CN115413018A - Channel state information processing method, terminal, and computer-readable storage medium - Google Patents
Channel state information processing method, terminal, and computer-readable storage medium Download PDFInfo
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Abstract
The invention discloses a channel state information processing method, a terminal and a computer readable storage medium, wherein the channel state information processing method applied to a first device comprises the following steps: acquiring channel information; determining a transmission matrix according to the channel information; and sending the transmission matrix to second equipment so that the second equipment positions the first equipment according to the transmission matrix. Based on the method, the first equipment obtains the transmission matrix through extracting the channel information characteristics, and then feeds the transmission matrix back to the second equipment, so that the second equipment can output the positioning result according to the transmission matrix, and accurate positioning of the first equipment is realized.
Description
Technical Field
The embodiment of the invention relates to the technical field of wireless communication, in particular to a channel state information processing method, a terminal and a computer readable storage medium.
Background
The positioning technology has wide application in our life and production, such as map navigation, object positioning of intelligent factories, fire fighting positioning, logistics positioning and the like. Moreover, the positioning accuracy is more and more demanding, for example, the positioning requirements for machines or articles in factories reach even centimeter level.
In the related art, if the Non-Line Of Sight (NLOS) exists between the terminal to be located and the location device, the location accuracy is not high, and the location accuracy is not ideal, which cannot meet the requirements Of life and production. Artificial Intelligence (AI) is one of the more popular research points at present, and is used for positioning, so that the positioning accuracy can be greatly improved. However, parameters involved in artificial intelligence, and in particular deep learning, typically reach the scale of tens of millions, or even hundreds of millions, which is not easy for these parameters to be universally used in simple terminal devices. Generally, a terminal can obtain relatively ideal channel information, and therefore, how the terminal feeds back the channel information to a base station or a positioning server is used to achieve accurate positioning of the terminal is a problem to be solved urgently.
Disclosure of Invention
The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.
The embodiment of the invention provides a channel state information processing method, a terminal, network equipment, a base station and a computer readable storage medium, which are used for realizing accurate positioning of the terminal based on extraction and feedback of channel information characteristics.
In a first aspect, an embodiment of the present invention provides a channel state information processing method, which is applied to a first device, and the method includes:
acquiring channel information;
determining a transmission matrix according to the channel information;
and sending the transmission matrix to second equipment so that the second equipment positions the first equipment according to the transmission matrix.
In a second aspect, an embodiment of the present invention provides a channel state information processing method, which is applied to a second device, and the method includes:
obtaining a transmission matrix from a first device;
and positioning the first equipment according to the transmission matrix.
In a third aspect, an embodiment of the present invention provides a terminal, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the channel state information processing method as described above in the first aspect when executing the computer program.
In a fourth aspect, an embodiment of the present invention provides a base station, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the channel state information processing method as described above in the second aspect when executing the computer program.
In a fifth aspect, an embodiment of the present invention provides a location server, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the channel state information processing method as described above in the second aspect when executing the computer program.
In a sixth aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer-executable program is stored, where the computer-executable program is used to make a computer execute the channel state information processing method according to the first aspect or the channel state information processing method according to the second aspect.
The embodiment of the invention comprises the following steps: the first device obtains the channel information, determines a transmission matrix according to the channel information, and then sends the transmission matrix to the second device, so that the second device positions the first device according to the transmission matrix. Based on the method, the first equipment obtains the transmission matrix through extracting the channel information characteristics, and then feeds the transmission matrix back to the second equipment, so that the second equipment can output the positioning result according to the transmission matrix, and accurate positioning of the first equipment is realized.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the example serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a flowchart of a channel state information processing method applied to a first device according to an embodiment of the present invention;
fig. 2 is a flowchart of sub-steps in a channel state information processing method applied to a first device according to an embodiment of the present invention;
fig. 3 is a flowchart of a channel state information processing method applied to a second device according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a terminal provided by an embodiment of the present invention;
fig. 5 is a schematic diagram of a base station according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a positioning server according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It should be understood that in the description of the embodiments of the present invention, a plurality (or a plurality) means two or more, more than, less than, more than, etc. are understood as excluding the number, and more than, less than, etc. are understood as including the number. If the description of "first", "second", etc. is used for the purpose of distinguishing technical features, it is not intended to indicate or imply relative importance or to implicitly indicate the number of indicated technical features or to implicitly indicate the precedence of the indicated technical features.
The positioning technology has wide application in our life and production, such as map navigation, object positioning of intelligent factories, fire fighting positioning, logistics positioning and the like. Moreover, the positioning accuracy is more and more demanding, for example, the positioning requirements for machines or articles in factories reach even centimeter level.
In the related art, if the Non-Line Of Sight (NLOS) exists between the terminal to be located and the location device, the location accuracy is not high, and the location accuracy is not ideal, which cannot meet the requirements Of life and production. Artificial Intelligence (AI) is one of the popular research points at present, and the accuracy of positioning can be greatly improved by using AI for positioning. However, parameters involved in artificial intelligence, and in particular deep learning, typically reach the scale of tens of millions, or even hundreds of millions, which is not easy for these parameters to be universally used in simple terminal devices. Generally, a terminal can obtain relatively ideal channel information, and therefore how the terminal feeds back the channel information to a base station or a positioning server to achieve accurate positioning of the terminal becomes a problem to be solved urgently.
The embodiment of the invention provides a channel state information processing method, a terminal and a computer readable storage medium. Based on the method, the first equipment obtains the transmission matrix through extracting the channel information characteristics, and then feeds the transmission matrix back to the second equipment, so that the second equipment can output the positioning result according to the transmission matrix, and accurate positioning of the first equipment is realized.
As shown in fig. 1, fig. 1 is a flowchart of a channel state information processing method according to an embodiment of the present invention. The channel state information processing method is applied to a first device, wherein the first device can be a terminal, and the channel state information processing method includes, but is not limited to, the following steps:
and 103, sending the transmission matrix to the second equipment so that the second equipment positions the first equipment according to the transmission matrix. The first device obtains the channel information, determines a transmission matrix according to the channel information, and then sends the transmission matrix to the second device, so that the second device positions the first device according to the transmission matrix. Based on the method, the first equipment obtains the transmission matrix through extracting the channel information characteristics, and then feeds the transmission matrix back to the second equipment, so that the second equipment can output the positioning result according to the transmission matrix, and accurate positioning of the first equipment is realized.
The matrix of this embodiment may be a one-dimensional or more matrix, array, vector, tensor.
It is noted that the first device includes, but is not limited to, a terminal, and the second device includes, but is not limited to, a base station and a positioning server. As for the terminal, the terminal may be a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA), a handheld device with a Wireless communication function, a computing device or other processing device connected to a Wireless modem, a vehicle-mounted device, a wearable device, or a terminal device in a 5G network or a future 5G or above network, and the like, and the present embodiment is not limited thereto. For the base station, the base station may be an evolved Node B (eNB or eNodeB) in Long Term Evolution (Long Term Evolution), a base station device in a 5G network, or a base station in a future communication system, and the like, and the base station may include one or more of various network side devices and Location Management Function (LMF) devices, such as various macro base stations, micro base stations, home base stations, radio remotes, routers, location servers (location servers), or primary cells (primary cells) and cooperative cells (secondary cells).
For the acquisition of the channel information, the first device may acquire the positioning reference signal from the second device, and obtain the channel information according to the positioning reference signal. For the Positioning Reference Signal, the Positioning Reference Signal may include a downlink Positioning Reference Signal (PRS) used for Positioning in a downlink and an uplink Sounding Reference Signal (SRS) used for Positioning, and may also be other Reference signals used for Positioning. In a positioning method based on terminal assisted positioning or based on a terminal, a base station or a positioning server sends a PRS to a terminal needing positioning, and the terminal obtains channel information related to positioning through the PRS. For each terminal, each transmit antenna of the base station goes to each receive antenna of the terminal, which may be denoted as N bs *N c Of a complex matrix of (a), wherein N c Is the number of subcarriers in the frequency domain, and N bs For the number of alternative positioning base stations, the ith row of the complex matrix represents the channel information from the terminal to the ith base station, the first dimension is the dimension of the base station, the second dimension is the dimension of the channel, of course, the channel information corresponding to the method of the present invention may also be in the form of the dimension of the base station after the dimension of the channel, that is, N c *N bs A complex matrix of (a). The base station processes the transmission matrix to output a positioning result, where the positioning result may be location information of the terminal, such as location information in absolute coordinates, location information in relative coordinates, or parameters used to further estimate user location information, such as time arrival, signal arrival angle, and signal departure angle.
It can be understood that, in order to reduce the complexity of the terminal, an artificial intelligence positioning module may be disposed at the base station or the positioning server, the terminal feeds back the transmission matrix to the base station or the positioning server, and the artificial intelligence positioning module processes the transmission matrix to output a positioning result.
It is to be understood that step 102 may include the following sub-steps:
intercepting the channel information to obtain an interception matrix;
and quantizing the intercepted matrix to obtain a transmission matrix.
It is understood that, in step 102, truncating the channel information to obtain a truncation matrix may include the following sub-steps:
converting the channel information into a time domain channel to obtain a time domain matrix;
and intercepting the time domain matrix to obtain an intercepted matrix.
It is understood that, in step 102, after the channel information is truncated to obtain the truncation matrix, the following sub-steps are further included:
and performing real number processing on the intercepted matrix to obtain a real number matrix.
It is understood that, in step 102, after performing real number processing on the truncated matrix to obtain a real number matrix, the following sub-steps are further included:
and sampling the real matrix to obtain a sampling matrix.
It is to be understood that, in step 102, after sampling the real matrix to obtain a sampling matrix, the following sub-steps are further included:
and feeding back the position of the sampling point corresponding to the sampling matrix to the second equipment.
It is understood that, in step 102, after sampling the real matrix to obtain a sampling matrix, the following sub-steps are further included:
and carrying out normalization processing on the sampling matrix to obtain a normalization matrix.
It is understood that, in step 102, after the sampling matrix is normalized to obtain a normalized matrix, the following sub-steps are further included:
and quantizing the normalized matrix to obtain a quantized matrix, and determining a transmission matrix according to the quantized matrix.
It is to be understood that, as shown in fig. 2, step 102 may further include the following sub-steps:
1023, performing real number processing on the intercepted matrix to obtain a real number matrix;
at step 1027, a transmission matrix is determined from the quantization matrix.
In an embodiment, in the process of preprocessing the channel information, the channel information may be converted into a time-domain channel, that is, an Inverse Discrete Fourier Transform (IDFT) or Inverse Fast Fourier Transform (IFFT) is performed on a channel from the terminal to each base station, so as to Transform the channel information into a time-domain matrix, that is, perform time-domain channel conversion on the channel from the terminal to each base station, where the time-domain matrix is N bs *N t Or Nt x N bs Of a complex matrix of N t The number of time domain sampling points is a positive integer, and can be greater than or equal to N c Positive integer of (1), N c The number of subcarriers in the frequency domain, such as 1024, 2048, 4096, etc., is related to the bandwidth of the positioning. And the number of located base stations N bs May also be more, such as N bs Not less than 8, it is known that N is fed back bs *N t The complex matrix overhead is large. Therefore, further processing is required, such as transforming the channel information to the time domain to obtain a time domain matrix, where the time domain matrix is N bs *N t Of complex matrix or Nt x N bs Of a complex matrix of N t Number of time-domain samples, typically N c Integer multiples of. Intercepting the time domain matrix for a certain window length, for example, intercepting the kth column to the k + L-1 column of the time domain matrix to obtain an intercepted matrix, wherein k and L are positive integers, and k is greater than 0, k + L-1 is less than N c For example, k =1,l =16,64,128,256. Then pairIntercepting the matrix, performing real number processing to obtain a real number matrix, then sampling the real number matrix to obtain a sampling matrix, performing normalization processing on the sampling matrix to obtain a normalization matrix, quantizing the normalization matrix to obtain a quantization matrix, determining a transmission matrix according to the quantization matrix, and feeding back the transmission matrix to the base station.
It should be noted that, performing real quantization processing on the truncated matrix, so as to convert the truncated matrix from a complex matrix to a real matrix, may be obtained through one of the following real quantization processing: taking an absolute value of each element, taking a real number part of each element, taking an absolute value of the real number part of each element, taking an imaginary number part of each element, taking an absolute value of the imaginary number part of each element, and obtaining a corresponding real number matrix through each real number processing, wherein the final real number matrix is obtained by connecting at least one real number matrix, for example, taking the absolute value of each element to obtain a real number matrix H1, taking the real number part of each element to obtain a real number matrix H2, taking the absolute value of the real number part of each element to obtain a real number matrix H3, taking the imaginary number part of each element to obtain a real number matrix H4, taking the absolute value of the imaginary number part of each element to obtain a real number matrix H5, connecting C matrixes of H1 to H5 to obtain a real number matrix, wherein the real number matrix is a matrix with s is Nt C, or Nb of the dimensionality Nb, wherein the third dimensionality is Nb, and the channel can be in front of the base station, and the base station can be positioned in front of the base station.
It is understood that the real matrix includes C matrices, and the sampling of the real matrix may include, but is not limited to, any of the following:
selecting the first L1 elements from each row of elements of each matrix included in the real matrix; or,
selecting the last L1 elements from each row of elements of each matrix included in the real number matrix; or,
selecting continuous L1 elements containing the elements with the maximum value in each row of elements of each matrix included in the real number matrix; or,
selecting the first L1 elements with the maximum value from each row of elements of each matrix included in the real matrix; or,
selecting L1 elements which are larger than a preset value from each row of elements of each matrix included in the real number matrix;
and acquiring position information of L1 elements on each row of elements of each matrix, which is included in the real number matrix, wherein the position information of the L1 elements on each row of elements of each matrix, which is included in the real number matrix, is position information of sampling points, acquiring the sampling matrix through sampling the real number matrix, and feeding back the position information of the sampling points corresponding to the sampling matrix to the second equipment.
Wherein, each row of the time domain matrix comprises L elements, and L1 is less than L.
It is understood that the real number matrix includes C matrices, and the sampling of the real number matrix may further include, but is not limited to, any one of the following:
selecting the first L1 elements from each column of elements of each matrix included in the real number matrix; or,
selecting the last L1 elements from each column of elements of each matrix included in the real number matrix; or,
selecting continuous L1 elements containing the element with the maximum value from each column element of each matrix included in the real number matrix; or,
selecting the first L1 elements with the maximum value from each column of elements of each matrix included in the real matrix; or,
selecting L1 elements which are larger than a preset value from each column of elements of each matrix included in the real matrix;
and acquiring position information of L1 elements on each row of elements of each matrix included in the real number matrix, wherein the position information of L1 elements on each row of elements of each matrix included in the real number matrix is position information of sampling points, acquiring the sampling matrix by sampling the real number matrix, and feeding back the position information of the sampling points corresponding to the sampling matrix to the second equipment.
Wherein, each column of the time domain matrix comprises L elements, and L1 is less than L.
It is to be understood that the real number matrix includes C matrices, and a real number matrix is sampled, where each matrix of the real number matrix is a vector, that is, channel information corresponding to each base station, and may further include, but is not limited to, any one of the following:
selecting the first L1 elements of each matrix included in the real number matrix; or,
l1 elements are selected from each matrix included in the real number matrix; or,
selecting continuous L1 elements containing the elements with the maximum value in each matrix included in the real number matrix; or,
selecting the first L1 elements with the maximum value in each matrix included in the real number matrix; or,
selecting L1 elements which are larger than a preset value in each matrix included in the real number matrix;
and acquiring position information of the L1 elements of each matrix included in the real number matrix, wherein the position information of the L1 elements of each matrix included in the real number matrix is the position information of the sampling points, acquiring the sampling matrix by sampling the real number matrix, and feeding back the position information of the sampling points corresponding to the sampling matrix to the second equipment.
Wherein, each column of the time domain matrix comprises L elements, and L1 is less than L.
Here, the position of the sampling point may also be interchanged with the following concepts, and the sampling point position information, the index corresponding to the sampling point, or the time sequence corresponding to the sampling point, or the window position of the sampling point, the multipath delay index, the multipath delay, the multipath arrival time, the relative value of the multipath arrival time, the multipath arrival time index, and the multipath position information may also be used.
It is to be understood that the normalization process performed on the sampling matrix may include any one of the following:
in an embodiment, for each element in a real number matrix, normalizing the element according to a first scaling factor, where the first scaling factor is a maximum value of absolute values in all elements of the real number matrix or is obtained maximum channel state information; it should be noted that the obtained channel state information may be channel state information of the second device set measured by the first device to the first device.
In one embodiment, the matrix obtained by normalizing the real number matrix in the K-th dimension is a normalized matrix, where K is a positive integer and 0 < K < 4. For example, when K = 1-dimensional normalization, the operation performed is: multiplying each element of the ith row of each matrix contained in the real matrix by the second scaling factor of the ith row, or dividing each element of the ith row of each matrix contained in the real matrix by the reciprocal of the second scaling factor of the ith row, wherein the second scaling factor of the ith row is the maximum of the absolute values of the elements of the ith row, i =1, …, N. It should be noted that, the second scaling factor is a maximum value of absolute values in an ith row element of the real matrix or channel state information from the first device to the second device mapped to the ith row of the real matrix, and i is equal to the number of the second devices. For example, when K =2 d normalization, the operation performed is: multiplying each element of the ith column in the real matrix by a second scaling factor of the ith column or dividing each element of the ith column in the real matrix by the inverse of the second scaling factor of the ith column, wherein the second scaling factor of the ith column is the maximum of the absolute values of the elements of the ith column, i =1, …, N. It should be noted that the second scaling factor is a maximum value of an absolute value in an ith column element of the real matrix or channel state information from the first device to the second device mapped to the ith column of the real matrix, and i is equal to the number of the second devices.
It should be noted that a constant may be added or subtracted to all elements of the real matrix, and then the normalization process is performed, or a constant, such as 0.5, an average value of all elements of the real matrix, etc., may be added or subtracted after the normalization process, or a variance obtained by adding or subtracting a constant to all elements of the real matrix and then dividing by all elements of the real matrix is performed.
It should be noted that, a constant may be added or subtracted to or from all elements of the K-th dimension of the real matrix, and then the normalization processing of the matrix in the K-th dimension is performed, or a constant, such as 0.5, is added or subtracted after the normalization processing of the matrix in the K-th dimension, an average value of all elements of the real matrix in the K-th dimension, and so on, or a variance obtained by adding or subtracting a constant to or from all elements of the real matrix in the K-th dimension and then dividing the sum by all elements of the real matrix in the K-th dimension, where K is a number of 0, 1, 2, 3, 4, 5, and 6.
In one embodiment, after converting the truncated matrix from the complex matrix to the real matrix, each element of the real matrix may be divided by a scaling factor s, where s may take one of the following values: s is the maximum value of the absolute values of all elements of HT1, and s is the maximum channel state information from the terminal to all base stations, where the channel state information includes but is not limited to one of the following: reference Signal Received Power (RSRP), signal-to-noise and interference ratio (SINR), reference Signal Received Quality (RSRQ).
In one embodiment, after converting the truncated matrix from a complex matrix to a real matrix, the elements of the ith row (column) of the real matrix may be divided by a scaling factor s i Wherein s is i May be one of the following: s i Is the maximum of the absolute values of the elements of the ith row (column) of HT1, s i Channel state information from the terminal to the ith base station, wherein the channel state information includes but is not limited to one of the following: reference signal received power, RSRP, signal to interference plus noise ratio, SINR, reference signal received quality, RSRQ, i =1, …, N bs 。
It is understood that, for a real matrix, the real matrix can be obtained by one of the following real quantization processes: taking an absolute value of each element, taking a real number part of each element, taking an absolute value of the real number part of each element, taking an imaginary number part of each element, taking an absolute value of the imaginary number part of each element, and obtaining a corresponding real number matrix through each real number processing, wherein the final real number matrix is obtained by connecting at least one real number matrix, for example, taking the absolute value of each element to obtain a real number matrix H1, taking the real number part of each element to obtain a real number matrix H2, taking the absolute value of the real number part of each element to obtain a real number matrix H3, taking the imaginary number part of each element to obtain a real number matrix H4, taking the absolute value of the imaginary number part of each element to obtain a real number matrix H5, then connecting C matrixes of H1 to H5 to obtain a real number matrix which is an s Nt matrix or Nb of Nbs = s = C, and taking the third dimension Nbs = C as a channel, and the channel can be positioned in front of the base station, and the base station.
For example, taking the absolute value of the real part or real part for each element of the truncated matrix forms a real part matrix, taking the absolute value of the imaginary part or imaginary part for each element of the truncated matrix forms an imaginary part matrix, and combining the real part matrix and imaginary part matrix into a three-dimensional matrix of N bs * And L is a matrix of 2, and when the value of the third dimension of the truncated matrix is 1, the truncated matrix is correspondingly a real part matrix, and when the value of the third dimension is 2, the truncated matrix is correspondingly an imaginary part matrix.
In one embodiment, after converting the real matrix from the complex matrix to the real matrix and the imaginary matrix, the element of the ith row (or column) of the real matrix and/or the imaginary matrix is divided by a scaling factor s i Wherein s is i May be one of the following: s is i Is the maximum of the absolute values of the elements of the ith row (or column) of the real and/or imaginary matrix of HT1, s i Channel state information from the terminal to the ith base station, wherein the channel state information includes but is not limited to one of the following: reference signal received power, RSRP, signal to interference plus noise ratio, SINR, reference signal received quality, RSRQ, i =1, …, N bs 。
It is to be understood that the quantization of the normalized matrix may include any one of:
for each element in the normalized matrix, bit quantizing the element according to a first scaling factor; or,
for each element in the normalized matrix, the element is bit quantized according to a second scaling factor corresponding to the ith row (or column) in which the element is located.
In an embodiment, each element of the normalized matrix is quantized to obtain a quantized matrix quantization matrix. For example, each element in the normalization matrix may be quantized by Bbit. The number of quantization bits for the element of the ith row (or column) and the scaling factor s of the ith row (or column) may also be i In connection with s i The larger the number of quantized bits. E.g. s i Quantization with 4 bits above threshold t1, quantization with 3 bits above t2, quantization with 1 bit above t3, etc., where t1 is greater than t2 and t2 is greater than t3.
In an embodiment, the transmission matrix is determined according to the quantization matrix, and specifically, if the quantization matrix is a two-dimensional matrix, the first L1 column or the last L1 column of the quantization matrix is selected to form a compressed matrix HI, and the transmission matrix is fed back in the physical layer and/or higher layer signaling. After receiving the transmission matrix, the base station or the positioning server may obtain a matrix with L columns by complementing L-L1 columns of zeros for the transmission matrix. If the quantization matrix is a three-dimensional matrix, the front L1 column or the rear L1 column of the real part matrix and the imaginary part matrix are respectively taken to form a compressed real part matrix and an imaginary part matrix, the compressed real part matrix and the compressed imaginary part matrix are combined into the three-dimensional matrix to obtain a transmission matrix, and the transmission matrix is fed back in the physical layer and/or the high layer signaling. After receiving the transmission matrix, the base station or the positioning server may obtain a matrix with L columns by complementing L-L1 columns of zeros to the real part matrix and the imaginary part matrix of the transmission matrix.
In an embodiment, for the feedback manner, consecutive L1 elements of the element having the maximum value in the quantization matrix may be fed back, for example, the first L1 samples of the element having the maximum value, or the last L1 samples of the element having the maximum value, or L1 samples with the element having the maximum value as a midpoint. Specifically, if the quantization matrix is a two-dimensional matrix, the ith row of the quantization matrix is selected, and the value a having the largest absolute value among the elements of the ith row is selected i And recording the column index j corresponding to the ith row, selecting L1 continuous indexes I including the index j, assigning the values except the index I as a constant c, wherein c can be zero, feeding back the index I through the physical layer and/or the high layer signaling,and values corresponding to L1 consecutive samples including index j. After the base station or the positioning server receives the transmission matrix and the index I, the constant c is assigned through values outside the index I, wherein c can be zero, and the value corresponding to the index I is a feedback value, so that the transmission matrix is formed. If the quantization matrix is a three-dimensional matrix, the real part matrix and the imaginary part matrix are respectively operated like a two-dimensional matrix, so that the real part matrix and the imaginary part matrix of the transmission matrix are obtained.
In an embodiment, for the feedback manner, the elements of the first L1 maximum values in the quantization matrix may be fed back. Specifically, if HT2 is a two-dimensional matrix, an ith row of the quantization matrix is selected, L1 values a having the largest absolute value among elements of the ith row are selected, where a is a vector of 1 × L1, and a column index I corresponding to the selected L1 sampling points in the ith row is recorded, and all values except the index I are assigned as a constant c, where c may be zero. And feeding back the index I and the values of L1 sampling points corresponding to the index I through physical layer and/or high layer signaling. After the base station or the positioning server receives the transmission matrix and the index I, values outside the index I are assigned to be a constant c, wherein c can be zero, and a value corresponding to the index I is a feedback value A, so that the transmission matrix is formed. If the quantization matrix is a three-dimensional matrix, the real part matrix and the imaginary part matrix are respectively operated like a two-dimensional matrix, so that the real part matrix and the imaginary part matrix of the transmission matrix are obtained.
In an embodiment, for the feedback manner, L1 elements of the quantization matrix larger than a preset value may be fed back. Specifically, if the quantization matrix is a two-dimensional matrix, an ith row of the quantization matrix is selected, ki values a with absolute values greater than a threshold value T in the elements of the ith row are selected, a is a vector of 1 × li, and column indexes I corresponding to the selected L1 sampling points in the ith row are recorded, values except the indexes I are all assigned to be constants c, and c can be zero. And feeding back the index I and the values of L1 sampling points corresponding to the index I through physical layer and/or high layer signaling. After the base station or the positioning server receives the transmission matrix and the index I, the base station or the positioning server assigns values outside the index I as constants c, wherein c can be zero, and the value corresponding to the index I is a feedback value A, so that a matrix transmission matrix is formed. If the quantization matrix is a three-dimensional matrix, the real part matrix and the imaginary part matrix are respectively operated like a two-dimensional matrix, so that the real part matrix and the imaginary part matrix of the transmission matrix are obtained.
In these embodiments, the index I may be referred to as position information of the sampling point.
As shown in fig. 3, fig. 3 is a flowchart of a channel state information processing method according to an embodiment of the present invention. The channel state information processing method may be applied to a second device, where the second device may be a base station or a positioning server, and the channel state information processing method includes, but is not limited to, the following steps:
The second device obtains the transmission matrix from the first device and then locates the first device according to the transmission matrix. Based on this, the second equipment can be according to transmission matrix output positioning result to realize the accurate location to first equipment.
The matrix of this embodiment may be a one-dimensional or more matrix, array, vector, tensor.
It is noted that the first device includes, but is not limited to, a terminal, and the second device includes, but is not limited to, a base station and a positioning server. As for the terminal, the terminal may be a cellular phone, a cordless phone, a session initiation protocol phone, a wireless local loop station, a personal digital assistant, a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, a vehicle-mounted device, a wearable device, or a terminal device in a 5G network or a future 5G or above network, and the like, which is not limited in this embodiment. For the base station, the base station may be an evolved node b in LTE, a base station device in a 5G network, or a base station in a future communication system, and the base station may include one or more of various macro base stations, micro base stations, home base stations, radio remotes, routers, location servers, or various network side devices and positioning management function devices, such as a main cell and a cooperative cell.
It should be noted that, the second device may send a positioning reference signal to the first device, and obtain a transmission matrix obtained by the first device processing the positioning reference signal, and for the positioning reference signal, the positioning reference signal may include a downlink positioning reference signal PRS used for positioning in a downlink and an uplink sounding reference signal SRS used for positioning, but may also be other reference signals used for positioning. In a positioning method based on terminal assisted positioning or based on a terminal, a base station or a positioning server sends a PRS to a terminal needing positioning, and the terminal obtains channel information related to positioning through the PRS. For each terminal, each transmit antenna of the base station goes to each receive antenna of the terminal, which may be denoted as N bs *N c Of a complex matrix of (a), wherein N c Is the number of subcarriers in the frequency domain, and N bs For the number of the alternative positioning base stations, the ith row of the complex matrix represents the channel information from the terminal to the ith base station. The base station processes the transmission matrix to output a positioning result, where the positioning result may be location information of the terminal, such as location information in absolute coordinates, location information in relative coordinates, or parameters used to further estimate user location information, such as time arrival, signal arrival angle, and signal departure angle.
It can be understood that, in order to reduce the complexity of the terminal, an artificial intelligence positioning module may be disposed at the base station or the positioning server, the terminal feeds back the transmission matrix to the base station or the positioning server, and the artificial intelligence positioning module processes the transmission matrix to output a positioning result.
As shown in fig. 4, an embodiment of the present invention further provides a terminal.
Specifically, the terminal includes: one or more processors and memory, one processor and memory being exemplified in fig. 4. The processor and memory may be connected by a bus or other means, such as by a bus in FIG. 4.
The memory, which is a non-transitory computer-readable storage medium, may be used to store a non-transitory software program and a non-transitory computer-executable program, such as the channel state information processing method in the above-described embodiments of the present invention. The processor implements the channel state information processing method in the above-described embodiment of the present invention by running a non-transitory software program and a program stored in a memory.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data and the like required to perform the channel state information processing method in the above-described embodiment of the present invention. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The non-transitory software program and the program required to implement the channel state information processing method in the above-described embodiments of the present invention are stored in the memory, and when executed by one or more processors, the channel state information processing method in the above-described embodiments of the present invention is executed, for example, the method steps 101 to 103 in fig. 1, and the method steps 1021 to 1027 in fig. 2 described above are executed, and the terminal acquires the channel information, determines the transmission matrix according to the channel information, and then sends the transmission matrix to the base station, so that the base station locates the terminal according to the transmission matrix. Based on the method, the terminal obtains the transmission matrix through extracting the channel information characteristics, and then feeds the transmission matrix back to the base station, so that the base station can output the positioning result according to the transmission matrix, and the accurate positioning of the terminal is realized.
As shown in fig. 5, an embodiment of the present invention further provides a base station.
Specifically, the base station includes: one or more processors and memory, one processor and memory being exemplified in fig. 5. The processor and memory may be connected by a bus or other means, such as by a bus in FIG. 5.
The memory, which is a non-transitory computer-readable storage medium, may be used to store a non-transitory software program and a non-transitory computer-executable program, such as the channel state information processing method in the above-described embodiments of the present invention. The processor implements the channel state information processing method in the above-described embodiment of the present invention by running the non-transitory software program and the program stored in the memory.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data and the like required to perform the channel state information processing method in the above-described embodiment of the present invention. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The non-transitory software program and program required to implement the channel state information processing method in the above-described embodiments of the present invention are stored in a memory, and when being executed by one or more processors, the channel state information processing method in the above-described embodiments of the present invention is executed, for example, the method steps 301 to 302 in fig. 3 described above are executed, and the base station acquires the transmission matrix from the terminal and locates the terminal according to the transmission matrix. Based on this, the base station can output the positioning result according to the transmission matrix, thereby realizing accurate positioning of the terminal.
As shown in fig. 6, an embodiment of the present invention further provides a location server.
Specifically, the positioning server includes: one or more processors and memory, one processor and memory being exemplified in fig. 6. The processor and memory may be connected by a bus or other means, such as by a bus in FIG. 6.
The memory, as a non-transitory computer-readable storage medium, may be used to store a non-transitory software program and a non-transitory computer-executable program, such as the channel state information processing method in the embodiments of the present invention described above. The processor implements the channel state information processing method in the above-described embodiment of the present invention by running a non-transitory software program and a program stored in a memory.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data and the like required to perform the channel state information processing method in the above-described embodiment of the present invention. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The non-transitory software program and the program required for implementing the channel state information processing method in the above-described embodiments of the present invention are stored in a memory, and when executed by one or more processors, perform the channel state information processing method in the above-described embodiments of the present invention, for example, perform the method steps 301 to 302 in fig. 3 described above, and the positioning server acquires the transmission matrix from the terminal and then positions the terminal according to the transmission matrix. Based on this, the positioning server can output the positioning result according to the transmission matrix, thereby realizing accurate positioning of the terminal.
Furthermore, an embodiment of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer-executable program, where the computer-executable program is executed by one or more control processors, and the computer-executable program may cause the one or more processors to perform the channel state information processing method in the foregoing embodiment of the present invention, for example, perform the method steps 101 to 103 in fig. 1, the method steps 1021 to 1027 in fig. 2, or the method steps 301 to 302 in fig. 3 described above. The first device obtains the channel information, determines a transmission matrix according to the channel information, and then sends the transmission matrix to the second device, so that the second device positions the first device according to the transmission matrix. Based on the method, the first equipment obtains the transmission matrix through extracting the channel information characteristics, and then feeds the transmission matrix back to the second equipment, so that the second equipment can output the positioning result according to the transmission matrix, and accurate positioning of the first equipment is realized.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable programs, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable programs, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
While the preferred embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that the foregoing and various other changes, omissions and deviations in the form and detail thereof may be made without departing from the scope of this invention.
Claims (22)
1. A channel state information processing method is applied to a first device, and comprises the following steps:
acquiring channel information;
determining a transmission matrix according to the channel information;
and sending the transmission matrix to second equipment so that the second equipment positions the first equipment according to the transmission matrix.
2. The method of claim 1, wherein the determining a transmission matrix according to the channel information comprises:
intercepting the channel information to obtain an interception matrix;
and quantizing the interception matrix to obtain a transmission matrix.
3. The method of claim 2, wherein the truncating the channel information to obtain a truncation matrix comprises:
converting the channel information into a time domain channel to obtain a time domain matrix;
and intercepting the time domain matrix to obtain an intercepted matrix.
4. The method of claim 3, wherein after the truncating the channel information to obtain a truncation matrix, further comprising:
and performing real number processing on the intercepted matrix to obtain a real number matrix.
5. The method of claim 4, wherein after the real processing on the truncated matrix to obtain a real matrix, the method further comprises:
and sampling the real matrix to obtain a sampling matrix.
6. The method of claim 5, wherein after the sampling the real matrix to obtain a sampling matrix, further comprising:
and feeding back the position information of the sampling points corresponding to the sampling matrix to the second equipment.
7. The method of claim 5, wherein after the sampling the real matrix to obtain a sampling matrix, further comprising:
and carrying out normalization processing on the sampling matrix to obtain a normalization matrix.
8. The method of claim 7, wherein after the normalizing the sampling matrix to obtain a normalized matrix, further comprising:
and quantizing the normalized matrix to obtain a quantized matrix, and determining a transmission matrix according to the quantized matrix.
9. The method of claim 3, wherein converting the channel information to a time domain channel comprises:
and performing inverse discrete Fourier transform or inverse fast Fourier transform processing on the channel information.
10. The method of claim 3, wherein the truncating the time domain matrix comprises:
and intercepting the time domain matrix according to a preset window length parameter.
11. The method according to claim 7, wherein the normalizing the sampling matrix comprises any one of:
for each element in the real matrix, performing normalization processing on the element according to a first scaling factor, wherein the first scaling factor is the maximum value of absolute values in all elements of the real matrix or the maximum acquired channel state information;
and normalizing the real number matrix in the K dimension to obtain a normalized matrix, wherein K is an integer and is more than or equal to 0 and less than or equal to 6.
12. The method according to claim 4, wherein the real matrix comprises at least one of a real matrix obtained by taking an absolute value of a real part or a real part for each element of the truncated matrix, an imaginary matrix obtained by taking an absolute value of an imaginary part or an imaginary part for each element of the truncated matrix, and a magnitude matrix obtained by taking an absolute value for each element of the truncated matrix.
13. The method of claim 11, wherein the channel state information comprises any one of:
reference signal received power, RSRP;
signal to interference plus noise ratio, SINR;
reference signal received quality, RSRQ.
14. The method of claim 11, wherein the quantizing the normalized matrix comprises:
for each element in the normalized matrix, bit quantizing the element according to the first scaling factor; or,
for each element of the ith row in the normalization matrix, carrying out bit quantization on the element according to a second scaling factor corresponding to the ith row in which the element is positionedWherein the second scaling factor is the maximum of the absolute values in the ith row or ith column element of the real matrix, i =1, …, N bs Wherein, N is bs The number of the second devices; or,
for each element in the ith column in the normalized matrix, performing bit quantization on the element according to a second scaling factor corresponding to the ith column in which the element is located, wherein the second scaling factor is the maximum value of absolute values in the ith column of the real matrix, i =1, …, N bs Wherein N is bs Is the number of the second devices.
15. The method of claim 5, wherein the sampling the real matrix comprises:
selecting first L1 elements from each row of elements of each matrix included in the real matrix; or,
selecting the last L1 elements from each row of elements of each matrix included in the real number matrix; or,
selecting continuous L1 elements containing the elements with the maximum value from each row of elements of each matrix included in the real number matrix; or,
selecting the first L1 elements with the maximum value from each row of elements of each matrix included in the real number matrix; or,
selecting L1 elements which are larger than a preset value from each row of elements of each matrix included in the real matrix;
acquiring position information of L1 elements corresponding to each row of elements of each matrix included in the real number matrix;
wherein each row of the time domain matrix comprises L elements, and L1 is less than L.
16. The method of claim 5, wherein the sampling the real matrix comprises:
selecting the first L1 elements from each column of elements of each matrix included in the real matrix; or,
selecting the last L1 elements from each column of elements of each matrix included in the real number matrix; or,
selecting continuous L1 elements containing the element with the maximum value in each column element of each matrix included in the real number matrix; or,
selecting the first L1 elements with the maximum value from each column of elements of each matrix included in the real number matrix; or,
selecting L1 elements which are larger than a preset value from each row of elements of each matrix included in the real matrix;
acquiring position information of the L1 elements on each column of elements of each matrix included in the real matrix;
wherein each column of the time domain matrix comprises L elements, and L1 is less than L.
17. The method of claim 5, wherein the sampling the real matrix comprises:
selecting first L1 elements from all elements of each matrix included in the real matrix; or,
selecting the last L1 elements from all elements of each matrix included in the real number matrix; or,
selecting continuous L1 elements containing the element with the maximum value from all elements of each matrix included in the real number matrix; or,
selecting the first L1 elements with the maximum value from all the elements of each matrix included in the real matrix; or,
selecting L1 elements which are larger than a preset value from all elements of each matrix included in the real number matrix;
acquiring position information of the L1 elements of each matrix included in the real number matrix;
each matrix included in the real number matrix is a one-dimensional vector, the time domain matrix includes L elements, and L1 is smaller than L.
18. A channel state information processing method is applied to a second device, and comprises the following steps:
obtaining a transmission matrix from a first device;
and positioning the first equipment according to the transmission matrix.
19. A terminal, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the channel state information processing method according to any one of claims 1 to 17 when executing the computer program.
20. A base station, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the channel state information processing method according to claim 18 when executing the computer program.
21. A positioning server, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor implements the channel state information processing method according to claim 18 when executing the computer program.
22. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer-executable program for causing a computer to execute the channel state information processing method according to any one of claims 1 to 17 or the channel state information processing method according to claim 18.
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| PCT/CN2022/091031 WO2022247599A1 (en) | 2021-05-26 | 2022-05-05 | Channel state information processing method, terminal, and computer readable storage medium |
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| CN106550447B (en) * | 2015-09-21 | 2020-04-03 | 中兴通讯股份有限公司 | Terminal positioning method, device and system |
| CN108168559B (en) * | 2017-12-26 | 2020-05-05 | 西京学院 | Indoor positioning system and method based on distributed antenna |
| WO2019148511A1 (en) * | 2018-02-05 | 2019-08-08 | Telefonaktiebolaget Lm Ericsson (Publ) | Method and devices for estimation of mimo channel state information |
| CN112491750A (en) * | 2019-09-12 | 2021-03-12 | 华为技术有限公司 | Method and apparatus for perceptual measurement |
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