A capacity optimization method for composite frame short packet cooperative synesthesia systems based on RIS

Abstract

This invention discloses a capacity optimization method for a composite frame short packet cooperative sensing system based on RIS. In a multi-base station cooperative short packet sensing system, an optimization function is established with the objective of maximizing the achievable capacity of communication users and detection targets. A one-dimensional search method is used to obtain the allocation scheme of pilot block length and data block length in the composite frame structure. A first-order Taylor estimation method, a logarithmic transformation method, and a quadratic transformation method are used to obtain a joint design scheme of active and passive beamforming for the sensing base station and RIS. After iterative optimization, the optimal solution of block length allocation and active and passive beamforming scheme is obtained, thereby maximizing the total achievable capacity of the system. Based on the designed composite frame structure, and with the assistance of RIS, the proposed joint design scheme of block length allocation and active and passive beamforming can provide ultra-reliable low-latency communication and sensing services for 6G vertical application scenarios, effectively improving the overall communication performance of the short packet cooperative sensing system.

Description

Technical Field

[0001] This invention belongs to the field of communication and sensing integration technology, specifically relating to a capacity optimization method for a composite frame short packet cooperative sensing system based on RIS. Background Technology

[0002] Sensing and sensing integration is considered one of the potential key technologies for sixth-generation mobile communication systems (6G). It integrates communication and sensing functions simultaneously through the sharing of hardware and software resources, endowing existing cellular systems with inherent sensing capabilities. Existing large-scale infrastructure, such as base stations, will be able to provide ubiquitous sensing services. New vertical application scenarios such as 6G smart homes and smart factories place higher demands on sensing accuracy and coverage. A single sensing and sensing node, due to its limited sensing capabilities and available resources, struggles to meet these high-quality sensing requirements. A multi-base station collaborative sensing and sensing mechanism can utilize existing communication infrastructure and signals for simultaneous communication and sensing. Through the collaboration of multiple sensing base stations, it can effectively overcome the limitations of a single sensing and sensing base station in terms of sensing capabilities and perspective.

[0003] In practical systems, the coverage of base station communication signals is often limited, and when using communication signals for sensing, it often fails to meet the required sensing coverage. Reconfigurable Intelligent Surfaces (RIS), as another potential key technology for 6G, can create additional line-of-sight links, assisting sensing base stations in expanding their sensing and communication coverage. Furthermore, to meet the demands of new 6G vertical application scenarios for ultra-reliable, low-latency connections, 6G sensing base stations will provide communication and sensing services through short packet transmission and finite block length coding.

[0004] Currently, RIS-based sensing integration methods mainly focus on long packet transmission, with sensing base stations using infinite block length coding to provide synchronous communication and sensing services. For emerging 6G vertical applications, researching multi-site collaborative sensing integration methods under short packet transmission is crucial. Existing, limited sensing integration methods based on short packet transmission mostly consider fixed sensing pilot block lengths and data transmission block lengths. This fixed sensing block length allocation strategy struggles to meet the dynamic and complex sensing performance requirements and trade-offs in emerging 6G vertical application scenarios. Furthermore, existing RIS-based sensing integration methods mostly consider single-site sensing scenarios; research on RIS-assisted multi-site collaborative sensing integration is still in its early stages. In future practical 6G scenarios, various types of heterogeneous base stations (such as sensing duplex base stations and pure communication base stations) will coexist to provide heterogeneous collaborative communication and sensing functions, meeting the demands of 6G vertical application scenarios for higher-quality communication and sensing performance. Summary of the Invention

[0005] To address the aforementioned issues, this invention discloses a capacity optimization method for a composite frame short packet cooperative sensing system based on RIS. With the assistance of RIS, multiple sensing base stations simultaneously provide communication and sensing services to multiple communication users and a single sensing target outside the coverage area within the integrated sensing cycle. Addressing the dynamic and complex sensing performance trade-offs in emerging 6G vertical application scenarios, a flexible composite frame structure is proposed to dynamically adjust the pilot block length and data block length used for communication and sensing. Based on the cooperative sensing results of multiple sensing base stations, this assists a pure communication base station in providing communication services to the detection target within a pure communication cycle. Through the joint optimization of a finite block length allocation strategy for sensing pilots and data, a beamforming strategy for the transmitting signals of sensing base stations, and the dependency matrix of RIS, a significant improvement in the total achievable capacity of communication users and detection targets in the short packet transmission cooperative sensing integrated system is achieved.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] A capacity optimization method for a composite frame short packet cooperative synesthetic system based on RIS, specifically including the following steps:

[0008] Step S1: Design the short packet transmission composite frame structure for each inductive base station;

[0009] Step S2: Determine the local detection probability and local false alarm probability of each sensing base station;

[0010] Step S3: Confirm The probability of cooperative detection and the probability of cooperative false alarm among integrated sensing base stations;

[0011] Step S4: Determine the integration cycle of induction and communication The total reachable capacity of each communication user and the reachable capacity of the detected target within a pure communication cycle;

[0012] Step S5: Determine the transmission cycle throughout the entire transmission period. Total achievable system capacity for communication users and detection targets;

[0013] Step S6: Based on the pilot block length in the composite frame structure Related to data block length allocation strategies and beamforming of the transmitting base station And the phase shift matrix of RIS Construct an optimization objective function with the goal of maximizing the total reachable capacity of the system. ;

[0014] Step S7: Based on the optimization objective function Using a convex optimization algorithm, the pilot block length allocation strategy is optimized. Optimize;

[0015] Step S8: Based on the optimization objective function And the optimized block length allocation strategy The transmitting beamforming of the inductive base station is achieved using the first-order Taylor estimation method, the logarithmic transform method, and the quadratic transformation method. and the phase shift matrix of RIS Perform joint optimization;

[0016] Step S9: Repeat steps S7 to S8 until the preset number of iterations is reached to obtain the block length allocation strategy that maximizes the system's capacity. Beamforming for transmission from a sensor base station And the phase shift matrix of RIS .

[0017] The beneficial effects of this invention are as follows:

[0018] The present invention provides a capacity optimization method for short-packet cooperative sensing systems based on RIS and composite frame structures. This method considers the demand for ultra-reliable low-latency connections in new 6G vertical application scenarios. Under packet transmission conditions, it meets the dynamic and complex sensing performance requirements in new 6G vertical application scenarios by designing a flexible composite frame structure. To address the limitations of a single sensing base station in terms of coverage and sensing capabilities, a RIS-assisted multi-base station cooperative sensing integration method is designed. This method jointly optimizes the allocation strategy of pilot block length and data block length in the composite frame structure, the transmit beamforming of the sensing base station, and the phase shift matrix of the RIS, dynamically coordinating sensing performance and maximizing the total achievable capacity of the cooperative sensing integration system. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the process of the present invention.

[0020] Figure 2 This is a system model diagram of the present invention.

[0021] Figure 3 This is a diagram of the short packet composite frame structure of the present invention. Detailed Implementation

[0022] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0023] This invention discloses a capacity optimization method for a composite frame short packet cooperative sensing system based on RIS (Reactive Information Framework). In a system where multiple base stations utilize short packet communication for cooperative communication and sensing, an optimization function is constructed with the goal of maximizing the total reachable capacity of multiple communication users and one detection target. A one-dimensional search method is used to derive the allocation scheme of the pilot block length for sensing and the data block length for communication in the composite sensing frame structure. First-order Taylor estimation, logarithmic transformation, and quadratic transform methods are used to derive the transmit beamforming scheme and RIS phase shift matrix design scheme for the sensing base station. After iterative optimization, the optimal solutions for the pilot and data block length allocation scheme, the transmit beamforming design scheme, and the phase shift matrix design scheme are obtained, thereby maximizing the total reachable capacity of communication users and the detection target in the short packet cooperative sensing system. With the assistance of RIS and multi-base station collaborative sensing results, the present invention designs a composite frame structure and proposes a joint design scheme of flexible allocation of pilot and data block lengths, beamforming of the sensing base station transmission, and RIS phase shift matrix. This scheme can provide ultra-reliable and low-latency sensing services for communication users and sensing targets in 6G vertical application scenarios that perform short packet transmission, and effectively improve the overall communication performance of the collaborative sensing integrated system.

[0024] like Figure 1 The aforementioned method for capacity optimization of a composite frame short packet cooperative synesthetic system based on RIS includes the following specific steps:

[0025] Step S1: The short packet transmission composite frame structure design of the short packet cooperative sensing system is as follows: The block length of the entire short packet transmission cycle is... This includes the block length of the short packet transmission sensing integrated cycle. And the block length of the pure communication cycle that follows short packet transmission Among them, the block length of the synesthetic integrated cycle. In the text, the block length of each subframe is... The frame structure of the first subframe includes a pilot block length for sensing. And what followed was used to The length of the data block provided to each user for communication services , where each user The data block length is The block length of a short packet transmission's pure communication cycle. In the text, the length of the data block used for communication is... The block length of the synesthetic integration cycle. In the frame structure of all subframes except the first, there is no pilot section for sensing; only the data section for communication is included, with a data block length of [length missing]. .

[0026] Step S2: Any sensing base station The obtained local detection probability and local false alarm probability They are

[0027] (1);

[0028] (2);

[0029] in, Indicates the sampling frequency. Indicates the detection threshold. Indicates a sensor base station The power coefficient assigned to the pilot signal sequence used for sensing. Indicates a sensor base station and the Channels between RIS Indicates the first The channel between the RIS and the detection target Indicates a sensor base station Receiver noise, It is a complementary cumulative distribution function, and the superscript H indicates the conjugate transpose operation.

[0030] Step S3: Cooperative detection probability of individual sensing base stations With the probability of false alarms in collaboration They are

[0031] (3);

[0032] (4);

[0033] Step S4: Within the integration cycle of sensory communication The sum of the achievable capacity of each communication user And the achievable capacity of target detection within a pure communication cycle. They are

[0034] (5);

[0035] in, Indicates communication user Signal-to-interference-to-noise ratio, RIS With communication users The channel between, yes The Columns represent sensor base stations To communication users Transmit beamforming, yes The Columns represent sensor base stations To communication users Transmit beamforming; Indicates communication user The received noise power; Indicates a sensor base station The power coefficient assigned to the data signal sequence used for communication. Indicates the probability of error. Indicates channel dispersion;

[0036] (6);

[0037] in, This represents the probability that the target actually exists. This represents the signal-to-noise ratio of the detected target. This represents the channel between the pure communication base station and the detection target. This indicates the transmit beamforming of a pure communication base station. This indicates the received noise power of the detected target.

[0038] Step S5: During the entire short packet transmission cycle, The total reachable system capacity for communication users and detection targets is:

[0039] (7);

[0040] Step S6: Construct an optimization function with the objective of maximizing the total reachable capacity of the system. for:

[0041] The constraints are (8);

[0042] in, This indicates the maximum transmission power of each sensing base station. This represents the minimum threshold for the probability of collaborative detection. RIS The Phase shift of each element.

[0043] Step S7: Beamforming the transmission beam of the given sensing base station And the phase shift matrix of RIS Under the premise that, in the first In this iteration, a convex optimization algorithm is used to solve for the constraint-satisfied solution. and Pilot block length allocation strategy .

[0044] Step S8: Solve for the transmit beamforming of the inductive base station. And the phase shift matrix of RIS include:

[0045] Step S8.1: Given a block length allocation strategy Under the premise of this, the first-order Taylor estimation method, logarithmic transformation method, and quadratic transformation method are used to construct the transmit beamforming of the sensing base station. And the phase shift matrix of RIS Joint optimization function :

[0046] (9);

[0047] The constraints are

[0048] ;

[0049] in, Represents auxiliary variables. Represents an auxiliary vector.

[0050] ,

[0051] ,

[0052] ,

[0053] Indicates the first The result obtained in the next iteration The value,

[0054] ,

[0055] ,

[0056] ;

[0057] ,

[0058] ,

[0059] Indicates the first The result obtained in the next iteration The value of .

[0060] Step S8.2: Based on the objective function In the In the next iteration, update according to the following formula :

[0061] (10);

[0062] Step S8.3: Update by solving the following subproblems. The constraints are ,

[0063] (11);

[0064] in, ;

[0065] .

[0066] Step S8.4: According to Using the phase rotation method, the following subproblems are solved to update the process. The constraints are and ,

[0067] (12);

[0068] in, ;

[0069] ,

[0070] .

[0071] Step S9: Repeat steps S7 to S8 until the preset number of iterations is reached. Stop iterating when the system reaches its maximum capacity to obtain the block length allocation strategy that maximizes the total reachable capacity of the system. Transmit beamforming strategy Phase shifting strategy of RIS .

[0072] It should be noted that the above content merely illustrates the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. For those skilled in the art, various improvements and modifications can be made without departing from the principle of the present invention, and all such improvements and modifications fall within the scope of protection of the claims of the present invention.

Claims

1. A capacity optimization method for a RIS-based composite frame short packet cooperative synesthetic system, characterized in that, Includes the following steps: Step S1: Design the short packet transmission composite frame structure for each sensing base station, as follows: The block length of the entire short packet transmission cycle is This includes the block length of the short packet transmission sensing integrated cycle. And the block length of the pure communication cycle that follows short packet transmission Among them, the block length of the synesthetic integration cycle In the text, the block length of each subframe is... The frame structure of the first subframe includes a pilot block length for sensing. And what followed was used to The length of the data block provided to each user for communication services , where each user The data block length is The block length of a short packet transmission's pure communication cycle. In the text, the length of the data block used for communication is... ; Block length of the synesthetic integration cycle In the frame structure of all subframes except the first, there is no pilot section for sensing; only the data section for communication is included, with a data block length of [length missing]. ; Step S2: Determine the local detection probability and local false alarm probability of each sensing base station; Step S3: Confirm The probability of cooperative detection and the probability of cooperative false alarm among integrated sensing base stations; Step S4: Determine the integration cycle of induction and sensory integration The total reachable capacity of each communication user and the reachable capacity of the detected target within a pure communication cycle; Step S5: Determine the transmission cycle throughout the entire transmission period. Total achievable system capacity for communication users and detection targets; Step S6: Based on the pilot block length in the composite frame structure Related to data block length allocation strategies and beamforming of the transmitting base station And the phase shift matrix of RIS Construct an optimization objective function with the goal of maximizing the total reachable capacity of the system. ; Step S7: Based on the optimization objective function Using a convex optimization algorithm, the pilot block length allocation strategy is optimized. Optimize; Step S8: Based on the optimization objective function And the optimized block length allocation strategy The transmitting beamforming of the inductive base station is achieved using the first-order Taylor estimation method, the logarithmic transform method, and the quadratic transformation method. and the phase shift matrix of RIS Perform joint optimization; Step S9: Repeat steps S7 to S8 until the preset number of iterations is reached to obtain the block length allocation strategy that maximizes the system's capacity. Beamforming for transmission from a sensor base station And the reflection and refraction coefficient matrix of RIS .

2. The capacity optimization method for a RIS-based composite frame short packet cooperative synesthesia system according to claim 1, characterized in that, In step S2, any sensing base station The obtained local detection probability and local false alarm probability They are （1）； （2）； in, Indicates the sampling frequency. Indicates the detection threshold. Indicates a sensor base station The power coefficient assigned to the pilot signal sequence used for sensing. Indicates a sensor base station and the Channels between RIS Indicates the first The channel between the RIS and the detection target Indicates a sensor base station Receiver noise, It is a complementary cumulative distribution function.

3. The capacity optimization method for a RIS-based composite frame short packet cooperative synesthesia system according to claim 2, characterized in that, In step S3, Collaborative detection probability of a single integrated sensing base station With the probability of false alarms in collaboration They are （3）； （4）。 4. The capacity optimization method for a RIS-based composite frame short packet cooperative synesthesia system according to claim 3, characterized in that, In step S4, during the inductive integration cycle The sum of the achievable capacity of each communication user And the achievable capacity of target detection within a pure communication cycle. They are （5）； in, Indicates communication user Signal-to-interference-to-noise ratio, RIS With communication users The channel between, yes The Columns represent the communication base stations. To communication users Transmit beamforming, yes The Columns represent the communication base stations. To communication users Transmit beamforming, Indicates communication user The received noise power; Indicates a sensor base station The power coefficient assigned to the data signal sequence used for communication. Indicates the probability of error. Indicates channel dispersion; （6）； in, This represents the probability that the target actually exists. This represents the signal-to-noise ratio of the detected target. This represents the channel between the pure communication base station and the detection target. This indicates the transmit beamforming of a pure communication base station. This indicates the received noise power of the detected target.

5. The capacity optimization method for a RIS-based composite frame short packet cooperative synesthesia system according to claim 4, characterized in that, In step S5, throughout the entire short packet transmission period, The total reachable system capacity for communication users and detection targets is: （7）。 6. The capacity optimization method for a RIS-based composite frame short packet cooperative synesthesia system according to claim 5, characterized in that, In step S6, an optimization function is constructed with the objective of maximizing the total reachable capacity of the system. for: The constraints are (8); in, This indicates the maximum transmission power of each sensing base station. This represents the minimum threshold for the probability of collaborative detection. RIS The Phase shift of each element.

7. The capacity optimization method for a RIS-based composite frame short packet cooperative synesthesia system according to claim 6, characterized in that, Step S7: Beamforming the transmission beam of the given sensing base station. And the phase shift matrix of RIS Under the premise that, in the first In this iteration, a convex optimization algorithm is used to solve for the constraint-satisfied solution. and Pilot block length allocation strategy .

8. The capacity optimization method for a RIS-based composite frame short packet cooperative synesthesia system according to claim 7, characterized in that, Step S8: Solve for the transmit beamforming of the inductive base station. And the phase shift matrix of RIS ,include: Step S8.1: Given a block length allocation strategy Under the premise of this, the first-order Taylor estimation method, logarithmic transformation method, and quadratic transformation method are used to construct the transmit beamforming of the sensing base station. And the phase shift matrix of RIS Joint optimization function : （9）； The constraints are ; ； in, Represents auxiliary variables. Represents an auxiliary vector. ， ， ， Indicates the first The result obtained in the next iteration The value, ， ， ； ， ； Indicates the first The result obtained in the next iteration The value; Step S8.2: Based on the objective function In the In the next iteration, update according to the following formula : （10）； Step S8.3: Update by solving the following subproblems. The constraints are , （11）； in, ; ； Step S8.4: According to Using the phase rotation method, the following subproblems are solved to update the process. The constraints are and , （12）； in, ; ； 。 9. A capacity optimization method for a RIS-based composite frame short packet cooperative synesthesia system according to claim 8, characterized in that, Step S9 repeats steps S7 to S8 repeatedly until the preset number of iterations is reached. Stop iterating when the system reaches its maximum capacity to obtain the block length allocation strategy that maximizes the total reachable capacity of the system. Transmit beamforming strategy Phase shifting strategy of RIS .

Images