Performance optimization method for communication and perception integrated system

Abstract

The invention discloses a communication perception integrated system performance optimization method. The method comprises the following steps: firstly, establishing a communication and perception integrated system, and respectively establishing a communication model and a perception model according to message transmission, so as to obtain an optimization target of maximizing a user rate under a constraint condition. And finally, carrying out conversion solution on the optimization target by utilizing a weighted minimum error algorithm and a block descent algorithm to obtain an optimal solution of the optimization target. The transmission rate of the user is high, the energy consumption of the base station is small, and the overall performance of the system is high.

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] In traditional communication methods, signal perception and communication capabilities are often considered separately and not as a whole. As a result, when using traditional communication methods and parameters, the efficiency of equipment and frequency utilization is low, the user's transmission rate is slow, the base station consumes a lot of energy, and the overall system performance is low.

[0004] The prior art discloses a security beamforming method for an RSMA-ISAC system. The method includes a communication module and a perception module; the communication module performs rate segmentation and encoding on user messages to obtain a public data stream and a private data stream, and both are added with interference symbols and mapped to the transmitting antenna through a precoder to obtain an ISAC signal; the perception module sets an objective function and decodes after solving the constraint function to obtain a security beam. The method does not comprehensively consider the two functions of communication and perception. Summary of the invention

[0005] The present invention aims at the defects of the prior art, such as slow user transmission rate, large 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. The technical solution of the present invention is as follows:

[0007] A communication perception integrated system performance optimization method, comprising:

[0008] S1: Establish a communication and perception integrated 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, and establish a perception model based on the perception tasks of the base station and the reconfigurable smart surface to the existing perception targets;

[0009] S2: According to the communication model and the perception model, an optimization goal of maximizing the minimum user rate under the conditions of the perception power threshold constraint, the transmission power constraint, and the constant modulus constraint is established;

[0010] 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.

[0011] Furthermore, a communication model is established according to message transmission among a base station, a 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 consists of the user's private part {m p,1,...,m p,k}Encoded; data stream[s c ,s p,1 ,...,s p,k ]Use the 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; φ represents the reconfigurable smart surface parameters; σ is the additive white Gaussian noise power at the kth user;

[0026] Furthermore, the perception model is established according to the perception tasks of the base station and the reconfigurable intelligent surface to 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 ; φ n 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 g is the effective perceived power.

[0042] Furthermore, in step S3, the optimization objective is transformed and solved by using a weighted minimum mean square error algorithm and a 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: Propose a second optimization goal based on the first optimization goal and in combination with 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: Using the block coordinate descent method to solve the third optimization objective and the fourth optimization objective 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; φ n is the parameter of the nth element in the reconfigurable smart surface, P0 is the minimum perceived power, P t is the total transmitted power; R c,k Decode s for the kth user c The rate of 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 parameter, 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 achievable rate for users, 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 nis the parameter of the nth element in the reconfigurable smart surface, P0 is the minimum perceived power, P t is the total transmitted power; P g is the effective perceived power.

[0064] Furthermore, in step S304, the third optimization objective and the fourth optimization objective are solved by 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 second reconfigurable smart surface, the reflection phase shift matrix θ 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: According to 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 , the first public flow transmission rate c 1 ;

[0068] S30404: According to the third minimum user achievable rate t 3and the second minimum achievable user 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 , the 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, set the reflection phase shift matrix Θ of the third reconfigurable smart surface 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 reflective 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 achievable rate for users, 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 n is the parameter of the nth element in the reconfigurable smart surface, P0 is the minimum perceived power, P t is the total transmitted power; 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}, φ n is the parameter of the nth element in the reconfigurable smart surface, t is the minimum achievable rate for the user, 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, 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 common 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 common 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] The present invention establishes an integrated system of synaesthesia. On the basis of making full use of hardware facilities and spectrum resources, the communication perception resources are adjusted according to the communication and perception tasks, thereby improving the performance of the integrated system of communication perception. Communication signals are used for simultaneous and co-frequency perception, thereby improving the utilization efficiency of hardware equipment and time-frequency resources, increasing the degree of freedom of beamforming, and thereby improving the performance of the integrated system of communication perception. At the same time, the deployment of reconfigurable intelligent surfaces brings better system gain, thereby increasing the upper limit of communication perception capabilities. The weighted minimum mean square error algorithm and the block coordinate descent method are used to solve the optimization target, thereby improving the performance of the communication system and the fairness of communication users. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 A flow chart of a communication perception integrated system performance optimization method provided in an embodiment.

[0091] Figure 2 A structural diagram of the communication model and perception model provided for the implementation example.

[0092] Figure 3 A flow chart for converting and solving the optimization target using a weighted minimum mean square error algorithm and a block coordinate descent method is provided in an embodiment.

[0093] Figure 4 A flowchart for solving the third optimization objective and the fourth optimization objective using the block coordinate descent method provided in the embodiment.

[0094] Figure 5 A graph showing the change 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 A graph of the minimum user rate variation of various algorithms with variation of transmit power under a constant perceived power threshold provided by an embodiment. DETAILED DESCRIPTION

[0097] The drawings are for illustrative purposes only and should not be construed as limiting the present patent;

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

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

[0100] The technical solution of the present invention is further described below in conjunction with 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 a communication and perception integrated 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, and establish a perception model based on the perception tasks of the base station and the reconfigurable smart surface to the existing perception targets;

[0104] S2: According to the communication model and the perception model, an optimization goal of maximizing the minimum user rate under the conditions of the perception power threshold constraint, the transmission power constraint, and the constant modulus constraint is established;

[0105] 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.

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

[0107] Furthermore, if Figure 2 As shown, a communication model is established based on the message transmission between the base station, the 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 consists of the user's private part {m p,1 ,...,m p,k}Encoded; data stream[s c ,s p,1 ,...,s p,k ]Use the 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; φ 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, 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 ; φ n 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 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 by 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: Propose a second optimization goal based on the first optimization goal and in combination with 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: Using the block coordinate descent method to solve the third optimization objective and the fourth optimization objective 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; φ n is the parameter of the nth element in the reconfigurable smart surface, P0 is the minimum perceived power, P t is the total transmitted power; R c,kDecode s for the kth user c The rate of 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 parameter, 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 achievable rate for users, 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 n is the parameter of the nth element in the reconfigurable smart surface, P0 is the minimum perceived power, P t is the total transmitted power; 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 equalizers for public and private flows respectively.

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

[0169]

[0170] The 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-to-noise ratio of the decoded public stream is

[0181]

[0182] The signal-to-interference-to-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 the rate and the 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 by 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 second reconfigurable smart surface, the reflection phase shift matrix θ 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: According to 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 , the first public flow transmission rate c 1 ;

[0203] S30404: According to the third minimum user achievable rate t 3 and the second minimum achievable user 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 , the 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, set the reflection phase shift matrix Θ of the third reconfigurable smart surface 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 reflective 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;

[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 achievable rate for users, 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 n is the parameter of the nth element in the reconfigurable smart surface, P0 is the minimum perceived power, P t is the total transmitted power; 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 respectively represent the first precoder parameters of the public stream and the first precompiler parameters of the private stream; 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}, φ n is the parameter of the nth element in the reconfigurable smart surface, t is the minimum achievable rate for the user, 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, 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 common 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 ,gc,1 ,...g c,k ],g c,k is the common 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 is as follows:

[0235]

[0236] Among them, φ t 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] For which φ H Performing a first-order Taylor expansion on Aφ gives the Taylor expansion.

[0242] like Figure 5 , Figure 6 , Figure 7As shown in the figure, RSMARIS represents the rate division multiple access synaesthesia integration system assisted by reconfigurable smart surface; RSMAnoRIS represents the rate division multiple access synaesthesia integration system without deploying reconfigurable smart surface; SDMARIS represents the space division multiple access synaesthesia integration system assisted by reconfigurable smart surface; SDMAnoRIS represents the space division multiple access synaesthesia integration system without deploying reconfigurable smart surface; FDRCRIS represents the frequency division communication perception system assisted by reconfigurable smart surface, 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 deploying reconfigurable smart surface, 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 adopts reconfigurable smart surface assisted rate division multiple access (RSMA), and the algorithm has better performance and faster convergence. Rate division multiple access can manage interference between users and between communication and perception due to the existence of common flow, so the gain of the reconfigurable smart surface assisted rate division multiple access algorithm is better.

[0244] It should be noted that if Figure 6 As shown in the figure, under the condition of constant transmission power, the algorithm of the present invention using reconfigurable smart surface assisted rate division multiple access (RSMA) performs better than other schemes. 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 in the figure, under the condition of constant sensing power threshold, the algorithm performance of the present invention using reconfigurable smart surface assisted rate division multiple access (RSMA) is better than other schemes. Since rate division multiple access has a common stream that can continuously interfere with each other, the application of rate division multiple access algorithm has a greater gain than SDMA in the scenario of low transmission power. The algorithm without reconfigurable smart surface cannot complete the synaesthesia task in the scenario of low transmission power.

[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 only used for illustrative purposes and should not be construed as limiting this patent;

[0248] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A communication perception integrated system performance optimization method, 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 according to the message transmission between the base station, the reconfigurable intelligent surface and a number of users, and a perception model is established according to the perception tasks of the base station and the reconfigurable intelligent surface to the existing perception targets; S2: According to the communication model and the perception model, an optimization goal of maximizing the minimum user rate under the conditions of the perception power threshold constraint, the transmission power constraint, and the constant modulus constraint is established; 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.

2. According to claim 1, a communication perception integrated system performance optimization method is characterized in that: 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 consists of the user's private part {m p,1 ,...,m p,k }Encoded; data stream[s c ,s p,1 ,...,s p,k ]Use the 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; φ represents the reconfigurable smart surface parameters; σ is the additive white Gaussian noise power at the kth user.

3. According to claim 2, a communication perception integrated system performance optimization method is characterized in that: The perception model is established based on the perception tasks of the base station and the reconfigurable intelligent surface to 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, 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 ].

4. According to claim 3, a communication perception integrated system performance optimization method is characterized in that: 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: The constraints are: 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 ; φ n 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.

5. According to claim 4, a communication perception integrated system performance optimization method is characterized in that: In step S3, the optimization target is transformed and solved by 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: Propose a second optimization goal based on the first optimization goal and in combination with a weighted minimum mean square error algorithm; S303: Split the second optimization objective into a third optimization objective and a fourth optimization objective; S304: Using the block coordinate descent method to solve the third optimization objective and the fourth optimization objective to obtain the optimal solution of the optimization objective.

6. A communication perception integrated system performance optimization method according to claim 5, characterized in that: The first optimization goal is: The constraints are: 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; φ n is the parameter of the nth element in the reconfigurable smart surface, P0 is the minimum perceived power, P t is the total transmitted power; R c,k Decode s for the kth user c The rate of P g is the effective perceived power.

7. A communication perception integrated system performance optimization method according to claim 6, characterized in that: The second optimization objective is: The constraints are: in, is the set of users {1,...,k}, ω is the MSE weight vector, g is the equalizer vector, W represents the precoder parameter, 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 achievable rate for users, 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 n is the parameter of the nth element in the reconfigurable smart surface, P0 is the minimum perceived power, P t is the total transmitted power; P g is the effective perceived power.

8. A communication perception integrated system performance optimization method according to claim 7, characterized in that: The third optimization goal is: The constraints are: 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 achievable rate for users, 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 n is the parameter of the nth element in the reconfigurable smart surface, P0 is the minimum perceived power, P t is the total transmitted power; P g is the effective perceived power.

9. A communication perception integrated system performance optimization method according to claim 8, characterized in that: The fourth optimization goal is: The constraints are: 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: in, is the set of users {1,...,k}, φ n is the parameter of the nth element in the reconfigurable smart surface, t is the minimum achievable rate for the user, 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, 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 common 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 common 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.

10. A communication perception integrated system performance optimization method according to claim 9, characterized in that: In step S304, the third optimization objective and the fourth optimization objective are solved by 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 second reconfigurable smart surface, the reflection phase shift matrix θ 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: According to 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 , the first public flow transmission rate c 1 ; S30404: According to the third minimum user achievable rate t 3 and the second minimum achievable user 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 , the 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, set the reflection phase shift matrix Θ of the third reconfigurable smart surface 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 reflective 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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