A method for optimizing a rate-splitting multiple access enhanced cell-free wireless communication system

Abstract

The application discloses a kind of rate splitting multiple access enhanced non-cellular communication system optimization methods, comprising the following steps: establishing system performance analytical framework, in considering imperfect channel state information, construct the closed-form analytical expression of user rate and perceived signal-to-interference-and-noise ratio of system downlink;According to system performance analytical framework, construct performance optimization model;Performance optimization model maximizes system total communication rate as optimization goal, minimizes user rate and perceived signal-to-interference-and-noise ratio as constraint condition;The constraint condition with non-convex characteristics in performance optimization model is transformed into second-order cone constraint, and then the optimal power allocation strategy is obtained by iterative algorithm;Verify the performance gain of optimal power allocation strategy.The application provides a joint optimization method oriented to communication performance, which can efficiently solve the resource allocation problem under the condition of imperfect channel state information, so as to maximize the system communication throughput while ensuring the basic sensing performance.

Description

Technical Field

[0001] This invention relates to the field of wireless communication, and more particularly to an optimization method for a cellular inductively coupled system with enhanced rate splitting multiple access. Background Technology

[0002] With the generational shift in mobile communication technology from fifth to sixth generation, the cellular-free distributed network paradigm has become a key technology for building future sixth-generation networks due to its superior performance in signal coverage balance and spectrum resource utilization. Against this backdrop, sensing technology, which integrates communication functions with sensing capabilities to more efficiently utilize increasingly scarce spectrum resources, has also become a cutting-edge research area.

[0003] However, the coexistence of communication and sensing functions presents a complex resource allocation trade-off problem for the system. The system must maximize communication throughput while ensuring the quality of basic sensing services. Currently, the mainstream technical approach often employs space division multiple access (SDMA) schemes, but this scheme is highly sensitive to the accuracy of channel state information. Under non-ideal conditions where channel information contains errors, the interference suppression effect between users will be significantly reduced, making it difficult for relevant optimization algorithms to simultaneously ensure the performance indicators of communication and sensing.

[0004] To address this limitation, Rate Split Access (RSA), with its more robust interference management capabilities, has emerged as a promising alternative. This technology deconstructs each user's information into a public stream that can be decoded by all users and a private stream that can be decoded by designated users, thus providing greater design freedom for the system. While applying this technology to non-cellular sensing integration scenarios opens up new possibilities for performance improvements, it also dramatically increases the complexity of resource scheduling. Network operators must collaboratively design highly correlated variables such as the power and rate splitting coefficients of the public and private streams, constituting a highly coupled non-convex programming problem.

[0005] In summary, although rate splitting multiple access (RSMA) technology shows great promise in non-cellular sensing convergence systems, a mature and efficient resource allocation mechanism that can fully unleash its performance potential is still lacking. Therefore, how to create an optimization algorithm that balances computational complexity and execution efficiency under non-ideal channel conditions, with maximizing communication throughput as the core criterion while ensuring basic sensing service quality, has become an urgent research topic in this cutting-edge field. Summary of the Invention

[0006] To address the technical challenge of lacking efficient resource allocation algorithms in existing non-cellular sensing integrated systems with rate splitting multiple access assistance, this invention provides a communication performance-oriented joint optimization method. This method can efficiently solve the resource allocation problem under imperfect channel state information conditions, thereby maximizing the system's communication throughput while ensuring basic sensing performance.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] An optimization method for a rate-split multiple access enhanced cellular sensorless system includes the following steps:

[0009] S1. Establish a system performance analysis architecture. Considering imperfect channel state information, construct closed-form analytical expressions for the user rate and perceived signal-to-interference-plus-noise ratio of the system downlink.

[0010] S2. Based on the system performance analysis architecture, construct a performance optimization model; the performance optimization model takes maximizing the total system communication rate as the optimization objective and minimizing the user rate and the perceived signal-to-interference-plus-noise ratio as constraints.

[0011] S3. Transform the non-convex constraint conditions in the performance optimization model into second-order cone constraints, and then obtain the optimal power allocation strategy through an iterative algorithm.

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

[0013] Preferably, S1 includes:

[0014] S11. Obtain access point information, single-antenna user information, radar information, and a sensed target information of the non-cellular sensing integrated system; use the pilot transmission-based channel estimation method to calculate the minimum mean square error estimate and covariance matrix of the direct channel between the access point and the user.

[0015] 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;

[0016] S13. Using the UatF-based capacity limit analysis method, calculate the user's public flow rate, private flow rate, and rate.

[0017] S14. Calculate the perceived signal-to-interference-plus-noise ratio.

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

[0019]

[0020] Where C1 and C2 represent relaxation constraints related to the public flow rate and private flow rate, respectively; C3 and C4 specify the minimum threshold values ​​for user rate and perceived signal-to-noise ratio, respectively; and C5 constrains the maximum transmit power of each AP. , , and Represents slack variables. and This represents the minimum rate and the perceived signal-to-interference-plus-noise ratio (SNR) threshold.

[0021] Preferably, in S3, the following convex inequality is used:

[0022]

[0023] Constraints The constraint is transformed into a second-order cone constraint as follows:

[0024]

[0025] Among them, superscript Indicates the index of the convex approximation iteration of the sequence. and Refers to the first The optimal solution obtained through a series of convex approximation iterations; based on the optimal solution, a definition can be made. (Right now The value of the denominator at this iteration point is And the corresponding signal-to-interference-plus-noise ratio (SIR / NOT) value .

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

[0027]

[0028] Constraints The second-order cone form is expressed as:

[0029] .

[0030] As a preferred option, regarding the constraints By adopting constraints The same convex inequality transforms it into a second-order cone constraint form:

[0031]

[0032] in, for The denominator term, and and At the iteration points respectively The value calculated at that location; The value is:

[0033] .

[0034] As a preferred option, regarding the constraints By the following inequality:

[0035]

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

[0037] .

[0038] As a preferred approach, in S3, an efficient solution is achieved using a convex optimization toolbox that includes CVX.

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

[0040] 1. The overall system performance has been significantly improved, especially in terms of communication.

[0041] The optimization algorithm proposed in this invention can effectively tap the performance potential of rate splitting multiple access (SMI) in complex interference scenarios. Through refined joint design of resources such as power allocation for public and private flows, it effectively manages interference between users while balancing the performance relationship between communication and sensing. Compared with traditional SMI optimization schemes that rely on imperfect channel state information, this invention significantly improves key communication indicators such as total system communication rate and user fairness, while ensuring that sensing performance meets preset quality of service requirements, achieving a higher level of overall system performance.

[0042] 2. It is highly robust to imperfect channel state information and is closer to practical applications.

[0043] Existing space division multiple access (SDMA) schemes suffer from drastic performance degradation when channel state information is imperfect. This invention addresses this pain point in practical applications. The algorithm fully leverages the characteristic of the "common codestream" in rate splitting multiple access (RSMA) technology, which is insensitive to channel state information errors, and through optimized design, transforms a portion of the interference into a useful component, thereby significantly reducing the system's performance dependence on the accuracy of channel state information. Therefore, this invention maintains superior performance even under non-ideal channel conditions, exhibiting stronger environmental adaptability and engineering practical value.

[0044] 3. It provides an efficient solution to complex non-convex optimization problems, representing a technological breakthrough.

[0045] The resource allocation problem arising from introducing Rate Split Multiple Access (RSMA) into a cellular-free, integrated communication system is a highly coupled non-convex optimization challenge, lacking direct and efficient solutions in existing technologies. This invention innovatively employs a sequential convex approximation framework, using ingenious mathematical transformations to decompose the previously intractable non-convex problem into a series of standard, easily solvable second-order cone programming subproblems. This not only solves the original problem's solution dilemma but also provides a systematic and feasible technical path for handling such complex resource management problems. Unlike traditional solutions that only consider ideal conditions, this invention considers non-ideal factors in real-world communication, establishing a performance analysis method based on a system performance analysis architecture and utilizing convex approximation optimization strategies. The communication technology of this invention considers the most popular Rate Split Multiple Access (RSMA) technology, a potential technology for future 6G and 7G technologies. Attached Figure Description

[0046] To more clearly illustrate the technical solution of the present invention, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the drawings described below are merely some exemplary embodiments of the present invention and are not intended to limit the entirety of the invention. For those skilled in the art, other drawings can be derived from these drawings without creative effort, and these drawings also fall within the protection scope of the present invention.

[0047] Figure 1 This is a schematic diagram of the method flow of Embodiment 1 of the present invention;

[0048] Figure 2 This is a performance comparison curve of the system and rate as a function of the total number of access points under different comparison schemes in Embodiment 1 of the present invention.

[0049] Figure 3 This is a performance comparison curve of the system and rate as the number of antennas at each access point varies under different comparison schemes in Embodiment 1 of the present invention. Detailed Implementation

[0050] To make the technical means, inventive features, objectives, and effects of the invention readily understandable, the invention is further described below with reference to specific illustrations. However, the invention is not limited to the embodiments described below.

[0051] It should be noted that the structures, proportions, sizes, etc., illustrated in the accompanying drawings of this specification are only used to complement the content disclosed in the specification for those skilled in the art to understand and read, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0052] Example 1:

[0053] This invention aims to address the technical challenges of low resource allocation efficiency and difficulty in synergistically improving communication and sensing performance in existing rate-split multiple access enhanced cellular sensorless integrated systems. To this end, embodiments of this invention provide a highly efficient iterative optimization algorithm that can jointly optimize wireless resources such as transmit precoding and power allocation of the system under practical constraints such as spatially correlated Ricean fading channels and imperfect channel state information, thereby significantly improving system communication performance while ensuring the quality of sensing services. The following will be discussed in conjunction with the appended specifications. Figures 1 to 3 The specific implementation methods of the present invention will be described in further detail so that those skilled in the art can better understand and implement the present invention.

[0054] like Figure 1 The method for optimizing a rate-split multiple access enhanced cellular sensorless system, as shown, specifically includes:

[0055] Step S1: Establish a system performance analysis architecture. Considering imperfect channel state information, derive a closed-form analytical expression for the system's downlink achievable rate and perceived signal-to-interference-plus-noise ratio.

[0056] S11. The non-cellular sensing integrated system considered in this patent includes One access point Single-antenna user There are one radar and one sensed target. The sets of indices for the access point, radar, and user are represented as follows: , and A channel estimation method based on pilot transmission is adopted, letting... Indicates the first The access point and the first Direct connection channel between users This indicates the channel. The minimum mean square error estimate, whose covariance matrix is ​​expressed as follows: and .

[0057] S12. In a rate-split multiple access enhanced cellular sensorless integrated system, all access points simultaneously transmit a common data stream to both the user and the target. Private data stream and perceptual flow , No. The user and the The signal received by the radar can be described as follows:

[0058]

[0059]

[0060] Where the superscript T represents the transpose operator, and These represent the transmit power of the data stream and the sensing stream, respectively. Power division factor, Indicates the first Private data streams for each user Indicates related The power control coefficient, Indicates related The power control coefficient, , and Then they respectively represent the relevant , and Beamforming matrix, and This represents Gaussian white noise. Furthermore, Indicates the first The first access point and the first The radars communicate with each other via a target sensing channel matrix. Indicates the first The first access point and the first Direct channel matrix between radars.

[0061] S13. Using the UatF (Use-and-then-Forget) based capacity limit analysis method, the following can be obtained: The rate expression for each user is:

[0062]

[0063] in, For common flow rate, For private flow rates, their expressions are as follows:

[0064]

[0065]

[0066] in, The length of a coherent interval The length used for data transmission within a coherent interval. This is the length used for sensing within a coherent interval. Other variables appearing in the above equation are defined as follows.

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] In the above formula, This represents the diagonal matrix operator. The matrix trace operator is represented by the superscript H, which indicates the conjugate transpose operator. and Represents the line-of-sight component of the channel. Indicates the pilot assignment status. Indicates noise power. and This indicates the channel estimation component.

[0074] S14. Similarly, a lower limit expression for the perceived signal-to-interference-plus-noise ratio can be obtained by calculation as follows:

[0075]

[0076] in,

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] Step S2: Based on this analytical architecture, construct a performance optimization model with the goal of maximizing the total communication rate of the system and the previously derived perceptual signal-to-interference-plus-noise ratio and the rate of each user as constraints.

[0083] Considering communication-centric applications, with the goal of maximizing the overall system rate and the constraints of minimum user rate and minimum perceived signal-to-interference-plus-noise ratio (SIR / NRR) for service quality, the optimization problem can be modeled as follows:

[0084]

[0085] Where C1 and C2 represent relaxation constraints related to the public flow rate and private flow rate, respectively; C3 and C4 specify the minimum threshold values ​​for user rate and perceived signal-to-noise ratio, respectively; and C5 constrains the maximum transmit power of each AP. , , and Represents slack variables. and This represents the minimum rate and the perceived signal-to-interference-plus-noise ratio (SNR) threshold.

[0086] Step S3: Considering the non-convex nature of this optimization problem, an iterative algorithm is used to solve for the optimization variables, including the power allocation factor, until the algorithm converges, obtaining a set of optimal power allocation strategies.

[0087] S31, due to optimization issues Constraints , and The non-convex nature of the problem makes the whole problem a matter of optimization variables. and Non-convex programming problems are difficult to solve directly. To achieve an efficient solution to these problems, this invention proposes a method based on sequential convex approximation, which transforms non-convex constraints into second-order cone constraints. Through this transformation, the original non-convex problem can be reconstructed into a computationally tractable optimization problem without changing its core structure.

[0088] For constraints The constraint is then transformed into a second-order cone constraint. This is based on the following convex inequality.

[0089]

[0090] Constraints Rewritten as

[0091]

[0092] Among them, superscript Indicates the index of the convex approximation iteration of the sequence. and Refers to the first The optimal solution is obtained through a series of convex approximation iterations. Based on this iterative solution, the following definition can be made: (Right now The value of the denominator at this iteration point is And the corresponding signal-to-interference-plus-noise ratio (SIR / NOT) value .

[0093] Furthermore, define the following auxiliary variables:

[0094]

[0095] Then constraints The second-order cone form can be expressed as:

[0096]

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

[0098] Similarly, for constraints By applying the same convex inequality, it is transformed into a second-order cone constraint form, and the specific result is as follows:

[0099]

[0100] in, for The denominator term, and and At the iteration points respectively The value calculated therein. Furthermore, The value is

[0101]

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

[0103]

[0104] Based on the above conditions, the transformed constraints can be easily verified. convexity.

[0105] For constraints Using the following inequality

[0106]

[0107] It can be transformed into a second-order cone form, that is

[0108]

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

[0110] After completing the constraints , and After the convex transformation, the original optimization problem In the The approximate convex form in the subsequence convex approximation iteration can be expressed as:

[0111]

[0112] S32, Optimization Problem This is a second-order cone programming problem, which can be efficiently solved using convex optimization toolboxes such as CVX. Based on the above transformation, this invention proposes a method for solving the original problem. The iterative method, with its specific steps, is as follows:

[0113] 1. Initialize parameters

[0114] Randomly generate a feasible positive solution as the initial tuple. Set a convergence tolerance to determine whether the algorithm has converged. Specify the maximum number of iterations and initialize the iteration index. .

[0115] 2. Iterative solution

[0116] In the In the next iteration, the approximate convex optimization problem of the current iteration is solved. Determine the solution set for the current iteration. And calculate the current total rate based on the solution. .

[0117] 3. Update the optimal solution

[0118] Update the current iterative solution obtained in step 2 with the recorded optimal solution, i.e., perform the operation. , , where the symbol This represents the element-wise product.

[0119] 4. Determine the termination condition

[0120] Determine if the iteration process meets the preset termination condition. The termination condition is: the current iteration number. Exceeding the maximum number of iterations , or and rate The relative change is less than the preset convergence tolerance. That is, satisfying .

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

[0122] If the termination condition is not met, the iteration index is incremented by one, i.e. Then return to step 2.

[0123] 5. Output Results

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

[0125] Step S4: In the system simulation environment, compare the performance of the obtained optimal power allocation strategy with the benchmark algorithm to verify the performance gain of this method relative to existing technologies.

[0126] In a simulation environment of a non-cellular sensing integrated system, performance comparison and verification are conducted, including comparing the system performance of the following two schemes:

[0127] Option 1: Under rate splitting multiple access, adopt the optimal power allocation strategy determined by the aforementioned inductive joint optimization method;

[0128] Option 2: A baseline scheme using an equal power allocation strategy under rate splitting multiple access;

[0129] Option 3: Under space division multiple access, adopt the optimal power allocation strategy determined by the aforementioned inductive joint optimization method;

[0130] Option 4: Under space division multiple access, adopt the optimal power allocation strategy determined by the aforementioned inductive joint optimization method;

[0131] By comparing the performance differences between Scheme 1 and the other three schemes, the performance gain of the joint optimization method of the present invention relative to the equal power allocation strategy and the performance gain of rate split multiple access relative to space division multiple access are verified.

[0132] The performance of the technical solution of the present invention will be further explained below with reference to simulation experiments.

[0133] Figure 2 The simulation results demonstrate the comparison of the proposed optimization method with other benchmark solutions in terms of downlink and rate performance under different access point configurations. Simulation parameters are set as follows: , , , , , , , , The simulation scenario is The figure clearly shows that, under all access point configurations, the rate splitting multiple access scheme (shown by the green bars) using the optimization algorithm of this invention achieved the highest system performance and rate, fully demonstrating the effectiveness of the proposed optimization algorithm in improving communication performance. Furthermore, by comparing the rate splitting multiple access scheme (green bars) with the space-division multiple access scheme (red bars) using the optimization algorithm of this invention, it can be seen that even under the same optimization algorithm, the rate splitting multiple access architecture itself can bring a significant rate gain compared to the space-division multiple access scheme. Simultaneously, comparing the optimization algorithm of this invention with the traditional "equal power allocation" benchmark scheme (green vs. blue, orange vs. pink), the performance of the proposed optimization algorithm far exceeds that of the simple equal power allocation strategy under both rate splitting multiple access and space-division multiple access architectures, highlighting the superiority of the algorithm in refined management of wireless resources.

[0134] Figure 3 The performance comparison results of system and rate vary with the number of antennas configured at each access point under different benchmark schemes are presented. Across the entire test range from 4 to 14 antennas, the proposed "rate splitting multiple access + optimization method" (green bars in the figure) consistently achieved the highest system and rate, continuously and significantly outperforming all benchmark comparison schemes. Comparing the optimization algorithm of this invention with the simple benchmark strategy of "equal power allocation" (green vs. blue, orange vs. pink), it can be seen that the refined resource scheduling algorithm of this invention brings a huge performance gain, proving that it does not simply rely on the hardware advantages of multiple antennas, but achieves a performance leap through intelligent optimization. Finally, as the number of antennas per AP increases, the performance of all schemes improves, but the performance advantage of the proposed scheme (i.e., the gap between the green bars and other bars) also increases synchronously. This indicates that the optimization algorithm proposed in this invention can more fully utilize the spatial degrees of freedom and beamforming gain brought by multi-antenna technology, and its superiority is more prominent in systems with stronger hardware capabilities.

[0135] In summary, the performance optimization algorithm proposed in this invention effectively solves the complex resource allocation problem in non-cellular sensing integrated networks by performing fine-grained joint scheduling of radio resources in rate-division multiple access systems. Simulation results consistently show that the method of this invention can significantly improve system communication throughput while ensuring sensing performance. Its overall performance is superior to traditional space division multiple access schemes and other benchmark strategies, demonstrating excellent practical application value and broad application prospects. It should be noted that the above content is only a specific embodiment of this invention. For those skilled in the art, various improvements and optimizations can be made without departing from the basic principles of this invention, and these equivalent modifications and alternatives should also be considered within the protection scope of this invention.

Claims

1. An optimization method for a rate-split multiple access enhanced cellular sensorless system, characterized in that, Includes the following steps: S1. Establish a system performance analysis architecture. Considering imperfect channel state information, construct closed-form analytical expressions for the user rate and perceived signal-to-interference-plus-noise ratio of the system downlink. S2. Based on the system performance analysis architecture, construct a performance optimization model; The performance optimization model aims to maximize the total communication rate of the system, while minimizing the user rate and the perceived signal-to-interference-plus-noise ratio. The performance optimization model is represented as follows: ； Where C1 and C2 represent relaxation constraints related to the public flow rate and private flow rate, respectively; C3 and C4 specify the minimum threshold values ​​for user rate and perceived signal-to-noise ratio, respectively; and C5 constrains the maximum transmit power of each AP. , , and Represents slack variables. and This represents the minimum rate and the perceived signal-to-interference-plus-noise ratio (SINNR) threshold. S3. Transform the non-convex constraint conditions in the performance optimization model into second-order cone constraints, and then obtain the optimal power allocation strategy through an iterative algorithm. S4. Verify the performance gain of the optimal power allocation strategy.

2. The optimization method for a rate-split multiple access enhanced cellular sensorless system according to claim 1, characterized in that, S1 includes: S11. Obtain access point information, single antenna user information, radar information, and a sensed target information from the non-cellular sensing integrated system. A channel estimation method based on pilot transmission is adopted to calculate the minimum mean square error estimate and covariance matrix of the direct 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. Using the UatF-based capacity limit analysis method, calculate the user's public flow rate, private flow rate, and rate. S14. Calculate the perceived signal-to-interference-plus-noise ratio.

3. The optimization method for a rate-split multiple access enhanced cellular sensorless system according to claim 2, characterized in that, In S3, based on the following convex inequality: ； Constraints The constraint is transformed into a second-order cone constraint as follows: ； Among them, superscript Indicates the index of the convex approximation iteration of the sequence. and Refers to the first The optimal solution obtained through a series of convex approximation iterations; based on the optimal solution, define... The value at this iteration point is And the corresponding signal-to-interference-plus-noise ratio (SIR / NOT) value .

4. The optimization method for a rate-split multiple access enhanced cellular sensorless system according to claim 3, characterized in that, Define the following auxiliary variables: ； Constraints The second-order cone form is expressed as: 。 5. The optimization method for a rate-split multiple access enhanced cellular sensorless system according to claim 4, characterized in that, For constraints By adopting constraints The same convex inequality transforms it into a second-order cone constraint form: ； in, for The denominator term, and and At the iteration points respectively The value calculated at that location; The value is: 。 6. The optimization method for a rate-split multiple access enhanced cellular sensorless system according to claim 2, characterized in that, For constraints By the following inequality: ； Transform it into a second-order cone form, that is 。 7. The optimization method for a rate-split multiple access enhanced cellular sensorless system according to claim 1, characterized in that, In S3, efficient solutions are achieved using a convex optimization toolbox that includes CVX.

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