A performance optimization method for communication and perception integrated system

Abstract

This invention discloses a method for optimizing the performance of an integrated communication and perception system. This method first establishes an integrated communication and perception system and then builds a communication model and a perception model based on message transmission. This method then derives the optimization objective of maximizing user data rates while satisfying constraints. Finally, a weighted minimum error algorithm and a block descent algorithm are used to transform and solve the optimization objective, yielding the optimal solution. This method achieves high user data rates, low base station energy consumption, and high overall system performance.

Description

Technical Field

[0001] The present invention relates to the field of communications, and more specifically, to a method for optimizing the performance of a communication-awareness integrated system. Background Art

[0002] With the development of the times, communication frequency bands are moving towards higher millimeter waves and terahertz, with larger bandwidths and denser distribution of large-scale antenna arrays. Therefore, a single system can integrate wireless signal perception and communication capabilities, thereby enabling each system to improve the performance of each other.

[0003] Traditional communication methods often consider signal perception and communication capabilities separately, rather than as a whole. Consequently, using traditional communication methods and parameters results in inefficient equipment and frequency utilization, slow user transmission rates, high base station energy consumption, and poor overall system performance.

[0004] The prior art discloses a secure beamforming method for an RSMA-ISAC system. This method includes a communication module and a perception module. The communication module performs rate splitting and encoding on user messages to generate public and private data streams. Interference symbols are added to these two streams, and these are mapped to the transmit antenna via a precoder to generate the ISAC signal. The perception module sets an objective function, solves it based on a constraint function, and then decodes it to generate a secure beam. This method does not comprehensively consider both communication and perception functions. Summary of the Invention

[0005] The present invention aims to solve the defects of the prior art, such as slow user transmission rate, high base station energy consumption and low overall system performance, and provides a method for optimizing the performance of a communication perception integrated system.

[0006] The primary purpose of the present invention is to solve the above technical problems, and the technical solutions of the present invention are as follows:

[0007] A method for optimizing the performance of a communication-awareness integrated system, comprising:

[0008] S1: Establish an integrated communication and perception system, including a base station, a reconfigurable smart surface, and several users. Establish a communication model based on the message transmission between the base station, the reconfigurable smart surface, and several users. Establish a perception model based on the perception tasks of the base station and the reconfigurable smart surface on existing perception targets.

[0009] S2: Based on the communication model and the perception model, establish an optimization goal of maximizing the minimum user rate under the conditions of the perception power threshold constraint, the transmit power constraint, and the constant modulus constraint;

[0010] S3: Using the weighted minimum mean square error algorithm and the block coordinate descent method to transform and solve the optimization objective to obtain the optimal solution of the optimization objective.

[0011] Furthermore, a communication model is established based on message transmission among the base station, the reconfigurable smart surface, and a plurality of users, including:

[0012] The base station generates a baseband signal transmission signal, which is directly transmitted to each user through a first channel, and is also transmitted to the reconfigurable smart surface through a second channel, and then transmitted to each user through a third channel;

[0013] The baseband signal transmission signal is:

[0014]

[0015] The signal received by the kth downlink communication user is:

[0016]

[0017] The kth user decodes s c The signal-to-interference-noise ratio is:

[0018]

[0019] The kth user decodes s p,k The signal-to-interference-noise ratio is:

[0020]

[0021] The kth user decodes s c The rate is:

[0022] R c,k =log2(1+γ c,k )

[0023] The kth user decodes s p,k The rate is:

[0024] R p,k =log2(1+γ p,k )

[0025] Among them, k is the user serial number, K is the total number of users, is the set of users {1,...,k}, m k is the message of the kth user, m k Split into common part m c,k 、Private part m p,k , public streams c The common part of all users {m c,1 ,...,m c,k} are jointly encoded, private stream {s p,1 ,...,s p,k} respectively by the user's private part {m p,1,...,m p,k} encoded; data stream [s c ,s p,1 ,...,s p,k ]Use precoder[w c ,w1,...,w k ] to encode; is the first channel from the base station to the kth user, h r,k is the third channel from the reconfigurable smart surface to the kth user, G is the second channel from the base station to the reconfigurable smart surface, Θ is the reflection phase shift matrix of the reconfigurable smart surface; represents the reconfigurable smart surface parameters; σ is the additive white Gaussian noise power at the kth user;

[0026] Furthermore, a perception model is established based on the perception tasks of the base station and the reconfigurable intelligent surface on the existing perception target, including:

[0027] While the base station directly senses the existing sensing target through the fourth channel, it also senses the existing sensing target through the second channel to the reconfigurable smart surface through the fifth channel.

[0028] The channel between the base station and the sensing target is:

[0029]

[0030] The effective perceived power is:

[0031]

[0032] Among them, h d,t is the channel from the base station to the sensing target, h r,t is the channel from the reconfigurable smart surface to the sensing target, G is the channel from the base station to the reconfigurable smart surface, Θ is the reflection phase shift matrix of the reconfigurable smart surface, W represents the precoder parameter, W = [w c ,w1,...,w k ].

[0033] Furthermore, the optimization goal of maximizing the minimum user rate under the conditions of satisfying the perceived power threshold constraint, the transmit power constraint, and the constant modulus constraint is:

[0034]

[0035] The constraints are:

[0036] P g ≥P0

[0037]

[0038] c≥0

[0039]

[0040]

[0041] in, is the set of users {1,...,k}, C k is the public stream s of the kth user c The transmission rate, c=[C1,...,C k ] T ; is the parameter of the nth element in the reconfigurable smart surface, W represents the precoder parameter, P0 is the minimum perceived power, P t is the total transmitted power, Θ is the reflection phase shift matrix of the reconfigurable smart surface; R c,k Decode s for the kth user c The rate, R p,k Decode s for the kth user p,k The rate of P g is the effective perceived power.

[0042] Furthermore, in step S3, the optimization objective is transformed and solved using the weighted minimum mean square error algorithm and the block coordinate descent method, including:

[0043] S301: introducing a slack variable to express the minimum user achievable rate, and the optimization objective can be converted into a first optimization objective;

[0044] S302: Proposing a second optimization objective based on the first optimization objective and a weighted minimum mean square error algorithm;

[0045] S303: Split the second optimization objective into a third optimization objective and a fourth optimization objective;

[0046] S304: solving the third optimization objective and the fourth optimization objective using the block coordinate descent method to obtain the optimal solution of the optimization objective.

[0047] Furthermore, the first optimization objective is:

[0048]

[0049] The constraints are:

[0050]

[0051] P g ≥P0

[0052]

[0053] c≥0

[0054]

[0055] in, is the set of users {1,...,k}, C k is the public stream s of the kth user c The transmission rate, c=[C1,...,C k ] T ; R p,k Decode s for the kth user p,k rate, W represents the precoder parameter, Θ is the reflection phase shift matrix of the reconfigurable smart surface; t is the minimum user achievable rate; is the parameter of the nth element in the reconfigurable smart surface, P0 is the minimum perceived power, P t is the total power of the transmission; R c,k Decode s for the kth user c The rate of P g is the effective perceived power.

[0056] Furthermore, the second optimization objective is:

[0057]

[0058] The constraints are:

[0059]

[0060] P g ≥P0

[0061] c≥0

[0062]

[0063] in, is the set of users {1,...,k}, ω is the MSE weight vector, g is the equalizer vector, W represents the precoder parameters, Θ is the reflection phase shift matrix of the reconfigurable smart surface; C k is the public stream s of the kth user c The transmission rate, c = [C1, ..., C k ] T ; t is the minimum user achievable rate, ξ c，k is the public stream s of the kth user c The augmented weighted mean square error, ξ p，k is the private stream s of the kth user p，k The augmented weighted mean square error of ; is the parameter of the nth element in the reconfigurable smart surface, P0 is the minimum perceived power, P t is the total power transmitted; P g is the effective perceived power.

[0064] Furthermore, in step S304, the third optimization objective and the fourth optimization objective are solved using the block coordinate descent method, including:

[0065] S30401: Initialize the first precoder parameter W 1 , the reflection phase shift matrix Θ of the first reconfigurable smart surface 1 , the first minimum user achievable rate t 1 ; The first precoder parameter W 1 As the second precoder parameter W 2 , the first minimum user achievable rate t 1 As the second minimum user achievable rate t 2 ; The reflection phase shift matrix Θ of the first reconfigurable smart surface 1 As the reflection phase shift matrix Θ of the second reconfigurable smart surface 2 ; Set the first minimum user achievable rate t 1 As the fourth minimum user achievable rate t 4 and the sixth minimum user achievable rate t 6 ;

[0066] S30402: Based on the first precoder parameter W 1 , the reflection phase shift matrix Θ of the first reconfigurable smart surface 1 , the first minimum user achievable rate t 1 , calculate the first MSE weight vector ω 1 , the first equalizer vector g 1 ;

[0067] S30403: Based on the second precoder parameter W 2 , the reflection phase shift matrix Θ of the first reconfigurable smart surface 1 , the first MSE weight vector ω 1 , the first equalizer vector g 1 , the second minimum user achievable rate t 2 Calculate the third optimization target and obtain the third precoder parameter W 3 , the third minimum user achievable rate t 3 , first public flow transmission rate c 1 ;

[0068] S30404: According to the third minimum user achievable rate t 3and the second minimum user achievable rate t 2 , determine whether the first exit condition is met; if so, set the third precoder parameter W 3 As the new first precoder parameter W 1 , execute step S30405; if not reached, set the third precoder parameter W 3 As the new second precoder parameter W 2 , the third smallest user achievable rate t 3 As the new second minimum user achievable rate t 2 , execute step S30403;

[0069] S30405: According to the reflection phase shift matrix Θ of the second reconfigurable smart surface 2 , the first precoder parameter W 1 , first public flow transmission rate c 1 , the first MSE weight vector ω 1 , the first equalizer vector g 1 , the fourth minimum user achievable rate t 4 , calculate the fourth optimization objective and obtain the reflection phase shift matrix Θ of the third reconfigurable smart surface 3 , the fifth minimum user achievable rate t 5 ;

[0070] S30406: According to the fifth minimum user achievable rate t 5 and the fourth minimum user achievable rate t 4 , determine whether the second exit condition is met; if so, the reflection phase shift matrix Θ of the third reconfigurable smart surface is 3 The reflective phase-shift matrix Θ as the first new reconfigurable smart surface 1 , the fifth smallest user achievable rate t 5 As the new sixth minimum user achievable rate t 6 , execute step S30407; if not reached, set the reflection phase shift matrix Θ of the third reconfigurable smart surface 3 The reflection phase shift matrix Θ as a new second reconfigurable smart surface 2 , the fifth smallest user achievable rate t 5 As the new fourth minimum user achievable rate t 4 , execute step S30405;

[0071] S30407: According to the sixth minimum user achievable rate t 6 and the first minimum user achievable rate t 1 , determine whether the third exit condition is met; if so, the sixth minimum user achievable rate t 6For the optimal minimum user achievable rate, the first precoder parameter W 1 is the optimal precoder parameter, the reflection phase shift matrix Θ of the first reconfigurable smart surface 1 is the reflection phase shift matrix of the optimal reconfigurable smart surface, the first common flow transmission rate c 1 is the optimal public stream transmission rate; if not reached, execute step S30402.

[0072] Furthermore, the third optimization objective is:

[0073]

[0074] The constraints are:

[0075] P g ≥P0

[0076] c≥0

[0077]

[0078] in, is the set of users {1, ..., k}, W represents the precoder parameters; C k is the public stream s of the kth user c The transmission rate, c = [C1, ..., C k ] T ; t is the minimum user achievable rate, ξ c，k is the public stream s of the kth user c The augmented weighted mean square error, ξ p，k is the private stream s of the kth user p，k The augmented weighted mean square error of ; is the parameter of the nth element in the reconfigurable smart surface, P0 is the minimum perceived power, P t is the total power transmitted; P g is the effective perceived power.

[0079] Furthermore, the fourth optimization objective is:

[0080]

[0081] The constraints are:

[0082] L≥P0

[0083]

[0084]

[0085] Among them, Q p,k ,q p,k、b p,k , Q c,k ,q c,k 、b c,k The calculation formula is as follows:

[0086]

[0087] in, is the set of users {1,...,k}, is the parameter of the nth element in the reconfigurable smart surface, t is the minimum user achievable rate, P0 is the minimum perceived power, C k is the public stream s of the kth user c The transmission rate, To take the real part operation, L is The first-order Taylor expansion of ; W represents the precoder parameter, ω is the MSE weight vector, ω=[ω p,1 ,...ω p,k ,ω c,1 ,...ω c,k ],ω c is the public flow MSE weight vector, ω c,k is the public flow MSE weight vector of the kth user, ω p,k is the MSE weight vector of the private stream of the kth user; g is the equalizer vector, g = [g p,1 ,...g p,k ,g c,1 ,...g c,k ], g c,k is the public flow equalizer vector of the kth user, g p,k is the equalizer vector of the private stream of the kth user; is the channel from the base station to the kth user, h r,k is the channel from the reconfigurable smart surface to the k-th user, G is the channel from the base station to the reconfigurable smart surface; σ is the additive white Gaussian noise power at the k-th user.

[0088] Compared with the prior art, the present invention has the following beneficial effects:

[0089] This invention establishes an integrated communication and perception system. While fully utilizing hardware facilities and spectrum resources, it adjusts communication and perception resources based on communication and perception tasks, thereby improving the performance of the integrated communication and perception system. Simultaneous and co-frequency perception using communication signals improves the efficiency of hardware equipment and time-frequency resources, increases the freedom of beamforming, and thus enhances the performance of the integrated communication and perception system. Furthermore, the deployment of reconfigurable smart surfaces improves system gain, thereby raising the upper limit of communication and perception capabilities. A weighted minimum mean square error algorithm and block coordinate descent method are used to solve the optimization objective, thereby improving communication system performance and fairness for communication users. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 A flow chart of a method for optimizing the performance of a communication-aware integrated system is provided in an embodiment.

[0091] Figure 2 The communication model and perception model structure diagram provided for the embodiment.

[0092] Figure 3 The embodiment provides a flow chart for transforming and solving the optimization objective using the weighted minimum mean square error algorithm and the block coordinate descent method.

[0093] Figure 4 This is a flowchart for solving the third and fourth optimization objectives using the block coordinate descent method provided in the embodiment.

[0094] Figure 5 This is a graph showing changes in the minimum user rate of various algorithms provided in the embodiments as the number of iterations changes.

[0095] Figure 6 The embodiment provides a graph showing the minimum user rate variation of various algorithms as the perceived power threshold changes under constant transmit power.

[0096] Figure 7 The embodiment provides a graph showing the minimum user rate variation of various algorithms as the transmit power changes under a constant perceived power threshold. DETAILED DESCRIPTION

[0097] The accompanying drawings are for illustrative purposes only and are not to be construed as limiting this patent;

[0098] In order to better illustrate this embodiment, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product size;

[0099] It is understandable to those skilled in the art that some well-known structures and descriptions thereof may be omitted in the drawings.

[0100] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0101] Example

[0102] like Figure 1 As shown, a communication perception integrated system performance optimization method includes:

[0103] S1: Establish an integrated communication and perception system, including a base station, a reconfigurable smart surface, and several users. Establish a communication model based on the message transmission between the base station, the reconfigurable smart surface, and several users. Establish a perception model based on the perception tasks of the base station and the reconfigurable smart surface on existing perception targets.

[0104] S2: Based on the communication model and the perception model, establish an optimization goal of maximizing the minimum user rate under the conditions of the perception power threshold constraint, the transmit power constraint, and the constant modulus constraint;

[0105] S3: Using the weighted minimum mean square error algorithm and the block coordinate descent method to transform and solve the optimization objective to obtain the optimal solution of the optimization objective.

[0106] It should be noted that reconfigurable smart surface is also called RIS.

[0107] Furthermore, if Figure 2 As shown in FIG, a communication model is established based on message transmission between a base station, a reconfigurable smart surface, and several users, including:

[0108] The base station generates a baseband signal transmission signal, which is directly transmitted to each user through a first channel, and is also transmitted to the reconfigurable smart surface through a second channel, and then transmitted to each user through a third channel;

[0109] The baseband signal transmission signal is:

[0110]

[0111] The signal received by the kth downlink communication user is:

[0112]

[0113] The kth user decodes s c The signal-to-interference-noise ratio is:

[0114]

[0115] The kth user decodes s p,k The signal-to-interference-noise ratio is:

[0116]

[0117] The kth user decodes s c The rate is:

[0118] R c,k =log2(1+γ c,k )

[0119] The kth user decodes s p,k The rate is:

[0120] R p,k =log2(1+γ p,k )

[0121] Among them, k is the user serial number, K is the total number of users, is the set of users {1,...,k}, m k is the message of the kth user, m k Split into common part m c,k 、Private part m p,k , public streams c The common part of all users {m c,1 ,...,m c,k} are jointly encoded, private stream {s p,1 ,...,s p,k} respectively by the user's private part {m p,1 ,...,m p,k} encoded; data stream [s c ,s p,1 ,...,s p,k ]Use precoder[w c ,w1,...,w k ] to encode; is the first channel from the base station to the kth user, h r,k is the third channel from the reconfigurable smart surface to the kth user, G is the second channel from the base station to the reconfigurable smart surface, Θ is the reflection phase shift matrix of the reconfigurable smart surface; represents the reconfigurable smart surface parameters; σ is the additive white Gaussian noise power at the kth user;

[0122] In a specific embodiment,

[0123] Furthermore, if Figure 2 As shown in FIG, the perception model is established based on the perception tasks of the base station and the reconfigurable intelligent surface to the existing perception target, including:

[0124] While the base station directly senses the existing sensing target through the fourth channel, it also senses the existing sensing target through the second channel to the reconfigurable smart surface through the fifth channel.

[0125] The channel between the base station and the sensing target is:

[0126]

[0127] The effective perceived power is:

[0128]

[0129] Among them, h d,t is the channel from the base station to the sensing target, h r,t is the channel from the reconfigurable smart surface to the sensing target, G is the channel from the base station to the reconfigurable smart surface, Θ is the reflection phase shift matrix of the reconfigurable smart surface, W represents the precoder parameter, W = [w c ,w1,...,w k ].

[0130] In a specific embodiment,

[0131] Furthermore, the optimization goal of maximizing the minimum user rate under the conditions of satisfying the perceived power threshold constraint, the transmit power constraint, and the constant modulus constraint is:

[0132]

[0133] The constraints are:

[0134] P g ≥P0

[0135]

[0136] c≥0

[0137]

[0138] in, is the set of users {1,...,k}, C k is the public stream s of the kth user c The transmission rate, c=[C1,...,C k ] T ; is the parameter of the nth element in the reconfigurable smart surface, W represents the precoder parameter, P0 is the minimum perceived power, P t is the total transmitted power, Θ is the reflection phase shift matrix of the reconfigurable smart surface; R c,k Decode s for the kth user c The rate, R p,k Decode s for the kth user p,k The rate of P g is the effective perceived power.

[0139] It should be noted that the precoder parameter W is an active beam, and the reflection phase shift matrix Θ of the reconfigurable smart surface is a passive beam.

[0140] Furthermore, if Figure 3 As shown, in step S3, the optimization objective is transformed and solved using the weighted minimum mean square error algorithm and the block coordinate descent method, including:

[0141] S301: introducing a slack variable to express the minimum user achievable rate, and the optimization objective can be converted into a first optimization objective;

[0142] S302: Proposing a second optimization objective based on the first optimization objective and a weighted minimum mean square error algorithm;

[0143] S303: Split the second optimization objective into a third optimization objective and a fourth optimization objective;

[0144] S304: solving the third optimization objective and the fourth optimization objective using the block coordinate descent method to obtain the optimal solution of the optimization objective.

[0145] Furthermore, the first optimization objective is:

[0146]

[0147] The constraints are:

[0148]

[0149] P g ≥P0

[0150]

[0151] c≥0

[0152]

[0153] in, is the set of users {1,...,k}, C k is the public stream s of the kth user c The transmission rate, c=[C1,...,C k ] T ; R p,k Decode s for the kth user p,k rate, W represents the precoder parameter, Θ is the reflection phase shift matrix of the reconfigurable smart surface; t is the minimum user achievable rate; is the parameter of the nth element in the reconfigurable smart surface, P0 is the minimum perceived power, P t is the total power of the transmission; R c,kDecode s for the kth user c The rate of P g is the effective perceived power.

[0154] Furthermore, the second optimization objective is:

[0155]

[0156] The constraints are:

[0157]

[0158] P g ≥P0

[0159] c≥0

[0160]

[0161] in, is the set of users {1,...,k}, ω is the MSE weight vector, g is the equalizer vector, W represents the precoder parameters, Θ is the reflection phase shift matrix of the reconfigurable smart surface; C k is the public stream s of the kth user c The transmission rate, c=[C1,...,C k ] T ; t is the minimum user achievable rate, ξ c,k is the public stream s of the kth user c The augmented weighted mean square error, ξ p,k is the private stream s of the kth user p,k The augmented weighted mean square error of ; is the parameter of the nth element in the reconfigurable smart surface, P0 is the minimum perceived power, P t is the total power transmitted; P g is the effective perceived power.

[0162] It should be noted that the second optimization objective can be derived from the first optimization objective, and the process is as follows:

[0163] The public stream decoded by the kth user

[0164]

[0165] The private stream decoded by the kth user

[0166]

[0167] Among them, g c,k and g p,k They are balancers for public and private flows respectively.

[0168] It can be obtained that the public mean square error of the kth user is

[0169]

[0170] Private mean square error of the kth user

[0171]

[0172] in

[0173]

[0174] make Available

[0175]

[0176] In summary, the minimum mean square error of the public flow of the kth user can be obtained

[0177]

[0178] Minimum mean square error of the private flow of the kth user

[0179]

[0180] The signal-to-interference-noise ratio of the decoded public stream is

[0181]

[0182] The signal-to-interference-and-noise ratio of the decoded private stream is

[0183]

[0184] It can be obtained that the rate at which the kth user decodes the public stream is

[0185]

[0186] The rate at which the kth user decodes the private stream

[0187]

[0188] It can be obtained that the augmented weighted mean square error of the kth user's public flow is

[0189]

[0190] Augmented weighted mean square error of the kth user's private stream

[0191]

[0192] After optimizing the equalizer and weights, the relationship between rate and weighted minimum mean square error can be obtained.

[0193]

[0194] According to the first-order optimality condition, the value of the optimal equalizer is

[0195]

[0196] The optimal weight is

[0197]

[0198] In summary, the first optimization objective can be derived as the second optimization objective.

[0199] Furthermore, if Figure 4 As shown, in step S304, the third optimization objective and the fourth optimization objective are solved using the block coordinate descent method, including:

[0200] S30401: Initialize the first precoder parameter W 1 , the reflection phase shift matrix Θ of the first reconfigurable smart surface 1 , the first minimum user achievable rate t 1 ; The first precoder parameter W 1 As the second precoder parameter W 2 , the first minimum user achievable rate t 1 As the second minimum user achievable rate t 2 ; The reflection phase shift matrix Θ of the first reconfigurable smart surface 1 As the reflection phase shift matrix Θ of the second reconfigurable smart surface 2 ; Set the first minimum user achievable rate t 1 As the fourth minimum user achievable rate t 4 and the sixth minimum user achievable rate t 6 ;

[0201] S30402: Based on the first precoder parameter W 1 , the reflection phase shift matrix Θ of the first reconfigurable smart surface 1 , the first minimum user achievable rate t 1 , calculate the first MSE weight vector ω 1 , the first equalizer vector g 1 ;

[0202] S30403: Based on the second precoder parameter W 2 , the reflection phase shift matrix Θ of the first reconfigurable smart surface 1 , the first MSE weight vector ω 1 , the first equalizer vector g 1 , the second minimum user achievable rate t2 Calculate the third optimization target and obtain the third precoder parameter W 3 , the third minimum user achievable rate t 3 , first public flow transmission rate c 1 ;

[0203] S30404: According to the third minimum user achievable rate t 3 and the second minimum user achievable rate t 2 , determine whether the first exit condition is met; if so, set the third precoder parameter W 3 As the new first precoder parameter W 1 , execute step S30405; if not reached, set the third precoder parameter W 3 As the new second precoder parameter W 2 , the third smallest user achievable rate t 3 As the new second minimum user achievable rate t 2 , execute step S30403;

[0204] S30405: According to the reflection phase shift matrix Θ of the second reconfigurable smart surface 2 , the first precoder parameter W 1 , first public flow transmission rate c 1 , the first MSE weight vector ω 1 , the first equalizer vector g 1 , the fourth minimum user achievable rate t 4 , calculate the fourth optimization objective and obtain the reflection phase shift matrix Θ of the third reconfigurable smart surface 3 , the fifth minimum user achievable rate t 5 ;

[0205] S30406: According to the fifth minimum user achievable rate t 5 and the fourth minimum user achievable rate t 4 , determine whether the second exit condition is met; if so, the reflection phase shift matrix Θ of the third reconfigurable smart surface is 3 The reflective phase-shift matrix Θ as the first new reconfigurable smart surface 1 , the fifth smallest user achievable rate t 5 As the new sixth minimum user achievable rate t 6 , execute step S30407; if not reached, set the reflection phase shift matrix Θ of the third reconfigurable smart surface 3 The reflection phase shift matrix Θ as a new second reconfigurable smart surface 2 , the fifth smallest user achievable rate t 5 As the new fourth minimum user achievable rate t4 , execute step S30405;

[0206] S30407: According to the sixth minimum user achievable rate t 6 and the first minimum user achievable rate t 1 , determine whether the third exit condition is met; if so, the sixth minimum user achievable rate t 6 For the optimal minimum user achievable rate, the first precoder parameter W 1 is the optimal precoder parameter, the reflection phase shift matrix Θ of the first reconfigurable smart surface 1 is the reflection phase shift matrix of the optimal reconfigurable smart surface, the first common flow transmission rate c 1 is the optimal public stream transmission rate; if not reached, execute step S30402.

[0207] Furthermore, the third optimization objective is:

[0208]

[0209] The constraints are:

[0210] P g ≥P0

[0211] c≥0

[0212]

[0213] in, is the set of users {1, ..., k}, W represents the precoder parameters; C k is the public stream s of the kth user c The transmission rate, c = [C1, ..., C k ] T ; t is the minimum user achievable rate, ξ c，k is the public stream s of the kth user c The augmented weighted mean square error, ξ p，k is the private stream s of the kth user p，k The augmented weighted mean square error of ; is the parameter of the nth element in the reconfigurable smart surface, P0 is the minimum perceived power, P t is the total power transmitted; P g is the effective perceived power.

[0214] It should be noted that the constraint calculation process of the third optimization objective is as follows:

[0215] Using Taylor expansion We can get:

[0216]

[0217] in, and They represent the first precoder parameters of the public stream and the first precompiler parameters of the private stream respectively; the third optimization objective is converted into a convex problem, which can be solved using the convex optimization problem solver cvx.

[0218] Furthermore, the fourth optimization objective is:

[0219]

[0220] The constraints are:

[0221] L≥P0

[0222]

[0223] Among them, Q p,k ,q p,k 、b p,k , Q c,k ,q c,k 、b c,k The calculation formula is as follows:

[0224]

[0225]

[0226] in, is the set of users {1,...,k}, is the parameter of the nth element in the reconfigurable smart surface, t is the minimum user achievable rate, P0 is the minimum perceived power, C k is the public stream s of the kth user c The transmission rate, To take the real part operation, L is The first-order Taylor expansion of ; W represents the precoder parameter, ω is the MSE weight vector, ω=[ω p,1 ,...ω p,k ,ω c,1 ,...ω c,k ],ω c is the public flow MSE weight vector, ω c,k is the public flow MSE weight vector of the kth user, ω p,k is the MSE weight vector of the private stream of the kth user; g is the equalizer vector, g = [g p,1 ,...g p,k ,g c,1 ,...g c,k ], g c,kis the public flow equalizer vector of the kth user, g p,k is the equalizer vector of the private stream of the kth user; is the channel from the base station to the kth user, h r,k is the channel from the reconfigurable smart surface to the k-th user, G is the channel from the base station to the reconfigurable smart surface; σ is the additive white Gaussian noise power at the k-th user.

[0227] It should be noted that the fourth optimization objective can be derived by adding the following conditions to the third optimization objective:

[0228] because

[0229]

[0230] Available

[0231]

[0232] In a specific embodiment, the following constraints are solved using a manifold-based algorithm, for example, a Riemann gradient algorithm:

[0233]

[0234] In a specific embodiment, The first-order Taylor expansion L of is as follows:

[0235]

[0236] in, are the reconfigurable smart surface parameters of the previous iteration.

[0237] The derivation process of the Taylor expansion is:

[0238] Depend on

[0239] make

[0240] in

[0241] To which Performing a first-order Taylor expansion gives the Taylor expansion.

[0242] like Figure 5 、 Figure 6 、 Figure 7As shown in the figure, RSMARIS represents the reconfigurable smart surface assisted rate division multiple access synaesthesia integration system; RSMAnoRIS represents the rate division multiple access synaesthesia integration system without the deployment of reconfigurable smart surface; SDMARIS represents the reconfigurable smart surface assisted space division multiple access synaesthesia integration system; SDMAnoRIS represents the space division multiple access synaesthesia integration system without the deployment of reconfigurable smart surface; FDRCRIS represents the frequency division communication perception system assisted by reconfigurable smart surface, in which the communication and perception tasks use orthogonal frequencies, share the transmission power, and the communication method adopts rate division multiple access; FDRCnoRIS represents the frequency division communication perception system without the deployment of reconfigurable smart surface, in which the communication and perception tasks use orthogonal frequencies, share the transmission power, and the communication method adopts rate division multiple access.

[0243] It should be noted that if Figure 5 As shown, the present invention uses reconfigurable smart surface-assisted rate division multiple access (RSMA), which achieves better algorithm performance and faster convergence. Because of the presence of common flows, RSMA can manage interference between users and between communication and perception, resulting in better gain for the reconfigurable smart surface-assisted rate division multiple access algorithm.

[0244] It should be noted that if Figure 6 As shown in the figure, under the condition of constant transmit power, the algorithm of the present invention using reconfigurable smart surface assisted rate division multiple access (RSMA) outperforms other solutions. The algorithm without reconfigurable smart surface cannot complete the synaesthesia task in the scenario with high perception power threshold.

[0245] It should be noted that if Figure 7 As shown, under the condition of a constant sensing power threshold, the algorithm of the present invention using reconfigurable smart surfaces to assist rate division multiple access (RSMA) outperforms other solutions. Because RSMA has common streams that can continuously cancel interference, RSMA achieves greater gains than SDMA in low-transmit power scenarios. Algorithms that do not include reconfigurable smart surfaces are unable to complete the synaesthesia task in low-transmit power scenarios.

[0246] The same or similar reference numerals correspond to the same or similar components;

[0247] The terms used in the drawings to describe positional relationships are for illustrative purposes only and should not be construed as limiting this patent;

[0248] Obviously, the above embodiments of the present invention are merely examples for the purpose of clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the performance of a communication-aware integrated system, characterized in that: include: S1: Establish a communication and perception integrated system, including a base station, a reconfigurable smart surface, and several users; A communication model is established based on the message transmission between the base station, the reconfigurable smart surface and several users, and a perception model is established based on the perception tasks of the base station and the reconfigurable smart surface on the existing perception targets; S2: Based on the communication model and the perception model, establish an optimization goal of maximizing the minimum user rate under the conditions of the perception power threshold constraint, the transmit power constraint, and the constant modulus constraint; S3: using a weighted minimum mean square error algorithm and a block coordinate descent method to transform and solve the optimization objective to obtain an optimal solution to the optimization objective; The communication model is established based on the message transmission between the base station, the reconfigurable smart surface and several users, including: The base station generates a baseband signal transmission signal, which is directly transmitted to each user through a first channel, and is also transmitted to the reconfigurable smart surface through a second channel, and then transmitted to each user through a third channel; The baseband signal transmission signal is: The signal received by the kth downlink communication user is: The kth user decodes s c The signal-to-interference-noise ratio is: The kth user decodes s p,k The signal-to-interference-noise ratio is: The kth user decodes s c The rate is: R c,k =log2(1+γ c,k ) The kth user decodes s p,k The rate is: R p,k =log2(1+γ p,k ) Among them, k is the user serial number, K is the total number of users, is the set of users {1,...,k}, m k is the message of the kth user, m k Split into common part m c,k 、Private part m p,k , public streams c The common part of all users {m c,1 ,...,m c,k } are jointly encoded, private stream {s p,1 ,...,s p,k } respectively by the user's private part {m p,1 ,...,m p,k } encoded; data stream [s c ,s p,1 ,...,s p,k ]Use precoder[w c ,w1,...,w k ] to encode; is the first channel from the base station to the kth user, h r,k is the third channel from the reconfigurable smart surface to the kth user, G is the second channel from the base station to the reconfigurable smart surface, Θ is the reflection phase shift matrix of the reconfigurable smart surface; represents the reconfigurable smart surface parameters; σ is the additive white Gaussian noise power at the kth user; The perception model is established based on the perception tasks of the base station and reconfigurable intelligent surface on the existing perception targets, including: While the base station directly senses the existing sensing target through the fourth channel, it also senses the existing sensing target through the second channel to the reconfigurable smart surface through the fifth channel. The channel between the base station and the sensing target is: The effective perceived power is: Among them, h d,t is the channel from the base station to the sensing target, h r,t is the channel from the reconfigurable smart surface to the sensing target, W = [w c ,w1,...,w k ]; The optimization goal of maximizing the minimum user rate under the conditions of the perceived power threshold constraint, the transmit power constraint, and the constant modulus constraint is: The constraints are: P g ≥P0 c≥0 in, is the set of users {1,...,k}, C k is the public stream s of the kth user c The transmission rate, c=[C1,...,C k ] T ; is the parameter of the nth element in the reconfigurable smart surface, P0 is the minimum perceived power, P t is the total power of the transmission; R c,k Decode s for the kth user c The rate, R p,k Decode s for the kth user p,k The rate of P g is the effective perceived power.

2. The method for optimizing the performance of a communication-awareness integrated system according to claim 1, wherein: In step S3, the optimization objective is transformed and solved using the weighted minimum mean square error algorithm and the block coordinate descent method, including: S301: introducing a slack variable to express the minimum user achievable rate, and the optimization objective can be converted into a first optimization objective; S302: Proposing a second optimization objective based on the first optimization objective and a weighted minimum mean square error algorithm; S303: Split the second optimization objective into a third optimization objective and a fourth optimization objective; S304: solving the third optimization objective and the fourth optimization objective using the block coordinate descent method to obtain the optimal solution of the optimization objective.

3. The method for optimizing the performance of a communication-awareness integrated system according to claim 2, wherein: The first optimization goal is: The constraints are: P g ≥P0 c≥0 Among them, C k is the public stream s of the kth user c The transmission rate, c=[C1,...,C k ] T ; R p,k Decode s for the kth user p,k rate, W represents the precoder parameter; t is the minimum user achievable rate; P0 is the minimum perceived power, P t is the total power of the transmission; R c,k Decode s for the kth user c The rate of P g is the effective perceived power.

4. The method for optimizing the performance of a communication-awareness integrated system according to claim 3, wherein: The second optimization goal is: The constraints are: P g ≥P0 c≥0 Where Θ is the MSE weight vector, g is the equalizer vector; C k is the public stream s of the kth user c The transmission rate, c=[C1,...,C k ] T ; t is the minimum user achievable rate, ξ c,k is the public stream s of the kth user c The augmented weighted mean square error, ξ p,k is the private stream s of the kth user p,k The augmented weighted mean square error; P0 is the minimum perceptual power, P t is the total power transmitted; P g is the effective perceived power.

5. A communication perception integrated system performance optimization method according to claim 4, characterized in that: The third optimization goal is: The constraints are: P g ≥P0 c≥0 in, is the set of users {1,...,k}; C k is the public stream s of the kth user c The transmission rate, c=[C1,...,C k ] T ; t is the minimum user achievable rate, ξ c,k is the public stream s of the kth user c The augmented weighted mean square error, ξ p,k is the private stream s of the kth user p,k The augmented weighted mean square error; P0 is the minimum perceptual power, P t is the total power transmitted; P g is the effective perceived power.

6. A communication perception integrated system performance optimization method according to claim 5, characterized in that: The fourth optimization objective is: The constraints are: L≥P0 Among them, Q p,k ,q p,k 、b p,k , Q c,k ,q c,k 、b c,k The calculation formula is as follows: Where t is the minimum user achievable rate, P0 is the minimum perceived power, C k is the public stream s of the kth user c The transmission rate, To take the real part operation, L is The first-order Taylor expansion of ω is the MSE weight vector, ω=[ω p,1 ,...ω p,k ,ω c,1 ,...ω c,k ],ω c is the public flow MSE weight vector, ω c,k is the public flow MSE weight vector of the kth user, ω p,k is the MSE weight vector of the private stream of the kth user; g is the equalizer vector, g = [g p,1 ,...g p,k ,g c,1 ,...g c,k ], g c,k is the public flow equalizer vector of the kth user, g p,k is the equalizer vector of the private stream of the kth user; is the channel from the base station to the kth user, h r,k is the channel from the reconfigurable smart surface to the kth user.

7. A communication perception integrated system performance optimization method according to claim 6, characterized in that: In step S304, the third optimization objective and the fourth optimization objective are solved using the block coordinate descent method, including: S30401: Initialize the first precoder parameter W 1 , the reflection phase shift matrix Θ of the first reconfigurable smart surface 1 , the first minimum user achievable rate t 1 ; The first precoder parameter W 1 As the second precoder parameter W 2 , the first minimum user achievable rate t 1 As the second minimum user achievable rate t 2 ; The reflection phase shift matrix Θ of the first reconfigurable smart surface 1 As the reflection phase shift matrix Θ of the second reconfigurable smart surface 2 ; Set the first minimum user achievable rate t 1 As the fourth minimum user achievable rate t 4 and the sixth minimum user achievable rate t 6 ; S30402: Based on the first precoder parameter W 1 , the reflection phase shift matrix Θ of the first reconfigurable smart surface 1 , the first minimum user achievable rate t 1 , calculate the first MSE weight vector ω 1 , the first equalizer vector g 1 ; S30403: Based on the second precoder parameter W 2 , the reflection phase shift matrix Θ of the first reconfigurable smart surface 1 , the first MSE weight vector ω 1 , the first equalizer vector g 1 , the second minimum user achievable rate t 2 Calculate the third optimization target and obtain the third precoder parameter W 3 , the third minimum user achievable rate t 3 , first public flow transmission rate c 1 ; S30404: According to the third minimum user achievable rate t 3 and the second minimum user achievable rate t 2 , determine whether the first exit condition is met; if so, set the third precoder parameter W 3 As the new first precoder parameter W 1 , execute step S30405; if not reached, set the third precoder parameter W 3 As the new second precoder parameter W 2 , the third smallest user achievable rate t 3 As the new second minimum user achievable rate t 2 , execute step S30403; S30405: According to the reflection phase shift matrix Θ of the second reconfigurable smart surface 2 , the first precoder parameter W 1 , first public flow transmission rate c 1 , the first MSE weight vector ω 1 , the first equalizer vector g 1 , the fourth minimum user achievable rate t 4 , calculate the fourth optimization objective and obtain the reflection phase shift matrix Θ of the third reconfigurable smart surface 3 , the fifth minimum user achievable rate t 5 ; S30406: According to the fifth minimum user achievable rate t 5 and the fourth minimum user achievable rate t 4 , determine whether the second exit condition is met; if so, the reflection phase shift matrix Θ of the third reconfigurable smart surface is 3 The reflective phase-shift matrix Θ as the first new reconfigurable smart surface 1 , the fifth smallest user achievable rate t 5 As the new sixth minimum user achievable rate t 6 , execute step S30407; if not reached, set the reflection phase shift matrix Θ of the third reconfigurable smart surface 3 The reflection phase shift matrix Θ as a new second reconfigurable smart surface 2 , the fifth smallest user achievable rate t 5 As the new fourth minimum user achievable rate t 4 , execute step S30405; S30407: According to the sixth minimum user achievable rate t 6 and the first minimum user achievable rate t 1 , determine whether the third exit condition is met; if so, the sixth minimum user achievable rate t 6 For the optimal minimum user achievable rate, the first precoder parameter W 1 is the optimal precoder parameter, the reflection phase shift matrix Θ of the first reconfigurable smart surface 1 is the reflection phase shift matrix of the optimal reconfigurable smart surface, the first common flow transmission rate c 1 is the optimal public stream transmission rate; if not reached, execute step S30402.

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