an embodiment of the present application.
  • FIG. 4 is a schematic diagram of an OFDM-based massive MIMO antenna system in an embodiment of the present application.
  • FIG. 5 is a schematic diagram of the training of the perception matrix in the embodiment of the present application.
  • FIG. 6 is a schematic diagram of a LAMP network structure in an embodiment of the present application.
  • FIG. 7 is a schematic diagram of the t-th layer structure of the LAMP network in the embodiment of the present application.
  • FIG. 8 is a schematic diagram of a pilot signal sending device in an embodiment of the present application.
  • FIG. 9 is a schematic diagram of a channel estimation device in an embodiment of the present application.
  • FIG. 10 is a schematic diagram of a terminal in an embodiment of the present application.
  • FIG. 11 is a schematic diagram of network-side equipment in an embodiment of the present application.
  • Fig. 12 is a schematic diagram of a communication device in an embodiment of the present application.
  • first, second and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specified order or sequence. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or described herein and that "first" and “second” distinguish objects. It is usually one category, and the number of objects is not limited. For example, there may be one or more first objects.
  • “and” in the specification and claims indicates at least one of the connected objects, and the character “/" generally indicates that the related objects are an "or” relationship.
  • New Radio New Radio
  • LTE Long Term Evolution
  • LTE-A Long Term Evolution-Advanced
  • CDMA code division multiple access
  • time division multiple access Time Division Multiple Access
  • TDMA time division multiple access
  • FDMA frequency division multiple access
  • OFDMA orthogonal frequency Orthogonal Frequency Division Multiple Access
  • SC-FDMA Single-carrier Frequency-Division Multiple Access
  • system and “network” in the embodiments of the present application are often used interchangeably, and the described technology can be used for the above-mentioned system and radio technology, and can also be used for other systems and radio technologies.
  • the following description describes the NR system for exemplary purposes, and NR terminology is used in most of the following descriptions, but these techniques are also applicable to applications other than NR system applications, such as 6th Generation (6G) communication systems.
  • 6G 6th Generation
  • This scheme utilizes the limited scattering characteristics of massive MIMO channels and the closely arranged characteristics of base station antenna arrays, and analyzes the structural sparseness between channels in different antenna space angle domains. Taking advantage of this property, massive MIMO channel estimation is transformed into a sparse signal recovery problem for structured compressed sensing.
  • the base station sends a small number of non-orthogonal pilots to reduce pilot overhead. The number of pilots is much smaller than the number of antennas.
  • Sparse signal recovery algorithms such as Orthogonal Matching Pursuit (OMP) or Approximate Message Passing (Approximate Message Passing, AMP) to solve this problem.
  • OMP Orthogonal Matching Pursuit
  • AMP Approximate Message Passing
  • compressed sensing compressed sensing
  • CSI Channel State Information
  • CS-like solutions strongly rely on the sparsity assumption of the channel in the spatial angle domain, and many real channels in actual scenarios do not have absolute sparsity. Therefore, CS-based channel estimation and feedback schemes rely on the prior assumption of perfect sparsity of the CSI matrix, and perform poorly on massive MIMO channels that only satisfy the approximate sparsity condition. The performance is difficult to meet the requirements of the actual system.
  • the wireless communication system includes a terminal 11 , a terminal 12 and a network side device 13 .
  • the terminal can also be called terminal equipment or UE, and the terminal can be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer) or a notebook computer, a personal digital assistant (Personal Digital Assistant, PDA), Pocket PC, netbook, ultra-mobile personal computer (UMPC), mobile internet device (Mobile Internet Device, MID), augmented reality (augmented reality, AR)/virtual reality (virtual reality, VR) equipment, robot , wearable device (Wearable Device), vehicle-mounted equipment (Vehicle User Equipment, VUE), pedestrian terminal (Pedestrian User Equipment, PUE), smart home (home equipment with wireless communication functions, such as refrigerators, TVs, washing machines or furniture, etc.
  • wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), Smart wristbands, smart clothing, game consoles, etc. It should be noted that, the embodiment of the present application does not limit the specific types of the terminal 11 and the terminal 12 .
  • the network side device 13 may be a base station or a core network, where a base station may be called a Node B, an evolved Node B, an access point, a Base Transceiver Station (Base Transceiver Station, BTS), a radio base station, a radio transceiver, a basic service Basic Service Set (BSS), Extended Service Set (ESS), Node B, Evolved Node B (gNB), Home Node B, Home Evolved Node B, Wireless Local Area Network (WLAN) ) access point, Wireless Fidelity (WiFi) node, Transmitting Receiving Point (TRP), wireless access network node, or some other appropriate term in the field, as long as the same technical effect is achieved , the base station is not limited to specified technical terms. It should be noted that in the embodiment of the present application, only the base station in the NR system is taken as an example, but the specific type of the base station is not limited.
  • Step 201 the first communication device sends the first pilot signal in the first matrix
  • the first matrix is an N ⁇ M matrix
  • the i-th row and the j-th column element in the first matrix represent the guides sent by the i-th resource element (Resource Element, RE) and the j-th antenna frequency signal
  • the N is the number of antennas of the first communication device
  • the M is the number of REs sending pilot signals on each resource block (Resource Block, RB)
  • the M is smaller than the N
  • the N and M All are integers greater than 1.
  • the base station sends a pilot channel state information reference signal (Channel State Information-Reference Signal, CSI-RS).
  • CSI-RS Channel State Information-Reference Signal
  • the resource block (Resource Block, RB) is taken as the basic unit.
  • Each RB contains 12 subcarriers in the frequency domain and 6-7 OFDM symbols in the time domain.
  • the compressed sensing method is used to perform pilot compression.
  • the second matrix is an N ⁇ M sensing matrix
  • the third matrix is a transformation matrix from the air domain to the beam domain, so that multiple antenna frequency domains showing frequency selectivity can be converted to beam domain delay domain paths.
  • a limited number of multipath channels are jointly estimated, which greatly reduces the pilot overhead.
  • the third matrix is an N-dimensional discrete Fourier transform (Discrete Fourier Transform, DFT) matrix
  • the third matrix is the Kronik product of an N1 - dimensional DFT matrix and an N2- dimensional DFT matrix, wherein the N1 is the number of rows comprising the first antenna array, and the N2 is the first antenna The number of columns in the array, the first antenna array includes N antennas.
  • the first communication device uses the DFT matrix of the space domain as a pilot transmission symbol on each RB, and compresses the transmitted first pilot signal by using a perceptual matrix operation.
  • the method further includes:
  • the second matrix is trained based on the first pilot signal.
  • the training of the second matrix based on the first pilot signal includes:
  • the first communication device performs fast Fourier transform on the first pilot signal to obtain a pilot signal with a delay path;
  • the first communication device estimates the pilot signal of the delay path according to the AMP algorithm to obtain a first estimation error
  • the first communications device trains the second matrix based on the first estimation error.
  • the method further includes:
  • the first communication device sends the trained second matrix to the second communication device.
  • the method further includes:
  • Training parameters of learned approximate message passing (Learned Approximate Message Passing, LAMP) based on the first pilot signal.
  • the step of training parameters of LAMP based on the first pilot signal includes:
  • the first communication device performs fast Fourier transform on the first pilot signal to obtain a pilot signal with a delay path;
  • the first communication device performs an overall channel estimation of learning approximate message passing LAMP according to the pilot signal of the delay path, and obtains a second estimation error;
  • the first communication device trains parameters of the LAMP according to the second estimation error.
  • the method further includes:
  • the first communication device sends the trained parameters of the LAMP to the second communication device.
  • the pilot overhead is reduced.
  • the embodiment of the present application provides a channel estimation method, and the specific steps include: step 301 and step 302 .
  • Step 301 the second communication device receives the first pilot signal in the first matrix sent by the first communication device;
  • Step 302 The second communication device performs channel estimation according to the first pilot signal
  • the first matrix is an N ⁇ M matrix
  • the i-th row and the j-th column in the first matrix represent the pilot signal sent by the i-th RE and the j-th antenna
  • i is greater than or equal to 1
  • j is a positive integer greater than or equal to 1
  • M is the number of antennas sent on each RB
  • N is the number of antennas of the first communication device
  • M is the number of antennas sent on each RB
  • the number of REs of the pilot signal, the M is smaller than the N, and both N and M are integers greater than 1.
  • the second communication device jointly processes the pilot signals of all RBs in the frequency domain, and first performs Inverse Fast Fourier Transform (IFFT) on the received pilot signals of each RB of each port. ), into the delay domain. Then the signals of each port and each time delay are combined to perform the overall channel estimation of learning approximate message passing LAMP. Finally, the estimated signal is transferred to the frequency domain to obtain the channel of each antenna in each RB, and complete the final channel estimation.
  • IFFT Inverse Fast Fourier Transform
  • the third matrix is an N-dimensional DFT matrix
  • the third matrix is the Kronik product of an N1 - dimensional DFT matrix and an N2- dimensional DFT matrix, wherein the N1 is the number of rows comprising the first antenna array, and the N2 is the first antenna The number of columns in the array, the first antenna array includes N antennas.
  • the second communication device performs channel estimation according to the first pilot signal, including:
  • the second communication device performs overall channel estimation of the LAMP according to the pilot signal of the delay path, and obtains a first channel estimation result.
  • the second communication device performs channel estimation according to the first pilot signal, further comprising:
  • the second communication device obtains, according to the first channel estimation result, a second channel estimation result of each antenna on a channel corresponding to an RB.
  • the second communication device obtains a second channel estimation result of each antenna on a channel corresponding to an RB according to the first channel estimation result, including:
  • the second communication device converts the first channel estimation result into an M ⁇ N channel matrix, and the first channel estimation result is a channel vector of a spatial beam;
  • the second communication device converts the M ⁇ N channel matrix into a frequency domain and a space domain, and obtains a second channel estimation result of each antenna on a channel corresponding to an RB.
  • the method further includes:
  • the second communication device estimates the pilot signal of each delay path according to the AMP algorithm to obtain a first estimation error
  • the second communications device trains the second matrix based on the first estimation error.
  • the method further includes:
  • the second communication device performs overall channel estimation of LAMP according to the pilot signal of the delay path, and obtains a second estimation error
  • the second communication device trains parameters of the LAMP according to the second estimation error.
  • the second communication device performs overall channel estimation of LAMP according to the pilot signal of the delay path, and obtains the second estimation error, including:
  • the second communication device splices the pilot signals of various delay paths together, and the spliced signal matrix is a fourth matrix, and the fourth matrix is a unit matrix whose dimension is the number of RBs and a sensing matrix on each RB
  • the Kronick product
  • the second communication device performs LAMP channel estimation according to the fourth matrix to obtain a second estimation error.
  • the pilot overhead is reduced.
  • the following takes the first communication device as the sending end and the second communication device as the receiving end as an example.
  • the OFDM-based massive MIMO antenna system shown in Figure 4 is used to combine the sparsity of the channel space domain (or described as the space domain) and the sparsity of the delay domain, through the space domain and delay
  • the domain joint compression optimized pilot design and channel estimation can complete the final channel estimation of all antennas on all RBs in an OFDM-based massive MIMO antenna system at one time.
  • each receiving antenna at the receiving end performs channel estimation independently, it can be designed by considering only one receiving antenna. That is, a large-scale antenna system of N ⁇ 1 is considered.
  • the pilot signal is sent in units of RBs, so channel estimation can be performed independently on each RB.
  • the number of REs transmitting pilot signals on each RB is M, and M is much smaller than the number N of transmitting antennas, so that the purpose of pilot overhead compression is achieved.
  • the transmitted symbol of the j-th antenna is expressed as c i,j
  • the transmitted symbols of the N antennas on all the M REs can be expressed as an N ⁇ M matrix C.
  • W 1 generally adopts a random matrix.
  • W 1 is obtained by training with a deep learning method through a large amount of channel data.
  • W 2 is the conversion matrix from the space domain to the beam domain, and for the linear array, W 2 is the N-dimensional DFT matrix.
  • pilot symbols are sent on M REs, and after inverse discrete Fourier transform (Inverse Discrete Fourier Transform, IDFT) (OFDM modulation), they are sent out through N antennas.
  • IDFT inverse discrete Fourier Transform
  • h i the channel on the i-th RB, which is a 1 ⁇ N channel vector, and the mathematical expression is:
  • h i [h i,1 h i,2 ... h i,N ]
  • n i is the noise received on the M REs on the i-th RB, and n i is a 1 ⁇ M vector.
  • received signals on K RBs are jointly processed.
  • R [r 1 r 2 ... r K ] T
  • R is the matrix of pilot signals received on all RBs
  • r k is the pilot signal received on the kth RB
  • T is the transposed symbol . Therefore:
  • definition is the channel of N transmit antennas on K RBs, and is a value to be estimated.
  • Be a sparse channel in the beam domain of N transmit antennas on K RBs.
  • a K-dimensional IDFT is first performed on the received pilot signal r in the RB dimension to obtain
  • R d is a K ⁇ M matrix.
  • first estimate That is, the channel gain in the beam domain delay domain.
  • a two-step deep learning training method is used to achieve optimal performance.
  • the first step is to train the optimal perception matrix A, because In this way, only W 1 can be trained, see FIG. 5 .
  • W 1 Since W 1 is operated on each RB, and W 1 on each RB is the same. Therefore, W 1 is trained separately on each RB, and there is no need to perform overall operations on all RBs. Since the final channel estimation is performed in the delay domain, W1 is trained separately on each delay path in the delay domain. Perform IFFT on the received pilot signal of each RB of each port, convert it into the delay domain for IDFT, estimate the pilot signal of each delay path with the AMP algorithm, and use the estimation error to train W 1 .
  • the pilot signal expression where r di is a 1 ⁇ M vector is a 1 ⁇ N vector.
  • W1 is trained to obtain the optimal perception matrix.
  • the perception matrix is regarded as a linear neural network. Compared with the traditional neural network, it has no offset and activation function. Find the optimal perception matrix through training.
  • the perception matrix at the sending end, the AMP recovery algorithm at the receiving end, the massive MIMO channel, and the noise introduced are regarded as a neural network for training.
  • the training data comes from a large number of channels in the beam domain delay domain, and the goal of training optimization (cost function cost function) is the output of the neural network at the output end and actual channel The mean square error between them is the smallest.
  • the optimized sensing matrix W 1 obtained from the training can be used for pilot transmission and channel estimation.
  • the base station uses the transmit signal (symbol) of each transmit antenna on each pilot RE of each RB according to the optimized sensing matrix.
  • the mobile user first performs IFFT on the pilot of each RB of each port, and converts it into extended domain. Then combine the signals of each port and each time delay to perform the overall channel estimation of LAMP.
  • the LAMP estimation result is the channel of each beam on each delay path LAMP training is based on to carry out.
  • the specific training process is as follows:
  • the LAMP network is constructed based on the traditional compressed sensing algorithm - AMP.
  • the iterative solution process of the AMP algorithm is expanded into a neural network, and its linear operation coefficient and nonlinear shrinkage parameters are jointly optimized.
  • the values of these distribution parameters can be obtained through the training process of the deep neural network. It is worth noting that during training, the sending end uses the optimized perception matrix that has been trained in the first stage.
  • the network structure of LAMP is constructed according to the AMP algorithm.
  • the LAMP algorithm expands the iterative operation process of the AMP algorithm into a deep neural network.
  • the specific network structure is shown in Figure 6.
  • the LAMP network structure has some unique designs, as follows: (1) There is an extra branch in the LAMP network, as shown in Figure 7 1, which corresponds to the Onsager correction item in the AMP algorithm to speed up convergence; (2) the LAMP algorithm’s The nonlinear function is a contraction function derived from a specific signal estimation problem, rather than an activation function that has no clear physical meaning in order to introduce a nonlinear function in a general neural network; (3) the noise parameter in the contraction function It is related to the residual and can be updated layer by layer. Because of the above characteristics, LAMP network can be more suitable for sparse signal recovery than general neural network.
  • the LAMP algorithm combines the deep learning and the AMP algorithm, takes the advantages of the two, not only utilizes the powerful learning ability of the deep neural network, but also retains the function of the AMP algorithm to realize sparse signal recovery.
  • the supervised learning method is adopted, and the input data set Perform network parameter training, where y d represents a low-dimensional observation signal, and x d represents a high-dimensional sparse signal.
  • the LAMP network is constructed on the basis of the AMP iterative algorithm, the network can be trained layer by layer in the process of training the network to realize the joint optimization of linear operation coefficients and nonlinear shrinkage parameters.
  • each layer defines a loss function separately, and the specific definitions are as follows:
  • y d represents a low-dimensional observation signal
  • x d represents a high-dimensional sparse signal
  • L t ( ⁇ ) is the loss function of the t-th layer, is the estimation result of x d in layer t.
  • the linear operation coefficient matrix B t and the nonlinear shrinkage parameter ⁇ t are trained separately and then jointly optimized in each layer.
  • pilot transmission and channel estimation can be performed.
  • the sending end it is also possible to independently perform the sensing matrix W 1 operation on each RB.
  • the pilot signals of all RBs are jointly processed in the frequency domain to perform channel estimation.
  • the pilot signals received by each port on each RB are collected together for IFFT and converted into the delay domain.
  • the signals of each port and each time delay to perform the overall channel estimation of LAMP.
  • the channel vector of the spatial beam is estimated
  • the channel vector Convert to M ⁇ N channel matrix Then perform DFT operation to convert to frequency domain and beam domain, and finally use W2 to transfer the result to frequency domain and space domain.
  • W2 Mathematically expressed as In this way, the channels of each antenna in each RB are obtained, and the final channel estimation is completed.
  • an embodiment of the present application provides an apparatus for sending a pilot signal, which is applied to a first communication device.
  • the apparatus 800 includes:
  • the first sending module 801 is configured to send the first pilot signal in the first matrix
  • the first matrix is an N ⁇ M matrix
  • the i-th row and the j-th column in the first matrix represent the pilot signal sent by the i-th RE and the j-th antenna
  • i is greater than or equal to 1 and less than or equal to a positive integer of N
  • j is a positive integer greater than or equal to 1 and less than or equal to M
  • the N is the number of antennas of the first communication device
  • the M is the number of antennas transmitted on each RB
  • the M is smaller than the N
  • both N and M are integers greater than 1.
  • the first matrix is determined based on the second matrix and the third matrix
  • the second matrix is an N ⁇ M sensing matrix
  • the third matrix is the transformation matrix
  • the third matrix is an N-dimensional DFT matrix
  • the third matrix is the Kronik product of an N1 - dimensional DFT matrix and an N2- dimensional DFT matrix, wherein the N1 is the number of rows comprising the first antenna array, and the N2 is the first antenna The number of columns in the array, the first antenna array includes N antennas.
  • the device also includes:
  • the device also includes:
  • a first training module configured to train the second matrix based on the first pilot signal.
  • the first training module includes:
  • the first processing unit is configured to perform fast Fourier transform on the first pilot signal to obtain a pilot signal with a delay path;
  • the second processing unit is configured to estimate the pilot signal of the delay path according to the AMP algorithm to obtain a first estimation error
  • a third processing unit configured to train the second matrix according to the first estimation error.
  • the device also includes:
  • a second sending module configured to send the trained second matrix to the second communication device.
  • the device also includes:
  • a second training module configured to train parameters of the LAMP based on the first pilot signal.
  • the second training module includes:
  • the fourth processing unit is configured to perform fast Fourier transform on the first pilot signal to obtain a pilot signal with a delay path;
  • the fifth processing unit is configured to perform overall channel estimation of LAMP according to the pilot signal of the delay path to obtain a second estimation error
  • a sixth processing unit configured to train parameters of the LAMP according to the second estimation error.
  • the device also includes:
  • a third sending module configured to send the trained parameters of the LAMP to the second communication device.
  • the device provided by the embodiment of the present application can realize each process realized by the method embodiment shown in FIG. 2 and achieve the same technical effect. To avoid repetition, details are not repeated here.
  • an embodiment of the present application provides a channel estimation apparatus, which is applied to a second communication device.
  • the apparatus 900 includes:
  • the first receiving module 901 is configured to receive the first pilot signal in the first matrix sent by the first communication device;
  • a channel estimation module 902 configured for the second communication device to perform channel estimation according to the first pilot signal
  • the first matrix is an N ⁇ M matrix
  • the i-th row and the j-th column in the first matrix represent the pilot signal sent by the i-th RE and the j-th antenna
  • i is greater than or equal to 1
  • j is a positive integer greater than or equal to 1
  • M is the number of antennas sent on each RB
  • N is the number of antennas of the first communication device
  • M is the number of antennas sent on each RB
  • the number of REs of the pilot signal, the M is smaller than the N, and both N and M are integers greater than 1.
  • the first matrix is determined based on the second matrix and the third matrix
  • the second matrix is an N ⁇ M sensing matrix
  • the third matrix is the transformation matrix
  • the third matrix is an N-dimensional DFT matrix
  • the third matrix is the Kronik product of an N1 - dimensional DFT matrix and an N2- dimensional DFT matrix, wherein the N1 is the number of rows comprising the first antenna array, and the N2 is the first antenna The number of columns in the array, the first antenna array includes N antennas.
  • the channel estimation module 902 is further used to:
  • the overall channel estimation of the LAMP is performed to obtain a first channel estimation result.
  • the channel estimation module 902 is further used to:
  • a second channel estimation result of each antenna on a channel corresponding to an RB is obtained.
  • the channel estimation module 902 is further used to:
  • the device also includes:
  • a seventh processing module configured to perform fast Fourier transform on the first pilot signal to obtain a pilot signal with a delay path
  • An eighth processing module configured to estimate the pilot signal of each delay path according to the AMP algorithm, to obtain a first estimation error
  • a ninth processing module configured to train the second matrix according to the first estimation error.
  • the device also includes:
  • a tenth processing module configured to perform fast Fourier transform on the first pilot signal to obtain a pilot signal with a delay path
  • the eleventh processing module is used for carrying out the overall channel estimation of LAMP according to the pilot signal of delay path, obtains the second estimation error;
  • a twelfth processing module configured to train parameters of LAMP according to the second estimation error.
  • the eleventh processing module is further configured to: splice pilot signals of various delay paths together, the spliced signal matrix is a fourth matrix, and the fourth matrix is a unit matrix whose dimension is the number of RBs and the Kronik product of the sensing matrix on each RB; according to the fourth matrix, LAMP channel estimation is performed to obtain a second estimation error.
  • the device provided by the embodiment of the present application can implement the various processes implemented by the method embodiment shown in FIG. 3 and achieve the same technical effect. To avoid repetition, details are not repeated here.
  • FIG. 10 is a schematic diagram of a hardware structure of a terminal implementing an embodiment of the present application.
  • the terminal 1000 includes, but is not limited to: a radio frequency unit 1001, a network module 1002, an audio output unit 1003, an input unit 1004, a sensor 1005, a display unit 1006, a user At least some components of the input unit 1007, the interface unit 1008, the memory 1009, and the processor 1010 and the like.
  • the terminal 1000 can also include a power supply (such as a battery) for supplying power to various components, and the power supply can be logically connected to the processor 1010 through the power management system, so as to manage charging, discharging, and power consumption through the power management system. Management and other functions.
  • a power supply such as a battery
  • the terminal structure shown in FIG. 10 does not constitute a limitation on the terminal, and the terminal may include more or fewer components than shown in the figure, or combine certain components, or arrange different components, which will not be repeated here.
  • the input unit 1004 may include a graphics processor (Graphics Processing Unit, GPU) 10041 and a microphone 10042, and the graphics processor 10041 is used for the image capture device (such as the image data of the still picture or video obtained by the camera) for processing.
  • the display unit 1006 may include a display panel 10061, and the display panel 10061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like.
  • the user input unit 1007 includes a touch panel 10061 and other input devices 10072 .
  • the touch panel 10061 is also called a touch screen.
  • the touch panel 10061 may include two parts, a touch detection device and a touch controller.
  • Other input devices 10072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, switch buttons, etc.), trackballs, mice, and joysticks, which will not be repeated here.
  • the radio frequency unit 1001 receives the downlink data from the network side device, and processes it to the processor 1010; in addition, sends the uplink data to the network side device.
  • the radio frequency unit 1001 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.
  • the memory 1009 can be used to store software programs or instructions as well as various data.
  • the memory 1009 may mainly include a program or instruction storage area and a data storage area, wherein the program or instruction storage area may store an operating system, at least one application program or instruction required by a function (such as a sound playback function, an image playback function, etc.) and the like.
  • the memory 1009 may include a high-speed random access memory, and may also include a nonvolatile memory, wherein the nonvolatile memory may be a read-only memory (Read-Only Memory, ROM), a programmable read-only memory (Programmable ROM) , PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electrically erasable programmable read-only memory (Electrically EPROM, EEPROM) or flash memory.
  • ROM Read-Only Memory
  • PROM programmable read-only memory
  • PROM erasable programmable read-only memory
  • Erasable PROM Erasable PROM
  • EPROM electrically erasable programmable read-only memory
  • EEPROM electrically erasable programmable read-only memory
  • flash memory for example at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device.
  • the processor 1010 may include one or more processing units; optionally, the processor 1010 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface and application programs or instructions, etc., Modem processors mainly handle wireless communications, such as baseband processors. It can be understood that the foregoing modem processor may not be integrated into the processor 1010 .
  • the terminal provided by the embodiment of the present application can realize each process realized by the method embodiment shown in FIG. 2 or FIG. 3 and achieve the same technical effect. To avoid repetition, details are not repeated here.
  • the embodiment of the present application also provides a network side device.
  • the network side device 1100 includes: an antenna 1101 , a radio frequency device 1102 , and a baseband device 1103 .
  • the antenna 1101 is connected to the radio frequency device 1102 .
  • the radio frequency device 1102 receives information through the antenna 1101, and sends the received information to the baseband device 1103 for processing.
  • the baseband device 1103 processes the information to be sent and sends it to the radio frequency device 1102
  • the radio frequency device 1102 processes the received information and sends it out through the antenna 1101 .
  • the foregoing frequency band processing device may be located in the baseband device 1103 , and the method performed by the network side device in the above embodiments may be implemented in the baseband device 1103 , and the baseband device 1103 includes a processor 1104 and a memory 1105 .
  • the baseband device 1103 may include, for example, at least one baseband board, and the baseband board is provided with a plurality of chips, as shown in FIG.
  • the baseband device 1103 may also include a network interface 1106 for exchanging information with the radio frequency device 1102, such as a common public radio interface (common public radio interface, CPRI).
  • a network interface 1106 for exchanging information with the radio frequency device 1102, such as a common public radio interface (common public radio interface, CPRI).
  • CPRI common public radio interface
  • the network side device in this embodiment of the present application further includes: instructions or programs stored in the memory 1105 and executable on the processor 1104 .
  • processor 1104 invokes instructions or programs in the memory 1105 to execute the methods performed by the modules shown in FIG. 8 or 9 and achieve the same technical effect. To avoid repetition, details are not repeated here.
  • this embodiment of the present application further provides a communication device 1200, including a processor 1201, a memory 1202, and programs or instructions stored in the memory 1202 and operable on the processor 1201,
  • a communication device 1200 including a processor 1201, a memory 1202, and programs or instructions stored in the memory 1202 and operable on the processor 1201
  • the communication device 1200 is a terminal
  • the program or instruction is executed by the processor 1201
  • each process of the above method embodiment in FIG. 2 or FIG. 3 can be realized, and the same technical effect can be achieved.
  • the communication device 1200 is a network-side device
  • the program or instruction is executed by the processor 1201
  • each process of the method embodiment in FIG. 2 or FIG. 3 described above can be achieved, and the same technical effect can be achieved. To avoid repetition, details are not repeated here. .
  • the embodiment of the present application also provides a computer program/program product, the computer program/program product is stored in a non-volatile storage medium, and the computer program/program product is executed by at least one processor to realize the Or the steps of the processing method described in FIG. 3 .
  • the embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by the processor, each process of the method embodiment shown in FIG. 2 or FIG. 3 above is implemented. , and can achieve the same technical effect, in order to avoid repetition, it will not be repeated here.
  • the processor is the processor in the terminal or the network side device described in the foregoing embodiments.
  • the readable storage medium includes computer readable storage medium, such as computer read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
  • the embodiment of the present application also provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the above described in Figure 2 or Figure 3
  • the chip includes a processor and a communication interface
  • the communication interface is coupled to the processor
  • the processor is used to run programs or instructions to implement the above described in Figure 2 or Figure 3
  • the chip mentioned in the embodiment of the present application may also be called a system-on-chip, a system-on-chip, a system-on-a-chip, or a system-on-a-chip.
  • the term “comprising”, “comprising” or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase “comprising a " does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element.
  • the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved. Functions are performed, for example, the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.
  • the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation.
  • the technical solution of the present application can be embodied in the form of computer software products, which are stored in a storage medium (such as ROM/RAM, magnetic disk, etc.) , CD-ROM), including several instructions to make a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of the present application.

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Abstract

本申请公开了一种导频信号发送方法、信道估计方法、装置及设备，该方法包括：第一通信设备发送第一矩阵中的第一导频信号；其中，所述第一矩阵为N×M矩阵，所述第一矩阵中的第i行，第j列的元素表示通过第i个RE，第j个天线发送的导频信号，i为大于或等于1，小于或等于N的正整数，j为大于或等于1，小于或等于M的正整数，所述N为所述第一通信设备的天线数量，所述M为每个RB上发送导频信号的RE的数量，所述M小于所述N，所述N和M均为大于1的整数。

Description

导频信号发送方法、信道估计方法、装置及设备
相关申请的交叉引用
本申请主张在2021年10月12日在中国提交的中国专利申请No.202111187971.8的优先权，其全部内容通过引用包含于此。
技术领域
本申请属于通信技术领域，具体涉及一种导频信号发送方法、信道估计方法、装置及设备。
背景技术
利用大规模多输入多输出(Multiple-input Multiple-output，MIMO)技术形成大规模天线阵列，可以同时支持更多用户发送和接收信号，从而将移动网络的信道容量以及数据流量提升数十倍或更大，同时能实现多用户之间干扰的急剧降低。因此从它被提出就一直受到广大研究人员的持续高度关注。为了支持宽带无线通信，从第四代移动通信技术(fourth generation，4G)开始正交频分复用(Orthogonal Frequency Division Multiplexing，OFDM)就成为移动通信的底层技术。它可以有效的对抗多径干扰，将频域频率选择性信道划分为多个平衰落的子信道来支持无线传输。OFDM结合大规模MIMO已经是现在和未来无线通信的基本框架。
然而在大规模MIMO系统中，由于天线数量增加，导频开销和信道估计的复杂度都有数量级的增加。如何降低导频开销是当前亟待解决的技术问题。
发明内容
本申请实施例提供一种导频信号发送方法、信道估计方法、装置及设备，能够解决如何降低导频开销的问题。
第一方面，提供一种导频信号发送方法，包括：
第一通信设备发送第一矩阵中的第一导频信号；
其中，所述第一矩阵为N×M矩阵，所述第一矩阵中的第i行，第j列的 元素表示通过第i个RE，第j个天线发送的导频信号，i为大于或等于1，小于或等于N的正整数，j为大于或等于1，小于或等于M的正整数，所述N为所述第一通信设备的天线数量，所述M为每个RB上发送导频信号的RE的数量，所述M小于所述N，所述N和M均为大于1的整数。
第二方面，提供一种信道估计方法，其特征在于，包括：
第二通信设备接收第一通信设备发送的第一矩阵中的第一导频信号；
所述第二通信设备根据所述第一导频信号进行信道估计；
其中，所述第一矩阵为N×M矩阵，所述第一矩阵中的第i行，第j列的元素表示通过第i个RE，第j个天线发送的导频信号，i为大于或等于1，小于或等于N的正整数，j为大于或等于1，小于或等于M的正整数，所述N为所述第一通信设备的天线数量，所述M为每个RB上发送导频信号的RE的数量，所述M小于所述N，所述N和M均为大于1的整数。
第三方面，提供一种导频信号发送装置，包括：
第一发送模块，用于发送第一矩阵中的第一导频信号；
其中，所述第一矩阵为N×M矩阵，所述第一矩阵中的第i行，第j列的元素表示通过第i个RE，第j个天线发送的导频信号，i为大于或等于1，小于或等于N的正整数，j为大于或等于1，小于或等于M的正整数，所述N为所述第一通信设备的天线数量，所述M为每个RB上发送导频信号的RE的数量，所述M小于所述N，所述N和M均为大于1的整数。
第四方面，提供一种信道估计装置，包括：
第一接收模块，用于接收第一通信设备发送的第一矩阵中的第一导频信号；
信道估计模块，用于所述第二通信设备根据所述第一导频信号进行信道估计；
其中，所述第一矩阵为N×M矩阵，所述第一矩阵中的第i行，第j列的元素表示通过第i个RE，第j个天线发送的导频信号，i为大于或等于1，小于或等于N的正整数，j为大于或等于1，小于或等于M的正整数，所述N为所述第一通信设备的天线数量，所述M为每个RB上发送导频信号的RE的数量，所述M小于所述N，所述N和M均为大于1的整数。
第五方面，提供一种通信设备，包括：处理器、存储器及存储在所述存储器上并可在所述处理器上运行的程序，所述程序被所述处理器执行时实现如第一方面或第二方面所述的方法的步骤。
第六方面，提供一种可读存储介质，所述可读存储介质上存储程序或指令，所述程序或指令被处理器执行时实现如第一方面或第二方面所述的方法的步骤。
第七方面，提供一种计算机程序产品，所述计算机程序产品被存储在非瞬态的存储介质中，所述计算机程序产品被至少一个处理器执行以实现如第一方面或第二方面所述的方法的步骤。
第八方面，提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现如第一方面或第二方面所述的方法。在本申请实施例中，第一通信设备发送第一矩阵中第一导频信号，由于第一矩阵为N×M矩阵，其中N为第一通信设备的天线数量，M为每个RB上发送导频信号的RE的数量，通过将M设置为小于N，从而达到导频开销压缩的目的，在保持系统的信道估计精度的基础上，减少导频开销。
附图说明
图1是本申请实施例可应用的一种无线通信系统的示意图；
图2是本申请实施例中导频信号发送方法的流程图；
图3是本申请实施例中信道估计方法的流程图；
图4是本申请实施例中基于OFDM的大规模MIMO天线系统的示意图；
图5是本申请实施例中感知矩阵的训练示意图；
图6是本申请实施例中LAMP网络结构示意图；
图7是本申请实施例中LAMP网络的第t层结构的示意图；。
图8是本申请实施例中导频信号发送装置的示意图；
图9是本申请实施例中信道估计装置的示意图；
图10是本申请实施例中终端的示意图；
图11是本申请实施例中网络侧设备的示意图；
图12是本申请实施例中通信设备的示意图。
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所获得的所有其他实施例，都属于本申请保护的范围。
本申请的说明书和权利要求书中的术语“第一”、“第二”等是用于区别类似的对象，而不用于描述指定的顺序或先后次序。应该理解这样使用的术语在适当情况下可以互换，以便本申请的实施例能够以除了在这里图示或描述的那些以外的顺序实施，且“第一”、“第二”所区别的对象通常为一类，并不限定对象的个数，例如第一对象可以是一个，也可以是多个。此外，说明书以及权利要求中“和”表示所连接对象的至少其中之一，字符“/”一般表示前后关联对象是一种“或”的关系。
值得指出的是，本申请实施例所描述的技术不限于新空口(New Radio，NR)系统、长期演进型(Long Term Evolution，LTE)/LTE的演进(LTE-Advanced，LTE-A)系统，还可用于其他无线通信系统，诸如码分多址(Code Division Multiple Access，CDMA)、时分多址(Time Division Multiple Access，TDMA)、频分多址(Frequency Division Multiple Access，FDMA)、正交频分多址(Orthogonal Frequency Division Multiple Access，OFDMA)、单载波频分多址(Single-carrier Frequency-Division Multiple Access，SC-FDMA)和其他系统。本申请实施例中的术语“系统”和“网络”常被可互换地使用，所描述的技术既可用于以上提及的系统和无线电技术，也可用于其他系统和无线电技术。以下描述出于示例目的描述了NR系统，并且在以下大部分描述中使用NR术语，但是这些技术也可应用于NR系统应用以外的应用，如第6代(6 th Generation，6G)通信系统。
大规模MIMO系统中，由于天线数量巨大，信道估计与反馈所需的导频开销和反馈开销巨大。为了降低导频开销和反馈开销，利用大规模MIMO信道在空间角度域的稀疏特性，提出基于压缩感知理论的信道估计方案。
该方案利用大规模MIMO信道的有限散射特性和基站天线阵列紧密排列的特性，分析得到不同天线空间角度域信道之间具有结构化稀疏特性。利用这一特性，把大规模MIMO信道估计转化为结构化压缩感知的稀疏信号恢复问题。具体而言，基站发送的少数非正交导频来降低导频开销。导频数量要远远小于天线数量。移动用户接收到导频信号后，然后利用正交匹配追踪(Orthogonal Matching Pursuit，OMP)或者近似消息传递(Approximate Message Passing，AMP)等压缩感知的稀疏信号恢复算法来解决这一问题。理论分析和仿真结果都表明，在空间稀疏性明显的情况下，基于结构化压缩感知的信道估计与反馈方法能够以较低的导频开销准确获取大规模MIMO信道状态信息。
虽然压缩感知(compressed sensing，CS)被视为较有潜力的信道状态信息(Channel State Information，CSI)开销降低方法。但是这一类的解决方案本身具有一些固有的问题。
其一，CS类解决方案强烈依赖于信道在空间角度域的稀疏性假设，而实际场景中的很多真实信道并不是都具有绝对的稀疏性。因而基于CS的信道估计和反馈方案依赖于对CSI矩阵完美稀疏的先验假设，在只满足近似稀疏条件的大规模MIMO信道上表现不佳。性能难以达到实际系统的要求。
其二，当把压缩感知用在基于OFDM的大规模MIMO系统时，无法有效的整合频域和空域资源。压缩感知很难在实际系统中应用。也就是说在基于OFDM的大规模MIMO系统，由于信道的频率选择性，不同子频段上的信道是不同的。因此，需要在每个子频段上同时进行大规模MIMO的信道估计。因此即使此时使用压缩感知也仍然存在巨大的导频开销。
参见图1，图中示出本申请实施例可应用的一种无线通信系统的框图。无线通信系统包括终端11、终端12和网络侧设备13。其中，终端也可以称作终端设备或者UE，终端可以是手机、平板电脑(Tablet Personal Computer)、膝上型电脑(Laptop Computer)或称为笔记本电脑、个人数字助理(Personal Digital Assistant，PDA)、掌上电脑、上网本、超级移动个人计算机(ultra-mobile personal computer，UMPC)、移动上网装置(Mobile Internet Device，MID)、增强现实(augmented reality，AR)/虚拟现实(virtual reality，VR)设备、机器人、 可穿戴式设备(Wearable Device)、车载设备(Vehicle User Equipment，VUE)、行人终端(Pedestrian User Equipment，PUE)、智能家居(具有无线通信功能的家居设备，如冰箱、电视、洗衣机或者家具等)等终端侧设备，可穿戴式设备包括：智能手表、智能手环、智能耳机、智能眼镜、智能首饰(智能手镯、智能手链、智能戒指、智能项链、智能脚镯、智能脚链等)、智能腕带、智能服装、游戏机等。需要说明的是，在本申请实施例并不限定终端11、终端12的具体类型。
网络侧设备13可以是基站或核心网，其中，基站可被称为节点B、演进节点B、接入点、基收发机站(Base Transceiver Station，BTS)、无线电基站、无线电收发机、基本服务集(Basic Service Set，BSS)、扩展服务集(Extended Service Set，ESS)、B节点、演进型B节点(gNB)、家用B节点、家用演进型B节点、无线局域网(Wireless Local Area Network，WLAN)接入点、无线保真(Wireless Fidelity，WiFi)节点、发送接收点(Transmitting Receiving Point，TRP)、无线接入网节点或所述领域中其它某个合适的术语，只要达到相同的技术效果，所述基站不限于指定技术词汇，需要说明的是，在本申请实施例中仅以NR系统中的基站为例，但是并不限定基站的具体类型。
参见图2，本申请实施例提供一种导频信号发送方法，具体步骤如下：
步骤201：第一通信设备发送第一矩阵中的第一导频信号；
其中，所述第一矩阵为N×M矩阵，所述第一矩阵中的第i行，第j列的元素表示通过第i个资源单元(Resource Element，RE)，第j个天线发送的导频信号，i为大于或等于1，小于或等于N的正整数(即i＝1～N)，j为大于或等于1，小于或等于M的正整数(即j＝1～M)，所述N为所述第一通信设备的天线数量，所述M为每个资源块(Resource Block，RB)上发送导频信号的RE的数量，所述M小于所述N，所述N和M均为大于1的整数。
以第一通信设备为基站为例，基站发送导频信道状态信息参考信号(Channel State Information-Reference Signal，CSI-RS)。在导频设计和资源分配时，以资源块(Resource Block，RB)为基本单位。每个RB在频域包含12个子载波，在时域包含6-7个OFDM符号。对于每一个RB，都利用压缩感知的方法来进行导频压缩。
在本申请一种实施方式中，所述第一矩阵是基于第二矩阵和第三矩阵确定的，比如所述第一矩阵＝第二矩阵(W 1)×第三矩阵(W 2)，所述第二矩阵为N×M的感知矩阵，所述第三矩阵为空域到波束域的转换矩阵，这样可以将多个天线频域呈现出频率选择性的信道转换到波束域时延域的径数有限的多径信道联合进行估计，从而大幅降低了导频开销。
在本申请一种实施方式中，所述第三矩阵为N维的离散傅里叶变换(Discrete Fourier Transform，DFT)矩阵；
或者，
所述第三矩阵为N 1维的DFT矩阵和N 2维的DFT矩阵的克罗尼克积，其中，所述N 1是包括第一天线阵列中的行数，所述N 2为第一天线阵列中的列数，所述第一天线阵列包括N个天线。
在本申请实施例中，第一通信设备在每个RB上用空域的DFT矩阵作为导频发送符号，采用感知矩阵操作对发送的第一导频信号进行压缩。
在本申请一种实施方式中，所述方法还包括：
基于所述第一导频信号训练所述第二矩阵。
在本申请一种实施方式中，所述基于所述第一导频信号训练所述第二矩阵包括：
所述第一通信设备对所述第一导频信号进行快速傅里叶变换，得到时延径的导频信号；
所述第一通信设备根据AMP算法对所述时延径的导频信号进行估计，得到第一估计误差；
所述第一通信设备根据所述第一估计误差训练所述第二矩阵。
在本申请一种实施方式中，所述方法还包括：
所述第一通信设备将训练后的所述第二矩阵发送给所述第二通信设备。
在本申请一种实施方式中，所述方法还包括：
基于所述第一导频信号训练学习近似消息传递(Learned Approximate Message Passing，LAMP)的参数。
在本申请一种实施方式中，所述基于所述第一导频信号训练LAMP的参数的步骤，包括：
所述第一通信设备对所述第一导频信号进行快速傅里叶变换，得到时延径的导频信号；
所述第一通信设备根据所述时延径的导频信号，进行学习近似消息传递LAMP的整体信道估计，得到第二估计误差；
所述第一通信设备根据所述第二估计误差训练LAMP的参数。
在本申请一种实施方式中，所述方法还包括：
所述第一通信设备将训练后的所述LAMP的参数发送给所述第二通信设备。
在本申请实施例中，在保持系统的信道估计精度的基础上，减少导频开销。
参见图3，本申请实施例提供一种信道估计方法，具体步骤包括：步骤301和步骤302。
步骤301：第二通信设备接收第一通信设备发送的第一矩阵中的第一导频信号；
步骤302：所述第二通信设备根据所述第一导频信号进行信道估计；
其中，所述第一矩阵为N×M矩阵，所述第一矩阵中的第i行，第j列的元素表示通过第i个RE，第j个天线发送的导频信号，i为大于或等于1，小于或等于N的正整数，j为大于或等于1，小于或等于M的正整数，，所述N为所述第一通信设备的天线数量，所述M为每个RB上发送导频信号的RE的数量，所述M小于所述N，所述N和M均为大于1的整数。
在本申请实施例中，第二通信设备对频域所有RB的导频信号联合处理，首先对每一个端口的各个RB的导频接收信号进行快速傅里叶逆变换(Inverse Fast Fourier Transform，IFFT)，转成时延域。然后把各个端口和各个时延的信号结合起来，进行学习近似消息传递LAMP的整体信道估计。最后将估计得到的信号再转到频域，得到各个天线在各个RB的信道，完成最后的信道估计。
在本申请一种实施方式中，第一矩阵是基于第二矩阵和第三矩阵确定的，比如所述第一矩阵＝第二矩阵×第三矩阵，所述第二矩阵为N×M的感知矩阵，所述第三矩阵为空域到波束域的转换矩阵，这样可以将多个天线频域呈现出 频率选择性的信道转换到波束域时延域的径数有限的多径信道联合进行估计，从而大幅降低了导频开销。
在本申请一种实施方式中，所述第三矩阵为N维的DFT矩阵；
或者，
所述第三矩阵为N 1维的DFT矩阵和N 2维的DFT矩阵的克罗尼克积，其中，所述N 1是包括第一天线阵列中的行数，所述N 2为第一天线阵列中的列数，所述第一天线阵列包括N个天线。
在本申请一种实施方式中，所述第二通信设备根据所述第一导频信号进行信道估计，包括：
所述第二通信设备对所述第一导频信号进行快速傅里叶变换，得到时延径的导频信号；
所述第二通信设备根据所述时延径的导频信号，进行LAMP的整体信道估计，得到第一信道估计结果。
可选地，第二通信设备根据所述第一导频信号进行信道估计，还包括：
所述第二通信设备根据所述第一信道估计结果，得到各个天线在对应RB的信道的第二信道估计结果。
在本申请一种实施方式中，所述第二通信设备根据所述第一信道估计结果，得到各个天线在对应RB的信道的第二信道估计结果，包括：
所述第二通信设备将所述第一信道估计结果转换为M×N的信道矩阵，所述第一信道估计结果为空域波束的信道向量；
所述第二通信设备将所述M×N的信道矩阵转换到频域和空域，得到各个天线在对应RB的信道的第二信道估计结果。
在本申请一种实施方式中，所述方法还包括：
所述第二通信设备对所述第一导频信号进行快速傅里叶变换，得到时延径的导频信号；
所述第二通信设备根据AMP算法对所述每个时延径的导频信号进行估计，得到第一估计误差；
所述第二通信设备根据所述第一估计误差训练所述第二矩阵。
在本申请一种实施方式中，所述方法还包括：
所述第二通信设备对所述第一导频信号进行快速傅里叶变换，得到时延径的导频信号；
所述第二通信设备根据时延径的导频信号，进行LAMP的整体信道估计，得到第二估计误差；
所述第二通信设备根据所述第二估计误差训练LAMP的参数。
在本申请一种实施方式中，所述第二通信设备根据时延径的导频信号，进行LAMP的整体信道估计，得到第二估计误差，包括：
所述第二通信设备将各个时延径的导频信号拼接在一起，拼接后的信号矩阵为第四矩阵，所述第四矩阵是维度为RB个数的单位阵和每个RB上感知矩阵的克罗尼克积；
所述第二通信设备根据所述第四矩阵，进行LAMP的信道估计，得到第二估计误差。
在本申请实施例中，在保持系统的信道估计精度的基础上，减少导频开销。
下面以第一通信设备为发送端，第二通信设备为接收端为例。
在本申请实施例中，利用图4所示的基于OFDM的大规模MIMO天线系统，将信道空域(或者描述为空间域)的稀疏性和时延域的稀疏性相结合，通过空域和时延域联合压缩优化的导频设计和信道估计，可以一次完成基于OFDM的大规模MIMO天线系统中所有天线在所有RB上的最终的信道估计。
在基于OFDM的大规模MIMO天线系统中，发送端有N个天线，由于接收端的每个接收天线都是独立进行信道估计的，因此可以只考虑一个接收天线来设计。即考虑N×1的大规模天线系统。在发送端，以RB为单位发送导频信号，所以每个RB上都可以单独进行信道估计。每个RB上发送导频信号的RE的个数为M，M要远远小于发送天线数N，从而达到了导频开销压缩的目的。
如果在第i个RE上，第j个天线的发送符号表示为c i,j，则所有M个RE上在N个天线的发送符号可以表示为一个N×M的矩阵C。
Figure PCTCN2022124029-appb-000001
其中C由两个矩阵相乘得到，即C＝W 2W 1，其中W 1为N×M感知矩阵，用来进行导频开销的压缩。
在传统的压缩感知方法中，W 1一般采用随机矩阵，在本申请的实施例中，W 1通过大量信道数据，采用深度学习的方法训练得到。W 2为空域到波束域的转换矩阵，对于线阵，W 2为N维的DFT矩阵。对于N 1×N 2的面阵，W 2为N 1维的DFT矩阵和N 2维的DFT矩阵的克罗尼克积(W 2＝kron(N 1维的DFT矩阵，N 2维的DFT矩阵)。
在每个RB上，基于C，在M个RE上发送导频符号，经过离散傅里叶逆变换(Inverse Discrete Fourier Transform，IDFT)(OFDM调制)后，通过N个天线发送出去。
在接收端，只考虑一个接收天线，该天线上的接收信号经过FFT(OFDM解调后)，得到各个RB上的信号。其在每个RB上的M个RE上收到导频信号，为1×M的向量。在第i个RB上接收导频信号r i＝[r i,1 r i,2 ... r i,M]可表示为r i＝h iC+n i。其中h i为第i个RB上的信道，其为1×N的信道向量，数学表达式为：
h i＝[h i,1 h i,2 ... h i,N]
n i为第i个RB上M个RE上收到的噪声，n i为1×M向量。
在本申请的实施例中，K个RB上的接收信号是联合处理的。定义R＝[r 1 r 2 ... r K] T，R为所有RB上收到的导频信号的矩阵，r k在第k个RB上接收到的导频信号，T为转置符号。由此可得：
Figure PCTCN2022124029-appb-000002
Figure PCTCN2022124029-appb-000003
定义
Figure PCTCN2022124029-appb-000004
为K个RB上N个发送天线的信道，是要估计的值。
定义
Figure PCTCN2022124029-appb-000005
为K个RB上N个发送天线的波束域稀疏信道。
可得，
Figure PCTCN2022124029-appb-000006
本申请实施例中，为了在时延域进行信道估计，先对接收到的导频信号r在RB维度上做K维的IDFT，得到
Figure PCTCN2022124029-appb-000007
定义H d＝W IDFTH和
Figure PCTCN2022124029-appb-000008
可得：
Figure PCTCN2022124029-appb-000009
R d为K×M矩阵。
其中
Figure PCTCN2022124029-appb-000010
Figure PCTCN2022124029-appb-000011
是第l个时延径上波束域的信道向量，
Figure PCTCN2022124029-appb-000012
为1×N的向量。
这里首先估计
Figure PCTCN2022124029-appb-000013
即波束域时延域信道增益。
由于
Figure PCTCN2022124029-appb-000014
将(R d) T写成KM×1的向量
Figure PCTCN2022124029-appb-000015
Figure PCTCN2022124029-appb-000016
写成KM×1的向量
Figure PCTCN2022124029-appb-000017
定义感知矩阵
Figure PCTCN2022124029-appb-000018
为KM×KN矩阵，A为波束域和时延域整体信道估计时等效的感知矩阵，I k为k阶单位矩阵。
Figure PCTCN2022124029-appb-000019
其中
Figure PCTCN2022124029-appb-000020
和A是已知的，
Figure PCTCN2022124029-appb-000021
同时具有波束域和时延域的稀疏性，就可以利用压缩感知的方法求解。
在本申请实施例中，采用两步深度学习训练方法来实现最优的性能，第一步为训练最优的感知矩阵A，由于
Figure PCTCN2022124029-appb-000022
这样可以只训练W 1，参见图5。
由于W 1是在每个RB上操作的，并且每个RB上的W 1都相同。因此W 1是在每个RB上单独训练的，不需要在所有RB做整体的操作。由于最后的信道估计是在时延域进行的，所以W 1是在时延域的每个时延径上单独训练的。对每一个端口的各个RB的导频接收信号进行IFFT，转成时延域对于IDFT后，将每个时延径的导频信号用AMP算法进行估计，利用估计误差来训练W 1。
Figure PCTCN2022124029-appb-000023
分解为：
Figure PCTCN2022124029-appb-000024
不失一般性，对于第i个时延径，导频信号表达式
Figure PCTCN2022124029-appb-000025
其中r di为1×M向量，
Figure PCTCN2022124029-appb-000026
为1×N向量。这时，用大量的r di和
Figure PCTCN2022124029-appb-000027
为训练数据，对W1进行训练，得到最优的感知矩阵。
为了通过神经网络的方法寻找最优的感知矩阵，将感知矩阵看成是一层线性的神经网络，和传统的神经网络相比，它没有偏移量和激活函数。通过训练来找到最优的感知矩阵。
训练时，将发送端的感知矩阵和接收端AMP恢复算法以及大规模MIMO信道以及引入噪声整体看成是一个神经网络进行训练。
训练数据来自于大量的波束域时延域信道，训练优化的目标(代价函数cost function)为输出端神经网络的输出
Figure PCTCN2022124029-appb-000028
和实际信道
Figure PCTCN2022124029-appb-000029
之间的均方误差最小。 即
Figure PCTCN2022124029-appb-000030
训练完成后，就可以利用训练得到的优化的感知矩阵W 1进行导频发送和信道估计。基站根据优化的感知矩阵利用各个发送天线在各个RB的各个导频RE上的发送信号(符号)，在移动用户端，移动用户首先对每一个端口的各个RB的导频进行IFFT，转成时延域。然后把各个端口和各个时延的信号结合起来，进行LAMP的整体信道估计。LAMP估计结果是各个时延径上各个波束的信道
Figure PCTCN2022124029-appb-000031
LAMP训练是基于
Figure PCTCN2022124029-appb-000032
来进行的。训练时，通过大量的
Figure PCTCN2022124029-appb-000033
和
Figure PCTCN2022124029-appb-000034
数据，得到LAMP各次迭代的B t和
Figure PCTCN2022124029-appb-000035
完成LAMP的参数训练，B t和
Figure PCTCN2022124029-appb-000036
分别对应神经网络中的线性加权矩阵与非线性激活函数。具体训练过程如下：
LAMP网络是基于传统的压缩感知算法——AMP构建的，将AMP算法的迭代求解过程展开为神经网络，将其线性操作系数与非线性收缩参数联合优化。可以通过深度神经网络的训练过程得到这些分布参数的取值。值得注意的是在进行训练时，发送端用的是第一阶段已经训练好的优化的感知矩阵。
不同于一般的神经网络结构，LAMP的网络结构是依据AMP算法构建的。实际上，LAMP算法就是将AMP算法的迭代运算过程展开为深度神经网络，具体网络结构如图6所示。
对于压缩感知常用的y＝Ax+n场景，从已知的(y,A)中估计x稀疏信号恢复，用
Figure PCTCN2022124029-appb-000037
表示原始稀疏信号的一个估计矢量，y是观测矢量，v是残差矢量。观测矢量y作为所有层的共同输入，前一层对估计矢量
Figure PCTCN2022124029-appb-000038
和残差矢量v的输出分别作为后一层估计矢量
Figure PCTCN2022124029-appb-000039
和残差矢量v的输入。在经过T层神经网络后，输出对原始稀疏信号的最终估计
Figure PCTCN2022124029-appb-000040
第t层的网络结构如图7所示。该结构与AMP算法中的迭代过程完全对应，逐步实现了估计矢量
Figure PCTCN2022124029-appb-000041
和残差矢量v的更新。其中B t和
Figure PCTCN2022124029-appb-000042
分别对应神经网络中的线性加权矩阵与非线性激活函数。
LAMP网络结构有些独特的设计，具体如下：(1)LAMP网络中多出来一条支路，如图7中①所示，对应于AMP算法中加快收敛的Onsager校正项；(2)LAMP算法中的非线性函数是由具体的信号估计问题推导出的收缩函数，而不是一般神经网络中为了引入非线性功能而无明确物理意义的激活函数；(3)收缩函数中的噪声参数
Figure PCTCN2022124029-appb-000043
与残差有关，可以逐层更新。正是由于以上特点，LAMP网络可以比一般的神经网络更适合稀疏信号恢复问题。
LAMP算法将深度学习和AMP算法相结合，取两者之优势，既利用了深度神经网络强大的学习能力又保留了AMP算法实现稀疏信号恢复的功能。
在本实施例中，采用监督学习方式，通过输入数据集
Figure PCTCN2022124029-appb-000044
进行网络参数训练，其中y d表示低维的观测信号，x d表示高维的稀疏信号。为了进一步提高算法性能，充分利用神经网络强大的学习能力，在每层中，除了非线性参数θ t逐层更新之外，线性算子B也可以逐层更新(在AMP算法中B＝A T)。于是，在LAMP网络中，需要训练的参数集为
Figure PCTCN2022124029-appb-000045
由于LAMP网络是在AMP迭代算法的基础上构建的，在训练网络过程中可以采用逐层训练的方式训练该网络，实现线性操作系数和非线性收缩参数的联合优化。不同于一般神经网络中仅仅定义一个损失函数，在LAMP网络中，为实现逐层训练，每一层都分别定义了损失函数，具体定义如下：
Figure PCTCN2022124029-appb-000046
y d表示低维的观测信号，x d表示高维的稀疏信号
L t(Φ)为第t层的损失函数，
Figure PCTCN2022124029-appb-000047
为第t层对x d的估计结果。
另外，为了避免网络过拟合，线性操作系数矩阵B t和非线性收缩参数θ t在每层中采取先分别优化再联合优化的方式训练。
感知矩阵W 1和LAMP的参数训练完成后，就可以进行导频发送和信道估计。在发送端，也可以采用每个RB上独立进行感知矩阵W 1操作。但是在接收端，在频域所有RB的导频信号联合处理，进行信道估计。首先对每一 个端口在各个RB上收到的导频信号收集到一起进行IFFT，转成时延域。然后把各个端口和各个时延的信号结合起来，进行LAMP的整体信道估计，此时估计到的是空域波束的信道向量
Figure PCTCN2022124029-appb-000048
首先要将信道向量
Figure PCTCN2022124029-appb-000049
转为M×N的信道矩阵
Figure PCTCN2022124029-appb-000050
再进行DFT操作转换成频域和波束域，最后利用W 2将结果转到频域和空域。数学表示为
Figure PCTCN2022124029-appb-000051
从而得到各个天线在各个RB的信道，完成最终的信道估计。
参见图8，本申请实施例提供一种导频信号发送装置，应用于第一通信设备，该装置800包括：
第一发送模块801，用于发送第一矩阵中的第一导频信号；
其中，所述第一矩阵为N×M矩阵，所述第一矩阵中的第i行，第j列的元素表示通过第i个RE，第j个天线发送的导频信号，i为大于或等于1，小于或等于N的正整数，j为大于或等于1，小于或等于M的正整数，所述N为所述第一通信设备的天线数量，所述M为每个RB上发送导频信号的RE的数量，所述M小于所述N，所述N和M均为大于1的整数。
在本申请的一种实施方式中，所述第一矩阵是基于第二矩阵和第三矩阵确定的，所述第二矩阵为N×M的感知矩阵，所述第三矩阵为空域到波束域的转换矩阵。
在本申请的一种实施方式中，所述第三矩阵为N维的DFT矩阵；
或者，
所述第三矩阵为N 1维的DFT矩阵和N 2维的DFT矩阵的克罗尼克积，其中，所述N 1是包括第一天线阵列中的行数，所述N 2为第一天线阵列中的列数，所述第一天线阵列包括N个天线。
在本申请的一种实施方式中，所述装置还包括：
在本申请的一种实施方式中，所述装置还包括：
第一训练模块，用于基于所述第一导频信号训练所述第二矩阵。
在本申请的一种实施方式中，第一训练模块包括：
第一处理单元，用于对所述第一导频信号进行快速傅里叶变换，得到时延径的导频信号；
第二处理单元，用于根据AMP算法对所述时延径的导频信号进行估计，得到第一估计误差；
第三处理单元，用于根据所述第一估计误差训练所述第二矩阵。
在本申请的一种实施方式中，所述装置还包括：
第二发送模块，用于将训练后的所述第二矩阵发送给所述第二通信设备。
在本申请的一种实施方式中，所述装置还包括：
第二训练模块，用于基于所述第一导频信号训练LAMP的参数。
在本申请的一种实施方式中，第二训练模块包括：
第四处理单元，用于对所述第一导频信号进行快速傅里叶变换，得到时延径的导频信号；
第五处理单元，用于根据所述时延径的导频信号，进行LAMP的整体信道估计，得到第二估计误差；
第六处理单元，用于根据所述第二估计误差训练LAMP的参数。
在本申请的一种实施方式中，所述装置还包括：
第三发送模块，用于将训练后的所述LAMP的参数发送给所述第二通信设备。
本申请实施例提供的装置能够实现图2所示的方法实施例实现的各个过程，并达到相同的技术效果，为避免重复，这里不再赘述。
参见图9，本申请实施例提供一种信道估计装置，应用于第二通信设备，该装置900包括：
第一接收模块901，用于接收第一通信设备发送的第一矩阵中的第一导频信号；
信道估计模块902，用于所述第二通信设备根据所述第一导频信号进行信道估计；
其中，所述第一矩阵为N×M矩阵，所述第一矩阵中的第i行，第j列的元素表示通过第i个RE，第j个天线发送的导频信号，i为大于或等于1，小于或等于N的正整数，j为大于或等于1，小于或等于M的正整数，，所述N为所述第一通信设备的天线数量，所述M为每个RB上发送导频信号的RE的数量，所述M小于所述N，所述N和M均为大于1的整数。
在本申请的一种实施方式中，所述第一矩阵是基于第二矩阵和第三矩阵确定的，所述第二矩阵为N×M的感知矩阵，所述第三矩阵为空域到波束域的转换矩阵。
在本申请的一种实施方式中，所述第三矩阵为N维的DFT矩阵；
或者，
所述第三矩阵为N 1维的DFT矩阵和N 2维的DFT矩阵的克罗尼克积，其中，所述N 1是包括第一天线阵列中的行数，所述N 2为第一天线阵列中的列数，所述第一天线阵列包括N个天线。
在本申请的一种实施方式中，信道估计模块902进一步用于：
对所述第一导频信号进行快速傅里叶变换，得到时延径的导频信号；
根据所述时延径的导频信号，进行LAMP的整体信道估计，得到第一信道估计结果。
在本申请的一种实施方式中，信道估计模块902进一步用于：
根据所述第一信道估计结果，得到各个天线在对应RB的信道的第二信道估计结果。
在本申请的一种实施方式中，信道估计模块902进一步用于：
将所述第一信道估计结果转换为M×N的信道矩阵，所述第一信道估计结果为空域波束的信道向量；将所述M×N的信道矩阵转换到频域和空域，得到各个天线在对应RB的信道的第二信道估计结果。
在本申请的一种实施方式中，所述装置还包括：
第七处理模块，用于对所述第一导频信号进行快速傅里叶变换，得到时延径的导频信号；
第八处理模块，用于根据AMP算法对所述每个时延径的导频信号进行估计，得到第一估计误差；
第九处理模块，用于根据所述第一估计误差训练所述第二矩阵。
在本申请的一种实施方式中，所述装置还包括：
第十处理模块，用于对所述第一导频信号进行快速傅里叶变换，得到时延径的导频信号；
第十一处理模块，用于根据时延径的导频信号，进行LAMP的整体信道 估计，得到第二估计误差；
第十二处理模块，用于根据所述第二估计误差训练LAMP的参数。
可选地，第十一处理模块进一步用于：将各个时延径的导频信号拼接在一起，拼接后的信号矩阵为第四矩阵，所述第四矩阵是维度为RB个数的单位阵和每个RB上感知矩阵的克罗尼克积；根据所述第四矩阵，进行LAMP的信道估计，得到第二估计误差。
本申请实施例提供的装置能够实现图3所示的方法实施例实现的各个过程，并达到相同的技术效果，为避免重复，这里不再赘述。
图10为实现本申请实施例的一种终端的硬件结构示意图，该终端1000包括但不限于：射频单元1001、网络模块1002、音频输出单元1003、输入单元1004、传感器1005、显示单元1006、用户输入单元1007、接口单元1008、存储器1009、以及处理器1010等中的至少部分部件。
本领域技术人员可以理解，终端1000还可以包括给各个部件供电的电源(比如电池)，电源可以通过电源管理系统与处理器1010逻辑相连，从而通过电源管理系统实现管理充电、放电、以及功耗管理等功能。图10中示出的终端结构并不构成对终端的限定，终端可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件布置，在此不再赘述。
应理解的是，本申请实施例中，输入单元1004可以包括图形处理器(Graphics Processing Unit，GPU)10041和麦克风10042，图形处理器10041对在视频捕获模式或图像捕获模式中由图像捕获装置(如摄像头)获得的静态图片或视频的图像数据进行处理。显示单元1006可包括显示面板10061，可以采用液晶显示器、有机发光二极管等形式来配置显示面板10061。用户输入单元1007包括触控面板10061以及其它输入设备10072。触控面板10061，也称为触摸屏。触控面板10061可包括触摸检测装置和触摸控制器两个部分。其它输入设备10072可以包括但不限于物理键盘、功能键(比如音量控制按键、开关按键等)、轨迹球、鼠标、操作杆，在此不再赘述。
本申请实施例中，射频单元1001将来自网络侧设备的下行数据接收后，给处理器1010处理；另外，将上行的数据发送给网络侧设备。通常，射频单元1001包括但不限于天线、至少一个放大器、收发信机、耦合器、低噪声放 大器、双工器等。
存储器1009可用于存储软件程序或指令以及各种数据。存储器1009可主要包括存储程序或指令区和存储数据区，其中，存储程序或指令区可存储操作系统、至少一个功能所需的应用程序或指令(比如声音播放功能、图像播放功能等)等。此外，存储器1009可以包括高速随机存取存储器，还可以包括非易失性存储器，其中，非易失性存储器可以是只读存储器(Read-Only Memory，ROM)、可编程只读存储器(Programmable ROM，PROM)、可擦除可编程只读存储器(Erasable PROM，EPROM)、电可擦除可编程只读存储器(Electrically EPROM，EEPROM)或闪存。例如至少一个磁盘存储器件、闪存器件、或其它非易失性固态存储器件。
处理器1010可包括一个或多个处理单元；可选地，处理器1010可集成应用处理器和调制解调处理器，其中，应用处理器主要处理操作系统、用户界面和应用程序或指令等，调制解调处理器主要处理无线通信，如基带处理器。可以理解的是，上述调制解调处理器也可以不集成到处理器1010中。
本申请实施例提供的终端能够实现图2或图3所示的方法实施例实现的各个过程，并达到相同的技术效果，为避免重复，这里不再赘述。
参见图11，本申请实施例还提供了一种网络侧设备。如图11所示，该网络侧设备1100包括：天线1101、射频装置1102、基带装置1103。天线1101与射频装置1102连接。在上行方向上，射频装置1102通过天线1101接收信息，将接收的信息发送给基带装置1103进行处理。在下行方向上，基带装置1103对要发送的信息进行处理，并发送给射频装置1102，射频装置1102对收到的信息进行处理后经过天线1101发送出去。
上述频带处理装置可以位于基带装置1103中，以上实施例中网络侧设备执行的方法可以在基带装置1103中实现，该基带装置1103包括处理器1104和存储器1105。
基带装置1103例如可以包括至少一个基带板，该基带板上设置有多个芯片，如图11所示，其中一个芯片例如为处理器1104，与存储器1105连接，以调用存储器1105中的程序，执行以上方法实施例中所示的网络设备操作。
该基带装置1103还可以包括网络接口1106，用于与射频装置1102交互 信息，该接口例如为通用公共无线接口(common public radio interface，CPRI)。
具体地，本申请实施例的网络侧设备还包括：存储在存储器1105上并可在处理器1104上运行的指令或程序。
可以理解的是，处理器1104调用存储器1105中的指令或程序执行图8或图9所示各模块执行的方法，并达到相同的技术效果，为避免重复，故不在此赘述。
可选的，如图12所示，本申请实施例还提供一种通信设备1200，包括处理器1201，存储器1202，存储在存储器1202上并可在所述处理器1201上运行的程序或指令，例如，该通信设备1200为终端时，该程序或指令被处理器1201执行时实现上述图2或图3方法实施例的各个过程，且能达到相同的技术效果。该通信设备1200为网络侧设备时，该程序或指令被处理器1201执行时实现上述图2或图3方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
本申请实施例还提供一种计算机程序/程序产品，所述计算机程序/程序产品被存储在非易失的存储介质中，所述计算机程序/程序产品被至少一个处理器执行以实现如图2或图3所述的处理的方法的步骤。
本申请实施例还提供一种可读存储介质，所述可读存储介质上存储有程序或指令，该程序或指令被处理器执行时实现上述图2或图3所示方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
其中，所述处理器为上述实施例中所述的终端或网络侧设备中的处理器。所述可读存储介质，包括计算机可读存储介质，如计算机只读存储器(Read-Only Memory，ROM)、随机存取存储器(Random Access Memory，RAM)、磁碟或者光盘等。
本申请实施例还提供了一种芯片，所述芯片包括处理器和通信接口，所述通信接口和所述处理器耦合，所述处理器用于运行程序或指令，实现上述图2或图3所示方法实施例的各个过程，且能达到相同的技术效果，为避免重复，这里不再赘述。
应理解，本申请实施例提到的芯片还可以称为系统级芯片，系统芯片，芯片系统或片上系统芯片等。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。此外，需要指出的是，本申请实施方式中的方法和装置的范围不限按示出或讨论的顺序来执行功能，还可包括根据所涉及的功能按基本同时的方式或按相反的顺序来执行功能，例如，可以按不同于所描述的次序来执行所描述的方法，并且还可以添加、省去、或组合各种步骤。另外，参照某些示例所描述的特征可在其他示例中被组合。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以计算机软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
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 (22)

  1. 一种导频信号发送方法，包括：
    第一通信设备发送第一矩阵中的第一导频信号；
    其中，所述第一矩阵为N×M矩阵，所述第一矩阵中的第i行，第j列的元素表示通过第i个资源单元RE，第j个天线发送的导频信号，i为大于或等于1，小于或等于N的正整数，j为大于或等于1，小于或等于M的正整数，所述N为所述第一通信设备的天线数量，所述M为每个资源块RB上发送导频信号的RE的数量，所述M小于所述N，所述N和M均为大于1的整数。
  2. 根据权利要求1所述的方法，其中，所述第一矩阵是基于第二矩阵和第三矩阵确定的，所述第二矩阵为N×M的感知矩阵，所述第三矩阵为空域到波束域的转换矩阵。
  3. 根据权利要求2所述的方法，其中，所述第三矩阵为N维的离散傅里叶变换DFT矩阵；
    或者，
    所述第三矩阵为N 1维的DFT矩阵和N 2维的DFT矩阵的克罗尼克积，其中，所述N 1是第一天线阵列中的行数，所述N 2为第一天线阵列中的列数，所述第一天线阵列包括N个天线。
  4. 根据权利要求2所述的方法，其中，所述方法还包括：
    基于所述第一导频信号训练所述第二矩阵。
  5. 根据权利要求4所述的方法，其中，基于所述第一导频信号训练所述第二矩阵的步骤，包括：
    所述第一通信设备对所述第一导频信号进行快速傅里叶变换，得到时延径的导频信号；
    所述第一通信设备根据近似消息传递AMP算法对所述时延径的导频信号进行估计，得到第一估计误差；
    所述第一通信设备根据所述第一估计误差训练所述第二矩阵。
  6. 根据权利要求4或5所述的方法，其中，所述方法还包括：
    所述第一通信设备将训练后的所述第二矩阵发送给第二通信设备。
  7. 根据权利要求1所述的方法，其中，所述方法还包括：
    基于所述第一导频信号训练LAMP的参数。
  8. 根据权利要求7所述的方法，其中，所述基于所述第一导频信号训练LAMP的参数的步骤，包括：
    所述第一通信设备对所述第一导频信号进行快速傅里叶变换，得到时延径的导频信号；
    所述第一通信设备根据所述时延径的导频信号，进行学习近似消息传递LAMP的整体信道估计，得到第二估计误差；
    所述第一通信设备根据所述第二估计误差训练LAMP的参数。
  9. 根据权利要求7或8所述的方法，其中，所述方法还包括：
    所述第一通信设备将训练后的所述LAMP的参数发送给第二通信设备。
  10. 一种信道估计方法，包括：
    第二通信设备接收第一通信设备发送的第一矩阵中的第一导频信号；
    所述第二通信设备根据所述第一导频信号进行信道估计；
    其中，所述第一矩阵为N×M矩阵，所述第一矩阵中的第i行，第j列的元素表示通过第i个RE，第j个天线发送的导频信号，i为大于或等于1，小于或等于N的正整数，j为大于或等于1，小于或等于M的正整数，所述N为所述第一通信设备的天线数量，所述M为每个RB上发送导频信号的RE的数量，所述M小于所述N，所述N和M均为大于1的整数。
  11. 根据权利要求10所述的方法，其中，所述第一矩阵是基于第二矩阵和第三矩阵确定的，所述第二矩阵为N×M的感知矩阵，所述第三矩阵为空域到波束域的转换矩阵。
  12. 根据权利要求11所述的方法，其中，所述第三矩阵为N维的DFT矩阵；
    或者，
    所述第三矩阵为N 1维的DFT矩阵和N 2维的DFT矩阵的克罗尼克积，其中，所述N 1是包括第一天线阵列中的行数，所述N 2为第一天线阵列中的列数，所述第一天线阵列包括N个天线。
  13. 根据权利要求10所述的方法，其中，所述第二通信设备根据所述第 一导频信号进行信道估计，包括：
    所述第二通信设备对所述第一导频信号进行快速傅里叶变换，得到时延径的导频信号；
    所述第二通信设备根据所述时延径的导频信号，进行LAMP的整体信道估计，得到第一信道估计结果。
  14. 根据权利要求13所述的方法，其中，所述第二通信设备根据所述第一导频信号进行信道估计，还包括
    所述第二通信设备根据所述第一信道估计结果，得到各个天线在对应RB的信道的第二信道估计结果。
  15. 根据权利要求14所述的方法，其中，所述第二通信设备根据所述第一信道估计结果，得到各个天线在对应RB的信道的第二信道估计结果，包括：
    所述第二通信设备将所述第一信道估计结果转换为M×N的信道矩阵，所述第一信道估计结果为空域波束的信道向量；
    所述第二通信设备将所述M×N的信道矩阵转换到频域和空域，得到各个天线在对应RB的信道的第二信道估计结果。
  16. 根据权利要求11所述的方法，其中，所述方法还包括：
    所述第二通信设备对所述第一导频信号进行快速傅里叶变换，得到时延径的导频信号；
    所述第二通信设备根据AMP算法对所述每个时延径的导频信号进行估计，得到第一估计误差；
    所述第二通信设备根据所述第一估计误差训练所述第二矩阵。
  17. 根据权利要求13所述的方法，其中，所述方法还包括：
    所述第二通信设备对所述第一导频信号进行快速傅里叶变换，得到时延径的导频信号；
    所述第二通信设备根据所述时延径的导频信号，进行LAMP的整体信道估计，得到第二估计误差；
    所述第二通信设备根据所述第二估计误差训练LAMP的参数。
  18. 根据权利要求17所述的方法，其中，所述第二通信设备根据所述时 延径的导频信号，进行LAMP的整体信道估计，得到第二估计误差，包括：
    所述第二通信设备将各个时延径的导频信号拼接在一起，拼接后的信号矩阵为第四矩阵，所述第四矩阵是维度为RB个数的单位阵和每个RB上感知矩阵的克罗尼克积；
    所述第二通信设备根据所述第四矩阵，进行LAMP的信道估计，得到第二估计误差。
  19. 一种导频信号发送装置，包括：
    第一发送模块，用于发送第一矩阵中的第一导频信号；
    其中，所述第一矩阵为N×M矩阵，所述第一矩阵中的第i行，第j列的元素表示通过第i个RE，第j个天线发送的导频信号，i为大于或等于1，小于或等于N的正整数，j为大于或等于1，小于或等于M的正整数，所述N为第一通信设备的天线数量，所述M为每个RB上发送导频信号的RE的数量，所述M小于所述N，所述N和M均为大于1的整数。
  20. 一种信道估计装置，包括：
    第一接收模块，用于接收第一通信设备发送的第一矩阵中的第一导频信号；
    信道估计模块，用于第二通信设备根据所述第一导频信号进行信道估计；
    其中，所述第一矩阵为N×M矩阵，所述第一矩阵中的第i行，第j列的元素表示通过第i个RE，第j个天线发送的导频信号，i为大于或等于1，小于或等于N的正整数，j为大于或等于1，小于或等于M的正整数，所述N为所述第一通信设备的天线数量，所述M为每个RB上发送导频信号的RE的数量，所述M小于所述N，所述N和M均为大于1的整数。
  21. 一种通信设备，包括：处理器、存储器及存储在所述存储器上并可在所述处理器上运行的程序，其中，所述程序被所述处理器执行时实现如权利要求1至18中任一项所述的方法的步骤。
  22. 一种可读存储介质，所述可读存储介质上存储程序或指令，其中，所述程序或指令被处理器执行时实现如权利要求1至18中任一项所述的方法的步骤。
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