Method for optimizing beam forming and resource allocation of general inductance calculation network based on full-duplex RSMA (Received Signal Moving Average)

Abstract

The invention belongs to the technical field of wireless communication, and particularly relates to a method for optimizing beam forming and resource allocation of a general inductance computing network based on full duplex RSMA, which comprises the following steps of: establishing an RSMA-ISCC system model integrally covering uplink and downlink based on an FD-RSMA-ISCC system framework; deriving expressions of uplink and downlink reachable rates of user equipment and sensing and computing performance of a base station in the system; the overall efficiency index of the RSMA-ISCC system model is described with a weighted minimum user rate, and a maximized optimization problem with communication, perception and calculation performance constraints is formed; and an inner and outer double-layer iterative algorithm based on SROCR, LCMV and SBOP methods is provided for solving, and a transceiver beam forming design, power distribution and DL-UE rate distribution scheme is obtained. The method is excellent in convergence speed and beam direction control capability, can effectively suppress interference among users, and remarkably improves the service quality of the users in the system.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a beamforming and resource allocation optimization method for a telecomputing network based on full-duplex RSMA. Background Art

[0002] As 6G networks drive the development of high-demand applications such as intelligent manufacturing, autonomous driving, and smart grids, Integrated Sensing, Communication, and Computation (ISCC) has become a key technology for achieving multi-task collaboration. Traditional, separate architectures, with independent deployment of each functional module, result in low resource utilization and limited collaboration, making it difficult to meet the global resource optimization needs of emerging applications.

[0003] Existing Integrated Sensing and Communication (ISAC) technology shares sensing and communication hardware resources through downlink (DL) waveforms, but IoT computing tasks still rely on uplink (UL) transmission from user equipment (UE). In full-duplex (FD) systems, DL-centric ISAC tasks and UL-centric computing tasks generate bidirectional co-channel interference. The time-frequency overlap of downlink sensing beams and uplink computing data streams leads to signal aliasing. Furthermore, multi-user competition for uplink resources makes traditional multiple access technologies difficult to adapt to dynamically changing network environments.

[0004] Therefore, there is an urgent need for a full-duplex RSMA-based inter-sensory computing network beamforming and resource allocation optimization method, which can jointly optimize the transceiver beamforming and power allocation to collaboratively suppress inter-function interference and inter-user interference, thereby improving the overall performance of the ISCC system. Summary of the Invention

[0005] The purpose of the present invention is to provide a full-duplex RSMA-based synaesthesia network beamforming and resource allocation optimization method, comprising the following steps:

[0006] Step S1: Establish an RSMA-ISCC system model covering uplink and downlink based on the FD-RSMA-ISCC system framework;

[0007] Step S2: Based on the RSMA-ISCC system model in step S1, derive expressions for uplink and downlink achievable rates of user equipment in the system and sensing and computing performance of base stations;

[0008] Step S3: Based on the achievable downlink and downlink rates of the user equipment, an overall performance indicator describing the RSMA-ISCC system model using a weighted minimum user rate is proposed, and a maximization optimization problem with communication, perception, and computing performance constraints is formulated based on the overall performance indicator;

[0009] Step S4: For the maximization optimization problem, an inner-outer double-layer iterative algorithm based on SROCR, LCMV and SBOP methods is proposed and applied to obtain the transceiver beamforming design, power allocation and DL-UE rate allocation scheme.

[0010] The FD-RSMA-ISCC system framework includes: a base station part, a user equipment part and a scatterer part;

[0011] The base station is responsible for the three functions of perception, communication, and computing. The base station transmits the downlink ISAC signal through the transmitting antenna, extracts the echo of the target perception signal through the receiving antenna, and provides edge computing services for uplink users.

[0012] The user equipment portion includes: a UL-UE and a DL-UE; the UL-UE divides the message into a private part and a public part according to the uplink RSMA principle, encodes them into independent information streams, and then allocates power and transmits them; the DL-UE sequentially decodes the public stream and the corresponding private stream from the downlink ISAC signal transmitted by the base station according to the downlink RSMA principle to obtain complete downlink information;

[0013] The scatterer part includes: a sensing target object and a clutter scatterer. The sensing target object has a predetermined radar scattering cross-section characteristic, and the clutter scatterer includes all non-target reflectors within the base station downlink beam coverage area. During the sensing signal propagation process, the sensing target object reflected signal carries effective sensing information, while the multipath reflected signal generated by the clutter scatterer will cause co-frequency interference to the base station.

[0014] The RSMA-ISCC system model that establishes an overall uplink and downlink coverage includes:

[0015] A base station is set up in the system, and the base station is equipped with N t transmit antennas and N r The antenna array consists of receiving antennas with a spacing of half a wavelength. The user equipment part includes K single-antenna DL-UEs and U single-antenna UL-UEs. The scatterer part includes M STs and L CLs. Let B be the system bandwidth, and define the index set K = {1, ..., K}, U = {1, ..., U}, M = {1, ..., M} and L = {1, ..., L} corresponding to DL-UE, UL-UE, ST and CL respectively. The channel from the base station to the downlink user equipment and the channel from the uplink user equipment to the base station are expressed as and

[0016] The ST is the sensing target; the CL is the clutter;

[0017] The echo paths from ST and CL are expressed as: where β i and θ i , for all i∈{K,U,M,L}, they represent the corresponding large-scale path loss and angle parameters respectively, where represents the antenna array response vector;

[0018] According to the uplink RSMA protocol, the uth UL-UE divides its uplink transmission information into two information streams s by information splitting and encoding. u,1 and s u,2 , and then allocate transmission power P to the two information flows respectively u,1 and P u,2 , then the transmitted signal of the u-th UL-UE is:

[0019]

[0020] At the base station, a single-layer downlink RSMA protocol is used, and each DL-UE message is split into a private part and a public part; all public parts are merged into a public stream through a combiner. c , the private parts are encoded separately as streams {x1,…,x K},by The index set of all DL-UE information flows; DL-UE information flows also need to be associated with the radar sequence used by the base station for perception The covariance matrix of the radar sequence is With general rank; the superimposed transmitted signal is expressed as follows:

[0021]

[0022] Where, and v c denote the precoding vectors corresponding to the private stream and the public stream respectively;

[0023] The covariance matrix of the transmitted signal is:

[0024]

[0025] Based on the above transmitted signal, the received signal of the kth DL-UE is expressed as:

[0026]

[0027] Where h u,k represents the interference channel between UL-UE and DL-UE, Indicates variance Additive white Gaussian noise; s u UL-UE uplink split information flow; Indicates the channel from the base station to the downlink user equipment;

[0028] The received signal of the base station is expressed as:

[0029]

[0030] Where H SI represents self-interference, and its elements are expressed as β SI is the power of the residual self-interference channel, z is the covariance Additive white Gaussian noise. Represents the echo paths from ST and CL.

[0031] The expressions for deriving the uplink and downlink achievable rates of the user equipment and the base station perception and computing performance in the system include:

[0032] For each DL-UE, first decode the received common stream x c , at this time, the private stream is regarded as interference; after the public stream is successfully eliminated through SIC technology, the private stream x is decoded. k ; The kth DL-UE decodes the common stream x c and private stream x k The SINRs are expressed as:

[0033]

[0034] Where, h u,k represents the interference channel between UL-UE and DL-UE; Indicates the channel from the base station to the downlink user equipment; and v c The public rate and private rate achievable by the precoding vectors corresponding to the private and public streams, respectively, are expressed as:

[0035] R k,c =Blog2(1+γ k,c ) (8)

[0036] R k,k =Blog2(1+γ k,k ) (9)

[0037] Where B refers to the base station bandwidth, B = G CL+H SI To ensure that each downlink user equipment can decode the common stream, the common rate R c Affected by all R k,c The constraint of the minimum value in R c =min k R k,c ; The total rate of the kth DL-UE is R k =R k,k +r k , where r k is the common rate portion allocated to the kth DL-UE;

[0038] At the base station receiving end, a receive beamformer w is applied to the base station receiving signal. s , the SINR of the perceived signal is expressed as follows:

[0039]

[0040] Where B = G CL +H SI ;H SI Represents self-interference, and the sensing function performance of the base station is defined as R s =Blog2(1+γ s );

[0041] After extracting the sensed echo from the received signal, the base station decodes the UL-UE transmission signal according to the uplink RSMA decoding principle; the UL-UE data stream is decoded in sequence according to the predetermined decoding order; Indicates s u,j The set of signal streams that generate interference; by applying the beamformer w u,j To suppress inter-functional interference, decode s u,j The SINR is expressed as:

[0042]

[0043] Where, represents the echo path from ST and CL, H SI represents self-interference), the uplink offloading achievable rate of the u-th UL-UE is expressed as: Where R u,j =Blog2(1+γ u,j ).

[0044] The method proposes to describe the overall performance index of the RSMA-ISCC system model using a weighted minimum user rate, and to formulate a maximization optimization problem with communication, perception, and computing performance constraints based on the overall performance index, including:

[0045] In the FD-RSMA-ISCC system, limited resources lead to complex performance trade-offs when simultaneously supporting perception, communication, and computing functions. Considering user fairness, WSMR is introduced to balance the minimum rate of all user devices in the system under perception and computing tasks, which is defined as:

[0046] WSMR=w DL min k∈k {R k}+w UL min u∈u {R u} (12)

[0047] Where w DL and w UL is the predefined rate weight; R k =R k,k +r k represents the total rate of the kth DL-UE; represents the uplink offloading achievable rate of the u-th UL-UE;

[0048] Transceiver beamforming through co-design and Power distribution scheme and common rate allocation for DL-UE To maximize WSMR; the joint optimization problem is formulated as follows:

[0049]

[0050] Where, Ensure that radar perception capabilities meet the minimum threshold for reliable perception; Ensure that the common stream can be decoded by all downlink user devices; and Ensure that the transmission power of the base station and uplink user equipment does not exceed their respective power budgets and Limit the amount of computational offload after aggregation to not exceed the computing capacity of the base station Where, ρ u Indicates the number of CPU cycles required per bit of data.

[0051] The inner and outer double-layer iterative algorithm based on the SROCR, LCMV and SBOP methods is used to solve the transceiver beamforming design, power allocation and DL-UE rate allocation scheme, including:

[0052] An inner and outer double-layer iterative algorithm based on SROCR, LCMV and SBOP methods is proposed to solve the problem:

[0053] Firstly, the optimization problem is reformulated as a semidefinite programming problem, and the SROCR method is applied to deal with the rank-one constraint.

[0054] Then the alternating optimization method is used to divide the variables into two groups Iterative updates are performed using the LCMV and SBOP methods respectively;

[0055] The specific algorithm is as follows:

[0056] 1) SDP Conversion and SROCR

[0057] Define a positive definite matrix in and is the index set of all DL-UE information flows;

[0058] Further definition as well as

[0059]

[0060] Rewrite the rate expression as:

[0061]

[0062] Where, and

[0063] By introducing slack variables Equation (13) is equivalently transformed into:

[0064]

[0065] Afterwards, the SROCR method is used to gradually relax the rank-one constraint into a convex form through iteration; in the tth iteration, the Relaxation:

[0066]

[0067] Where, is obtained in the t-1th iteration The principal eigenvector, is the relaxation parameter; by monotonically increasing The value of , combined with the step size δ (t-1) , the solving matrix will gradually approach the rank-one solution;

[0068] 2) Update by LCMV method

[0069] In the optimization problem, w s Affects perception constraints only Without affecting the objective function and other constraints; the optimal w is determined by maximizing the radar perception capability s , which is directly related to the perceived SINR; it is optimized and solved according to the SINR maximization criterion, as follows:

[0070]

[0071] By the matrix Perform generalized eigenvalue decomposition to solve;

[0072] However, when the matrix dimension is high, the computational complexity of generalized eigenvalue decomposition is high, which is particularly significant in scenarios where multiple targets coexist. The LCMV beamforming method is used to enhance target reception capability and suppress interference directions by imposing multiple linear constraints. The perceived SINR maximization problem is restructured as follows:

[0073]

[0074] Where, b is the response vector; according to the Lagrange multiplier method, the optimal solution to this problem is:

[0075]

[0076] Receive beamformer w u,j Affects decoding only u,j The SINR of the received stream is improved without affecting other streams. Based on the LCMV method, the optimal solution of the receive beamformer for each UL-UE is as follows:

[0077]

[0078] 3) Update {Q, P, R} by SBOP method

[0079] The problem after SDP equivalent transformation involves non-convex difference of logarithms. The SBOP method is used to transform the problem into a convex form by constructing a surrogate function.

[0080] Definition Lemma: Define the function g(α) = log(α) - αx + 1, where α > 0. For a given x > 0, when α * =x -1 When g(α) reaches its maximum value, and max α＞0 g(α)=-log(x);

[0081] According to the lemma, a set of auxiliary variables are introduced To construct the proxy problem of formula (18), the details are as follows:

[0082]

[0083] The above problem variables are decomposed into two variable blocks and When , it has block convexity;

[0084] For a given By solving the formula (24) iteratively, for a given Its update is the following closed-form solution:

[0085]

[0086] Substituting the obtained transceiver beamforming, power allocation scheme, and DL-UE optimal rate allocation scheme into formula (12), the optimal WSMR can be obtained, and the optimal overall system transmission performance under the constraints of communication, perception, and computing performance in the proposed FD-RSMA-ISCC system can be obtained.

[0087] Another object of the present invention is to provide a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the full-duplex RSMA-based synaesthesia network beamforming and resource allocation optimization method according to the present invention.

[0088] Another object of the present invention is to provide a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor executes the full-duplex RSMA-based telepathic computing network beamforming and resource allocation optimization method according to the present invention.

[0089] The beneficial effects of the present invention are:

[0090] (1) The present invention combines full-duplex technology to improve the utilization of time-frequency resources and avoid the loss of perception echo; uses RSMA technology combined with interference elimination to alleviate inter-user interference; and further suppresses inter-functional interference through transceiver beamforming and power allocation, thereby achieving seamless integration of perception, communication and computing functions in the FD-RSMA-ISCC system.

[0091] (2) This paper establishes an FD-RSMA-ISCC system model, derives the uplink and downlink achievable rates for the RSMA-based base station-user system, proposes WSMR as a transmission performance metric, and further constructs a WSMR maximization optimization problem. This method can improve the service quality of a given user while satisfying the constraints of perceived performance, power, and computational performance.

[0092] (3) In view of the high complexity and non-convexity of the WSMR maximization optimization problem, this paper proposes an inner and outer double-layer iterative algorithm based on SROCR, LCMV and SBOP to solve it, and verifies the effectiveness and fast convergence of this method through simulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0093] Figure 1 Schematic diagram of a flow chart of a full-duplex RSMA-based synaesthesia computing network beamforming and resource allocation optimization method of the present invention;

[0094] Figure 2 FD-RSMA-ISCC system model diagram in an embodiment of the present invention;

[0095] Figure 3 This is a flow chart of the inner and outer double-layer iterative algorithm proposed by the present invention;

[0096] Figure 4 The following is a comparison chart of the convergence performance of the proposed algorithm and different algorithms;

[0097] Figure 5 is the standardized transceiver beam pattern of the proposed algorithm in the embodiment;

[0098] Figure 6 Schematic diagram of comparison between perception thresholds and WSMR using different beamforming schemes in the embodiment;

[0099] Figure 7 FIG. 4 is a schematic diagram comparing the power budgets of different multiple access schemes and WSMR in an embodiment. DETAILED DESCRIPTION

[0100] The present invention provides a full-duplex RSMA-based telemetry network beamforming and resource allocation optimization method, which is further described in detail below with reference to the accompanying drawings.

[0101] like Figure 1 The embodiment of the present invention disclosed herein discloses a beamforming and resource allocation optimization method for a telecomputing network based on full-duplex RSMA, comprising the following steps:

[0102] Step S1: Establish an RSMA-ISCC system model covering uplink and downlink based on the FD-RSMA-ISCC system framework;

[0103] In this embodiment, an overall system model is established based on the FD-RSMA-ISCC system framework, taking into account multiple uplink and downlink user equipment, and an uplink and downlink RSMA transmission method is adopted between the base station and the user equipment. In the base station downlink transmission, the base station divides the message of each downlink user equipment into a public part and a private part, merges all the public parts into a public stream, and encodes the private parts into independent private streams respectively, and finally forms a downlink ISAC signal through the transmission beam. In the user equipment uplink transmission, the UL-UE divides its message into two parts, encodes them into independent information streams, and allocates different transmission powers to transmit public information and private information. At the receiving end, the base station first estimates the perceived echo in the received signal, and then performs interference cancellation and decoding on the uplink user signal. Based on the above process, the expression of the UL-UE transmission signal, the expression of the base station transmission signal, the expression of the signal of the base station receiving each part of the information, and the expression of the signal of the DL-UE receiving each part of the information are given.

[0104] The FD-RSMA-ISCC system framework includes: a base station part, a user equipment part and a scatterer part;

[0105] The base station is responsible for the three functions of perception, communication, and computing. The base station transmits the downlink ISAC signal through the transmitting antenna, extracts the echo of the target perception signal through the receiving antenna, and provides edge computing services for uplink users.

[0106] The user equipment portion includes: a UL-UE and a DL-UE; the UL-UE divides the message into a private part and a public part according to the uplink RSMA principle, encodes them into independent information streams, and then allocates power and transmits them; the DL-UE sequentially decodes the public stream and the corresponding private stream from the downlink ISAC signal transmitted by the base station according to the downlink RSMA principle to obtain complete downlink information;

[0107] The scatterer part includes: a sensing target object and a clutter scatterer. The sensing target object has a predetermined radar scattering cross-section characteristic, and the clutter scatterer includes all non-target reflectors within the base station downlink beam coverage area. During the sensing signal propagation process, the sensing target object reflected signal carries effective sensing information, while the multipath reflected signal generated by the clutter scatterer will cause co-frequency interference to the base station.

[0108] Step S2: Based on the RSMA-ISCC system model in step S1, derive expressions for uplink and downlink achievable rates of user equipment in the system and sensing and computing performance of base stations;

[0109] In this embodiment, based on the system model established in step S1, expressions for the signal-to-interference-plus-noise ratio (SINR) of each portion of information received by the UL-UE and DL-UE, as well as expressions for the SINR of each portion of information received by the base station are derived, and expressions for the uplink and downlink achievable rates of the user equipment in the system and the perception and computing performance of the base station are further derived.

[0110] Step S3: Based on the achievable downlink and downlink rates of the user equipment, an overall performance indicator describing the RSMA-ISCC system model using a weighted minimum user rate is proposed, and a maximization optimization problem with communication, perception, and computing performance constraints is formulated based on the overall performance indicator;

[0111] In this embodiment, based on the achievable uplink and downlink rates of user devices, the weighted sum of minimum user rates (WSMR) is proposed as a metric to measure overall transmission performance. To maximize the WSMR in an FD-RSMA-ISCC system, a maximization optimization problem with communication, perception, and computational performance constraints is constructed.

[0112] Step S4: For the maximization optimization problem, an inner-outer double-layer iterative algorithm based on SROCR, LCMV and SBOP methods is proposed and applied to obtain the transceiver beamforming design, power allocation and DL-UE rate allocation scheme.

[0113] In this embodiment, a dual-layer iterative algorithm based on sequential rank-one constraint relaxation (SROCR), linear constrained minimum variance (LCMV), and surrogate-based optimization procedure (SBOP) is proposed and applied to the aforementioned maximization optimization problem. In the outer layer, the problem is reformulated as a semidefinite program (SDP), and the rank constraint is solved using the SROCR method. In the inner layer, the solution is iteratively optimized using the LCMV and SBOP methods.

[0114] This embodiment specifically relates to a beamforming and resource allocation optimization method for a communication-perception-computing integrated network based on full-duplex rate division multiple access. The method is applicable to multi-task collaboration and interference management applications in 6G Internet of Things scenarios. The method is applicable to FD-RSMA-ISCC systems and optimizes transceiver beamforming and resource allocation through an inner and outer double-layer iterative algorithm based on SROCR, LCMV, and SBOP methods, thereby maximizing the weighted sum of system user rates, suppressing interference between users and between system functions, and improving system throughput.

[0115] like Figure 2 As shown, the FD-RSMA-ISCC system includes a base station, a user equipment, and a scatterer. The base station (BS) simultaneously performs the three functions of perception, communication, and computing: the base station's transmitting antenna transmits the downlink ISAC signal, extracts the echo of the target perception signal through the receiving antenna, and provides edge computing services. The user equipment part is specifically divided into uplink user equipment (UL-UE) and downlink user equipment (DL-UE). Among them, the UL-UE divides its message into two parts according to the uplink RSMA principle, encodes them into independent information streams, and then allocates power and transmits them; the DL-UE, according to the downlink RSMA principle, sequentially decodes the public stream and the corresponding private stream from the downlink ISAC signal transmitted by the base station to obtain complete downlink information. The scatterer part includes sensing targets (ST) and clutters (CL) within the sensing range of the base station during the downlink perception process.

[0116] This embodiment proposes an FD-RSMA-ISCC system framework. This framework leverages Rate-Splitting Multiple Access (RSMA) technology to split each message into multiple sub-messages that share the same spectrum resources. Successive Interference Cancellation (SIC) mitigates inter-user interference, thereby improving system performance in both downlink and uplink scenarios. Furthermore, transceiver beamforming technology provides additional degrees of freedom (DoFs) to mitigate interference between different functions.

[0117] In this embodiment, the applicable scenario is the FD-RSMA-ISCC system shown, which includes a base station part, a user equipment part, and a scatterer part. The base station simultaneously undertakes the three functions of perception, communication, and computing: the base station transmitting antenna transmits the downlink ISAC signal, extracts the echo of the target perception signal through the receiving antenna, and provides edge computing services for the UL-UE. The user equipment part is specifically divided into UL-UE and DL-UE. Among them, the UL-UE divides its message into two parts according to the uplink RSMA principle, encodes them into independent information streams, and then allocates power and transmits them; the DL-UE decodes the public stream and the corresponding private stream from the downlink ISAC signal transmitted by the base station in sequence according to the downlink RSMA principle to obtain complete downlink information. The scatterer part includes the perception targets and clutter within the perception range of the base station during the downlink perception process.

[0118] The following further describes in detail the sub-steps of the full-duplex RSMA-based synaesthesia computing network beamforming and resource allocation optimization method disclosed in the present invention in conjunction with the accompanying drawings.

[0119] Step S1: Establish an RSMA-ISCC system model covering uplink and downlink based on the FD-RSMA-ISCC system framework;

[0120] (1) Establish the overall system model based on the FD-RSMA-ISCC system framework. Set up a base station in the system, which is equipped with N t transmit antennas and N r The antenna array consists of K receiving antennas with a half-wavelength spacing; the user equipment part includes K single-antenna DL-UEs and U single-antenna UL-UEs; the scatterer part includes M STs and L CLs. Let B be the system bandwidth, and define the index set K = {1,…,K}, U = {1,…,U}, M = {1,…,M} and L = {1,…,L} corresponding to DL-UE, UL-UE, ST and CL respectively. The channel from the base station to the downlink user equipment and the channel from the uplink user equipment to the base station are expressed as

[0121] The ST is the sensing target; the CL is the clutter; the echo paths from the ST and CL are respectively expressed as: where β i and θ i , for all i∈{K,U,M,L}, they represent the corresponding large-scale path loss and angle parameters respectively, where represents the antenna array response vector;

[0122] According to the uplink RSMA protocol, the uth UL-UE divides its uplink transmission information into two information streams s by information splitting and encoding. u,1 and su,2 , and then allocate transmission power P to the two information flows respectively u,1 and P u,2 , then the transmitted signal of the u-th UL-UE is:

[0123]

[0124] At the base station, a single-layer downlink RSMA protocol is used, and each DL-UE message is split into a private part and a public part; all public parts are merged into a public stream through a combiner. c , the private parts are encoded separately as streams {x1,…,x K},by Represents the index set of all DL-UE information flows; DL-UE information flows also need to be associated with the radar sequence used by the base station for perception The covariance matrix of the radar sequence is With general rank. The superimposed transmitted signal is expressed as follows:

[0125]

[0126] Where, and represent the precoding vectors corresponding to the private stream and the public stream respectively;

[0127] Therefore, the covariance matrix of the transmitted signal is:

[0128]

[0129] Based on the above transmitted signal, the received signal of the kth DL-UE is expressed as:

[0130]

[0131] Where h u,k represents the interference channel between UL-UE and DL-UE, Indicates variance Additive White Gaussian Noise (AWGN); u UL-UE uplink split information flow; Indicates the channel from the base station to the downlink user equipment;

[0132] Similarly, the received signal of the base station is expressed as:

[0133]

[0134] Where H SI Represents self-interference (SI), and its elements are represented as β SI is the power of the residual self-interference channel, z is the covariance Additive white Gaussian noise. Represents the echo paths from ST and CL.

[0135] Step S2: Based on the overall system model established in step S1, derive expressions for uplink and downlink achievable rates of user equipment in the system and sensing and computing performance of base stations;

[0136] (2) Based on the system model established in step S1, the SINR expressions for each part of the information received by the DL-UE during the data transmission phase and the SINR expressions for each part of the information received by the base station are given, and the expressions for the uplink and downlink achievable rates of the user equipment and the base station perception calculation performance in the system are derived from them.

[0137] For each DL-UE, the received public stream is first decoded, and the private stream is considered as interference. After the public stream is successfully eliminated through the SIC technology, the private stream is decoded. The SINR of the public and private streams decoded by the kth DL-UE are expressed as:

[0138]

[0139] Where, h u,k represents the interference channel between UL-UE and DL-UE; Indicates the channel from the base station to the downlink user equipment; and v c The public rate and private rate achievable by the precoding vectors corresponding to the private and public streams, respectively, are expressed as:

[0140] R k,c =Blog2(1+γ k,c ) (8)

[0141] R k,k =Blog2(1+γ k,k ) (9)

[0142] Where B refers to the base station bandwidth, B = G CL +H SI To ensure that each downlink user equipment can decode the common stream, the common rate R c Affected by all R k,c The constraint of the minimum value in R c =min k R k,c ; The total rate of the kth DL-UE is R k =R k,k +r k , where rk is the common rate portion allocated to the kth DL-UE;

[0143] At the base station receiving end, a receive beamformer w is applied to the base station receiving signal. s , the SINR of the perceived signal is expressed as follows:

[0144]

[0145] Where B = G CL +H SI ;H SI Represents self-interference, and the sensing function performance of the base station is defined as R s =Blog2(1+γ s );

[0146] After extracting the sensed echo from the received signal, the base station decodes the UL-UE transmit signal according to the uplink RSMA decoding principle. The UL-UE data stream is decoded in sequence according to a predetermined decoding order. The decoded SINR is expressed as:

[0147]

[0148] Where, represents the echo path from ST and CL, H SI represents self-interference), the uplink offloading achievable rate of the u-th UL-UE is expressed as: Where R u,j =Blog2(1+γ u,j ).

[0149] Step S3: Based on the achievable uplink and downlink rates of the user equipment, a maximization optimization problem with communication, perception, and computing performance constraints is proposed with the weighted minimum user rate as the optimization target;

[0150] (3) Based on the achievable uplink and downlink rates of the user equipment derived in step S2, the weighted sum of minimum user rates (WSMR) is used as the overall transmission performance metric. To maximize the WSMR in the FD-RSMA-ISCC system, an optimization problem with communication, perception, and computational performance constraints is constructed.

[0151] In the FD-RSMA-ISCC system, limited resources lead to complex performance trade-offs when simultaneously supporting perception, communication, and computing functions. Considering user fairness, WSMR is introduced to balance the minimum rate of all user devices in the system under perception and computing tasks, which is defined as:

[0152]

[0153] Where w DL and w UL is the predefined rate weight; R k =R k,k +r k represents the total rate of the kth DL-UE; represents the uplink offloading achievable rate of the u-th UL-UE;

[0154] Transceiver beamforming through co-design and Power distribution scheme and common rate allocation for DL-UE To maximize WSMR; the joint optimization problem is formulated as follows:

[0155]

[0156] Where, ensure that the radar perception capability meets the minimum threshold for reliable perception; ensure that the common stream can be decoded by all downlink user equipment; and ensure that the transmission power of the base station and uplink user equipment does not exceed the sum of their respective power budgets; limit the amount of computational offload after aggregation to not exceed the computing capacity of the base station, where represents the number of CPU cycles required for each bit of data.

[0157] Step S4: For the maximization optimization problem, an inner-outer double-layer iterative algorithm based on SROCR, LCMV and SBOP methods is proposed and applied to obtain the transceiver beamforming design, power allocation and DL-UE rate allocation scheme.

[0158] (4) To solve the non-convex problem formula (13), an inner and outer double-layer iterative algorithm based on SROCR, LCMV and SBOP methods is proposed. The algorithm flow chart is as follows: Figure 3 As shown. First, the optimization problem is reconstructed into a semi-definite programming problem, and the SROCR method is applied to deal with the rank-one constraint. Then, the alternating optimization (AO) method is used to divide the variables into two groups. and {Q, P, R} are iteratively updated using the LCMV and SBOP methods, respectively. The specific algorithm is as follows:

[0159] 1) SDP Conversion and SROCR

[0160] Define a positive definite matrix in and is the index set of all DL-UE information flows;

[0161] Further definition as well as

[0162]

[0163] The rate expression can be rewritten as:

[0164]

[0165] Where, and

[0166] By introducing slack variables Equation (13) is equivalently transformed into:

[0167]

[0168] Afterwards, the SROCR method is used to gradually relax the rank-one constraint into a convex form through iteration. Specifically, in the tth iteration, Introduce a relaxed constraint:

[0169]

[0170] Where, is obtained in the t-1th iteration The principal eigenvector, is the relaxation parameter; by monotonically increasing The value of , combined with the step size δ (t-1) , the solving matrix will gradually approach the rank-one solution;

[0171] 2) Update by LCMV method

[0172] In the optimization problem, w s Affects perception constraints only Without affecting the objective function and other constraints. The optimal w is determined by maximizing the radar perception capability. s , which is directly related to the perceived SINR. It is optimized and solved according to the SINR maximization criterion, as follows:

[0173]

[0174] This problem is solved by The generalized eigenvalue decomposition is performed to solve the problem. However, when the matrix dimension is high, the computational complexity of the generalized eigenvalue decomposition is high, and this drawback is particularly significant in scenarios where multiple targets coexist. Therefore, the LCMV beamforming method is used to enhance the target reception capability and suppress the interference direction by imposing multiple linear constraints. The perceived SINR maximization problem is reformulated as:

[0175]

[0176] Where, b is the response vector; according to the Lagrange multiplier method, the optimal solution to this problem is:

[0177]

[0178] Similarly, the receive beamformer w u,j Affects decoding only u,j Based on the LCMV method, the optimal solution for the receive beamformer of each UL-UE is as follows:

[0179]

[0180] 3) Update {Q, P, R} by SBOP method

[0181] The problem after SDP equivalent transformation still contains non-convex logarithmic difference constraints. Therefore, the present invention adopts the SBOP method to transform it into a convex form by constructing a proxy function, and then reconstructs the original problem into a convex optimization problem for iterative solution.

[0182] Lemma: Define the function g(α) = log(α) - αx + 1, where α > 0. For a given x > 0, when α * =x -1 When g(α) reaches its maximum value, and max α＞0 g(α)=-log(x). (This lemma is proved by simple derivation)

[0183] According to the above lemma, a set of auxiliary variables are introduced To construct the proxy problem of optimizing formula (18), the details are as follows:

[0184]

[0185] The above problem variables are decomposed into two variable blocks and , it has block convexity. Specifically, for a given The solution is iteratively obtained by solving the formula (24), and The update of has the following closed-form solution:

[0186]

[0187] Substituting the obtained transceiver beamforming design, power allocation scheme, and DL-UE optimal rate allocation scheme into Equation (12), the optimal WSMR can be obtained, and the optimal overall system transmission performance under the communication, perception, and computing performance constraints in the proposed FD-RSMA-ISCC system model can be obtained.

[0188] In order to verify the effectiveness of the full-duplex RSMA-based synaesthesia computing network beamforming and resource allocation optimization method disclosed in the present invention, a specific embodiment is carried out to verify the effectiveness of the synaesthesia computing network beamforming and resource allocation optimization method based on full-duplex RSMA disclosed in the present invention.

[0189] Based on the above invention content, the specific numerical results of the embodiment are obtained through Monte Carlo simulation to verify the performance of the proposed solution. t = 8 transmit antennas and N r = A base station with 8 receiving antennas, and a polar coordinate system is established with it as the coordinate origin (0,0). There are K = 2 DL-UEs in the system, whose angles are {-50°, 35°} respectively; U = 2 UL-UEs, whose angles are {-45°, 40°} respectively; M = 2 STs, whose angles are {-20°, 20°} respectively; and L = 1 CL, whose angle is 0°. These user devices, sensing targets and interference debris are all distributed in an area with a radius of 50 to 100 meters centered on the base station. Considering that the wireless channel in the embodiment follows Rayleigh fading, the path loss model is PL (dB) = 35.3 + 37.6log 10 (d), where d is the distance (in meters). Other parameter settings: B = 1MHz, σ 2 =-114dBm,ρ=10 3 circle / bit, as well as

[0190] like Figure 4 and Figure 5 As shown in the figure, the optimization algorithm convergence performance and transceiver beamforming performance of the proposed scheme are achieved under a random channel, where ∈ AO =10 -4 .exist Figure 4 In this paper, Successive Convex Approximation (SCA) and Convex Difference Algorithm (DCA) are used as benchmark algorithms. It can be observed that all algorithms converge monotonically within 25 iterations, and the proposed algorithm has the fastest convergence speed. Figure 5As shown, the proposed algorithm effectively directs the transmit beamforming main lobe in the desired direction. In the receive beam, all beamformers successfully reach nulls in the directions of the interference and clutter. This demonstrates the superiority of the proposed method in terms of convergence speed and beam direction control capability.

[0191] Figure 6 The performance trade-off between WSMR and the perception threshold is demonstrated for different receive beamforming schemes, including the proposed LCMV scheme, zero-forcing (ZF), matched filtering (MF), and isotropic schemes. WSMR for all schemes decreases with increasing perception threshold, as tighter perception constraints reduce the system's available degrees of freedom, impacting communication and computational capabilities. The proposed LCMV scheme consistently outperforms the ZF, MF, and isotropic schemes by 11.4%, 27.3%, and 31.2%, respectively. This result confirms the superiority of the proposed LCMV approach in suppressing inter-function interference.

[0192] Figure 7 The relationship between the base station power budget and WSMR under different multiple access schemes is demonstrated, including RSMA, non-orthogonal multiple access (NOMA), space-division multiple access (SDMA), and time-division multiple access (TDMA). Figure 7 The results show that the RSMA scheme consistently outperforms the baseline scheme, achieving average gains of 5.7%, 18.6%, and 29.5% compared to NOMA, SDMA, and TDMA, respectively. This demonstrates the effectiveness of RSMA in suppressing inter-user interference.

[0193] Another embodiment of the present invention provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the full-duplex RSMA-based telepathic computing network beamforming and resource allocation optimization method according to the present invention.

[0194] Another embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor executes the full-duplex RSMA-based telepathic computing network beamforming and resource allocation optimization method according to the present invention.

[0195] The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by ordinary technicians in this field based on the contents disclosed in the present invention should be included in the protection scope recorded in the claims.

Claims

1. A beamforming and resource allocation optimization method for a full-duplex RSMA-based telemetry network, characterized in that: The following steps are involved: Step S1: Establish an RSMA-ISCC system model covering uplink and downlink based on the FD-RSMA-ISCC system framework; Step S2: Based on the RSMA-ISCC system model in step S1, derive expressions for uplink and downlink achievable rates of user equipment in the system and sensing and computing performance of base stations; Step S3: Based on the achievable downlink and downlink rates of the user equipment, an overall performance indicator describing the RSMA-ISCC system model using a weighted minimum user rate is proposed, and a maximization optimization problem with communication, perception, and computing performance constraints is formulated based on the overall performance indicator; Step S4: For the maximization optimization problem, an inner-outer double-layer iterative algorithm based on SROCR, LCMV and SBOP methods is proposed and applied to obtain the transceiver beamforming design, power allocation and DL-UE rate allocation scheme.

2. The beamforming and resource allocation optimization method for the full-duplex RSMA-based telemetry computing network according to claim 1 is characterized in that: The FD-RSMA-ISCC system framework includes: a base station part, a user equipment part and a scatterer part; The base station is responsible for the three functions of perception, communication, and computing. The base station transmits the downlink ISAC signal through the transmitting antenna, extracts the echo of the target perception signal through the receiving antenna, and provides edge computing services for uplink users. The user equipment portion includes: a UL-UE and a DL-UE; the UL-UE divides the message into a private part and a public part according to the uplink RSMA principle, encodes them into independent information streams, and then allocates power and transmits them; the DL-UE sequentially decodes the public stream and the corresponding private stream from the downlink ISAC signal transmitted by the base station according to the downlink RSMA principle to obtain complete downlink information; The scatterer part includes: a sensing target object and a clutter scatterer. The sensing target object has a predetermined radar scattering cross-section characteristic, and the clutter scatterer includes all non-target reflectors within the base station downlink beam coverage area. During the sensing signal propagation process, the sensing target object reflected signal carries effective sensing information, while the multipath reflected signal generated by the clutter scatterer will cause co-frequency interference to the base station.

3. The beamforming and resource allocation optimization method for the full-duplex RSMA-based telemetry network according to claim 1 is characterized in that: The RSMA-ISCC system model that establishes an overall uplink and downlink coverage includes: A base station is set up in the system, and the base station is equipped with N t transmit antennas and N r The antenna array consists of receiving antennas with a spacing of half a wavelength. The user equipment part includes K single-antenna DL-UEs and U single-antenna UL-UEs. The scatterer part includes M STs and L CLs. Let B be the system bandwidth, and define the index set K = {1, ..., K}, U = {1, ..., U}, M = {1, ..., M} and L = {1, ..., L} corresponding to DL-UE, UL-UE, ST and CL respectively. The channel from the base station to the downlink user equipment and the channel from the uplink user equipment to the base station are expressed as and The ST is the sensing target; the CL is the clutter; The echo paths from ST and CL are expressed as: where β i and θ i , for all i∈{K,U,M,L}, they represent the corresponding large-scale path loss and angle parameters respectively, where represents the antenna array response vector; According to the uplink RSMA protocol, the uth UL-UE divides its uplink transmission information into two information streams s by information splitting and encoding. u,1 and s u,2 , and then allocate transmission power P to the two information flows respectively u,1 and P u,2 , then the transmitted signal of the u-th UL-UE is: At the base station, a single-layer downlink RSMA protocol is used, and each DL-UE message is split into a private part and a public part; all public parts are merged into a public stream through a combiner. c , the private parts are encoded separately as streams {x1,...,x K },by The index set of all DL-UE information flows; DL-UE information flows also need to be associated with the radar sequence used by the base station for perception The covariance matrix of the radar sequence is With general rank; the superimposed transmitted signal is expressed as follows: Where, and v c denote the precoding vectors corresponding to the private stream and the public stream respectively; The covariance matrix of the transmitted signal is: Based on the above transmitted signal, the received signal of the kth DL-UE is expressed as: Where h u,k represents the interference channel between UL-UE and DL-UE, Indicates variance Additive white Gaussian noise; s u UL-UE uplink split information flow; Indicates the channel from the base station to the downlink user equipment; The received signal of the base station is expressed as: Where H SI represents self-interference, and its elements are expressed as β SI is the power of the residual self-interference channel, z is the covariance Additive white Gaussian noise, Represents the echo paths from ST and CL.

4. The beamforming and resource allocation optimization method for the full-duplex RSMA-based telemetry network according to claim 1 is characterized in that: The expressions for deriving the uplink and downlink achievable rates of the user equipment and the base station perception and computing performance in the system include: For each DL-UE, first decode the received common stream x c , at this time, the private stream is regarded as interference; after the public stream is successfully eliminated through SIC technology, the private stream x is decoded. k ; The kth DL-UE decodes the common stream x c and private stream x k The SINRs are expressed as: Where, h u,k represents the interference channel between UL-UE and DL-UE; Indicates the channel from the base station to the downlink user equipment; and v c The public rate and private rate achievable by the precoding vectors corresponding to the private and public streams, respectively, are expressed as: R k,c =Blog2(1+γ k,c ) (8) R k,k =Blog2(1+γ k,k ) (9) Where B refers to the base station bandwidth, B = G CL +H SI To ensure that each downlink user equipment can decode the common stream, the common rate R c Affected by all R k,c The constraint of the minimum value in R c =min k R k,c ; The total rate of the kth DL-UE is R k =R k,k +r k , where r k is the common rate portion allocated to the kth DL-UE; At the base station receiving end, a receive beamformer w is applied to the base station receiving signal. s , the SINR of the perceived signal is expressed as follows: Where B = G CL +H SI ;H SI Represents self-interference, and the sensing function performance of the base station is defined as R s =Blog2(1+γ s ); After extracting the sensed echo from the received signal, the base station decodes the UL-UE transmission signal according to the uplink RSMA decoding principle; the UL-UE data stream is decoded in sequence according to the predetermined decoding order; Indicates s u,j The set of signal streams that generate interference; by applying the beamformer w u,j To suppress inter-functional interference, decode s u,j The SINR is expressed as: Where, represents the echo path from ST and CL, H SI represents self-interference), the uplink offloading achievable rate of the u-th UL-UE is expressed as: Where R u,j =Blog2(1+γ u,j ).

5. The beamforming and resource allocation optimization method for the full-duplex RSMA-based telemetry network according to claim 1 is characterized in that: The method proposes to describe the overall performance index of the RSMA-ISCC system model using a weighted minimum user rate, and to formulate a maximization optimization problem with communication, perception, and computing performance constraints based on the overall performance index, including: In the FD-RSMA-ISCC system, limited resources lead to complex performance trade-offs when simultaneously supporting perception, communication, and computing functions. Considering user fairness, WSMR is introduced to balance the minimum rate of all user devices in the system under perception and computing tasks, which is defined as: Where w DL and w UL is the predefined rate weight; R k =R k,k +r k represents the total rate of the kth DL-UE; represents the uplink offloading achievable rate of the u-th UL-UE; Transceiver beamforming through co-design and Power distribution scheme and common rate allocation for DL-UE To maximize WSMR; the joint optimization problem is formulated as follows: Where, Ensure that radar perception capabilities meet the minimum threshold for reliable perception; Ensure that the common stream can be decoded by all downlink user devices; and Ensure that the transmission power of the base station and uplink user equipment does not exceed their respective power budgets and Limit the amount of computational offload after aggregation to not exceed the computing capacity of the base station Where, ρ u Indicates the number of CPU cycles required per bit of data.

6. The beamforming and resource allocation optimization method for the full-duplex RSMA-based telemetry network according to claim 1 is characterized in that: The inner and outer double-layer iterative algorithm based on the SROCR, LCMV and SBOP methods is used to solve the transceiver beamforming design, power allocation and DL-UE rate allocation scheme, including: An inner and outer double-layer iterative algorithm based on SROCR, LCMV and SBOP methods is proposed to solve the problem: first, the optimization problem is reconstructed as a semi-positive programming problem, and the SROCR method is applied to deal with the rank-one constraint; then, an alternating optimization method is used to divide the variables into two groups. and {Q, P, R} are iteratively updated by LCMV and SBOP methods respectively; the specific algorithm is as follows: 1) SDP Conversion and SROCR Define a positive definite matrix in and is the index set of all DL-UE information flows; Further definition as well as Rewrite the rate expression as: Where, and By introducing slack variables Equation (13) is equivalently transformed into: Afterwards, the SROCR method is used to gradually relax the rank-one constraint into a convex form through iteration; in the tth iteration, the rank(Q i )=1, Relaxation: Where, is obtained in the t-1th iteration The principal eigenvector, is the relaxation parameter; by monotonically increasing The value of , combined with the step size δ (t-1) , the solving matrix will gradually approach the rank-one solution; 2) Update by LCMV method In the optimization problem, w s Affects perception constraints only Without affecting the objective function and other constraints; the optimal w is determined by maximizing the radar perception capability s , which is directly related to the perceived SINR; it is optimized and solved according to the SINR maximization criterion, as follows: By the matrix Perform generalized eigenvalue decomposition to solve; However, when the matrix dimension is high, the computational complexity of generalized eigenvalue decomposition is high, which is particularly significant in scenarios where multiple targets coexist. The LCMV beamforming method is used to enhance target reception capability and suppress interference directions by imposing multiple linear constraints. The perceived SINR maximization problem is restructured as follows: Where, b is the response vector; according to the Lagrange multiplier method, the optimal solution to this problem is: Receive beamformer w u,j Affects decoding only u,j The SINR of the received stream is improved without affecting other streams. Based on the LCMV method, the optimal solution of the receive beamformer for each UL-UE is as follows: 3) Update {Q, P, R} by SBOP method The problem after SDP equivalent transformation involves non-convex difference of logarithms. The SBOP method is used to transform the problem into a convex form by constructing a surrogate function. Definition Lemma: Define the function g(α) = log(α) - αx + 1, where α > 0. For a given x > 0, when α * =x -1 When g(α) reaches its maximum value, and max α＞0 g(α)=-log(x); According to the lemma, a set of auxiliary variables are introduced To construct the proxy problem of formula (18), the details are as follows: The above problem variables are decomposed into two variable blocks and When , it has block convexity; For a given By solving the formula (24) iteratively, for a given Its update is the following closed-form solution: Substituting the obtained transceiver beamforming, power allocation scheme, and DL-UE optimal rate allocation scheme into formula (12), the optimal WSMR can be obtained, and the optimal overall system transmission performance under the constraints of communication, perception, and computing performance in the proposed FD-RSMA-ISCC system can be obtained.

7. A computer device, characterized in that: The invention comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor runs the computer program stored in the memory, the processor executes the full-duplex RSMA-based telepathic computing network beamforming and resource allocation optimization method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the processor executes the full-duplex RSMA-based telepathic computing network beamforming and resource allocation optimization method according to any one of claims 1 to 6.

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