Non-cellular large-scale MIMO system signal detection method based on GSIC algorithm

Abstract

The invention discloses a non-cellular large-scale MIMO system signal detection method based on a GSIC algorithm, and belongs to the technical field of wireless communication. The method comprises the following steps: constructing a quantization system model; grouping all the user equipments, the groups being continuously detected, and simultaneously detecting the user equipments within the same group; constructing a grouping SIC signal detection algorithm GSIC; detecting the received signal matrix by using an MRC-GSIC or ZF-GSIC algorithm to obtain a sending signal estimated value of the user equipment; an ideal ADC system is compared with an ADC quantized system through a simulation experiment, a simulation result shows that the spectral efficiency can be improved along with the increase of the number of groups, and the system can achieve high spectral efficiency and high energy efficiency close to SIC detection under the condition of fewer groups, so that the system has high application value. However, the calculation complexity is greatly reduced, so that the hardware cost and the energy consumption can be greatly reduced.

Description

Technical Field

[0001] The invention relates to a non-cellular large-scale MIMO system signal detection method based on a GSIC algorithm, and belongs to the technical field of wireless communication. Background Art

[0002] The non-cellular massive multiple-input multiple-output (MIMO) system has become one of the most promising key technologies in the future B5G (Beyond 5G) and sixth generation (6G) networks. In the non-cellular massive MIMO system, the system is based on distributed massive MIMO and breaks the division of cell boundaries. That is, in a wide area, a large number of access points (APs) and user equipment (UEs) are distributed at the same time. Different APs are connected to the central processing unit (CPU) through backhaul links and provide services to all users under the same time and frequency resources according to the instructions of the CPU. Since the non-cellular massive MIMO system greatly reduces the distance between users and APs, it has a strong spatial macro-diversity gain and the ability to resist path loss, and can greatly improve the service quality of edge users. However, non-cellular massive MIMO systems usually require the deployment of a large number of APs. When the AP is equipped with a full-precision analog-to-digital converter (ADC) for quantization processing, it is bound to bring high hardware costs and huge energy consumption. In order to deal with this problem, configuring a low-precision ADC at the AP for quantization processing is undoubtedly a more direct solution. Therefore, the research on non-cellular massive MIMO systems equipped with low-resolution ADC is of great theoretical value and practical significance.

[0003] Regarding the modeling of ADC quantization distortion, there are two main models recognized by the academic community, namely Bussgang decomposition theorem and additive quantization noise model (AQNM). Since the former is more cumbersome when measuring multi-bit quantization distortion, researchers usually only use it to model 1-bit ADC quantization, while the latter can be used to analyze the impact of arbitrary bit quantization due to its strong operability.

[0004] Existing spectral efficiency (SE) analysis of non-cellular massive MIMO systems mainly focuses on linear signal detection algorithms, while less research is done on nonlinear signal detection algorithms. Nonlinear successive interference cancellation (SIC) detection can not only improve SE, but also improve energy efficiency (EE). However, SIC detection leads to higher computational complexity than linear detection. Summary of the invention

[0005] In order to solve the problem that the current signal detection method in the non-cellular massive MIMO system cannot strike a balance between improving SE performance and reducing computational complexity, the present invention provides a non-cellular massive MIMO system signal detection method based on the GSIC algorithm, and the technical solution is as follows:

[0006] In a non-cellular massive MIMO signal detection method of the present invention, the system model to be detected is expressed as:

[0007]

[0008] The system model includes L access points AP and K user equipment UE, each AP is configured with N antennas, and each UE is configured with 1 antenna;

[0009] Among them, y l represents the signal received at the lth access point AP-1, x i represents the information symbol sent by the i-th user equipment UE-i, h il represents the channel vector between UE-i and AP-1, n l represents the additive white Gaussian noise on AP-1;

[0010] Characterized in that the method comprises:

[0011] Step 1: construct a quantization system model for the system model to be detected according to a linear additive quantization noise model AQNM;

[0012] Step 2: Divide all user equipment UEs into U groups evenly, each group consists of The groups are composed of user equipments UE, where 1≤U≤K, and these groups are detected continuously, and user equipments UE in the same group are detected simultaneously;

[0013] Step 3: Construct the group SIC signal detection algorithm GSIC. The GSIC signal detection algorithm is expressed as:

[0014]

[0015] in, represents the estimated value of the transmitted signal of the kth user equipment UE, v k is the receiving combination vector of the kth user equipment UE, α represents the distortion coefficient, Represents y l The quantized signal, represents the group number of the kth user equipment UE, h i Denotes the channel vector h il A collection of;

[0016] Step 4: Use the MRC-GSIC detection algorithm or the ZF-GSIC detection algorithm to detect the received signal matrix y q Perform detection to obtain the estimated value of the transmitted signal of the kth user equipment UE

[0017] Optionally, the step 4 uses the MRC-GSIC detection algorithm to detect the received signal matrix y q Perform detection to obtain the estimated value of the transmitted signal The process includes:

[0018] The receiving combination vector of the kth user equipment UE It is expressed as:

[0019]

[0020] in, represents the estimated value of the channel matrix, Representation Matrix The k-(u k -1) Column G, Representation Matrix The first (u k -1) G+1 column to K column;

[0021] The receiving combination vector of the kth user equipment UE Substitute into the GSIC signal detection algorithm to obtain the signal estimation value of the kth user equipment UE

[0022] Optionally, the step 4 uses the ZF-GSIC detection algorithm to detect the received signal matrix y q Perform detection to obtain the estimated value of the transmitted signal The process includes:

[0023] The received combination vector of the kth user equipment UE It is expressed as:

[0024]

[0025] in Indicates the uth k The combination vector of all UEs in the group is expressed as:

[0026]

[0027] The receiving combination vector of the kth user equipment UE Substitute into the GSIC signal detection algorithm to obtain the signal estimation value of the kth user equipment UE

[0028] Optionally, the additive Gaussian quantization distortion The covariance matrix of is expressed as:

[0029]

[0030] in, is the semi-positive definite covariance matrix describing the spatial correlation of the channel, p i represents the transmit power of the i-th user equipment UE, σ 2 represents additive white Gaussian noise n l The variance of , diag(·) is used to extract the diagonal elements and create a diagonal matrix, I N is the N×N identity matrix.

[0031] Optionally, the step 4 uses MRC-GSIC to perform signal detection. When U=K, G=1, u k = k, the receiving combination vector of the kth user equipment UE for:

[0032]

[0033] When U=1, G=K, u k =1, at this time the receiving combination vector of the kth user equipment UE for:

[0034]

[0035] Optionally, the step 4 uses the ZF-GSIC algorithm to perform signal detection. When U=K, G=1, u k = k, at this time, the receiving combination vector of the kth user equipment UE for:

[0036]

[0037] When U=1, G=K, u k =1, at this time, the receiving combination vector of the kth user equipment UE for:

[0038]

[0039] Optionally, after the grouping in step 2, the u-th group consists of UEs with sequence numbers k=G(u-1)+1,…,Gu, and the group number of the k-th UE is

[0040] Optionally, the channel vector between UE-i and AP-1 is a spatially correlated Rayleigh fading channel.

[0041] The present invention provides a non-cellular large-scale MIMO signal detection device, including a memory and a processor;

[0042] The memory is used to store computer programs;

[0043] The processor is used to implement the non-cellular massive MIMO signal detection method as described in any one of the above items when executing the computer program.

[0044] The present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the non-cellular large-scale MIMO signal detection method as described in any one of the above items is implemented.

[0045] The beneficial effects of the present invention are:

[0046] The present invention proposes a group SIC (GSIC) signal detection method, and respectively proposes MRC-GSIC and ZF-GSIC signal detection methods to detect a received signal matrix. The GSIC signal detection method is used to obtain a transmitted signal estimation value of each user equipment UE from the received signal matrix. At the same time, a linear maximum ratio combining (MRC) signal detection method, a zero forcing (ZF) signal detection method, a nonlinear MRC-SIC signal detection method and a ZF-SIC signal detection method can be used as four special cases of the GSIC signal detection method of the present invention under specific conditions.

[0047] Simulation results show that the use of the GSIC signal detection method proposed in the present invention to perform signal detection in a non-cellular massive MIMO system can maintain high spectral efficiency and energy efficiency while effectively reducing signal processing complexity, processing delay and implementation cost. When using a low-precision ADC and a small number of groups, the spectral efficiency and energy efficiency of the GSIC signal detection method proposed in the present invention are very close to those of traditional SIC detection, but the computational complexity can be greatly reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] FIG1( a ) is a cumulative distribution function diagram of the total spectrum efficiency of the signal detection method for a non-cellular massive MIMO system based on the GSIC algorithm of the present invention under ideal and non-ideal CSI conditions when L=100 and N=1.

[0050] FIG1( b ) is a cumulative distribution function diagram of the total spectrum efficiency of the signal detection method for a non-cellular massive MIMO system based on the GSIC algorithm of the present invention under ideal and non-ideal CSI conditions when L=50 and N=2.

[0051] Figure 2 This is a curve diagram showing how the average spectrum efficiency of each UE varies with the number of APs in the non-cellular massive MIMO system signal detection method based on the GSIC algorithm of the present invention under ideal and non-ideal CSI conditions.

[0052] Figure 3 The following is a curve diagram showing the change of the average spectrum efficiency of each UE with the number of APs when different detection algorithms are used under non-ideal CSI.

[0053] Figure 4 The following is a curve diagram showing the variation of the average spectrum efficiency of each UE with the number of antennas of each AP when different detection algorithms are used under non-ideal CSI.

[0054] FIG5(a) is a graph showing the average spectrum efficiency of each UE corresponding to different detection algorithms when U=2 and under non-ideal CSI and the variation with different quantization bit numbers.

[0055] FIG5( b ) is a graph showing the average spectrum efficiency of each UE corresponding to different detection algorithms when U=5 and under non-ideal CSI and the variation with different quantization bit numbers.

[0056] FIG6( a ) is a graph showing the energy efficiency corresponding to different detection algorithms when U=2 and the number of quantization bits.

[0057] FIG6( b ) is a graph showing the energy efficiency corresponding to different detection algorithms when U=5 and the number of quantization bits. DETAILED DESCRIPTION

[0058] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0059] Embodiment 1:

[0060] This embodiment provides a non-cellular massive MIMO signal detection method, and the system model to be detected is expressed as:

[0061]

[0062] The system model includes L access points AP and K user equipment UE, each AP is configured with N antennas, and each UE is configured with 1 antenna;

[0063] Among them, y l represents the signal received at the lth access point AP-1, x i represents the information symbol sent by the i-th user equipment UE-i, h il represents the channel vector between UE-i and AP-1, n l represents the additive white Gaussian noise on AP-1;

[0064] The method of this embodiment includes:

[0065] Step 1: Construct a quantization system model for the system model to be detected based on the linear additive quantization noise model AQNM;

[0066] Step 2: Divide all user equipment UEs into U groups evenly, each group consists of The groups are composed of user equipments UE, where 1≤U≤K, and these groups are detected continuously, and user equipments UE in the same group are detected simultaneously;

[0067] Step 3: Construct the group SIC signal detection algorithm GSIC. The GSIC signal detection algorithm is expressed as:

[0068]

[0069] in, represents the estimated value of the transmitted signal of the kth user equipment UE, v k is the receiving combination vector of the kth user equipment UE, α represents the distortion coefficient, Represents y l The quantized signal, represents the group number of the kth user equipment UE, h i Denotes the channel vector h il A collection of;

[0070] Step 4: Use the MRC-GSIC detection algorithm or the ZF-GSIC detection algorithm to detect the received signal matrix yq Perform detection to obtain the estimated value of the transmitted signal of the kth user equipment UE

[0071] Embodiment 2:

[0072] This embodiment provides a non-cellular massive MIMO signal detection method, and the system model applicable to the method is:

[0073]

[0074] In the above formula represents the signal received at the lth AP (AP-1), where The information symbol (i.e., the transmitted signal) sent by the i-th UE (UE-i) has a transmission power of p i ;h il is the channel vector between UE-i and AP-1; is the additive Gaussian white noise on AP-l, and its variance is σ 2 ; represents a complex Gaussian distribution.

[0075] This embodiment considers a non-cellular massive MIMO system in a spatially correlated Rayleigh fading channel, with L geographically distributed APs and K UEs, each AP is configured with N antennas, and each UE is configured with one antenna.

[0076] The channel is modeled as a spatially correlated Rayleigh fading distribution, so the channel vector h between UE-k and AP-l is kl It is expressed as:

[0077]

[0078] in is the semi-positive definite covariance matrix describing the spatial correlation of the channel; the average channel gain from an antenna at the lth AP to the kth UE is given by the normalized trace β kl =tr(R kl ) / N, which is the large-scale fading coefficient that describes geometric path loss and shadow fading.

[0079] After quantification using the AQNM method, the quantized signal is obtained

[0080]

[0081] In this embodiment, L APs receive the received data signal {y l:l=1,...,L} is sent to the CPU, which is responsible for detecting the data signal. That is, the AP acts as a relay and forwards its received signal to the CPU for processing. More precisely, each AP sends the received pilot signal and the received uplink data signal to the CPU, and the CPU performs channel estimation, reception combination and data detection. The received signal available on the CPU is represented as:

[0082]

[0083] Formula (4) can be expressed in a more concise form:

[0084]

[0085] Assumptions is the distribution of the aggregate channels, where the block diagonal spatial correlation matrix is ​​given by In addition, Defined as the channel matrix.

[0086] Since in actual situations, the channel state information (CSI) is usually obtained during the training phase. In the present invention, the minimum mean-square error (MMSE) method is used to estimate the channel. Since the centralized non-cellular massive MIMO system is considered, the CPU can use the channel statistics obtained at the AP and the received pilot signal to calculate all MMSE channel estimates. These estimates can be calculated individually without losing optimality. Then, the aggregate channel estimate can be obtained at the CPU, and the aggregate channel estimate for the kth UE can be expressed as:

[0087]

[0088] The aggregate channel estimation error of the kth UE can be expressed as In addition, the definition As an estimate of the channel matrix H.

[0089] The method comprises:

[0090] Step 1: Construct a quantization system model of a low-precision ADC based on a linear additive quantization noise model (AQNM).

[0091] The AQNM quantization system model is constructed using the following formula (7):

[0092]

[0093] in, represents the received signal vector at AP-1; is the information symbol (i.e., the transmitted signal) sent by the i-th UE, and its transmission power is p i ;h il is the channel vector between UE-i and AP-1;

[0094] is the additive Gaussian white noise on the lth AP, and its variance is σ 2 ; represents the quantized signal, α is the distortion coefficient, which depends on the number of quantized bits b; is with y l Uncorrelated additive Gaussian quantization distortion, The covariance matrix of is expressed as:

[0095]

[0096] The symbol diag(·) is used to extract the diagonal elements and create a diagonal matrix, I N is the N×N identity matrix.

[0097] Step 2: Divide all UEs into U groups evenly, each group consists of UEs, where 1≤U≤K.

[0098] The u-th group consists of UEs with sequence numbers k=G(u-1)+1,...,Gu. Therefore, the group number of the k-th UE is For example, consider a network of K=40 UEs, divided equally into U=10 groups. The number of UEs in each group is G=4. The group number of UE-7 is u7=2, and the second group contains UEs from UE-5 to UE-8. These groups are detected consecutively, and UEs in the same group are detected simultaneously.

[0099] Step 3: Construct a group SIC (GSIC) signal detection algorithm based on the idea of ​​SIC detection;

[0100] The GSIC signal detection algorithm is constructed using the following formula (9):

[0101]

[0102] in represents the estimated value of the transmitted signal of the kth UE, v k is the receiving combination vector of the kth UE, with the superscript H Denotes conjugate transpose.

[0103] Step 4: Use the MRC-GSIC detection algorithm or the ZF-GSIC detection algorithm to detect the received signal yq The transmitted signal of each user can be recovered.

[0104] Among them, the MRC-GSIC detection algorithm is used to detect the received signal y q The process of recovering the sent signal of each user is as follows:

[0105] Using formula (10) and the MRC-GSIC algorithm for signal detection, the receiving combination vector of the kth UE is It is expressed as:

[0106]

[0107] in, [H] :,n represents the nth column of the matrix H.

[0108] In particular, for the MRC-SIC detector, U = K, G = 1, u k = k, at this time, the receiving combination vector of the kth UE for:

[0109]

[0110] For MRC detection, U = 1, G = K, u k =1, then the receiving combination vector of the kth UE for:

[0111]

[0112] Using the ZF-GSIC detection algorithm, the received signal y q The process of recovering the sent signal of each user is as follows:

[0113] Using formula (13) and the ZF-GSIC algorithm for signal detection, the receiving combination vector of the kth UE is It is expressed as:

[0114]

[0115] in Indicates the uth k The combination vector of all UEs in the group is expressed as:

[0116]

[0117] In particular, for the ZF-SIC detector, U = K, G = 1, u k = k, at this time, the receiving combination vector of the kth UE for:

[0118]

[0119] For the ZF detector, U = 1, G = K, u k =1, at this time, the receiving combination vector of the kth UE for:

[0120]

[0121] Finally, the received combination vector of the kth UE is Substituting into the GSIC signal detection algorithm of formula (9), the estimated value of the transmitted signal of the kth UE can be obtained.

[0122] In order to make the purpose, technical solution and advantages of the present invention clearer, some classic detection algorithms will be used to compare with the present invention to show the superiority of the signal detection method based on the GSIC algorithm of the present invention in improving system performance.

[0123] The detection algorithms used for simulation are respectively based on the MRC detection algorithm, based on the MRC-SIC detection algorithm, based on the ZF detection algorithm, based on the ZF-SIC detection algorithm and the signal detection method based on the GSIC algorithm of the present invention.

[0124] Among them, the MRC and ZF detection algorithms are classic linear detection algorithms, and the MRC-SIC and ZF-SIC detection algorithms are classic nonlinear detection algorithms; the signal detection method based on the GSIC algorithm of the present invention can achieve system SE and EE performance close to that of the SIC detection algorithm with low complexity through fewer groupings.

[0125] The experimental results are shown in the simulation curve in the attached figure. It can be seen from the simulation curve that the spectrum efficiency of the system using the GSIC detection of the present invention can be improved as the number of groups increases, and when there are fewer groups, such as 5 groups, the system can achieve a spectrum efficiency and energy efficiency that is very close to that when SIC detection is used. In addition, the ideal ADC system and the ADC quantization system are compared through simulation experiments. The results show that when the number of quantization bits is 5 or 6, the spectrum efficiency and energy efficiency of the system can reach the optimal situation. This shows that under tolerable performance loss, a 5-bit ADC can be used to replace the ideal ADC, thereby significantly reducing hardware costs and energy consumption.

[0126] As shown in Figure 1(a) and Figure 1(b), the SE performance of the system under the ZF-GSIC detector is higher than that under the MRC-GSIC detector. This is because the ZF-GSIC detector can eliminate the interference between users. For low-precision ADC systems, the difference between ideal and non-ideal CSI cases is due to the error in channel estimation. Therefore, it can be seen that the SE performance of the system under ideal CSI is higher than that under non-ideal CSI. It can also be found that by comparing the results of different numbers of groups, more groups, such as 10 groups, can obtain higher system SE. From this conclusion, the SE of the system can be improved by increasing the number of groups. Figure 1(a) and Figure 1(b) show the cases when the number of APs and their antennas are L=100, N=1 and L=50, N=2 respectively. Figure 1(b) considers the same setting, but the number of APs equipped with multiple antennas is smaller. It can be seen from the figure that the overall trend of Figure 1(b) is the same as that of Figure 1(a), but the SE of Figure 1(b) is lost compared with Figure 1(a) due to the reduction of macro diversity, that is, the increase in the average distance from UE to AP.

[0127] like Figure 2 As shown in the figure, as the number of APs increases, the average spectrum efficiency of each UE gradually increases, so the system performance can be improved by increasing the number of APs. Under this simulation setting, it can also be seen that the spectrum efficiency during ZF-GSIC detection is higher than that during MRC-GSIC detection, and the higher the number of groups, the higher the spectrum efficiency. For example, the performance of 10 groups is higher than that of 2 groups, which further confirms that the system performance can be improved by increasing the number of GSIC detection groups.

[0128] like Figure 3 As shown, SIC detection provides the greatest spectral efficiency, ordinary linear detection provides the lowest spectral efficiency, and the GSIC detection proposed in the present invention is between the two. The simulation results confirm the rationality of MRC-GSIC and ZF-GSIC proposed in the present invention, and MRC and MRC-SIC detection are two special cases of MRC-GSIC detection, and ZF and ZF-SIC detection are two special cases of ZF-GSIC detection. In addition, it can be seen that the system performance of ZF, ZF-SIC, and ZF-GSIC detection is higher than that of MRC, MRC-SIC, and MRC-GSIC. This is because the ZF detector can eliminate interference between users. In this simulation setting, the number of GSIC detection groups is set to 5 groups. Figure 3 From the results in , we can find that by setting a small number of groups, the spectral efficiency of GSIC detection is very close to that of SIC detection, and it is also better than the system with linear detection.

[0129] like Figure 4As shown in the figure, as the number of antennas of each AP increases, the average spectrum efficiency of each UE gradually increases, so the system performance can be improved by increasing the number of AP antennas. In addition, it can be seen that under this simulation setting, the GSIC detection algorithm proposed in the present invention is between SIC and linear detection, which further confirms the rationality of GSIC detection in non-cellular systems. Figure 3 The results are similar to those of GSIC. When the number of GSIC detection groups is set to 5, it can be found that when the number of AP antennas changes, by setting a small number of groups, the system performance under GSIC detection is very close to that under SIC detection, which further confirms the advantage of GSIC detection.

[0130] As shown in Figures 5(a) and 5(b), the average spectrum efficiency of each UE gradually increases with the increase of the number of quantization bits. However, when the number of quantization bits reaches a certain number, the spectrum efficiency becomes the same as that in the ideal ADC case and no longer increases. In addition, it can be seen that the SE of the GSIC detector proposed in the present invention is between SIC and linear detection, and the system performance under ZF-GSIC detection is higher than that under MRC-GSIC detection. The simulation results further confirm the rationality of GSIC detection. In addition, as the number of quantization bits increases, the average SE is close to the performance of the ideal ADC. It can be seen that when the number of quantization bits is 6, the SE converges to a fixed bound. This means that the 6-bit ADC architecture can achieve the same performance as the ideal ADC. Figures 5(a) and 5(b) are the cases when the number of groups is 2 and 5, respectively. Comparison of the two figures shows that when the number of groups is larger, the SE performance is better. This is because the more groups there are, the closer the GSIC detector is to the SIC detection case, so the interference elimination performance is better. In addition, it can be found from Figures 5(a) and 5(b) that a small number of groups, such as 5 groups, can achieve higher system performance under GSIC detection.

[0131] As shown in Figure 6(a) and Figure 6(b), for all different detection algorithms, higher energy efficiency can be achieved at a lower number of quantization bits (such as b = 5). In addition, it can be seen that under different detectors, the energy efficiency increases with the increase of quantization bits, and then decreases due to severe quantization loss and increased total energy consumption. The energy efficiency of the SIC detector is the largest, the energy efficiency of the linear MRC and ZF detectors is relatively small, and the energy efficiency of the GSIC detector is between the two. The simulation results show that the energy efficiency of low-resolution ADC systems can be improved by properly selecting quantization bits. This insight shows that regardless of the detector type, low-resolution ADC architectures have great potential for improvement. Figure 6(a) and Figure 6(b) are the cases when the number of groups is U = 2 and 5, respectively. The value of the system energy efficiency of the GSIC receiver at U = 5 is closer to that of the SIC receiver, and this result also confirms that higher system performance can be achieved by adopting a smaller number of groups. In addition, the larger the number of groups, the higher the energy efficiency, that is, in Figure 6(a) and Figure 6(b), the energy efficiency at U = 5 is higher than that at U = 2. Through the simulation of energy efficiency performance, the effectiveness of the signal detection method based on the GSIC algorithm proposed in the present invention applied to the non-cellular system is further verified.

[0132] In summary, compared with linear detection, the SIC detection system has higher spectral efficiency and energy efficiency, and the system performance under ZF-GSIC detection is higher than that under MRC-GSIC detection. In addition, on the basis of effectively reducing the signal processing complexity, processing delay and implementation cost, using a low-precision ADC, with a small number of groups, the spectral efficiency and energy efficiency of the system under the signal detection method based on the GSIC algorithm proposed in the present invention are very close to those of traditional SIC detection.

[0133] Some steps in the embodiments of the present invention may be implemented using software, and the corresponding software program may be stored in a readable storage medium, such as a CD or a hard disk.

[0134] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A non-cellular massive MIMO signal detection method, the system model to be detected is expressed as: The system model includes L access points AP and K user equipment UE, each AP is configured with N antennas, and each UE is configured with 1 antenna; in, y l represents the signal received at the lth access point AP-1, x i represents the information symbol sent by the i-th user equipment UE-i, h il represents the channel vector between UE-i and AP-1, n l represents the additive white Gaussian noise on AP-1; Characterized in that the method comprises: Step 1: construct a quantization system model for the system model to be detected according to a linear additive quantization noise model AQNM; Step 2: Divide all user equipment UEs into U groups evenly, each group consists of The groups are composed of user equipments UE, where 1≤U≤K, and these groups are detected continuously, and user equipments UE in the same group are detected simultaneously; Step 3: Construct the group SIC signal detection algorithm GSIC. The GSIC signal detection algorithm is expressed as: in, represents the estimated value of the transmitted signal of the kth user equipment UE, v k is the receiving combination vector of the kth user equipment UE, α represents the distortion coefficient, Represents y l The quantized signal, represents the group number of the kth user equipment UE, h i Denotes the channel vector h il A collection of; Step 4: Use the MRC-GSIC detection algorithm or the ZF-GSIC detection algorithm to detect the received signal matrix y q Perform detection to obtain the estimated value of the transmitted signal of the kth user equipment UE 2. The method for detecting a non-cellular massive MIMO signal according to claim 1, wherein: The step 4 uses the MRC-GSIC detection algorithm to detect the received signal matrix y q Perform detection to obtain the estimated value of the transmitted signal The process includes: The receiving combination vector of the kth user equipment UE It is expressed as: in, represents the estimated value of the channel matrix, Representation Matrix The k-(u k -1) Column G, Representation Matrix The first (u k -1) G+1 column to K column; The receiving combination vector of the kth user equipment UE Substitute into the GSIC signal detection algorithm to obtain the signal estimation value of the kth user equipment UE 3. The method for detecting a non-cellular massive MIMO signal according to claim 2, wherein: The step 4 uses the ZF-GSIC detection algorithm to detect the received signal matrix y q Perform detection to obtain the estimated value of the transmitted signal The process includes: The received combination vector of the kth user equipment UE It is expressed as: in Indicates the uth k The combination vector of all UEs in the group is expressed as: The receiving combination vector of the kth user equipment UE Substitute into the GSIC signal detection algorithm to obtain the signal estimation value of the kth user equipment UE 4. The method for detecting a non-cellular massive MIMO signal according to claim 3, wherein: The additive Gaussian quantization distortion The covariance matrix of is expressed as: in, is the semi-positive definite covariance matrix describing the spatial correlation of the channel, p i represents the transmit power of the i-th user equipment UE, σ 2 represents additive white Gaussian noise n l The variance of , diag(·) is used to extract the diagonal elements and create a diagonal matrix, I N is the N×N identity matrix.

5. The method for detecting a non-cellular massive MIMO signal according to claim 2, wherein: Step 4 uses MRC-GSIC to detect signals. When U=K, G=1, u k = k, the receiving combination vector of the kth user equipment UE for: When U=1, G=K, u k =1, at this time the receiving combination vector of the kth user equipment UE for:

6. The method for detecting a non-cellular massive MIMO signal according to claim 3, wherein: Step 4 uses the ZF-GSIC algorithm to detect the signal. When U=K, G=1, u k = k, at this time, the receiving combination vector of the kth user equipment UE for: When U=1, G=K, u k =1, at this time, the receiving combination vector of the kth user equipment UE for:

7. The method for detecting a non-cellular massive MIMO signal according to claim 1, wherein: After the grouping in step 2, the uth group consists of UEs with sequence numbers k=G(u-1)+1,…,Gu, and the group number of the kth user equipment UE is 8. The method for detecting a non-cellular massive MIMO signal according to claim 1, wherein: The channel vector between UE-i and AP-1 is a spatially correlated Rayleigh fading channel.

9. A non-cellular massive MIMO signal detection device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is configured to implement the non-cellular massive MIMO signal detection method according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the non-cellular massive MIMO signal detection method as described in any one of claims 1 to 8 is implemented.

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