Limited feedback-based channel estimation method for MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) system

Abstract

The embodiment of the invention provides a limited feedback-based channel estimation method for an MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) system, which is applied to a base station end and used for estimating a downlink channel state, and comprises the following steps: S1, acquiring a preset vector quantization codebook for the MIMO system, the code vectors comprise a plurality of code vectors used for representing different pilot frequency receiving signal vectors and an index value of each code vector; s2, pilot signals are sent to the multiple user sides, signals fed back to the base station side by the multiple user sides according to pilot receiving signals of the user sides are received, and the fed-back signals are index values of code vectors most similar to quantization vectors of the pilot receiving signals searched by the user sides from the vector quantization codebook; s3, obtaining an estimated quantization vector of the pilot frequency receiving signal according to the vector quantization codebook and a signal fed back by each user side; and S4, performing channel estimation according to the estimation quantization vector by adopting a preset estimation mode to obtain a downlink channel state information matrix correspondingly estimated by each user side.

Description

Technical Field

[0001] The present invention relates to the field of wireless communications, in particular to the field of channel estimation, and more particularly to a channel estimation method based on limited feedback for a MIMO-OFDM system. Background Art

[0002] Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) technology is widely used in modern wireless communication systems for its outstanding multiplexing, high data transmission rate and high spectrum utilization. MIMO technology significantly improves the data transmission rate and system capacity by using multiple transmit and receive antennas on the same frequency band to transmit multiple data streams. The combination of MIMO technology and OFDM technology can effectively combat frequency selective fading and further enhance transmission performance; it can also greatly improve spectrum utilization through spatial multiplexing without adding additional spectrum resources, which is particularly important in today's wireless communication environment with tight spectrum resources.

[0003] According to whether a pilot signal is required, current channel estimation algorithms can be divided into two categories: blind channel estimation and pilot-based channel estimation. Blind channel estimation does not require a pilot signal. The pilot signal is used to estimate the channel through the second-order or high-order statistical information of the received signal. For example, reference [1] proposed a blind channel estimation algorithm that uses the statistical information of the average power of the received signal to convert the average power of the received signal into a quadratic equation containing the channel gain. The user terminal can estimate the MIMO downlink channel gain without any downlink pilot resources, but this algorithm is only applicable to time division duplex systems and has poor estimation accuracy. For example, reference [2] proposed a MIMO channel blind estimation algorithm based on expectation maximization. This algorithm uses the sparse characteristics of the channel in the angular domain to improve the channel estimation accuracy, but requires a large amount of calculation. Blind channel estimation has the advantages of less prior information and high spectral efficiency, but its application is severely limited by low channel estimation accuracy, high complexity, and poor real-time performance.

[0004] Although pilot-based channel estimation loses some spectral efficiency compared to blind channel estimation, it has been widely used due to its simple implementation. Common pilot-based channel estimation algorithms include the least squares (LS) method in reference [3] and the minimum mean square error (MMSE) in reference [4]. The LS algorithm is a simple interpolation-based method, which has poor performance due to the neglect of noise processing. The MMSE algorithm takes into account the complete channel statistics and noise variance, and its estimation accuracy is better than the LS algorithm, but it needs to invert the channel correlation matrix, which is computationally intensive and requires a priori channel statistics. Reference [5] proposes an approximately linear MMSE for fast fading channels. Due to the reduction of correlation and the size of the filter matrix, its complexity is much lower than the original MMSE.

[0005] The basic idea of ​​pilot-based channel estimation technology in MIMO-OFDM system is: first, the transmitter periodically sends a pre-designed pilot signal sequence; then, after receiving the pilot signal, the receiver generates a feedback data according to the received signal quality and conditions; finally, after receiving the feedback from the receiver, the transmitter can use this information to perform more accurate channel estimation. Since MIMO-OFDM system usually contains multiple subcarrier channels and multiple transmit and receive antenna pairs, the system needs to estimate many channel parameters. The traditional channel estimation algorithm based on pilot and feedback will cause the pilot and feedback overhead to increase sharply with the increase of subcarriers and transmit and receive antenna numbers, and the performance loss in terms of spectrum efficiency and complexity is serious, which limits its application. At present, for MIMO-OFDM system, some scholars have proposed channel estimation algorithms based on vector quantization, such as by feeding back quantized channel information or quantized received pilot information. Compared with the method of directly feeding back channel or pilot information, although the feedback overhead can be reduced to a certain extent, vector quantization introduces some quantization errors, which in turn causes the loss of channel estimation performance, and the quantized data information is still large, and the feedback overhead is large.

[0006] Therefore, in the existing MIMO-OFDM system, the pilot-based channel estimation technology has the problems of too much feedback information and large feedback overhead when feeding back channel information, which reduces the efficiency of wireless communication.

[0007] It should be noted that this background technology is only used to introduce the relevant information of the present invention to help understand the technical solution of the present invention, but it does not mean that the relevant information is necessarily the prior art. If there is no evidence that the relevant information has been disclosed before the application date of the present invention, the relevant information shall not be regarded as the prior art.

[0008] References:

[0009] [1]Ngo H Q,Larsson E G.Blind estimation of effective downlink channelgains in massive MIMO[C] / / 2015IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).IEEE,2015:2919-2923.

[0010] [2]Mezghani A,Swindlehurst A L.Blind estimation of sparse broadbandmassve MIMO channels with ideal and one-bit ADCs[J].IEEE Transactions onSignal Processing,2018,66(11):2972-2983.

[0011] [3]Chang M X.A new derivation of least-squares-fitting principle forOFDM channel estimation[J].IEEE transactions on wireless communications,2006,5(4):726-731.

[0012] [4]Minn H,Bhargava V K,Letaief K B.A combined timing and frequencysynchronization and channel estimation for OFDM[C] / / 2004IEEE InternationalConference on Communications(IEEE Cat.No.04CH37577).IEEE,2004,2:872-876.

[0013] [5] M,Mehlführer C,Wrulich M,et al.Doubly dispersive channel estimation with scalable complexity[C] / / 2010International ITG Workshop onSmart Antennas(WSA).IEEE,2010:251-256. Summary of the invention

[0014] Therefore, the object of the present invention is to overcome the above-mentioned defects of the prior art and provide a channel estimation method based on limited feedback for a MIMO-OFDM system.

[0015] The objective of the present invention is achieved through the following technical solutions:

[0016] According to a first aspect of the present invention, a channel estimation method is provided, which is applied to a base station end and is used to estimate the downlink channel state. The method includes: S1, obtaining a vector quantization codebook preset for a MIMO system, which includes multiple code vectors for representing different pilot reception signal vectors and an index value of each code vector; S2, sending pilot signals to multiple user terminals, and receiving signals that are fed back to the base station end by multiple user terminals based on their pilot reception signals, wherein the fedback signal is the index value of the code vector that is most similar to the quantization vector of the pilot reception signal searched by the user terminal from the vector quantization codebook; S3, obtaining an estimated quantization vector of the pilot reception signal based on the vector quantization codebook and the signal fed back by each user terminal; S4, performing channel estimation based on the estimated quantization vector using a preset estimation method to obtain an estimated downlink channel state information matrix corresponding to each user terminal.

[0017] In some embodiments of the present invention, in S1, the method for generating the vector quantization codebook includes: constructing a vector source training set for an indoor communication scenario, including obtaining multiple positions of the indoor communication scenario, calculating the pilot received signal vectors of users located at the multiple positions, and obtaining a vector source training set; according to the vector source training set and a preset number of quantization bits, using a vector quantization algorithm to generate a vector quantization codebook, which includes multiple code vectors and an index value corresponding to each code vector, and the preset number of quantization bits is the number of bits of the index value and is less than the number of bits of the code vector.

[0018] In some embodiments of the present invention, the method of constructing a vector source training set for indoor communication scenarios includes: using a random walk model to generate multiple positions where users randomly walk in a predetermined indoor space; based on the sent pilot signal and the channel model, calculating the pilot received signal vectors of multiple users who walk to each position to obtain a vector source training set.

[0019] In some embodiments of the present invention, in S4, the preset estimation method includes: constructing an optimal post-processing matrix optimization function based on the minimum mean square error criterion with the optimization goal of minimizing the gap between the real downlink channel state information matrix and the estimated downlink channel state information matrix; solving the optimal post-processing matrix based on the optimization function and the estimated quantization vector of the pilot received signal; and obtaining the estimated downlink channel state information matrix based on the optimal post-processing matrix and the estimated quantization vector of the pilot received signal.

[0020] In some embodiments of the present invention, the optimal post-processing matrix optimization function is as follows:

[0021]

[0022] in, represents minimizing the gap between the true downlink channel state information and the estimated downlink channel state information matrix, D represents the post-processing matrix, E{·} represents the expected calculation symbol, ‖·‖ 2 represents the vector's second norm, s = vec(H down ), vec(·) means to vectorize the matrix, H down represents the real downlink channel state information matrix, represents the estimated downlink channel state information matrix, represents the pilot received signal y pil The estimated value of s when ot is known.

[0023] In some embodiments of the present invention, the vector quantization algorithm includes performing multiple rounds of iterations according to a preset number of quantization bits, and each round of iteration includes: obtaining a code vector, wherein the first round uses a code vector calculated based on each sample and the number of samples in a vector source training set, and each round after the first round uses the code vector obtained in the previous round; obtaining a disturbance coefficient, and performing splitting calculation on the obtained code vector according to the disturbance coefficient to obtain multiple code vectors after splitting; and updating each of the multiple code vectors after splitting once or multiple times according to a preset rule to obtain the code vector of the current round.

[0024] In some embodiments of the present invention, the preset rules include: obtaining multiple code vectors of the current time, using the multiple code vectors after splitting for the first time, and using the multiple code vectors after the previous update each time after the first time; according to the multiple code vectors of the current time, dividing the vector source training set into multiple subsets based on the nearest neighbor rule, and each code vector of the current time corresponds to a subset; calculating the total distortion based on the divided multiple subsets, and judging whether the total distortion converges, if not converged, updating the multiple code vectors of the current time to obtain the updated multiple code vectors, including averaging the sum of all samples in the subset corresponding to each code vector to obtain the updated code vector, otherwise stopping the above updating process to obtain the code vector of the current round.

[0025] In some embodiments of the present invention, in S2, the feedback signal is obtained by the user end in the following processing manner: vector quantization is performed on the pilot received signal to obtain a quantization vector of the pilot received signal; the code vector most similar to the quantization vector of the pilot received signal is searched from the vector quantization code book, and the index value of the most similar code vector is used as the feedback signal.

[0026] In some embodiments of the present invention, the method of performing vector quantization on the pilot received signal is as follows:

[0027]

[0028] Among them, y pilot represents the quantized vector of the pilot received signal, vec(·) represents the matrix vectorization operation, Y pilot represents the pilot receiving signal, Φ represents the sending pilot signal, represents the tensor product, I Nr N r ×N r The identity matrix, N r Indicates the number of receiving antennas at all user terminals, H down represents the real downlink channel state information matrix, W pilot Represents the noise matrix when receiving the pilot signal.

[0029] According to a second aspect of the present invention, a communication system based on the method described in the first aspect of the present invention is provided, the system comprising multiple user terminals and base station terminals, wherein the base station terminal is configured to: obtain a vector quantization codebook preset for the MIMO system, which includes multiple code vectors for representing different pilot received signal vectors and an index value of each code vector; send pilot signals to multiple user terminals, and receive signals fed back by multiple user terminals based on their respective pilot received signals; obtain an estimated quantization vector of the pilot received signal based on the signal fed back by each user terminal and the vector quantization codebook; perform channel estimation based on the estimated quantization vector using a preset estimation method to obtain an index value of each The invention discloses a method for transmitting the downlink channel state information matrix estimated by the base station for each user terminal; and optimizing the communication parameter configuration of the base station according to the estimated downlink channel state information matrix estimated by each user terminal, and sending a data signal to each user terminal based on the optimized communication parameter configuration; each user terminal is configured to: obtain a vector quantization codebook preset for the MIMO system, search the vector quantization codebook for the index value of the code vector most similar to the quantization vector of its pilot reception signal according to its pilot reception signal, and feed back the index value of the most similar code vector as a signal to the base station; and receive the data signal sent by the base station, and obtain data according to its own position and the received data signal.

[0030] Compared with the prior art, the advantages of the present invention are:

[0031] The present invention sends pilot signals to multiple user terminals through a base station, performs vector quantization on the pilot reception signals of all user terminals, and feeds back the index value of the code vector with the most similar quantization vector searched from the vector quantization code book to the base station through the uplink, avoiding the feedback of many parameter information such as channel information or pilot signals, and only realizing the estimation by feeding back an index value, which greatly reduces the feedback data and feedback overhead of the present invention. Finally, the base station of the present invention can directly obtain the estimated quantization vector of the pilot reception signal from the vector quantization code book according to the signal fed back by each user terminal, and estimate the downlink channel state information matrix corresponding to each user terminal according to the estimated quantization vector, thereby reducing the computational complexity and improving the performance and efficiency of wireless communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The embodiments of the present invention are further described below with reference to the accompanying drawings, in which:

[0033] Figure 1 A schematic diagram of the transmission and receiving process of a MIMO-OFDM system model according to an embodiment of the present invention;

[0034] Figure 2 A schematic diagram of a flow chart of a channel estimation method based on limited feedback according to an embodiment of the present invention;

[0035] Figure 3 A schematic diagram of a block pilot transmission signal structure used according to an embodiment of the present invention;

[0036] Figure 4 A schematic diagram of a pilot transmission and feedback process in a MIMO-OFDM system according to an embodiment of the present invention;

[0037] Figure 5 A flow chart of a channel estimation method based on limited feedback for a MIMO-OFDM system according to an embodiment of the present invention;

[0038] Figure 6 A curve comparison diagram showing changes in the mean square error corresponding to the channel state information matrix estimated by the six schemes according to the embodiments of the present invention as the signal-to-noise ratio SNR changes;

[0039] Figure 7 A curve comparison diagram of mean square error generated when estimating a channel under different signal-to-noise ratios (SNRs) using the method of the present invention and a traditional vector quantization scheme according to an embodiment of the present invention versus the number of code vectors;

[0040] Figure 8 The figure is a comparison diagram of the computational delays of generating different numbers of code vectors by the method of the present invention according to an embodiment of the present invention and a traditional vector quantization scheme and a scalar quantization scheme. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below through specific embodiments in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0042] As mentioned in the background technology section, the existing pilot-based channel estimation technology for MIMO-OFDM systems has the problems of too much feedback information and high feedback overhead when feeding back channel information, which reduces the efficiency of wireless communication.

[0043] Based on the above problems, according to one embodiment of the present invention, the inventor proposes a channel estimation method based on limited feedback for a MIMO-OFDM system, which is used to estimate the downlink channel state. In the method of the present invention, first, a vector quantization codebook is preset for the MIMO system, which includes multiple code vectors for representing different pilot reception signal vectors and an index value of each code vector; then, the present invention sends a pilot signal to multiple user terminals through a base station, vector quantizes the pilot reception signals of all user terminals, and feeds back the index value of the code vector with the most similar quantization vector searched from the vector quantization codebook to the base station through the uplink, avoiding feedback of many parameter information such as channel information or pilot signals, and only realizing estimation by feeding back an index value, which greatly reduces the feedback data and feedback overhead of the present invention. Finally, the base station of the present invention can directly obtain the estimated quantization vector of the pilot reception signal from the vector quantization codebook according to the signal fed back by each user terminal, and estimate the downlink channel state information matrix corresponding to each user terminal according to the estimated quantization vector, thereby reducing the computational complexity and improving the performance and efficiency of wireless communication.

[0044] Before describing the channel estimation method of the present invention, the MIMO-OFDM system model is first introduced.

[0045] According to an embodiment of the present invention, a MIMO-OFDM system model is set, in which the base station uses N t The user receiving end uses N transmitting antennas. r There are N receiving antennas, where one user end generally has one receiving antenna. r Therefore, N user terminals are connected to the base station and all user terminals. t ×N r The MIMO-OFDM system uses OFDM technology and the total number of OFDM subcarriers is K. Figure 1 , which is a schematic diagram of the transmission and reception process of the MIMO-OFDM system model. Figure 1 The principles of the transmission and reception process of the MIMO-OFDM system model are explained as follows:

[0046] (1) Transmission process principle of MIMO-OFDM system model

[0047] According to one embodiment of the present invention, Figure 1 As shown, the sending end is the base station end, the 1st to the Nth t The signals sent by the transmitting antennas are recorded as For the nth t Transmitting antennas, n t =1,…,N t , in the nth OFDM symbol interval, the constellation modulation signal obtained by the transmitter after serial-to-parallel conversion can be expressed as k represents the number of OFDM subcarriers, Indicates the nth t The constellation modulation signal of the kth OFDM subcarrier of the nth OFDM symbol on the transmitting antenna, K represents the number of OFDM subcarriers, and the nth OFDM symbol can be obtained by inverse fast Fourier transform (IFFT). t The OFDM time domain signal on the transmitting antenna can be expressed as follows:

[0048]

[0049] in, Indicates the nth t The nth OFDM time domain signal on the transmitting antenna, IFFT{·} represents the inverse fast Fourier transform operation, N represents the number of OFDM symbols in the frequency domain. In order to avoid inter-symbol interference in the wireless channel, it is usually necessary to add a cyclic prefix (CP) before each OFDM time domain signal. The present invention considers adding an appropriate protection interval to the transmitted signal in the communication system, and does not consider the inter-symbol interference and synchronization error (mainly refers to the synchronization error in the data signal reception and processing time) problems of the communication system. The transmitting end then sends the OFDM time domain signal with the cyclic prefix after parallel-to-serial conversion.

[0050] (2) Principle of the receiving process of the MIMO-OFDM system model

[0051] Assuming that the wireless channel is a static or quasi-static channel and remains unchanged in each OFDM symbol period, then in the nth OFDM symbol period, after removing the cyclic prefix (removing the CP), the nth OFDM signal at the user receiving end is r The time domain baseband equivalent signal received by the root receiving antenna It can be expressed as follows:

[0052]

[0053] in, Indicates the nth t The nth transmitting antenna and r The channel gain between the root receiving antennas, Indicates the nth OFDM symbol period r The Gaussian white noise in the signal received by the root receiving antenna. r After the CP-free time domain baseband equivalent signal received by the transmitting antenna is subjected to serial-to-parallel conversion, fast Fourier transform (FFT) and parallel-to-serial conversion, the received signals are recorded as Among them, N at the user receiving end r Baseband equivalent time domain signal matrix received by the root receiving antenna It can be expressed as follows:

[0054]

[0055] Where P represents the transmission power of the signal. N represents the downlink in the MIMO-OFDM system model r ×N t The real downlink channel state information matrix of dimension is Indicates that N is included r ×N t elements, Represents K×N t dimensional transmission signal matrix, W represents the noise matrix when receiving the signal, which is composed of N r ×K are independent of each other and have a mean of 0 and a variance of The variable composition of (·) T The real downlink channel state information matrix can be understood as: the downlink channel state information matrix set by simulation, which can be calculated based on the relative spatial position and posture of the receiving antenna and the transmitting antenna.

[0056] According to one embodiment of the present invention, see Figure 2 , which is a flow chart of a channel estimation method based on limited feedback of the present invention. The channel estimation method is applied at the base station end to estimate the downlink channel state, and the estimation method includes steps S1, S2, S3 and S4. In order to better understand the present invention, each step is described in detail below in combination with a specific embodiment.

[0057] In step S1, a vector quantization codebook preset for a MIMO system is obtained, which includes a plurality of code vectors for representing different pilot received signal vectors and an index value of each code vector.

[0058] According to one embodiment of the present invention, the pilot transmission and reception process principle of the MIMO-OFDM system model is as follows:

[0059] Sending process: see Figure 3 , which is a schematic diagram of the block pilot transmission signal structure used, and the block pilot transmission signal structure corresponding to the channel with characteristics in the time direction and the frequency direction is shown in the figure. The present invention inserts block pilots at intervals in the time direction, such as the black solid circle part is the inserted block pilot, and the black hollow circle part is inserted with data, and the receiving end obtains the pilot reception signal. It is assumed that there are τ pilots in each OFDM symbol, and they are placed at intervals in time slots n0,…,n K-1 The nth row and nth row of the signal matrix X are sent. t Column Elements It can be expressed as follows:

[0060]

[0061] in, Indicates the nth t The signal sent by the transmitting antenna in the nth time slot is Indicates that the nth t The pilot signal sent by the transmitting antenna is Indicates that when the scale factor is m and the bias factor is d, the nth t The data signal sent by the transmitting antenna is is the number of sub-time slots between adjacent pilots, K represents the total number of subcarriers, and the frequency domain OFDM symbol can obtain K time domain complex signals after K-point IFFT transformation as shown in formula (1). The present invention adopts block pilot insertion, and the proportional factor is expressed as m. When n is D k When it is an integer multiple of k , nth t The transmitting antenna sends a pilot signal, and the sending pilot signal is expressed as On the contrary, when n=mD k +d and bias factor d=1,…,D k -1, the nth t The transmitting antenna sends a data signal, and the transmitted data signal is expressed as

[0062] Receiving process: According to equation (3), the pilot receiving signal It can be expressed as follows:

[0063]

[0064] Where P represents the transmission power of the signal. Indicates sending pilot signal, which is in matrix form φ 1,1 Indicates the first pilot signal sent by the first transmitting antenna, φ τ,1 represents the τth pilot signal sent by the first transmitting antenna, Indicates the nth t The first pilot signal sent by the transmitting antenna is Indicates the nth t The τth pilot signal is sent by the transmitting antenna, τ represents the number of pilots in each OFDM symbol, W pilot Represents the noise matrix when receiving the pilot signal, which is composed of N r ×τ are independent of each other and have a mean of 0 and a variance of The variable composition.

[0065] According to an embodiment of the present invention, based on the principle of the pilot transmission and reception process introduced in the above embodiment, the generation method of the vector quantization codebook in step S1 of the present invention is described. The present invention considers an indoor mobile communication scenario, and presets a vector quantization codebook for the MIMO system. Therefore, in step S1, the generation method of the vector quantization codebook includes steps S11 and S12. Among them, step S11, constructing a vector source training set for the indoor communication scenario, including obtaining multiple positions of the indoor communication scenario, calculating the pilot reception signal vector of the user located at multiple positions, and obtaining the vector source training set. Step S12, according to the vector source training set and the preset number of quantization bits, a vector quantization algorithm is used to generate a vector quantization codebook, which includes multiple code vectors and an index value corresponding to each code vector, and the preset number of quantization bits is the number of bits of the index value and is less than the number of bits of the code vector. The technical solution of this embodiment can at least achieve the following beneficial technical effects: simulate the communication of the indoor scene to construct the source training data set and the codebook, which is convenient for application in the actual indoor scene. Among them, the number of quantization bits refers to the number of bits used to represent the index value of the code vector in the quantization codebook, which determines the number of bits of the index value. The number of bits of the index value of the present invention is much smaller than the number of bits of the code vector, which effectively reduces the feedback overhead.

[0066] According to an embodiment of the present invention, in step S11, a vector source training set may be constructed for an indoor communication scenario by using a random walk model to generate multiple positions where a user randomly walks in a predetermined room; and according to the sent pilot signal and the channel model, the pilot received signal vectors of the multiple users walking in each position are calculated to obtain the vector source training set. In the top view, the shape of the predetermined room is a rectangle, a square, a circle, etc., and the channel model may be a channel model based on Rayleigh fading. The vector source training set is recorded as y1, y2, ..., y T They respectively represent the 1st sample, the 2nd sample, ..., the Tth sample in the source training set, and each sample is a pilot received signal vector.

[0067] According to one embodiment of the present invention, in step S11, the method of constructing a vector source training set for an indoor communication scenario may also be: manually randomly selecting multiple locations in a predetermined room, and calculating the pilot reception signal vectors of multiple users wandering to each location based on the sent pilot signal and the channel model to obtain the vector source training set.

[0068] According to an embodiment of the present invention, the smaller the number of quantization bits, the smaller the feedback overhead, but the estimation performance of the channel quantization feedback may be poor. Conversely, the larger the number of quantization bits, the larger the feedback overhead, and the better the estimation performance of the channel quantization feedback. Therefore, the present invention presets the number of quantization bits according to actual needs, for example, the preset number of quantization bits is 6, 8 or 10, etc., to minimize the feedback overhead while ensuring the estimation performance.

[0069] According to one embodiment of the present invention, in step S12, given a vector source training set Under the constraint of the preset quantization bit number B, the vector quantization algorithm includes performing multiple rounds of iterations according to the preset quantization bit number, and each round of iteration includes steps S121, S122 and S123:

[0070] Step S121: Obtain code vectors, wherein the first round uses code vectors calculated based on each sample and the number of samples in the vector source training set, and each round after the first round uses the code vector obtained in the previous round.

[0071] According to one embodiment of the present invention, in the first round, the number of code vectors N is set to 1, and the code vectors obtained in the first round are as follows:

[0072]

[0073] in, represents the code vector obtained by the first round of calculation, card(·) represents the number of samples in the vector source training set, t represents the sample number, and y t Represents the tth sample. The total distortion is calculated based on the calculated code vector in the first round, and the calculation method of the total distortion in the first round is as follows:

[0074]

[0075] Where, τ represents the number of pilots per OFDM symbol, ||·|| 2 Represents the square of the Euclidean distance.

[0076] Step S122: Obtain a disturbance coefficient, and perform splitting calculation on the obtained code vector according to the disturbance coefficient to obtain a plurality of split code vectors.

[0077] According to one embodiment of the present invention, the disturbance coefficients (1+ε) and (1-ε) are set, and the code vector is split and calculated. The split calculation process is as follows:

[0078]

[0079] in, represents the g-th code vector after the split in the 0th round (also called the first round) iteration, represents the g+Nth code vector after the split in the 0th iteration. g=1,…,N, where N represents the total number of code vectors obtained in the round. Indicates the gth code vector obtained in the current round. Indicatively, the code vector of the first round Taking splitting as an example, the code vector can be calculated according to formula (8): According to formula (9), it can be calculated at this time, Split into two code vectors, N takes the value of 1. In the next round of splitting, and Perform split calculations separately to obtain 4 code vectors, with N taking the value of 2.

[0080] According to an embodiment of the present invention, the number of iterations is equal to the preset number of quantization bits. If the preset number of quantization bits is B, the number of iterations is B times, and the codebook finally obtained includes 2 B code vectors, and the corresponding index value is represented by B bits. Schematically, if the preset number of quantization bits is 2, the iteration round is 2 times, and the final code book includes 4 code vectors, and the corresponding index values ​​are represented by 2 bits, which are 00, 01, 10 and 11 respectively. If the preset number of quantization bits is 3, the iteration round is 3 times, and the final code book includes 8 code vectors, and the corresponding index values ​​are represented by 3 bits, which are 000, 001, 010, 011, 100, 101, 110 and 111 respectively.

[0081] Step S123: Update each of the multiple code vectors after the splitting once or multiple times according to a preset rule to obtain the code vector of the current round.

[0082] According to one embodiment of the present invention, the preset rule includes the following steps S1231, S1232 and S1233:

[0083] Step S1231: Obtain multiple code vectors for the current time, use the multiple code vectors after splitting for the first time, and use the multiple code vectors after the previous update each time after the first time.

[0084] According to one embodiment of the present invention, the multiple code vectors obtained in step S122 are used for the first time. If the current iteration is the first round, and The next iteration of the current first round is the first use of and The four code vectors are obtained by splitting calculations separately.

[0085] Step S1232: According to the multiple code vectors of the current time, the vector source training set is divided into multiple subsets based on the nearest neighbor rule, and each code vector of the current time corresponds to a subset.

[0086] According to one embodiment of the present invention, the vector source training set is divided into Divide into n subsets, taking the multiple code vectors obtained in step S122 for the first time as an example, for the vector source training set Each sample y t , search for The minimum value of Represents the nth code vector after the i-th round of iterative splitting. In this way, multiple subsets are obtained so that each sample in the subset is consistent with the code vector The distance is less than the distance between the sample and any other code vector. Each subset is expressed as follows:

[0087]

[0088] in, represents the nth subset, Represents sample y t Code vector The Euclidean distance between the sample and any other code vector is smaller than the distance code vector between the sample and any other code vector. Indicates that the code vector is divided by the code vector in all code vectors at that time Other code vectors except With sample y t The distance is the closest, the code vector Can approximate sample y t .

[0089] According to one embodiment of the present invention, t The closest code vector Represents sample y t , specifically expressed as Wherein, Q(·) represents a vector quantization function. The present invention represents the pilot reception signal vector by a code vector, thereby generating a vector quantization code book, and subsequently directly searching the code book for the code vector most similar to the quantization vector of the pilot reception signal, thereby facilitating signal feedback and operation through the index value of the most similar code vector.

[0090] Step S1233: Calculate the total distortion according to the multiple divided subsets, and determine whether the total distortion converges. If not, update the multiple code vectors of the current round to obtain multiple updated code vectors, including averaging the sum of all samples in the subset corresponding to each code vector to obtain the updated code vector. Otherwise, stop the above updating process to obtain the code vector of the current round.

[0091] According to an embodiment of the present invention, the total distortion is calculated based on the multiple subsets divided at that time. The way is as follows:

[0092]

[0093] If the total distortion no longer decreases significantly or the number of updates meets the expected threshold, the code vector is stopped from being updated. Otherwise, multiple code vectors of the current time are updated, and the process from step S1231 to step S1233 is repeated to achieve multiple updates of the code vector. Since the present invention represents the pilot received signal vector by a code vector, the present invention updates the code vector by minimizing the distance between the samples in each subset and the code vector corresponding to the subset, thereby iteratively calculating a code vector that can more approximately represent the sample (i.e., the pilot received signal vector).

[0094] According to one embodiment of the present invention, each code vector is updated as follows:

[0095]

[0096] in, Indicates the nth code vector after the current update. The multiple code vectors obtained from the last update are used as the code vectors obtained in the current round. All code vectors in the vector quantization codebook are the multiple code vectors obtained from the last round of iteration.

[0097] In step S2, a pilot signal is sent to multiple user terminals, and signals fed back to the base station by multiple user terminals based on their pilot received signals are received. The fed-back signal is the index value of the code vector most similar to the quantization vector of its pilot received signal searched by the user terminal from the vector quantization code book.

[0098] According to one embodiment of the present invention, in S2, the feedback signal is obtained by the user end in the following processing manner: vector quantization is performed on the pilot received signal to obtain a quantization vector of the pilot received signal; the code vector most similar to the quantization vector of the pilot received signal is searched from the vector quantization code book, and the index value of the most similar code vector is used as the feedback signal.

[0099] According to one embodiment of the present invention, first, a pilot signal based on a combination code is selected, and the number of channels required for using the combination code is τ=C(N t ,N tθ), where θ represents the ratio of the maximum power to the average power of the transmitting antenna. Then, based on the downlink communication link, using N t The transmitting antenna sends a pilot signal Φ, which passes through the downlink channel Arrives at the receiving end, and quantizes the pilot received signal. That is, the same principle as the above formula (5), the pilot received signal matrix And it can be expressed as Equation (5) is equivalent to a vector expression, that is, the method of vector quantization of the pilot reception signal is as follows:

[0100]

[0101] Among them, y pilot represents the quantized vector of the pilot received signal, vec(·) represents the matrix vectorization operation, Y pilot represents the pilot receiving signal, Φ represents the sending pilot signal, represents the tensor product, N r ×N r The identity matrix, N r Indicates the number of receiving antennas at all user terminals, H down represents the real downlink channel state information matrix, W pilot Represents the noise matrix when receiving the pilot signal.

[0102] Based on the above operations, the matrix expression of the pilot signal transmitted to the user receiving end through the downlink can be equivalently converted into a vector form. Definition Then it has the following vector form:

[0103]

[0104] Then, the vector form y of (14) pilot Further scalar quantization is performed and expressed as follows:

[0105]

[0106] Among them, Q B (·) represents scalar quantization, c n Represents the vector quantization codebook The nth code vector in, code vector c n The corresponding index value is represented by i n , represents the pilot received signal y pilot The quantized vector is defined as pilot The code vector c with the highest similarity to the quantized vector n , that is, the code vector c nThe pilot received signal y can be approximately represented by pilot The quantized vector of the code vector with the highest similarity is i n Feedback to the base station.

[0107] According to one embodiment of the present invention, searching for the pilot received signal y pilot The code vector c with the highest similarity to the quantized vector n The methods include: calculating the quantized vector y pilot The similarity between each code vector in the vector quantization codebook is used to select a code vector with the highest similarity to the quantization vector to represent the pilot received signal y pilot quantized vector of .

[0108] In step S3, an estimated quantization vector of the pilot reception signal is obtained according to the vector quantization codebook and the signal fed back by each user terminal;

[0109] According to one embodiment of the present invention, the index value i of the quantized vector of the pilot reception signal used for channel state estimation is n The uplink communication link is fed back to the base station, and the base station's receiving index value y index It is expressed as follows:

[0110] y index =H up P up i n +N, (16)

[0111] Among them, H up represents the uplink channel state information matrix, P up represents the precoding matrix, which is used to perform linear transformation on the signal at the transmitting end to eliminate the interference between multiple antennas, and N represents the uplink channel noise. index Detection, obtain the estimated index value, the estimated index value result is expressed as follows:

[0112]

[0113] in, Represents the estimated result of the index value, Represents the generalized inverse computation of a matrix.

[0114] Finally, Demapping is performed to obtain an estimated quantized vector of the pilot reception signal. Demapping can be expressed as: in, Indicates the estimated index value Find the corresponding code vector from the vector quantization codebook, Represents the estimated quantized vector of the pilot received signal.

[0115] In step S4, a preset estimation method is used to perform channel estimation according to the estimated quantization vector to obtain an estimated downlink channel state information matrix corresponding to each user terminal.

[0116] According to an embodiment of the present invention, in S4, the preset estimation method includes the following steps S41, S42 and S43:

[0117] Step S41: Taking minimizing the gap between the real downlink channel state information matrix and the estimated downlink channel state information matrix as the optimization goal, construct an optimal post-processing matrix optimization function based on the minimum mean square error criterion (MMSE criterion).

[0118] According to one embodiment of the present invention, the optimal post-processing matrix optimization function is constructed as follows:

[0119]

[0120] in, represents minimizing the gap between the true downlink channel state information matrix and the estimated downlink channel state information matrix, D represents the post-processing matrix, E{·} represents the expected calculation symbol, ‖·‖ 2 represents the vector's second norm, s = vec(H down ), vec(·) means to vectorize the matrix, H down represents the real downlink channel state information matrix, represents the estimated downlink channel state information matrix, represents the pilot received signal y pilot The estimated value of s when is known.

[0121] The following is an explanation of the process principle of constructing the optimal post-processing matrix optimization function:

[0122] 1) Analyze the process of vector quantization of pilot received signals

[0123] According to an embodiment of the present invention, based on the Bussgang decomposition theorem, the process of performing vector quantization on the pilot reception signal in step S2 can be expressed as a linear process, that is, based on equation (15) in step S2, it can be further expressed as the following linear process:

[0124] c n =Gy pilot +e, (19)

[0125] Where G = E{c n (y pilot ) T}E{y pilot (y pilot )T} -1 is a coefficient matrix, e represents a weight vector, and E{·} represents a mean (expected) calculation symbol.

[0126] 2) Construct the optimal post-processing matrix optimization function based on the above linear process

[0127] According to one embodiment of the present invention, since the purpose is to design the optimal post-processing matrix D to minimize the gap between the real downlink channel state information matrix and the estimated downlink channel state information matrix. Therefore, the optimal post-processing matrix optimization function is initially expressed as The process of vector quantization of the pilot received signal can be expressed as a linear process. It can be further decomposed into the following form:

[0128]

[0129] in, represents the pilot received signal y pilot The estimated value of s when is known is independent of the matrix D.

[0130] Step S42: Based on the optimization function and the estimated quantization vector of the pilot reception signal, an optimal post-processing matrix is ​​obtained by solving.

[0131] According to one embodiment of the present invention, it is defined It can be further expressed as follows:

[0132]

[0133] in, N r ×N r In addition, let the second term on the right side of equation (18) be The expanded expression can be expressed as follows:

[0134]

[0135] Taking the first-order derivative of formula (22), the first-order derivative can be expressed as follows:

[0136]

[0137] make Assume that the estimated quantized vector of the pilot received signal is The actual quantized vector c of the pilot received signal n The error between can be ignored, that is At this time, the optimal post-processing matrix is ​​obtained by solving, that is, the closed-form solution of the optimal post-processing matrix is ​​as follows:

[0138]

[0139] Where D represents the optimal post-processing matrix obtained by solving.

[0140] Step S43: Obtain an estimated downlink channel state information matrix according to the optimal post-processing matrix and the estimated quantization vector of the pilot reception signal.

[0141] According to one embodiment of the present invention, the estimated downlink channel state information matrix is ​​obtained. In the following form:

[0142]

[0143] Here, Mat{·} represents an operation symbol for converting a vector into a matrix form.

[0144] The technical solution of the embodiment of step S4 can at least achieve the following beneficial technical effects: since the channel state obtained by the existing estimation is The channel state estimated by the present invention can be further calculated as follows: Improve estimation accuracy. Therefore, the present invention adopts vector quantization and minimum mean square error estimation method, represents vector quantization as a linear process based on Bussgang theorem, and then derives the closed-form solution of the post-processing matrix designed based on the minimum mean square error criterion (MMSE criterion), achieving the channel estimation technical effect of low complexity, low overhead and high estimation accuracy, reducing the channel estimation feedback overhead and improving the accuracy of channel estimation.

[0145] According to an embodiment of the present invention, a communication system based on the method described in the above embodiment is provided, comprising a plurality of user terminals and a base station terminal. Among them, the base station end is configured to: obtain a vector quantization codebook preset for the MIMO system, which includes multiple code vectors for representing different pilot reception signal vectors and an index value of each code vector; send pilot signals to multiple user terminals, and receive signals fed back by multiple user terminals based on their pilot reception signals; obtain an estimated quantization vector of the pilot reception signal based on the signal fed back by each user terminal and the vector quantization codebook; use a preset estimation method to perform channel estimation based on the estimated quantization vector to obtain a corresponding estimated downlink channel state information matrix for each user terminal; and optimize the communication parameter configuration of the base station end based on the estimated downlink channel state information matrix corresponding to each user terminal, and send a data signal to each user terminal based on the optimized communication parameter configuration; each user terminal is configured to: obtain a vector quantization codebook preset for the MIMO system, search the vector quantization codebook for the index value of the code vector most similar to the quantization vector of its pilot reception signal according to its pilot reception signal, and feed back the index value of the most similar code vector as a signal to the base station end; and receive the data signal sent by the base station end, and obtain data based on its own position and the received data signal.

[0146] According to an embodiment of the present invention, during the communication between the base station and the user end, first, the present invention considers the scenario that the base station needs to know the downlink channel state, and then optimizes the communication parameter configuration such as power allocation beamforming based on it. Therefore, the downlink channel state is estimated first, and the estimation process is referred to in Figure 4 , which is a schematic diagram of the pilot transmission and feedback process in the MIMO-OFDM system. In the downlink: the base station broadcasts the pilot signal through the antenna to send the pilot signal Φ to each user end, and the user end receives the pilot signal through the downlink channel H down The pilot signal transmitted is used to obtain the pilot received signal y p At this time, the user end performs vector quantization on its pilot reception signal, that is, executes the process of the above formulas (13)-(15) to obtain the pilot reception signal y pilot The quantized vector And search the index value i of the code vector that is most similar to the quantized vector of its pilot received signal from the vector quantization codebook n , and transmit the index value to the base station. In the uplink: the user end transmits the index value i n After being fed back to the base station as a feedback signal, the base station receives the signal through the uplink channel H up The transmitted feedback signal is used to obtain the received feedback signal Y q At this time, the base station executes the formula (17) of the above embodiment to calculate Y q Perform feedback signal detection to obtain the estimated index value right Demap to obtain the estimated quantized vector of the pilot received signal The base station constructs the optimal post-processing matrix optimization function. To solve the optimal post-processing matrix D, and then based on And the optimal post-processing matrix D is estimated to obtain the downlink channel state information matrix Then, the base station optimizes the communication parameter configuration of the base station according to the estimated downlink channel state information matrix corresponding to each user terminal, and sends data signals to each user terminal based on the optimized communication parameter configuration. Finally, each user terminal receives the data signal sent by the base station and obtains data according to its own position and the received data signal.

[0147] According to an embodiment of the present invention, based on the above embodiment, a schematic complete process of the channel estimation method of the present invention is described. Figure 5 , which is a flow chart of a channel estimation method based on limited feedback for a MIMO-OFDM system, comprising the following steps:

[0148] 1. Build a MIMO-OFDM system model, where the base station and user receiving end use N t Transmitting antennas and N r Root receiving antenna;

[0149] 2. The base station inserts block pilots in the time direction;

[0150] 3. Based on the random walk model, multiple positions are randomly generated, and then the pilot reception signal vectors of multiple users walking to each position are calculated and stored in the vector source training set. Under the constraint of the number of quantization bits, a vector quantization codebook is generated based on the vector quantization algorithm;

[0151] 4. The base station broadcasts a pilot signal to the user end through the transmitting antenna. The pilot signal reaches the user end through the downlink communication channel. The user end performs vector quantization on the pilot reception signal.

[0152] 5. Based on Bussgang decomposition theorem, the vector quantization process of the pilot reception signal is expressed as a linear process;

[0153] 6. Using the uplink communication channel, the index value corresponding to the code vector most similar to the quantized vector of the pilot reception signal is fed back to the base station end;

[0154] 7. Construct an optimal post-processing matrix optimization function based on the MMSE criterion, and estimate the downlink channel state information matrix based on the feedback index value and function solution.

[0155] In order to verify the beneficial effects of the present invention, the inventors conducted the following simulation comparison experiments:

[0156] 1) The change of mean square error generated by channel estimation with increasing signal-to-noise ratio under different code vector numbers

[0157] Parameter setting: In this simulation experiment, the preset quantization bit number B is set to 3 bits, 4 bits and 5 bits respectively. When the quantization bit number is 3, 4, and 5, the corresponding code vector number is 8, 16, and 32.

[0158] Scheme setting: 6 schemes are constructed for comparison, of which 3 schemes are the methods of the present invention constructed under the 3 different preset quantization bit numbers, which are the combined vector quantization of the pilot reception signal and the solution of the optimal post-processing matrix. The other 3 schemes are traditional vector quantization schemes constructed under the 3 different preset quantization bit numbers, which only perform vector quantization on the pilot reception signal.

[0159] Comparison results: The above 6 solutions were simulated and compared, and the comparison results were as follows: Figure 6 As shown in Figure 1, it is a curve comparison of the mean square error corresponding to the downlink channel state information matrix estimated by the six schemes as the signal-to-noise ratio SNR changes. The horizontal axis is the signal-to-noise ratio, and the vertical axis is the mean square error. Figure 6 It can be seen that in this simulation experiment, the channel estimation mean square error performance corresponding to the channel estimation results of the traditional vector quantization scheme and the method of the present invention increases with the increase of the number of code vectors, which means that the larger the number of quantization bits, the more accurate the quantization, and this change trend is consistent with the theoretical analysis. By comparing the two algorithms vertically, it can be found that under the same signal-to-noise ratio and the number of code vectors, the mean square error between the downlink channel state information estimated by the method of the present invention using the joint vector quantization and the optimal post-processing matrix and the actual downlink channel state information is smaller than the mean square error corresponding to the channel estimation based on the traditional vector quantization scheme, proving that the method of the present invention can greatly improve the quantization performance and reduce the error caused by the traditional vector quantization. At the same time, the quantization effect can also be improved by appropriately increasing the number of code vectors.

[0160] 2) Under different signal-to-noise ratios, the mean square error generated during channel estimation changes as the number of code vectors increases. Parameter setting: The signal-to-noise ratio SNR is set to 10dB, 20dB and 30dB respectively.

[0161] Scheme setting: 6 schemes were constructed for comparison, of which 3 schemes were constructed under the 3 different signal-to-noise ratios according to the method of the present invention, which combined vector quantization of the pilot reception signal and solving the optimal post-processing matrix. The other 3 schemes were traditional vector quantization schemes constructed under the 3 different signal-to-noise ratios, which only performed vector quantization on the pilot reception signal.

[0162] Comparison results: The above 6 solutions were simulated and compared, and the comparison results were as follows: Figure 7 As shown, it is a curve comparison of the mean square error generated by the method of the present invention and the traditional vector quantization scheme when estimating the channel under different signal-to-noise ratios SNRs as the code vector number changes, where the horizontal axis is the code vector number and the vertical axis is the mean square error. Figure 7 It can be seen that when the traditional vector quantization scheme performs channel estimation, the estimation effect does not improve significantly with the increase of the signal-to-noise ratio, and the mean square error only decreases slowly; on the contrary, each increase in the number of code vectors (increase in the number of quantization bits) will improve the quantization effect by one level. However, in the method of the present invention, when the number of quantization bits reaches more than 4 bits, the increase in the signal-to-noise ratio will significantly optimize the estimation effect, and the increase in the number of quantization bits will also improve the optimization effect to a greater extent, which further shows that the method of the present invention has great estimation superiority under both low and high signal-to-noise ratio conditions. In the signal-to-noise ratio range of 0 to 30dB, in terms of mean square error, the method of the present invention has at least 80% optimization over the channel estimation algorithm of the traditional vector quantization scheme, and this optimization is still increasing when the signal-to-noise ratio increases.

[0163] 3) Under different signal-to-noise ratios, the calculation delay generated during channel estimation changes as the number of code vectors increases. Parameter setting: The signal-to-noise ratio SNR is set to 10dB and 30dB respectively.

[0164] Scheme setting: 6 schemes are constructed for comparison, among which 2 schemes are the methods of the present invention constructed under the above 2 different signal-to-noise ratios. The methods of the present invention are to combine vector quantization of pilot received signals and solving the optimal post-processing matrix. There are also 2 schemes that are traditional vector quantization schemes constructed under the above 2 different signal-to-noise ratios. The traditional vector quantization scheme only performs vector quantization on pilot received signals. The last 2 schemes are traditional scalar quantization schemes constructed under the above 2 different signal-to-noise ratios. The traditional scalar quantization scheme only performs scalar quantization on pilot received signals.

[0165] Comparison results: The above six schemes were simulated and compared. This simulation experiment timed each algorithm implementation part respectively. Under the initial conditions of SNR of 10dB and 30dB, 100 tests were performed under different quantization bit numbers. Each experiment was iterated about 120 times on average. The comparison results are as follows: Figure 8 As shown in FIG. 1 , it is a comparison diagram of the computational delay of generating different numbers of code vectors by the method of the present invention and the traditional vector quantization scheme and scalar quantization scheme. The horizontal axis is the number of code vectors, and the vertical axis is the computational delay. Figure 8It can be seen that the two curves under different signal-to-noise ratios obtained by the same method almost overlap, which shows that the signal-to-noise ratio does not significantly affect the quantization time. Therefore, the curves of different signal-to-noise ratios can be analyzed together. The running time of the method of the present invention takes an average of 0.97 seconds, 1.12 seconds, 1.39 seconds, 1.95 seconds, and 3.08 seconds when the number of code vectors is 4, 8, 16, 32, and 64, respectively, which is 53.83% lower than the average delay under all code vector numbers corresponding to the traditional scalar quantization scheme, that is, compared with the traditional scalar quantization scheme, the operation rate of the method of the present invention is increased by about one time; compared with the vector quantization algorithm, the method of the present invention greatly improves the quantization effect while the operation rate only decreases slightly, and the performance is good.

[0166] In summary, compared with the existing scalar quantization and vector quantization technologies, the method of the present invention effectively reduces the number of bits of the signal fed back by the user end to the base station end during the downlink channel estimation process, and can obtain a more accurate estimated channel state information matrix, thereby improving the data transmission rate of the system.

[0167] It should be noted that although the above describes the various steps in a specific order, it does not mean that the steps must be executed in the above specific order. In fact, some of these steps can be executed concurrently or even in a different order as long as the required functions can be achieved.

[0168] The present invention may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present invention.

[0169] Computer readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. Computer readable storage medium can include, for example, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a protruding structure in a groove on which instructions are stored, and any suitable combination thereof.

[0170] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A channel estimation method, applied at a base station, for estimating a downlink channel state, characterized in that: Methods include: S1. Obtain a vector quantization codebook preset for a MIMO system, which includes a plurality of code vectors for representing different pilot received signal vectors and an index value of each code vector; S2, sending pilot signals to multiple user terminals, and receiving signals fed back by multiple user terminals to the base station according to their pilot reception signals, wherein the fedback signal is an index value of a code vector most similar to a quantization vector of the pilot reception signal searched by the user terminal from the vector quantization codebook; S3, obtaining an estimated quantization vector of the pilot reception signal according to the vector quantization codebook and the signal fed back by each user terminal; S4. Perform channel estimation according to the estimated quantization vector using a preset estimation method to obtain an estimated downlink channel state information matrix corresponding to each user terminal.

2. The method according to claim 1, characterized in that In the S1, the vector quantization codebook is generated in the following manner: Constructing a vector source training set for an indoor communication scenario, including obtaining multiple positions of the indoor communication scenario, calculating pilot reception signal vectors of users located at the multiple positions, and obtaining a vector source training set; According to the vector source training set and the preset quantization bit number, a vector quantization algorithm is used to generate a vector quantization codebook, which includes multiple code vectors and an index value corresponding to each code vector. The preset quantization bit number is the number of bits of the index value and is less than the number of bits of the code vector.

3. The method according to claim 2, characterized in that The method of constructing a vector source training set for an indoor communication scenario includes: Using the random walk model, multiple locations where users randomly walk in a predetermined room are generated; According to the sent pilot signal and the channel model, the pilot reception signal vectors of the multiple users wandering to various positions are calculated to obtain a vector source training set.

4. The method according to claim 1, characterized in that: In S4, the preset estimation method includes: Taking minimizing the gap between the real downlink channel state information matrix and the estimated downlink channel state information matrix as the optimization goal, an optimal post-processing matrix optimization function based on the minimum mean square error criterion is constructed; Based on the optimization function and the estimated quantized vector of the pilot reception signal, an optimal post-processing matrix is ​​obtained by solving; An estimated downlink channel state information matrix is ​​obtained according to the optimal post-processing matrix and the estimated quantization vector of the pilot reception signal.

5. The method according to claim 4, characterized in that The optimal post-processing matrix optimization function is as follows: in, represents minimizing the gap between the true downlink channel state information matrix and the estimated downlink channel state information matrix, D represents the post-processing matrix, E{·} represents the expected calculation symbol, ‖·‖ 2 Represents the vector's second norm, s = vec(H down ), vec(·) means to vectorize the matrix, H down represents the real downlink channel state information matrix, represents the estimated downlink channel state information matrix, represents the pilot received signal y pilot The estimated value of s when is known.

6. The method according to claim 2, characterized in that The vector quantization algorithm includes performing multiple rounds of iterations according to a preset number of quantization bits, and each round of iteration includes: Obtaining a code vector, wherein the first round uses a code vector calculated according to each sample and the number of samples in the vector source training set, and each round after the first round uses a code vector obtained in the previous round; Obtain a disturbance coefficient, and perform splitting calculation on the obtained code vector according to the disturbance coefficient to obtain multiple code vectors after splitting; Each of the multiple code vectors after the splitting is updated once or multiple times according to the preset rules to obtain the code vector of the current round.

7. The method according to claim 6, characterized in that The preset rules include: Obtain multiple code vectors of the current time, use multiple code vectors after splitting for the first time, and use multiple code vectors after the previous update each time after the first time; According to the multiple code vectors of the time, the vector source training set is divided into multiple subsets based on the nearest neighbor rule, and each code vector of the time corresponds to a subset; The total distortion is calculated based on the multiple divided subsets to determine whether the total distortion converges. If not, the multiple code vectors of the current round are updated to obtain multiple updated code vectors, including averaging the sum of all samples in the subset corresponding to each code vector to obtain the updated code vector. Otherwise, the above updating process is stopped to obtain the code vector of the current round.

8. The method according to claim 1, characterized in that In S2, the feedback signal is obtained by the user end in the following processing manner: Performing vector quantization on the pilot reception signal to obtain a quantized vector of the pilot reception signal; A code vector most similar to the quantized vector of the pilot received signal is searched from the vector quantization codebook, and an index value of the most similar code vector is used as a feedback signal.

9. The method according to claim 8, characterized in that The method of performing vector quantization on the pilot received signal is as follows: Among them, y pilot represents the quantized vector of the pilot received signal, vec(·) represents the matrix vectorization operation, Y pilot represents the pilot receiving signal, Φ represents the sending pilot signal, represents the tensor product, I Nr N r ×N r The identity matrix, N r Indicates the number of receiving antennas at all user terminals, H down represents the real downlink channel state information matrix, W pilot Represents the noise matrix when receiving the pilot signal.

10. A communication system based on the method according to any one of claims 1 to 9, characterized in that: It includes multiple user terminals and base station terminals, among which, The base station is configured as follows: Acquire a vector quantization codebook preset for the MIMO system, which includes a plurality of code vectors for representing different pilot received signal vectors and an index value of each code vector; Sending pilot signals to multiple user terminals, and receiving signals fed back by the multiple user terminals according to their respective pilot reception signals; Based on the signal fed back by each user terminal and the vector quantization codebook, an estimated quantization vector of the pilot reception signal is obtained; Performing channel estimation according to the estimated quantization vector using a preset estimation method to obtain an estimated downlink channel state information matrix corresponding to each user terminal; and Optimizing the communication parameter configuration of the base station according to the estimated downlink channel state information matrix corresponding to each user terminal, and sending a data signal to each user terminal based on the optimized communication parameter configuration; Each client is configured as: Obtain a vector quantization codebook preset for the MIMO system, search the vector quantization codebook for an index value of a code vector most similar to a quantization vector of the pilot received signal according to the pilot received signal, and feed back the index value of the most similar code vector as a signal to the base station; and Receive the data signal sent by the base station and obtain data based on its own position and the received data signal.

Images