Uplink received signal processing method and device

Abstract

The invention provides an uplink received signal processing method and device, and belongs to the technical field of communication. The method comprises the following steps: acquiring an uplink receiving signal; taking a target response matrix extracted from an uplink received signal and a communication signal as a target, and constructing a JED model based on a result of determining the target based on an MAP criterion; and carrying out iteration on the JED model based on an ADMM algorithm to obtain the target response matrix and the communication signal. According to the uplink received signal processing method and device provided by the invention, the technical effect of simultaneously analyzing the sensing signal and the communication signal from the uplink received signal can be realized, so that the interference between sensing and communication functions is reduced, and the sensing accuracy and the communication quality are improved.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method and device for processing an uplink received signal. Background Art

[0002] ISAC (Integrated Sensing and Communication) combines sensing and communication capabilities on a unified platform by leveraging shared hardware and radio resources. Specifically, the Radar-Communication (RadCom) base station utilizes non-orthogonal radio resources to perform communication and sensing tasks simultaneously, thereby improving spectrum efficiency and hardware utilization.

[0003] Existing technologies primarily focus on joint downlink transmission, where the base station simultaneously transmits communication signals to users and broadcasts sensing signals to the target. While this area has been extensively researched, significant challenges remain in jointly receiving the echo signal from the target and the uplink communication signal. A key issue in uplink ISAC systems is interference between the sensing and communication functions, which can compromise sensing accuracy and communication quality. Summary of the Invention

[0004] The present invention provides an uplink received signal processing method and device, which are used to solve the defect of interference between perception and communication functions in the prior art, and achieve the effect of improving the accuracy of perception and the quality of communication.

[0005] In a first aspect, the present invention provides a method for processing an uplink received signal, comprising: Get uplink receiving signal; The target response matrix and communication signal are extracted from the uplink received signal as the target, and the target structure is determined based on the MAP to build a JED (Joint Radar Signal Estimation and Communication Signal Detection) model; The JED model is iterated based on the ADMM algorithm to obtain the target response matrix and the communication signal.

[0006] In one embodiment, iterating the JED model based on the ADMM algorithm includes: Only in the last round of iteration of the ADMM algorithm, the communication signal intermediate variables are soft-demodulated to obtain the target response matrix and the communication signal.

[0007] In one embodiment, iterating the JED model based on the ADMM algorithm includes: In each iteration of the ADMM algorithm, the target response matrix intermediate variables are fused with the communication signal intermediate variables.

[0008] In one embodiment, the step of constructing a JED model based on the result of determining the target based on the MAP criterion includes: Determining a path to obtain the result based on the MAP criterion; The prior probability of the target response matrix and the joint likelihood probability of the communication signal and the target response matrix are fused into the path to construct the JED model.

[0009] In one embodiment, iterating the JED model based on the ADMM algorithm includes: During each iteration of the ADMM algorithm, the penalty term coefficient and the augmented Lagrangian penalty parameter corresponding to the current iteration are determined according to the uplink received signal and the result parameters obtained in the previous iteration; The result parameters include the split binary communication signal, the communication signal intermediate variable, and the Lagrange multiplier.

[0010] In one embodiment, determining the penalty term coefficient and augmented Lagrangian penalty parameter corresponding to the current iteration according to the uplink received signal and the intermediate parameters obtained in the previous iteration includes: Obtaining a penalty term coefficient and an augmented Lagrangian penalty parameter corresponding to a current iteration based on the uplink received signal, the intermediate parameters obtained in the previous iteration, and the target model; The target model includes T ADMM cascade layers, and the structure of each cascade layer corresponds to the iterative process of the ADMM algorithm; T ADMM is the number of iterations of the ADMM algorithm.

[0011] In a second aspect, the present invention provides an uplink received signal processing device, comprising: An acquisition module, used to acquire an uplink received signal; A modeling module is used to extract the target response matrix and the communication signal from the uplink received signal as the target, and to construct a JED model based on the result of determining the target based on the MAP criterion; A solution module is used to solve the JED problem based on the ADMM algorithm to obtain the target response matrix and the communication signal.

[0012] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the uplink received signal processing method as described in the first aspect above is implemented.

[0013] In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the uplink received signal processing method as described in the first aspect above.

[0014] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the uplink received signal processing method as described in the first aspect above.

[0015] The uplink received signal processing method and device provided by the present invention can achieve the technical effect of simultaneously parsing the perception signal and the communication signal from the uplink received signal by extracting the target response matrix and the communication signal from the uplink received signal as the target to construct a JED model, and iterating the JED model based on the ADMM algorithm, thereby reducing the interference between the perception and communication functions and improving the accuracy of perception and the quality of communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 It is a schematic diagram of an application scenario of the present invention.

[0018] Figure 2 It is a flowchart of the uplink received signal processing method provided by the present invention.

[0019] Figure 3 This is the target model provided by the present invention. t Functional module diagram of the layer.

[0020] Figure 4 2 is a schematic diagram comparing the SER and NMSE performance of JED-ADMM and JED-ADMMNet of the present invention at different numbers of iterations.

[0021] Figure 5 The figure is a schematic diagram comparing the SER and NMSE performance of the receiver of the present invention and other receivers at different communication powers.

[0022] Figure 6 The figure is a schematic diagram comparing the SER and NMSE performance of the receiver of the present invention and other receivers under different numbers of communication users (CUs).

[0023] Figure 7It is a structural diagram of the uplink received signal processing device provided by the present invention.

[0024] Figure 8 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0025] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0026] The following example illustrates the application scenario of the uplink received signal processing method provided in this application: like Figure 1 As shown in Figure 2, consider an ISAC system where the Radar-Communication Base Station (RadCom BS) contains M transmit antennas and N Receive antennas. BS serves K The single-antenna communication users (CUs) simultaneously sense an extended target (sensing task).

[0027] For uplink communication -ary rectangular quadrature amplitude modulation method ( -QAM) constellation set is , time slot l The communication signal at ,in , P c is the total communication power. K The channel to the BS is composed of Given, where β k represents the large-scale path loss, k Indicates the k CUs, represents small-scale fading, I N is the N-dimensional identity matrix. For simplicity, the communication matrix is ​​expressed as , assuming this is an accurate estimate.

[0028] For perception, the base station broadcasts radar detection waveforms ,in Indicates the length of the waveform signal, represents the total sensed power. During this process, all users operate in uplink mode and transmit their data to the base station. Perfect synchronization is assumed at the BS. Therefore, the BS simultaneously receives the sensing echo signal (generated by the extended target reflecting the base station's probe signal) and the communication signal, and the received uplink signal can be expressed as: (1) in , stands for Target Response Matrix (TRM), stands for Additive White Gaussian Noise (AWGN), where , represents the variance of the noise.

[0029] Based on the above content, it can be seen that in formula (1), Y represents the uplink received signal; X represents the communication signal; H represents the channel of the communication signal X; G is the target response matrix, which can represent the perception channel, and the position of the extended target can be determined through G; S represents the detection signal sent by the base station; and W represents noise.

[0030] The extended target is usually modeled as a surface composed of distributed point scatterers, which generate multiple reflection paths to the BS. Using the Swerling II target model with Gaussian distributed complex amplitudes and assuming that the receiving antennas are widely separated, the TRMG can be expressed as ,in The entries of are independent and identically distributed (iid) Gaussian variables with zero mean and unit variance, is the second-order statistical information.

[0031] Figure 2 Schematic diagram of the process of the uplink received signal processing method provided by the present invention, such as Figure 2 As shown, the method may include the following steps: Step 210: Acquire an uplink received signal; Step 220: Extract the target response matrix and the communication signal from the uplink received signal as the target, and build a JED (Joint Radar Signal Estimation and Communication Signal Detection) model based on the target determination result based on the MAP criterion; Step 230: Iterate the JED model based on the ADMM (Alternating Direction Method of Multipliers) algorithm to obtain a target response matrix and a communication signal.

[0032] It should be noted that the execution subject of the uplink received signal processing method provided by the present invention may be a network side device, such as a base station, etc. The following takes the base station executing the uplink received signal processing method provided by the present invention as an example to describe the technical solution of the present invention in detail.

[0033] In step 210, the base station may obtain an uplink receive signal from the terminal and the extended target, where the uplink receive signal includes target response matrix information and communication signal information.

[0034] In step 220 , a target response matrix and a communication signal extracted from the uplink received signal may be used as a target, and a JED model may be constructed based on a result of determining the target based on a MAP (Maximum A Posteriori) criterion.

[0035] Specifically, the purpose of the present invention is to extract the TRM G and the communication signal X from the uplink received signal Y. Therefore, the target response matrix and the communication signal are extracted from the uplink received signal as targets, and the result of determining the target based on the MAP criterion can be expressed as follows: (2) in, It is based on Bayes' rule.

[0036] Furthermore, the JED model can be constructed by first determining a path for obtaining the result based on the MAP criterion, and then integrating the prior probability of the target response matrix and the joint likelihood probability of the communication signal and the target response matrix into the path.

[0037] Specifically, formula (2) can be used as a path to obtain the target result, and then the prior probability of the target response matrix and the joint likelihood probability of the communication signal and the target response matrix are substituted into formula (2).

[0038] in, , since X is from the set The medium probability is selected, and the prior probability of G can be expressed as , the joint likelihood probability of X and G can be expressed as Therefore, the JED model of formula (2) can be further transformed into: (3) It can be understood that by integrating the prior probability of the target response matrix and the joint likelihood probability of the communication signal and the target response matrix into the JED model, the JED model can better express the intrinsic connection between the target response matrix and the communication signal, so that the final target response matrix and communication signal can be obtained by iterative optimization of the JED model.

[0039] In step 230 , the JED model of formula (3) may be iteratively optimized based on the ADMM algorithm to obtain the target response matrix G and the communication signal X.

[0040] The uplink received signal processing method provided by the present invention extracts a target response matrix and a communication signal from the uplink received signal as targets to construct a JED problem, and solves the JED problem based on the ADMM algorithm. This can achieve the technical effect of simultaneously parsing the perception signal and the communication signal from the uplink received signal, thereby reducing the interference between the perception and communication functions and improving the accuracy of perception and the quality of communication.

[0041] In one embodiment, step 230 may be implemented as follows: Will -QAM symbols are decomposed into a combination of multiple binary variables. Then, the transmitted communication signal X can be rewritten as ,in ,and , x qR is the in-phase component of the complex sign, x qI are the complex-signed quadrature components, j Is an imaginary unit.

[0042] In addition, by relaxing the integer constraint {-1, 1} to [-1, 1] and introducing a sum of quadratic penalty terms, the solution of Equation (3) can be approximated as: (4) in , α q ( α q ≥0) is the penalty term coefficient.

[0043] The added penalty function The optimal integer solution can be made more desirable. By introducing auxiliary variables , formula (4) can be equivalently expressed as: (5) The augmented Lagrangian function of formula (5) can be expressed as: (6) in, is the Lagrange multiplier, ρ >0 is the augmented Lagrangian penalty parameter.

[0044] Using the ADMM algorithm to solve formula (6), the framework can be expressed as: (7) in, t Indicates the t The index of the iteration, the total number of iterations of the ADMM algorithm is T ADMM .

[0045] It should be noted that solving A key challenge is This complicates finding the global optimum.

[0046] However, ensure For all q , allowing the augmented Lagrangian to be expressed wrt each It shows strong convexity, which enables the application of the fast coordinate descent (BCD) method. The gradient of .

[0047] In addition, due to Relative to is strongly convex, and is strongly convex with respect to G, so and G can be solved by differentiating Equation (6) and setting the derivative to 0.

[0048] In the present invention, the algorithm for iterating the JED model of formula (5) based on the ADMM algorithm is called JED-ADMM. In this algorithm, it is possible to pre-calculate before the iteration process and .

[0049] In calculation When , the element-wise operator Project each entry in the input matrix onto the interval [-1, 1] in both the real and imaginary parts. Formula (7) yields the specific expression of each intermediate variable: (8a) (8b) (8c) (8d) In one embodiment, iterating the JED model based on the ADMM algorithm may include: Only in the last iteration of the ADMM algorithm are the intermediate variables of the communication signal soft-demodulated to obtain the target response matrix and the communication signal.

[0050] Specifically, in the last iteration, in order to reduce the residual error of the communication signal term, the above formulas (8a)-(8d) can be improved by using The soft demodulation result is used to estimate TRM G. The final output G is given by given.

[0051] Therefore, formulas (8a)-(8d) can be rewritten as: (9a) (9b) (9c) (9d) Wherein, in formula (9b), demod represents a soft demodulation operation.

[0052] The uplink received signal processing method provided by the present invention can reduce the interference of communication residuals on the perception process by soft-demodulating the communication signal in the last iteration of the ADMM algorithm to obtain the final target response matrix and the communication signal, while not demodulating the communication signal in previous iterations.

[0053] In one embodiment, iterating the JED model based on the ADMM algorithm may include: In each iteration of the ADMM algorithm, the intermediate variables of the target response matrix are fused with the intermediate variables of the communication signal.

[0054] Specifically, in order to reduce the complexity of the ADMM algorithm, formula (9d) can be substituted into formula (9b) to calculate , as shown below: (10) Among them, formula (9d) can be executed only in the last iteration of the ADMM algorithm to estimate TRM G.

[0055] The uplink received signal processing method provided by the present invention reduces the iterative steps by fusing the intermediate iterative steps of the ADMM algorithm, thereby reducing the computational complexity of the algorithm and improving the uplink received signal processing efficiency.

[0056] In one embodiment, iterating the JED model based on the ADMM algorithm may include: In each iteration of the ADMM algorithm, the penalty term coefficient and augmented Lagrangian penalty parameter corresponding to the current iteration are determined based on the uplink received signal and the result parameters obtained in the previous iteration. The result parameters include split binary communication signals, communication signal intermediate variables, and Lagrange multipliers.

[0057] It should be noted that, from the above content, it can be seen that in the process of iterating the JED model based on the ADMM algorithm of the present invention, the penalty term coefficient α q and the augmented Lagrangian penalty parameter ρ This has a significant impact on the performance of the ADMM algorithm. Therefore, in order to further improve the performance of uplink received signal processing, the present invention proposes a target model to learn better parameters.

[0058] The target model can be a model-driven deep learning network, which is referred to as JED-ADMMNet in the present invention. The target model can include T ADMM cascade layers, and the structure of each cascade layer corresponds to the iterative process of the ADMM algorithm. Each layer has the same architecture but a different set of trainable parameters.

[0059] like Figure 3 As shown, the target model t The structure of the layer and the ADMM algorithm t The iterative processing process corresponds to integrating the trainable parameters and .exist Figure 3 middle, yes The intermediate operation module can be expressed as .

[0060] It should be noted that a major difficulty in designing the target model lies in the design of the loss function. Since the estimation accuracy of G is highly dependent on the estimation accuracy of X0 (as reflected in formula (9d)), when designing the loss function of the target model, we can focus on the accuracy of X0 estimation and define the loss function as: (11) in, and X are the target model t The output of the layer and the true transmission matrix.

[0061] In the ADMM algorithm t During the round of iteration, the uplink received signal (Y, H, S, R g ) and t -1 round of iteration results in the parameter, that is, the split binary communication signal , intermediate variables of communication signals , Lagrange multipliers Input into the target model and get the output of the target modelt Penalty coefficient corresponding to round iteration and the augmented Lagrangian penalty parameter .

[0062] It is understood that during the training process of the target model, the uplink received signal and the intermediate parameters obtained in the previous iteration can be used as samples, and the penalty coefficient and augmented Lagrangian penalty parameter corresponding to the current iteration can be used as labels to train the target model. After the target model is trained, the current estimated signal and intermediate results can be adjusted at each iteration based on the penalty coefficient and augmented Lagrangian penalty parameter output by the target model.

[0063] The uplink received signal processing method provided by the present invention can timely adjust the current estimated signal and intermediate results in each iteration by embedding trainable parameters into each round of ADMM iteration, thereby improving the processing performance of the uplink received signal.

[0064] In one example, the uplink received signal processing method provided by the present invention is specifically applied to a receiver, and the complexity of the receiver is compared with that of existing receivers, including ZF-ZF and ZFO-ZF receivers.

[0065] The complexity is measured by the number of real multiplication operations. Since JED-ADMM and JED-ADMMNet have the same complexity, the following will only focus on the complexity analysis of JED-ADMMNet.

[0066] The overall complexity of JED-ADMMNet consists of two components: the preprocessing stage and the iteration stage.

[0067] In the preprocessing stage, 、 and The complexity of the operations is 、 and .

[0068] In the iteration phase, the complexity of each iteration is divided into two parts: First, the execution of formula (9a) Q times, the complexity is ; Secondly, the complexity introduced by executing formula (11) is .

[0069] In the final iteration, equation (9d) is executed to calculate , whose complexity is Therefore, the total complexity of JED-ADMMNet is .

[0070] Table 1 lists the complexity of different receivers and provides numerical results for different scenarios, all of which use 4-QAM modulation. It should be noted that despite the higher complexity of the proposed receiver (tens of iterations are required to reach convergence) compared to recent studies, it successfully achieves significant performance improvements.

[0071] Table 1. Comparison of the complexity of different receivers

[0072] In terms of simulation results, the performance of perception and communication is evaluated using the normalized mean square error (NMSE) and symbol error rate (SER), respectively, where NMSE is defined as .

[0073] The simulation parameters are set as follows: , and In JED-ADMM, the penalty parameter is set to , and . Correlation matrix R g During the offline deep learning training phase, 10,000 training samples were generated and the model was trained for 300 cycles.

[0074] Figure 4 The performance of JED-ADMM and JED-ADMMNet receivers under 4-QAM modulation is shown to decrease with the number of ADMM iterations. T ADMM The system parameters are set to K =8, M =10, N =10, L =12 and For comparison purposes, the performance of the ZFO-ZF receiver is also shown in the figure. Due to parameter optimization, JED-ADMMNet always outperforms JED-ADMM. When , the performance of JED-ADMM and JED-ADMMNet is close to that of ZFO-ZF. T ADMM When it is increased to 30, both proposed receivers show significant improvements over ZFO-ZF in terms of NMSE and SER, with NMSE approaching convergence. For the proposed receiver, further increasing T ADMM It can improve communication performance, but it will increase complexity. T ADMM A balance can be achieved between receiver performance and complexity.

[0075] Figure 5 The SER and NMSE performance of the receiver under 4-QAM and 16-QAM modulation are compared. T ADMM Set to 30, other system parameters are the same as Figure 4 The parameters in the proposed JED-ADMMNet receiver are consistent. The proposed JED-ADMMNet receiver shows the best performance in both communication and perception. In terms of communication performance under 4-QAM modulation, the JED-ADMM receiver is better than the ZFO-ZF and ZF-ZF receivers in terms of SER. The JED-ADMMNet receiver further improves this performance, providing an additional gain of approximately 1.4 dB at the same SER level. In terms of perceptual performance, the JED-ADMM and JED-ADMMNet receivers significantly outperform the ZF-ZF and ZFO-ZF receivers. As the value of increases, the SER decreases continuously, while the NMSE curve increases initially and then decreases. This behavior of NMSE is due to the fact that the detection residual This is caused by the combined influence of detection performance and communication power.

[0076] Figure 6 The performance of the receiver is compared with different numbers of communicating users (CUs), where the parameters are set to M =14, N =14, L =16, and , using 16-QAM modulation. As the number of CUs increases, the mutual interference between the communication-radar and communication-communication links increases, resulting in a decrease in the communication and perception performance of all receivers. However, compared with the ZFO-ZF and ZF-ZF receivers, the JED-ADMM and JED-ADMMNet receivers show a slower increase in symbol error rate (SER) and normalized mean square error (NMSE). For example, K = 14, the NMSE of the ZFO-ZF and ZF-ZF receivers is close to 10, indicating that their perception capabilities have failed. In contrast, the JED-ADMM and JED-ADMMNet receivers proposed in this paper maintain stable perception performance. In addition, the SER of the ZFO-ZF and ZF-ZF receivers degrades to close to , and the receiver proposed in this invention achieves a close The lower SER shows excellent performance.

[0077] In summary, the uplink received signal processing method provided by the present invention can significantly improve the system communication symbol error rate (SER) and perceptual mean square error (NMSE) performance.

[0078] The uplink received signal processing device provided by the present invention is described below. The uplink received signal processing device described below and the uplink received signal processing method described above can refer to each other and can achieve the same technical effects, and will not be repeated here.

[0079] Figure 7 Schematic diagram of the structure of the uplink receiving signal processing device provided by the present invention. Figure 7 As shown, the device may include: An acquisition module 710 is configured to acquire an uplink received signal; A modeling module 720 is configured to extract a target response matrix and a communication signal from an uplink received signal as a target, determine a structure of the target based on a MAP criterion, and construct a JED model; The solving module 730 is configured to iterate the JED model based on the ADMM algorithm to obtain the target response matrix and the communication signal.

[0080] In one embodiment, the solution module 720 is specifically configured to: Only in the last round of iteration of the ADMM algorithm, the communication signal intermediate variables are soft-demodulated to obtain the target response matrix and the communication signal.

[0081] In one embodiment, the solution module 720 is specifically configured to: In each iteration of the ADMM algorithm, the target response matrix intermediate variables are fused with the communication signal intermediate variables.

[0082] In one embodiment, the solution module 720 is specifically configured to: A path for obtaining the result based on the MAP criterion; The prior probability of the target response matrix and the joint likelihood probability of the communication signal and the target response matrix are fused into the path to construct the JED model.

[0083] In one embodiment, the solution module 720 is specifically configured to: During each iteration of the ADMM algorithm, the penalty term coefficient and the augmented Lagrangian penalty parameter corresponding to the current iteration are determined according to the uplink received signal and the result parameters obtained in the previous iteration; The result parameters include the split binary communication signal, the communication signal intermediate variable, and the Lagrange multiplier.

[0084] In one embodiment, the solution module 720 is specifically configured to: Obtaining a penalty term coefficient and an augmented Lagrangian penalty parameter corresponding to a current iteration based on the uplink received signal, the intermediate parameters obtained in the previous iteration, and the target model; The target model includes T ADMM cascade layers, and the structure of each cascade layer corresponds to the iterative process of the ADMM algorithm; T ADMM is the number of iterations of the ADMM algorithm.

[0085] Figure 8 An example of a physical structure diagram of an electronic device is shown below. Figure 8 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the uplink received signal processing method described in any of the above embodiments, for example, including: Get uplink receiving signal; The target response matrix and the communication signal are extracted from the uplink received signal as the target, and the JED model is constructed based on the target determination result based on the MAP criterion; The JED model is iterated based on the ADMM algorithm to obtain the target response matrix and the communication signal.

[0086] Furthermore, the logic instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0087] On the other hand, the present invention further provides a computer program product, comprising a computer program. The computer program may be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the uplink received signal processing method described in any of the above embodiments, for example, including: Get uplink receiving signal; The target response matrix and the communication signal are extracted from the uplink received signal as the target, and the JED model is constructed based on the target determination result based on the MAP criterion; The JED model is iterated based on the ADMM algorithm to obtain the target response matrix and the communication signal.

[0088] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the uplink received signal processing method according to any one of the above embodiments is implemented, for example, including: Get uplink receiving signal; The target response matrix and the communication signal are extracted from the uplink received signal as the target, and the structure of the target is determined based on the MAP criterion to construct the JED model; The JED model is iterated based on the ADMM algorithm to obtain the target response matrix and the communication signal.

[0089] It should be noted that the network-side device in the present invention can be a base station or a core network, wherein the base station can be referred to as a node B, an evolved node B, an access point, a base transceiver station (BTS), a radio base station, a radio transceiver, a basic service set (BSS), an extended service set (ESS), a B node, an evolved B node (eNB), a home B node, a home evolved B node, a WLAN access point, a WiFi node, a transmitting receiving point (TRP), or other appropriate terms in the field. As long as the same technical effect is achieved, the base station is not limited to a specific technical vocabulary. It should be noted that in the embodiments of the present application, only a base station is used as an example, but the specific type of the base station is not limited.

[0090] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0091] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for processing an uplink received signal, characterized in that: include: Get uplink receiving signal; The target response matrix and the communication signal are extracted from the uplink received signal as the target, and the JED model is constructed based on the target determination result based on the MAP criterion; The JED model is iterated based on the ADMM algorithm to obtain the target response matrix and the communication signal.

2. The uplink received signal processing method according to claim 1, wherein: The iterating the JED model based on the ADMM algorithm includes: Only in the last round of iteration of the ADMM algorithm, the communication signal intermediate variables are soft-demodulated to obtain the target response matrix and the communication signal.

3. The uplink received signal processing method according to claim 1, wherein: The iterating the JED model based on the ADMM algorithm includes: In each iteration of the ADMM algorithm, the target response matrix intermediate variables are fused with the communication signal intermediate variables.

4. The uplink received signal processing method according to claim 1, wherein: The JED model is constructed based on the results of determining the target based on the MAP criterion, including: Determining a path to obtain the result based on the MAP criterion; The prior probability of the target response matrix and the joint likelihood probability of the communication signal and the target response matrix are fused into the path to construct the JED model.

5. The uplink received signal processing method according to any one of claims 1 to 4, characterized in that: The iterating the JED model based on the ADMM algorithm includes: During each iteration of the ADMM algorithm, the penalty term coefficient and the augmented Lagrangian penalty parameter corresponding to the current iteration are determined according to the uplink received signal and the result parameters obtained in the previous iteration; The result parameters include the split binary communication signal, the communication signal intermediate variable, and the Lagrange multiplier.

6. The uplink received signal processing method according to claim 5, wherein: The determining, based on the uplink received signal and the intermediate parameters obtained in the previous iteration, the penalty term coefficient and the augmented Lagrangian penalty parameter corresponding to the current iteration, includes: Obtaining a penalty term coefficient and an augmented Lagrangian penalty parameter corresponding to a current iteration based on the uplink received signal, the intermediate parameters obtained in the previous iteration, and the target model; The target model includes T ADMM cascade layers, and the structure of each cascade layer corresponds to the iterative process of the ADMM algorithm; T ADMM is the number of iterations of the ADMM algorithm.

7. An uplink received signal processing device, characterized in that: include: An acquisition module, used to acquire an uplink received signal; A modeling module is used to extract the target response matrix and the communication signal from the uplink received signal as the target, and to construct a JED model based on the result of determining the target based on the MAP criterion; A solution module is used to iterate the JED model based on the ADMM algorithm to obtain the target response matrix and the communication signal.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the uplink received signal processing method according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the uplink received signal processing method according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the uplink received signal processing method according to any one of claims 1 to 6 is implemented.

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