Rate splitting multiple access enhanced cellular-free sensing system optimization method

Abstract

The invention relates to a rate splitting multiple access enhanced cellular-free sensing system optimization method, which comprises the following steps of: establishing a system performance analysis architecture, and constructing a closed analysis expression of a user rate and a sensing signal to interference plus noise ratio of a system downlink under the condition of considering imperfect channel state information; according to the system performance analysis architecture, constructing a performance optimization model; the performance optimization model takes maximization of the total communication rate of the system as an optimization target and takes minimization of the user rate and the sensing signal to interference plus noise ratio as constraint conditions; a constraint condition with a non-convex characteristic in the performance optimization model is converted into a second-order cone constraint, and an optimal power distribution strategy is obtained through an iterative algorithm; and verifying the performance gain of the optimal power distribution strategy. The invention provides a joint optimization method oriented to communication performance, which can efficiently solve the problem of resource allocation under the condition of imperfect channel state information, thereby maximizing the communication throughput of the system while ensuring the basic perception performance.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wireless communication, in particular to a method for optimizing a non-cellular communication and sensing system with enhanced rate-splitting multiple access. BACKGROUND

[0002] With the generational change of mobile communication technology from the fifth generation to the sixth generation, the non-cellular distributed network paradigm has become a key technology for building future sixth-generation networks due to its excellent performance in signal coverage uniformity and spectrum resource utilization. In this context, in order to more efficiently utilize the increasingly tight spectrum resources, the integration of communication and sensing capabilities has also become a current frontier research field.

[0003] However, the coexistence of communication and sensing functions brings a complex resource allocation trade-off problem to the system. The system must maximize the communication throughput while ensuring the quality of basic sensing services. The current mainstream technical path mostly adopts the spatial division multiple access scheme, but this scheme is highly sensitive to the accuracy of channel state information. Under non-ideal conditions where channel information has errors, the interference suppression effect between users will be greatly reduced, which makes it difficult for related optimization algorithms to ensure the performance indicators of communication and sensing at the same time.

[0004] In view of this limitation, the rate-splitting multiple access technology with more robust interference management capability has become a very promising alternative. This technology decomposes the information of each user into a common code stream that all users can decode and a private code stream that only the designated user can decode, providing the system with more design freedom. Applying this technology to the non-cellular communication and sensing integrated scenario opens up new possibilities for performance improvement, but also leads to a sharp increase in the complexity of resource scheduling. Network operators must collaboratively design variables such as the power of the common stream and the private stream, the rate-splitting coefficient, and other highly correlated variables, which constitutes a highly coupled non-convex programming problem.

[0005] In summary, although the rate-splitting multiple access technology has broad application prospects in non-cellular communication and sensing integrated systems, mature and efficient resource allocation mechanisms that can fully release its performance potential are still lacking. Therefore, how to create an optimization algorithm that takes into account the computational complexity and execution efficiency under non-ideal channel conditions, with the core criterion of maximizing communication throughput while ensuring basic sensing service quality, has become an urgent research topic in this frontier field. SUMMARY

[0006] In order to solve the technical problem of lacking efficient resource allocation algorithm for the rate-splitting multiple access aided integrated system of cellular-free communication and sensing in the prior art, the application provides a communication performance oriented joint optimization method, which can efficiently solve the resource allocation problem under the condition of imperfect channel state information, so as to maximize the communication throughput of the system while ensuring the basic sensing performance.

[0007] In order to achieve the above purpose, the technical scheme adopted by the application is:

[0008] A rate-splitting multiple access enhanced integrated system of cellular-free communication and sensing optimization method, comprising the following steps:

[0009] S1, establishing a system performance analysis architecture, constructing a closed-form analytical expression of user rate and sensing signal-to-interference-and-noise ratio of the system downlink under the consideration of imperfect channel state information;

[0010] S2, constructing a performance optimization model according to the system performance analysis architecture; the performance optimization model takes maximizing the total communication rate of the system as the optimization objective, and takes minimizing the user rate and the sensing signal-to-interference-and-noise ratio as the constraint condition;

[0011] S3, transforming the constraint condition with non-convex characteristics in the performance optimization model into a second-order cone constraint, and then obtaining an optimal power allocation strategy through an iterative algorithm;

[0012] S4, verifying the performance gain of the optimal power allocation strategy.

[0013] Preferably, S1 comprises:

[0014] S11, obtaining the access point information, single-antenna user information, radar information and a sensed target information of the integrated system of cellular-free communication and sensing; using a pilot transmission based channel estimation method, calculating the minimum mean square error estimation and covariance matrix of the direct connection channel between the access point and the user;

[0015] S12, in the rate-splitting multiple access enhanced integrated system of cellular-free communication and sensing, all access points simultaneously transmit common data streams, private data streams and sensing streams to users and targets, and construct the signal model received by the first user and the first radar; S13, using a UatF based capacity limit analysis method to calculate the common stream rate, private stream rate and rate of the user;

[0016] S14, calculating the sensing signal-to-interference-and-noise ratio.

[0017] S14, calculating the sensing signal-to-interference-and-noise ratio.

[0018] Preferably, in S2, the performance optimization model is represented as follows: ​

[0019]

[0020] wherein C1 and C2 represent the relaxation constraints related to the common and private flow rates, respectively, C3 and C4 specify the minimum threshold values for the user rates and the perceived signal-to-noise ratio, respectively, and C5 constrains the maximum transmit power of each AP; , , and denote the relaxation variables, and denote the minimum threshold values for the rates and the perceived signal-to- noise ratio, respectively.

[0021] As a preference, in S3, the following convex inequality is used:

[0022]

[0023] The constraint condition is converted into a second-order cone constraint as follows:

[0024]

[0025] wherein the superscript denotes the index of the sequence convex approximation iteration, and denote the optimal solution obtained in the th sequence convex approximation iteration; based on the optimal solution, the value of (i.e., the denominator term of ) at the iteration point is defined as , and the corresponding signal-to-noise ratio value is defined as .

[0026] As a preference, the following auxiliary variables are defined:

[0027]

[0028] The second-order cone form of the constraint condition is expressed as:

[0029] .

[0030] As a preference, for the constraint condition , it is converted into a second-order cone constraint form by using the same convex inequality as that for the constraint condition :

[0031]

[0032] wherein is the denominator term of , and and are respectively the values calculated at iteration points ; The value of

[0033] .

[0034] As a preferred, for the constraint condition , through the following inequality:

[0035]

[0036] Convert it into a second-order cone form, that is

[0037] .

[0038] As a preferred, in S3, the convex optimization toolbox including CVX is used for effective solution.

[0039] Compared with the prior art, the beneficial effects of the present application are embodied in:

[0040] 1、System comprehensive performance is significantly improved, especially in the communication aspect gain is obvious

[0041] The optimization algorithm proposed in the present application can effectively tap the performance potential of rate splitting multiple access in a complex interference scenario. Through fine joint design of power allocation and other resources of public flow and private flow, the performance relationship between communication and sensing is balanced while effectively managing inter-user interference. Compared with traditional space division multiple access optimization schemes that rely on imperfect channel state information, the present application has a significant improvement in key communication indicators such as total communication rate of the system and user fairness, while also ensuring that the sensing performance meets the preset quality of service requirements, achieving a higher level of overall system performance.

[0042] 2、Robustness to imperfect channel state information is strong, closer to actual application

[0043] The existing space division multiple access scheme will deteriorate sharply when the channel state information is imperfect, and the present application is designed to address this pain point in actual application. The algorithm makes full use of the characteristics of the "public code stream" in the rate splitting multiple access technology that is not sensitive to channel state information errors, and through optimization design, part of the interference is converted into useful part, thereby greatly reducing the dependence of system performance on the accuracy of channel state information. Therefore, the present application can still maintain superior performance under non-ideal channel conditions, has stronger environmental adaptability and engineering practical value.

[0044] 3、Provided an effective solution to complex non-convex optimization problems, with a technical breakthrough

[0045] The resource allocation problem formed by introducing rate-splitting multiple access into the integrated system of non-cellular communication and sensing is a highly coupled non-convex optimization problem, and the existing technology lacks a direct and efficient solution method. The present application innovatively uses a sequential convex approximation framework, and through a clever mathematical transformation, the original difficult non-convex problem is decomposed into a series of standard, easy-to-solve second-order cone programming sub-problems. This not only solves the solution dilemma of the original problem, but also provides a systematic and feasible technical path for dealing with such complex resource management problems. Unlike traditional technical solutions that only consider ideal cases, the present application considers non-ideal factors in real communication, establishes a performance analysis method based on a system performance analytical framework, and uses a convex approximation optimization strategy. The communication technology of the present application considers the most popular rate-splitting multiple access technology, which is a potential technology for future 6G and 7G. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the present application, specific embodiments of the present application will be described in detail below with reference to the drawings. It should be understood that the drawings described below are only some exemplary embodiments of the present application, and do not limit the entire present application. For those skilled in the art, other drawings can be derived from these drawings without creative labor, and these drawings also fall within the scope of protection of the present application.

[0047] Fig. 1 is a method flowchart of embodiment 1 of the present application;

[0048] Fig. 2 is a performance comparison curve graph of system and rate varying with the total number of access points under different comparative schemes in embodiment 1 of the present application;

[0049] Fig. 3 is a performance comparison curve graph of system and rate varying with the number of antennas of each access point under different comparative schemes in embodiment 1 of the present application. DETAILED DESCRIPTION

[0050] In order to make the technical means, creative features, purposes and effects achieved by the application easy to understand, the present application will be further described in conjunction with specific drawings. However, the present application is not limited to the following embodiments.

[0051] It should be understood that the structures, proportions, sizes, etc. shown in the drawings attached to the present specification are only used to cooperate with the content disclosed in the specification, to be understood and read by those skilled in the art, and do not have technical substantive significance, and any modification of structure, change of proportion relationship or adjustment of size, without affecting the effects and purposes that can be achieved by the present application, should still fall within the scope of the technical content disclosed by the present application.

[0052] Embodiment 1

[0053] The present application aims to solve the technical problems of low resource allocation efficiency and difficult coordination of communication and sensing performance in the prior art of rate-splitting multiple access enhanced integrated system of cellular communication and sensing. To this end, the embodiment of the present application provides an efficient iterative optimization algorithm, which can jointly optimize the wireless resources such as transmit precoding and power allocation of the system under the consideration of practical constraints such as spatially correlated Rician fading channel and imperfect channel state information, thereby significantly improving the system communication performance while ensuring the quality of sensing service. The following will be described in combination with the accompanying drawings of the present application. Figs. 1 to 3 The specific implementation of the method of the present application will be further described in detail to enable those skilled in the art to better understand and implement the present application.

[0054] As shown in a rate-splitting multiple access enhanced integrated system of cellular communication and sensing optimization method, specifically comprising: Fig. 1

[0055] Step S1, establish a system performance analysis architecture, derive the closed-form analytical expressions of the achievable rate of the system downlink and the sensing signal-to-noise ratio under the consideration of imperfect channel state information

[0056] S11, the integrated system of cellular communication and sensing considered in the present patent includes access points, single-antenna users, radars and a sensed target. The subscript sets of access points, radars and users are respectively represented as , and . Using a channel estimation method based on pilot transmission, let represent the direct connection channel between the th access point and the th user, then represents the minimum mean square error estimation of the channel , and the covariance matrices are respectively represented as and .

[0057] S12, in the rate-splitting multiple access enhanced integrated system of cellular communication and sensing, all access points simultaneously transmit common data streams , private data streams and sensing streams to users and targets, the signals received by the th user and the th radar can be expressed as

[0058] ​

[0059]

[0060] where the superscript T denotes the transpose operator, and denote the transmit power of the data stream and the sensing stream, respectively, is the power splitting factor, denotes the private data stream of the th user, denotes the power control coefficient related to , denotes the power control coefficient related to , , and denote the beamforming matrix related to , and , and denote the Gaussian white noise. In addition, denotes the channel matrix between the th access point and the th radar via the sensing target, denotes the direct channel matrix between the th access point and the th radar.

[0061] S13, by using the capacity limit analysis method based on UatF (Use-and-then-Forget), the rate expression of the th user can be obtained as

[0062]

[0063] where is the common stream rate, is the private stream rate, and the expressions thereof are

[0064]

[0065]

[0066] where is the length of a coherence interval, is the length of a coherence interval used for data transmission, is the length of a coherence interval used for sensing. Other variables appearing in the above expressions are defined as follows.

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] In the above formula, denotes a diagonal matrix operator, denotes a matrix trace operator, and the superscript H denotes a conjugate transpose operator, and denotes a line-of-sight component of the channel, denotes a pilot allocation state, denotes a noise power, and denotes a channel estimation component.

[0074] S14, similarly, a lower bound expression of the achievable perceived SINR is calculated as

[0075]

[0076] wherein,

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] Step S2, based on the analytical framework, a performance optimization model is constructed to maximize the total system communication rate as the optimization objective, and the perceived SINR and user rate derived above are used as constraint conditions.

[0083] Considering the communication-oriented application scenario, the optimization problem can be modeled as

[0084]

[0085] wherein, C1 and C2 represent the relaxed constraints related to the public flow rate and the private flow rate, respectively, C3 and C4 specify the minimum threshold values of the user rate and the perceived SINR, respectively, and C5 constrains the maximum transmission power of each AP. , , and denote the slack variables, and denote the minimum rate and the perceived SIR threshold, respectively.

[0086] Step S3, for the non-convexity of the optimization problem, an iterative algorithm is adopted to solve the optimization variables including the power allocation factors until the algorithm converges, and a set of optimal power allocation strategies is obtained

[0087] S31, because the optimization problem has non-convexity in the constraint conditions , and , resulting in the problem as a whole to become a non-convex programming problem about optimization variables and , which is difficult to solve directly. In order to effectively solve the non-convex problem, the present application proposes a method based on sequential convex approximation, that is, to transform the non-convex constraint into a second-order cone constraint. Through this transformation, the original non-convex problem can be reconstructed into an optimization problem that is easy to calculate and handle.

[0088] The constraint condition is processed to convert it into a second-order cone constraint. Based on the following convex inequality,

[0089]

[0090] The constraint is rewritten as

[0091]

[0092] where the superscript denotes the index of the sequential convex approximation iteration, and denote the optimal solution obtained in the thsequential convex approximation iteration. Based on the iterative solution, the value of (i.e., the denominator item of ) at the iteration point is , and the corresponding SIR value is .

[0093] Further, the following auxiliary variables are defined:

[0094]

[0095] The second-order cone form of the constraint can be expressed as:

[0096]

[0097] The constraint formed by the above equation is a second order cone constraint, thus it is a convex constraint.

[0098] Similarly, for the constraint condition , it can be transformed into a second order cone constraint form by using the same convex inequality, and the specific result is:

[0099]

[0100] where, is the denominator term of , and and are the values calculated at the iteration point . In addition, the value of

[0101]

[0102] It is worth noting that the effectiveness of and after transformation is based on the following conditions:

[0103]

[0104] Based on the above conditions, it can be easily verified that the transformed constraint is convex.

[0105] For the constraint , it can be transformed into a second order cone form by using the following inequality

[0106]

[0107] , that is,

[0108]

[0109] Obviously, this constraint is also a convex constraint.

[0110] After completing the convex transformation of the constraints , and , the original optimization problem can be expressed in the approximate convex form in the first order sequence convex approximation iteration as:

[0111]

[0112] S32, the optimization problem is a second-order cone programming problem, which can be solved effectively by using convex optimization toolboxes such as CVX. Based on the above transformation, the iterative method for solving the original problem proposed by the application is as follows:

[0113] 1. Initialization of parameters

[0114] a feasible positive solution is randomly generated as an initial tuple , a convergence tolerance for judging whether the algorithm converges is set , the maximum number of iterations is specified , and the iteration index is initialized .

[0115] 2. Iterative solution

[0116] In the th iteration, the solution tuple of the current iteration is determined by solving the approximate convex optimization problem of the current iteration, and the total rate of the current iteration is calculated based on the solution .

[0117] 3. Update of the optimal solution

[0118] The solution of the current iteration obtained in step 2 is updated as the recorded optimal solution, that is, the operation is performed , where the symbol represents element-wise multiplication.

[0119] 4. Judgment of termination condition

[0120] It is judged whether the iteration process meets the preset termination condition. The termination condition is that the current iteration number exceeds the maximum number of iterations , or the relative change of the sum rate is less than the preset convergence tolerance , that is, it meets .

[0121] If the termination condition is met, the iteration process is ended, and step 4 is executed;

[0122] If the termination condition is not met, the iteration index is increased by one, that is , and step 2 is returned.

[0123] 5. Output of results

[0124] After the iteration process is ended, the final optimal solution recorded is output, and the optimal power allocation strategy is generated.

[0125] Step S4, in the system simulation environment, the obtained optimal power allocation strategy is compared with the benchmark algorithm in performance to verify the performance gain of the method relative to the prior art

[0126] In the simulation environment of the integrated system without cellular, performance comparison verification is carried out, and the verification includes comparing the system performance of the following two schemes:

[0127] Scheme one: the optimal power allocation strategy determined by the foregoing joint optimization method of sensing and communication is used under rate-splitting multiple access;

[0128] Scheme two: the benchmark scheme of equal power allocation strategy is used under rate-splitting multiple access;

[0129] Scheme three: the optimal power allocation strategy determined by the foregoing joint optimization method of sensing and communication is used under spatial division multiple access;

[0130] Scheme four: the optimal power allocation strategy determined by the foregoing joint optimization method of sensing and communication is used under spatial division multiple access;

[0131] By comparing the performance difference between scheme one and the other three schemes, the performance gain of the joint optimization method of the application relative to the equal power allocation strategy and the performance gain of rate-splitting multiple access relative to spatial division multiple access are verified.

[0132] The performance of the technical scheme of the application is further described below in combination with simulation experiments.

[0133] Fig. 2 The comparison results of the optimal method of the application and other benchmark schemes in the system downlink and rate performance under different numbers of access points are shown. The simulation parameters are set as , , , , , , , , , and the simulation scene is The green bar in the figure shows the rate-splitting multiple access scheme using the optimization algorithm of the present application. It can be clearly seen from the figure that the rate-splitting multiple access scheme using the optimization algorithm of the present application (shown by the green bar in the figure) achieves the highest system sum rate in all the configurations of the number of antennas. This fully proves the effectiveness of the optimization algorithm of the present application in improving the communication performance. In addition, by comparing the rate-splitting multiple access scheme using the optimization algorithm of the present application (green bar) with the spatial division multiple access scheme (red bar), it can be seen that even under the same optimization algorithm, the rate-splitting multiple access architecture itself can bring significant rate gain compared with the spatial division multiple access scheme. At the same time, the optimization algorithm of the present application is compared with the traditional "equal power allocation" benchmark scheme (green vs. blue, orange vs. pink). Under the rate-splitting multiple access architecture or the spatial division multiple access architecture, the performance of the optimization algorithm is far superior to the simple equal power allocation strategy, highlighting the superiority of the algorithm of the present application in fine management of wireless resources.

[0134] Fig. 3 The performance comparison results of the system sum rate with the change of the number of antennas configured for each access point under different benchmark schemes are shown. In the entire test range from 4 antennas to 14 antennas, the "rate-splitting multiple access + optimization method" proposed by the present application (green bar in the figure) always obtains the highest system sum rate, continuously and significantly leading all benchmark comparison schemes. By comparing the optimization algorithm of the present application with the simple benchmark strategy of "equal power allocation" (green vs. blue, orange vs. pink), it can be seen that the fine resource scheduling algorithm of the present application brings great performance gain, proving that it does not simply rely on the hardware advantage of multiple antennas, but realizes a leap in performance through intelligent optimization. Finally, with the increase of the number of antennas of each AP, the performance of all schemes is improved, but the performance advantage of the scheme of the present application (i.e. the gap between the green bar and other bars) also increases synchronously. This shows that the optimization algorithm proposed by the present application can more fully utilize the spatial degrees of freedom and beamforming gain brought by multiple antenna technology, and its superiority is more prominent in a system with stronger hardware capability.

[0135] To sum up, the performance optimization algorithm provided by the application effectively solves the complex resource allocation problem in the integrated network without a cellular network through fine joint scheduling of the wireless resources in the rate splitting multiple access system. The simulation results show that the method can greatly improve the system communication throughput while ensuring the sensing performance, and the overall performance is better than the traditional space division multiple access scheme and other benchmark strategies, which shows excellent practical application value and broad application prospect. It should be pointed out that the above content is only a specific embodiment of the application. For ordinary skilled persons in the art, various forms of improvement and optimization can be made without departing from the basic principles of the application, and these equivalent modifications and alternative schemes should also be considered within the protection scope of the application.

Claims

1. A method for optimizing a rate-splitting multiple access enhanced cell-free wireless communication system, the method comprising: determining a set of parameters for a rate-splitting multiple access enhanced cell-free wireless communication system; and optimizing the set of parameters for the rate-splitting multiple access enhanced cell-free wireless communication system. The method comprises the following steps: S1, establishing a system performance analysis architecture, considering imperfect channel state information, constructing a closed-form analytical expression of user rate and perceived signal-to-interference-and-noise ratio of a system downlink; S2, constructing a performance optimization model according to the system performance analysis architecture; The performance optimization model takes maximizing the total communication rate of the system as an optimization objective, and takes minimizing the user rate and the perceived signal-to-interference-and-noise ratio as constraint conditions; S3, transforming the constraint condition with non-convex characteristics in the performance optimization model into a second-order cone constraint, and then obtaining an optimal power allocation strategy through an iterative algorithm; S4, verifying the performance gain of the optimal power allocation strategy.

2. The method of claim 1, wherein the method further comprises: S1 comprises: S11, acquiring access point information, single-antenna user information, radar information and one perceived target information of a cell-free integrated system; A pilot transmission-based channel estimation method is used to calculate the minimum mean square error estimation and covariance matrix of the direct connection channel between the access point and the user; S12. In a rate-split multiple access enhanced cellular sensorless integrated system, all access points simultaneously transmit public data streams, private data streams, and sensing streams to users and targets, constructing the first... The user and the A signal model received by the radar; S13, a UatF-based capacity limit analysis method is used to calculate the common flow rate, private flow rate and rate of the user; S14, the perceived signal-to-interference-and-noise ratio is calculated.

3. The method of claim 1, wherein the method further comprises: In S2, the performance optimization model is represented as follows: ； wherein C1 and C2 represent relaxation constraints related to common and private flow rates, respectively, C3 and C4 specify minimum threshold values for user rates and perceived signal-to-noise ratio, respectively, and C5 constrains maximum transmit power of each AP; , , and denote relaxation variables, and denote minimum rate and perceived signal-to-interference-and-noise ratio threshold values.

4. The method of claim 3, wherein the method further comprises: In S3, based on the following convex inequality: ； The constraint condition is converted to a second-order cone constraint as follows: ； where the superscript denotes the index of the sequence convex approximation iteration, and denotes the optimal solution obtained in the th sequence convex approximation iteration; based on the optimal solution, the value of the function at the iteration point is defined as and the corresponding signal-to-interference-and-noise ratio value is . .

5. The method of claim 4, wherein the method further comprises: The following auxiliary variables are defined: ； Constraint is expressed in the form of a second-order cone as 。 6. The method of claim 4, wherein the method further comprises: For the constraints , we transform them into the second-order cone constraint form by adopting the same convex inequality as the constraints . ； wherein is the denominator term, and and are values calculated at iteration points respectively; the value of is: 。 7. The method of claim 3, wherein the method further comprises: For the constraint condition by the following inequality: ； It is converted into a second-order cone form, that is 。 8. The method of claim 1, wherein the method further comprises: In S3, a convex optimization toolbox including CVX is used for effective solution.

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