Deployment optimization method for high-frequency communication system assisted by multiple intelligent reflectors

Abstract

The present invention provides a high-frequency communication system assisted by multiple intelligent reflective surfaces and a deployment optimization method, which improves the communication performance in complex environments by optimizing multiple IRS (intelligent reflective surfaces) assisted MIMO (multiple input multiple output) systems. The system solves the problem of base station-user link obstruction, uses IRS single reflection to reduce path attenuation, and introduces an orthogonal deployment strategy to ensure that independent propagation paths maximize system capacity. The IRS element allocation and power allocation are optimized by the SCA (continuous convex approximation) algorithm to maximize the channel capacity. The method improves system capacity, has an efficient optimization algorithm, low deployment cost and is green and environmentally friendly, and is suitable for the next generation of efficient wireless communication systems.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless communications, and in particular to a high-frequency communication system assisted by multiple intelligent reflecting surfaces and a deployment optimization method. Background Art

[0002] In the field of wireless communications, MIMO (Multiple Input Multiple Output) technology has become an important means to improve channel capacity and communication performance. However, in Line-of-Sight (LoS) point-to-point MIMO systems, low-rank channels often significantly hinder the implementation of spatial multiplexing. Low-rank channels prevent signals from fully utilizing spatial resources, resulting in reduced transmission efficiency and limiting the overall performance of MIMO systems.

[0003] In order to overcome this technical bottleneck, the Intelligent Reflecting Surface (IRS) has been introduced into wireless communication systems as an emerging technology. IRS achieves intelligent channel reconstruction by adjusting the phase and amplitude of the incident signal, thereby improving the spatial multiplexing efficiency of the MIMO system. Compared with traditional relay stations, IRS does not require additional RF links and energy consumption, so it has the significant advantages of low cost and low energy consumption.

[0004] Most of the existing IRS research focuses on the optimization of a single IRS, which is to enhance the signal strength in a specific direction by adjusting the phase and amplitude of the IRS. However, the coverage and capability of a single IRS are limited, making it difficult to fully improve the performance of the MIMO system in a complex environment. In order to further improve system performance, it is necessary to explore the collaborative optimization method of multiple IRSs.

[0005] In a multi-antenna system, the direct link between the base station and the user equipment often becomes very weak due to obstacles, resulting in a significant decrease in channel capacity. By properly deploying multiple IRSs, multiple reflection links can be established between the base station and the user equipment, thereby reconstructing the channel environment and enhancing the reliability and efficiency of signal transmission. However, the deployment and resource allocation of multiple IRSs face many technical challenges, including how to avoid channel correlation, how to optimize the phase and amplitude allocation of IRSs, and how to determine the optimal deployment location of IRSs.

[0006] Therefore, it is necessary to propose a new technical solution to improve the above technical problems. Summary of the invention

[0007] In view of the defects in the prior art, the object of the present invention is to provide a high-frequency communication system assisted by multiple intelligent reflecting surfaces and a deployment optimization method.

[0008] A high-frequency communication system assisted by multiple intelligent reflective surfaces provided according to the present invention includes: a transmitter, multiple intelligent reflective surface systems and a receiver; the transmitter is equipped with a MIMO multi-antenna system; the intelligent reflective surface system assists the MIMO multi-antenna system in spatial multiplexing; and the receiver receives signals from multiple intelligent reflective surface systems.

[0009] Preferably, the intelligent reflective surface system comprises a plurality of reflective units and a controller; each reflective unit is arranged at a preset position and has no correlation with the channel; and the transmitter jointly optimizes the configuration and power allocation of the reflective units.

[0010] Preferably, the controller adopts a successive convex approximation (SCA) algorithm; the transmitter generates a control signal for the MIMO multi-antenna array, controls the amplitude and phase of the MIMO multi-antenna array through digital beamforming, and performs active beamforming; the controller generates a control signal for the intelligent reflector system unit, and performs passive beamforming by controlling the amplitude and phase of each reflector unit.

[0011] Preferably, the direct link between the transmitter and the receiver becomes weak due to obstacles; and the multi-intelligent reflecting surface system reconstructs the channel through a single reflection path.

[0012] Preferably, the MIMO multi-antenna system includes multiple transmitting antennas and receiving antennas; in a complex scenario where the base station-user link is blocked, resulting in extremely weak signals, the system optimizes the deployment and power allocation of the smart reflector system.

[0013] Preferably, a preset passive beamforming structure is adopted, and based on orthogonality considerations, the deployment design of the intelligent reflector system is performed; the intelligent reflector system is positioned along the discrete Fourier transform direction of the transmitter and the receiver.

[0014] Preferably, when the number of elements and the total power of the smart reflector system are limited, a successive convex approximation algorithm is used to jointly optimize the allocation of elements and the power allocation of the smart reflector system.

[0015] The present invention also provides a deployment optimization method for a high-frequency communication system assisted by multiple intelligent reflecting surfaces, the method applying the high-frequency communication system assisted by multiple intelligent reflecting surfaces described above, the method comprising the following steps:

[0016] Deployment steps: Deploy multiple intelligent reflective surface systems at non-collinear locations on a two-dimensional plane according to an orthogonal deployment strategy;

[0017] Allocation step: Use the successive convex approximation algorithm to perform element allocation and power optimization of the joint intelligent reflector system, transform the non-convex problem into a convex problem, and perform resource allocation.

[0018] Preferably, in the deployment step, all feasible deployment positions are selected and beam forming of the smart reflector system is optimized.

[0019] Preferably, in the allocation step, the smart reflector system elements and power are evenly allocated by an optimization algorithm.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. The present invention introduces multiple IRSs and adopts an orthogonal deployment strategy on a two-dimensional plane. The present invention ensures that each IRS provides an independent propagation path, significantly reducing interference and path loss between signals. This deployment method enables multi-path signals to be effectively transmitted on different paths, thereby greatly improving the channel capacity of the system. Combined with the successive convex approximation (SCA) algorithm, the present invention realizes the joint optimization of IRS element allocation and power allocation, further improving the system capacity. By optimizing the phase difference, element allocation, transmission covariance matrix and IRS deployment position of the IRS, the system can achieve the maximum beam gain and channel capacity under the constraints of the preset maximum transmission power and IRS unit transmission capacity.

[0022] 2. The joint optimization algorithm proposed in the present invention transforms the non-convex optimization problem into a convex optimization problem through the SCA method, thus achieving efficient resource allocation; the algorithm can quickly converge and find the optimal solution in complex multi-dimensional optimization problems, thereby significantly improving the performance and efficiency of the system; the reasonable setting of the initial value also avoids the algorithm from converging to a suboptimal solution, such as evenly distributing resources or allocating all elements to a single IRS, further improving the optimization effect;

[0023] 3. As an emerging technology, the IRS of the present invention has the advantages of low cost and low energy consumption. Compared with the traditional relay station, the IRS does not require additional radio frequency links and energy consumption, thereby reducing the hardware cost and power consumption of the system. The multi-IRS architecture proposed by the present invention is simple and easy to deploy, and is applicable to various wireless communication scenarios, providing new ideas and methods for the design of the next generation of efficient wireless communication systems.

[0024] 4. The low power consumption characteristics of the IRS system of the present invention meet the development requirements of green and energy-saving modern communication systems; by rationally configuring and optimizing the IRS, the present invention can significantly reduce the energy consumption of the system while maintaining high performance, thus achieving green communication;

[0025] 5. When the direct link between the base station and the user equipment becomes weak due to obstacles, the present invention deploys multiple IRSs to establish multiple reflection links between the base station and the user equipment, thereby enhancing the reliability and efficiency of signal transmission; especially in the case of high power and a large number of IRS units, the system exhibits superior performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0027] Figure 1 A schematic diagram of a multi-IRS-assisted MIMO multi-antenna system according to the present invention;

[0028] Figure 2 It is a schematic diagram showing the variation of the achievable rate with the number of IRS elements under different IRS conditions of the present invention;

[0029] Figure 3 It is a schematic diagram of the variation of the achievable rate with power under different IRS conditions of the present invention. DETAILED DESCRIPTION

[0030] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0031] Embodiment 1:

[0032] A high-frequency communication system assisted by multiple intelligent reflective surfaces provided according to the present invention includes: a transmitter, multiple intelligent reflective surface systems and a receiver; the transmitter is equipped with a MIMO multi-antenna system; the intelligent reflective surface system assists the MIMO multi-antenna system in spatial multiplexing; and the receiver receives signals from multiple intelligent reflective surface systems.

[0033] The intelligent reflector system includes multiple reflector units and a controller; each reflector unit is arranged at a preset position and has no correlation with the channel; the transmitter jointly optimizes the configuration and power allocation of the reflector unit. The controller adopts the successive convex approximation SCA algorithm; the transmitter generates a control signal for the MIMO multi-antenna array, controls the amplitude and phase of the MIMO multi-antenna array through digital beamforming, and performs active beamforming; the controller generates a control signal for the intelligent reflector system unit, and performs passive beamforming by controlling the amplitude and phase of each reflector unit. The direct link between the transmitter and the receiver becomes weak due to obstacles; the multi-intelligent reflector system reconstructs the channel through a single reflection path. The MIMO multi-antenna system includes multiple transmitting antennas and receiving antennas; in a complex scenario where the base station-user link is blocked and its signal is extremely weak, the system optimizes the deployment and power allocation of the intelligent reflector system. The deployment design of the intelligent reflector system is carried out using a preset passive beamforming structure and based on orthogonality considerations; the intelligent reflector system is positioned along the discrete Fourier transform direction of the transmitter and the receiver. When the number of elements and total power of smart reflector system are limited, a successive convex approximation algorithm is used to jointly optimize the allocation of elements and power allocation of smart reflector system.

[0034] The present invention also provides a deployment optimization method for a high-frequency communication system assisted by multiple intelligent reflecting surfaces, the method applying the high-frequency communication system assisted by multiple intelligent reflecting surfaces described above, the method comprising the following steps:

[0035] Deployment steps: Deploy multiple intelligent reflective surface systems at non-collinear locations on a two-dimensional plane according to an orthogonal deployment strategy;

[0036] Allocation step: Use the successive convex approximation algorithm to perform element allocation and power optimization of the joint intelligent reflector system, transform the non-convex problem into a convex problem, and perform resource allocation.

[0037] In the deployment step, all feasible deployment positions are selected and the smart reflector system beamforming is optimized.

[0038] In the allocation step, the smart reflector system elements and power are evenly allocated through an optimization algorithm.

[0039] Embodiment 2:

[0040] The present invention proposes a high-frequency communication system assisted by multiple intelligent reflectors and a deployment optimization method. By introducing multiple IRSs and combining orthogonal deployment strategies and successive convex approximation methods, efficient optimization of the multi-IRS system is achieved. Compared with a single IRS configuration, the multi-IRS system exhibits higher superiority in resource allocation and communication performance, providing new ideas and methods for the design of next-generation wireless communication systems. The present invention is based on the urgent need to improve the performance of MIMO systems, enhance signal transmission quality and coverage in the current wireless communication field, and aims to overcome the limitations of low-rank channels through the collaborative optimization of multiple IRSs, thereby achieving a performance leap in high-frequency communication systems.

[0041] The present invention provides a high-frequency communication system assisted by multiple intelligent reflectors and a deployment optimization method, comprising a base station, a multiple intelligent reflector system and a user device; the base station comprises a MIMO multi-antenna system; the multiple intelligent reflector system assists the MIMO multi-antenna system in spatial multiplexing, and optimizes the deployment and resource allocation of the intelligent reflectors to achieve the preset multi-user communication direction and channel gain, thereby improving the system capacity and performance. The present invention utilizes multiple intelligent reflector technology to assist the MIMO multi-antenna system, and significantly enhances the signal transmission quality by jointly optimizing the configuration and power allocation of the intelligent reflector units, especially in the case of high power and a large number of reflector units, showing superior performance.

[0042] A high-frequency communication system assisted by multiple intelligent reflectors includes a transmitter, multiple intelligent reflector (IRS) systems, and a receiver; the base station is equipped with a MIMO multi-antenna system; the intelligent reflector system assists the MIMO multi-antenna system in spatial multiplexing, and improves channel capacity and communication performance by optimizing the deployment and resource allocation of IRS units. The intelligent reflector system includes multiple reflector units and a controller; each reflector unit is arranged at a specific position to avoid channel correlation; the base station improves signal coverage and transmission efficiency by jointly optimizing the configuration and power allocation of the reflector unit. The controller adopts a successive convex approximation (SCA) algorithm to solve the allocation and power optimization problems of the intelligent reflector unit; the base station generates a control signal for the MIMO multi-antenna array, controls the amplitude and phase of the MIMO multi-antenna array through digital beamforming, and realizes active beamforming; the controller generates a control signal for the intelligent reflector unit, and realizes passive beamforming by controlling the amplitude and phase of each reflector unit. The direct link between the base station and the user equipment becomes weak due to obstacles; the multi-intelligent reflector system reconstructs the channel through a single reflection path to enhance the signal transmission quality; in the optimization process, it is assumed that the ideal channel state information is known to ensure the optimal configuration of each intelligent reflector unit. t There are N transmitting antennas, rWe solved the complex scenario where the direct link from the base station to the user is blocked, resulting in extremely weak signals. To simplify the problem, we assumed that all reflecting surfaces are at the same height, thus simplifying the system to two dimensions. In the system deployment, we represent the positions of the base station and the user in a two-dimensional Cartesian coordinate system as and The distances between the base station and the user are d t and d r The IRSk is equipped with a M k (=M v，k ×M h，k ) units, where UPA on IRSk is provided by M v，k Line and M h，k The spacing between all cells is d s The total number of IRS units that can be allocated is M max ,therefore The position of IRSk is expressed as set up and denote the transmitted signal vector and the effective base station-user MIMO channel respectively. Therefore, the signal received by the user is It can be expressed as:

[0043] y=Hx+z,

[0044] in, represents the additive white Gaussian noise at the user, whose power is σ 2 It should be noted that due to the multiplicative nature of the cascade path loss, multiple reflections from multiple IRSs will cause severe cascade path attenuation. Therefore, only a single reflection from each IRS is considered in this study.

[0045] We express the channel matrix from the base station to the kth IRS as The channel matrix from the kth IRS to the user is expressed as We assume that the distributed IRSs are deployed in ideal locations to ensure that there is a direct path (LoS) between the base station and the user. The channel matrix from the base station to the kth IRS can be expressed as:

[0046]

[0047] in represents the complex channel gain from the base station to the kth intelligent reflecting surface (IRS) link, β represents the channel power gain at a reference distance of 1 meter, and d 1，k represents the distance from the base station to the kth IRS. In the array response vector, as well as in and They represent the horizontal angle of arrival (AoA), vertical angle of arrival (AoA) and angle of departure (AoD) of the base station IRSk link respectively. In addition, and a S，k (·) denote the array response vectors at the base station and IRS k, respectively.

[0048] It is worth noting that the array response of the uniform planar array (UPA) can be decomposed into the form of a uniform linear array (ULA), namely In this study, we consider the far-field scenario. Therefore, the array response vector of ULA can be uniformly expressed as:

[0049] a N (X) = [1, e jX ,…,e jX(N-1) ].

[0050] Similar to the link from the base station to the kth IRS, the channel matrix from the kth IRS to the user can be expressed as:

[0051]

[0052] we will is represented as the passive beamforming matrix of the kth IRS, where Represents the element in the kth IRS To achieve the maximum reflection effect, we further set in And we assume perfect channel state information. Therefore, the effective base station-user MIMO channel assisted by K cooperative IRSs is modeled as:

[0053]

[0054] We aim to optimize the IRS phase difference Element allocation Transmit covariance matrix Q and IRS deployment location To maximize the channel capacity of the MIMO system considering multiple IRS assistance, assuming K≤min(N t , N r ), the mathematical expression of the problem is as follows:

[0055]

[0056] tr(Q)≤P,

[0057] Q ≥ 0,

[0058]

[0059] in, P represents the maximum transmission power of the base station, and Represents the set of optional deployment points for IRS.

[0060] In order to maximize the gain of each link through the IRS, we adopt a specific passive beamforming structure and design the IRS deployment based on orthogonality considerations;

[0061] To maximize each link gain through IRS, we adopt the following passive beamforming structure:

[0062]

[0063] in, yes The mth k elements, yes The mth k elements. By adopting the above configuration, f(Φ k )=M k It is worth noting that the strategy of maximizing link gain cannot guarantee optimality. However, under our proposed orthogonal deployment strategy, the link gain maximization method does achieve optimal performance.

[0064] The introduction of IRS enables the creation of a controllable scattering channel environment, potentially establishing favorable conditions for multi-stream transmission. However, due to the interference between different paths, we aim to achieve interference-free paths through orthogonal deployment, the conditions of which are as follows:

[0065]

[0066] With the transmitter and receiver positions fixed, we position the IRS along the discrete Fourier transform (DFT) direction of the transmitter and receiver. This arrangement meets the requirements of AOA and AoD discretization:

[0067]

[0068] We have and Therefore, our problem is transformed into a search task, which is to choose a point that maximizes the total rate given the known power allocation, IRS phase adjustment, and element distribution. It should be noted that this search is constrained: any two selected IRS cannot be collinear. This restriction stems from the fact that collinearity implies linear correlation, which will lead to strong correlation between channels and violate the principle of orthogonal deployment. Therefore, we must choose carefully during the search process.

[0069] This limitation stems from the fact that collinearity implies linear correlation, which leads to strong correlation between channels and violates the principle of orthogonal deployment. Therefore, we must choose carefully during the search process.

[0070] We first select compatible combinations, T Divide into non-overlapping subsets Θ T，1 , Θ T，2 , and Θ R Divide into Θ R，1 , Θ R，2 . Then, we will T，1 With Θ R，1 Pairing to form C 1 , and Θ T，2 With Θ R，2 Pairing to form C 2 We use a 1 、a 2 、b 1 and b 2 Respectively represent Θ T，1 , Θ T，2 , Θ R，1 and θ R，2 This method is due to the fact that not every Θ T The angles in can be compared with Θ R The specific division is determined by the site conditions. Assume that we select K IRSs in total, of which C 1 There are J in it, C 2 There are KJ in , and we can get T，1 and θ R，1 In the example above, we can generate J unique IRS deployment angle combinations by pairing elements without duplication. Similarly, we can generate J unique IRS deployment angle combinations from C 2 In this case, the pairing results of C1 and C2 are and possible combinations. By iterating J from 0 to min(K, a1, a2, b1, b2), we can generate all the required combinations. Finally, we can use the law of cosines to determine the specific deployment location of each IRS, thus obtaining

[0071] When the number of IRS elements and the total power are limited, element allocation and power allocation are performed for multiple IRS blocks to maximize the channel capacity;

[0072] We jointly optimize the IRS element allocation and power under fixed IRS deployment and phase. The MIMO channel capacity can also be expressed as:

[0073]

[0074] where pk represents the amount of transmit power allocated to the kth singular value of H and δk represents the kth singular value of H. In our orthogonal deployment, this means that δk is a singular value of RkΦkTk. Furthermore, we relax the discrete value M to its continuous counterpart In order to solve the problem of coupling terms, we introduce auxiliary variables sk and lk and define in And ξk is The square of the kth singular value of . Therefore, the optimization problem (P1) is simplified to:

[0075]

[0076] in, as well as After the above operations, the problem is still non-convex. Therefore, we use the SCA (Successive Convex Approximation) method to exist Chuhe exist Perform a first-order Taylor expansion at , and get its lower bound approximation:

[0077]

[0078] Therefore, the optimization problem of the tth iteration of SCA can be expressed as:

[0079]

[0080] Therefore, problem (P3) is a convex optimization problem in iteration t, which can be solved using CVX. After each iteration, and Updated to sk and The integer number of reflection elements can be reconstructed by rounding off consecutive solutions to the problem. It is worth noting that the SCA-based approach is very sensitive to the setting of the initial values. We propose to initialize the power allocated to each IRS to P / K and set the initial number of elements of the kth IRS to Mmax / (4k). This approach helps avoid convergence to solutions that evenly allocate resources or assign all elements to a single IRS.

[0081] A deployment optimization method for a high-frequency communication system assisted by multiple intelligent reflectors is provided. A multi-IRS-assisted wireless communication system is applied to achieve spatial multiplexing of a MIMO system by optimizing the deployment and allocation of IRS elements, thereby improving system performance. The method includes the following steps:

[0082] Deployment steps: On a two-dimensional plane, according to the orthogonal deployment strategy, select non-collinear locations to deploy multiple IRSs, ensuring that the location of each IRS avoids channel correlation.

[0083] Allocation step: The successive convex approximation (SCA) method is used to solve the joint IRS element allocation and power optimization problem, transforming the non-convex problem into a convex problem for efficient resource allocation.

[0084] In the deployment step, all feasible deployment locations are selected and IRS beamforming is optimized to maximize the gain of each link; in the allocation step, IRS elements and power are evenly allocated through an optimization algorithm to improve system capacity.

[0085] A multi-IRS-assisted wireless communication system according to the present invention includes a base station, multiple IRSs and user equipment;

[0086] The base station and the user equipment are respectively equipped with a multi-antenna system, the base station is equipped with multiple antennas, and the user equipment is equipped with multiple antennas;

[0087] Through the deployment and resource allocation of multiple IRSs, the spatial multiplexing efficiency and communication performance of the system are improved.

[0088] Preferably, the multi-IRS system comprises a plurality of reflective surface units, and each IRS is equipped with an intelligent controller for controlling the phase and amplitude of each reflective surface unit;

[0089] The direct link between the base station and the user equipment is weak. By deploying multiple IRSs, a line-of-sight (LoS) path is ensured between the base station and the user equipment at an ideal location, thereby enhancing the quality of the communication link.

[0090] Preferably, the deployment method comprises the following steps:

[0091] Deployment steps: On a two-dimensional plane, multiple IRSs are deployed in non-collinear locations according to an orthogonal deployment strategy to avoid channel correlation.

[0092] Allocation step: The successive convex approximation (SCA) method is used to solve the joint IRS element allocation and power optimization problem, transforming the non-convex problem into a convex problem for efficient resource allocation.

[0093] Preferably, the method comprises the following optimization steps:

[0094] Under the constraints of the preset maximum transmission power and the IRS unit transmission capability, an optimization problem is constructed to maximize the beam gain in the target direction;

[0095] Based on the continuous convex approximation (SCA) algorithm, all optimization variables are optimized and solved separately to obtain the optimal resource allocation solution.

[0096] Preferably, the system can significantly improve system performance under high power and a large number of IRS units. Compared with a single IRS configuration, a multi-IRS system exhibits higher superiority in resource allocation and communication performance.

[0097] Preferably, the MIMO multi-antenna system has a total of N t There are N transmitting antennas, r We solved the complex scenario where the direct link from the base station to the user is blocked, resulting in extremely weak signals. To simplify the problem, we assumed that all reflecting surfaces are at the same height, thus simplifying the system to two dimensions. In the system deployment, we represent the positions of the base station and the user in a two-dimensional Cartesian coordinate system as and The distances between the base station and the user are d t and d r The IRS k is equipped with a M k (=M v，k ×M h，k ) units, where UPA on IRS 1000 by M v，k Line and M h，k The spacing between all cells is d s The total number of IRS units that can be allocated is M max ,therefore The position of IRS k is expressed as set up and denote the transmitted signal vector and the effective base station-user MIMO channel respectively. Therefore, the signal received by the user is It can be expressed as:

[0098] y=Hx+z,

[0099] in, represents the additive white Gaussian noise at the user, whose power is σ 2 It should be noted that due to the multiplicative nature of the cascade path loss, multiple reflections from multiple IRSs will cause severe cascade path attenuation. Therefore, only a single reflection from each IRS is considered in this study.

[0100] We express the channel matrix from the base station to the kth IRS as The channel matrix from the kth IRS to the user is expressed as We assume that the distributed IRSs are deployed in ideal locations to ensure that there is a direct path (LoS) between the base station and the user. The channel matrix from the base station to the kth IRS can be expressed as:

[0101]

[0102] in represents the complex channel gain from the base station to the kth intelligent reflecting surface (IRS) link, β represents the channel power gain at a reference distance of 1 meter, and d 1，k represents the distance from the base station to the kth IRS. In the array response vector, as well as in and denote the horizontal angle of arrival (AoA), vertical angle of arrival (AoA) and angle of departure (AoD) of the base station IRS k link respectively. In addition, and a S,k (·) denote the array response vectors at the base station and IRS k, respectively.

[0103] It is worth noting that the array response of the uniform planar array (UPA) can be decomposed into the form of a uniform linear array (ULA), namely In this study, we consider the far-field scenario. Therefore, the array response vector of ULA can be uniformly expressed as:

[0104] a N (X) = [1, e jX ,...,e jX(N-1) ].

[0105] Similar to the link from the base station to the kth IRS, the channel matrix from the kth IRS to the user can be expressed as:

[0106]

[0107] we will is represented as the passive beamforming matrix of the kth IRS, where Represents the element in the kth IRS To achieve the maximum reflection effect, we further set in And we assume perfect channel state information. Therefore, the effective base station-user MIMO channel assisted by K cooperative IRSs is modeled as:

[0108]

[0109] We aim to optimize the IRS phase difference Element allocation Transmit covariance matrix Q and IRS deployment location To maximize the channel capacity of the MIMO system considering multiple IRS assistance, assuming K≤min(N t , N r ), the mathematical expression of the problem is as follows:

[0110]

[0111] tr(Q)≤P,

[0112] Q≥0,

[0113]

[0114] in, P represents the maximum transmission power of the base station, and Represents the set of optional deployment points for IRS.

[0115] When the optimization problem is a non-convex optimization problem about location deployment, IRS beam design, IRS element allocation and power allocation, we first consider location deployment and IRS beam design.

[0116] Preferably, in order to maximize the gain of each link through the IRS, we adopt a specific passive beamforming structure and design the deployment of the IRS based on orthogonality considerations;

[0117] To maximize each link gain through IRS, we adopt the following passive beamforming structure:

[0118]

[0119] in, yes The mth k elements, yes The mth k elements. By adopting the above configuration, f(Φ k )=M k It is worth noting that the strategy of maximizing link gain cannot guarantee optimality. However, under our proposed orthogonal deployment strategy, the link gain maximization method does achieve optimal performance.

[0120] The introduction of IRS enables the creation of a controllable scattering channel environment, potentially establishing favorable conditions for multi-stream transmission. However, due to the interference between different paths, we aim to achieve interference-free paths through orthogonal deployment, the conditions of which are as follows:

[0121]

[0122] With the transmitter and receiver positions fixed, we position the IRS along the discrete Fourier transform (DFT) direction of the transmitter and receiver. This arrangement meets the requirements of AOA and AoD discretization:

[0123]

[0124] We have and Therefore, our problem is transformed into a search task, which is to choose a point that maximizes the total rate given the known power allocation, IRS phase adjustment, and element distribution. It should be noted that this search is constrained: any two selected IRS cannot be collinear. This restriction stems from the fact that collinearity implies linear correlation, which will lead to strong correlation between channels and violate the principle of orthogonal deployment. Therefore, we must choose carefully during the search process.

[0125] This limitation stems from the fact that collinearity implies linear correlation, which leads to strong correlation between channels and violates the principle of orthogonal deployment. Therefore, we must choose carefully during the search process.

[0126] We first select compatible combinations, T Divide into non-overlapping subsets Θ T，1 , Θ T，2 , and Θ R Divide into Θ R,1 , Θ R,2 . Then, we will T，1 With Θ R，1 Pairing to form C 1 , and Θ T，2 With Θ R，2 Pairing to form C 2 We use a 1 、a 2 、b 1 and b 2 Respectively represent Θ T，1 , Θ T，2 , Θ R，1 and θ R，2 This method is due to the fact that not every Θ T The angles in can be compared with Θ R The specific division is determined by the site conditions. Assume that we select K IRSs in total, of which C 1 There are J in it, C 2 There are KJ in , and we can get T，1 and θ R，1 In the example above, we can generate J unique IRS deployment angle combinations by pairing elements without duplication. Similarly, we can generate J unique IRS deployment angle combinations from C 2 In this case, C1 and C 2 The pairing results are and possible combinations. By iterating J from 0 to min(K, a 1 , a 2 , b 1 , b 2 ), we can generate all the required combinations. Finally, we can use the law of cosines to determine the specific deployment location of each IRS, thus obtaining

[0127] For IRS element allocation and power allocation, a joint optimization algorithm based on SCA is adopted.

[0128] Preferably, we jointly optimize the IRS element allocation and power under fixed IRS deployment and phase. The MIMO channel capacity can also be expressed as:

[0129]

[0130] Among them, p k represents the amount of transmit power allocated to the kth singular value of H, δ k represents the kth unique value of H. Under our orthogonal deployment, this means that δ k YesR k Φ k T k Furthermore, we relax the discrete value M to its continuous counterpart In order to solve the problem of coupling terms, we introduce the auxiliary variable s k and l k , and define in And k yes The square of the kth singular value of . Therefore, the optimization problem (P1) is simplified to:

[0131]

[0132] in, as well as After the above operations, the problem is still non-convex. Therefore, we use the SCA (Successive Convex Approximation) method to exist Chuhe exist Perform a first-order Taylor expansion at , and get its lower bound approximation:

[0133]

[0134] Therefore, the optimization problem of the tth iteration of SCA can be expressed as:

[0135]

[0136] Therefore, problem (P3) is a convex optimization problem in iteration t, which can be solved using CVX. After each iteration, and Update to s respectively k and The integer number of reflection elements can be reconstructed by rounding off the successive solutions to the problem. It is worth noting that the SCA-based method is very sensitive to the setting of the initial value. We propose to initialize the power allocated to each IRS to P / K and set the initial number of elements of the kth IRS to M. max / (4k). This approach helps avoid convergence to solutions that evenly distribute resources or assign all elements to a single IRS.

[0137] According to a method for maximizing capacity of a multi-IRS-assisted MIMO system provided by the present invention, a multi-IRS-assisted MIMO system is applied, and multiple IRS systems assist the MIMO multi-antenna system to jointly perform channel optimization to achieve a preset capacity maximization configuration and obtain the expected capacity improvement.

[0138] Preferably, the method comprises the following steps:

[0139] Independent path configuration: Orthogonal placement ensures that each IRS provides an independent propagation path, and the optimal IRS phase shift is derived based on this configuration. By ensuring that each IRS is placed orthogonally, the system can obtain more spatial multiplexing gain on different propagation paths, significantly improving channel capacity.

[0140] Element Allocation Optimization: An efficient algorithm is proposed to jointly optimize the IRS element allocation and power allocation to maximize capacity. In this process, a continuous convex approximation algorithm is used to optimize all optimization variables to ensure that the optimization problem gradually converges to the optimal solution in each iteration.

[0141] Preferably, in the independent path configuration step, orthogonal placement is used to ensure that each IRS provides an independent propagation path to maximize system capacity. Specifically, the optimal position of each IRS is determined by mathematical model analysis so that it can provide maximum signal enhancement on different channel paths. In the element allocation optimization step, an efficient algorithm is proposed, which jointly optimizes IRS element allocation and power allocation to achieve optimal system capacity. In this process, the discrete values ​​of IRS elements are relaxed to continuous values ​​and gradually adjusted in each iteration, and finally the optimal element and power allocation scheme is found.

[0142] The embodiment of the present invention discloses a channel capacity maximization solution for a multi-IRS assisted MIMO system. Figure 1 As shown, it includes a MIMO multi-antenna system, multiple IRS reflection panels, an optimization algorithm, and a target channel capacity; it also includes an IRS deployment and beamforming design to maximize the system channel capacity.

[0143] When the multi-IRS assisted MIMO system transmits signals, the base station and multiple IRSs perform joint active and passive beamforming design to obtain the optimal channel capacity. The multi-IRS system assists the MIMO multi-antenna system to jointly perform beamforming, IRS phase adjustment and power allocation to obtain the maximum channel capacity.

[0144] In a multi-IRS-assisted MIMO system, the base station needs to allocate power to the transmit antenna array and control the transmit power of each data stream. The IRS implements passive beamforming by adjusting the phase of each reflector unit.

[0145] Multi-IRS assisted MIMO system: The base station and multiple IRSs are jointly optimized and designed together. The base station performs power allocation, and the IRS performs phase adjustment and component allocation.

[0146] Optimization of power allocation by the base station: Considering that the base station transmit power is P, the power of each data stream after the base station performs power allocation is p k , including the power allocation design for each data stream.

[0147] IRS performs phase adjustment and optimization of component allocation: IRS adjusts the phase of the incident signal, controls the IRS phase matrix to perform phase design of all elements, and optimizes the component allocation M of each IRS. k , so that the reflected signal can maximize the channel capacity.

[0148] Optimization scheme in multi-IRS assisted MIMO system: taking maximization of system channel capacity as performance index and limiting total transmit power and total number of IRS elements. By constructing an optimization problem with maximization of channel capacity, maximum transmit power and total number of IRS elements as constraints, the present invention realizes the scheme design of the multi-IRS assisted MIMO system.

[0149] Design of optimization scheme in multi-IRS assisted MIMO system: The multi-IRS assisted system introduced in this optimization problem is passive and does not require additional RF links to process signals. It implements beamforming in a passive form and can achieve better performance at a lower cost.

[0150] Optimization scheme design of multi-IRS assisted MIMO system: The channel capacity maximization optimization problem constructed is a non-convex optimization problem, and the global optimal solution cannot be directly obtained. The present invention is based on the orthogonal deployment strategy and continuous convex approximation algorithm, and optimizes all optimization variables to obtain a suboptimal solution with higher quality.

[0151] Aiming at the requirements of the next generation wireless network for channel capacity performance and system complexity, the present invention provides a novel design of a multi-IRS-assisted MIMO system.

[0152] According to the multi-IRS assisted MIMO system provided by the present invention, a joint design scheme for base station power allocation and IRS phase adjustment and component allocation is provided.

[0153] The base station needs to consider the power allocation design when transmitting signals and implement active transmit beamforming by controlling the power of each data stream. The IRS needs to control the phase of each reflector unit when assisting in transmitting signals to implement passive beamforming and optimize the component allocation of each IRS.

[0154] The MIMO multi-antenna system has a total of N t There are N transmitting antennas, r We solved the complex scenario where the direct link from the base station to the user is blocked, resulting in extremely weak signals. To simplify the problem, we assumed that all reflecting surfaces are at the same height, thus simplifying the system to two dimensions. In the system deployment, we represent the positions of the base station and the user in a two-dimensional Cartesian coordinate system as and The distances between the base station and the user are d k and d r The IRSk is equipped with a M k (=M v，k ×M h，k ) units, where UPA on IRSk is provided by M v，kLine and M h，k The spacing between all cells is d s The total number of IRS units that can be allocated is M max ,therefore The position of IRS k is expressed as set up and denote the transmitted signal vector and the effective base station-user MIMO channel respectively. Therefore, the signal received by the user is It can be expressed as:

[0155] y=Hx+z,

[0156] in, represents the additive white Gaussian noise at the user, whose power is σ 2 It should be noted that due to the multiplicative nature of the cascade path loss, multiple reflections from multiple IRSs will cause severe cascade path attenuation. Therefore, only a single reflection from each IRS is considered in this study.

[0157] We express the channel matrix from the base station to the kth IRS as The channel matrix from the kth IRS to the user is expressed as We assume that the distributed IRSs are deployed in ideal locations to ensure that there is a direct path (LoS) between the base station and the user. The channel matrix from the base station to the kth IRS can be expressed as:

[0158]

[0159] in represents the complex channel gain from the base station to the kth intelligent reflecting surface (IRS) link, β represents the channel power gain at a reference distance of 1 meter, and d 1，k represents the distance from the base station to the kth IRS. In the array response vector, as well as in and They represent the horizontal angle of arrival (AoA), vertical angle of arrival (AoA) and angle of departure (AoD) of the base station IRSk link respectively. In addition, and a S,k (·) denote the array response vectors at the base station and IRS k, respectively.

[0160] It is worth noting that the array response of the uniform planar array (UPA) can be decomposed into the form of a uniform linear array (ULA), namely In this study, we consider the far-field scenario. Therefore, the array response vector of ULA can be uniformly expressed as:

[0161] a N(X) = [1, e jX ,…,e jX(N-1) ].

[0162] Similar to the link from the base station to the kth IRS, the channel matrix from the kth IRS to the user can be expressed as:

[0163]

[0164] we will is represented as the passive beamforming matrix of the kth IRS, where Represents the element in the kth IRS To achieve the maximum reflection effect, we further set in And we assume perfect channel state information. Therefore, the effective base station-user MIMO channel assisted by K cooperative IRSs is modeled as:

[0165]

[0166] We aim to optimize the IRS phase difference Element allocation Transmit covariance matrix Q and IRS deployment location To maximize the channel capacity of the MIMO system considering multiple IRS assistance, assuming K≤min(N t , N r ), the mathematical expression of the problem is as follows:

[0167]

[0168] tr(Q)≤P,

[0169] Q≥0,

[0170]

[0171] in, P represents the maximum transmission power of the base station, and Represents the set of optional deployment points for IRS.

[0172] The present invention provides an IRS phase adjustment design and an IRS deployment strategy in a multi-IRS assisted MIMO system, and designs the unit phase of each IRS for IRSs at different positions, so as to maximize the channel capacity and ensure the total number of IRS elements.

[0173] (1) Obtain the IRS beam design and IRS deployment location

[0174] In order to maximize the gain of each link through the IRS, we adopt a specific passive beamforming structure and design the IRS deployment based on orthogonality considerations;

[0175] To maximize each link gain through IRS, we adopt the following passive beamforming structure:

[0176]

[0177] in, yes The mth k elements, yes The mth k elements. By adopting the above configuration, f(Φ k )=M k It is worth noting that the strategy of maximizing link gain cannot guarantee optimality. However, under our proposed orthogonal deployment strategy, the link gain maximization method does achieve optimal performance.

[0178] The introduction of IRS enables the creation of a controllable scattering channel environment, potentially establishing favorable conditions for multi-stream transmission. However, due to the interference between different paths, we aim to achieve interference-free paths through orthogonal deployment, the conditions of which are as follows:

[0179]

[0180] With the transmitter and receiver positions fixed, we position the IRS along the discrete Fourier transform (DFT) direction of the transmitter and receiver. This arrangement meets the requirements of AOA and AoD discretization:

[0181]

[0182] We have and Therefore, our problem is transformed into a search task, which is to choose a point that maximizes the total rate given the known power allocation, IRS phase adjustment, and element distribution. It should be noted that this search is constrained: any two selected IRS cannot be collinear. This restriction stems from the fact that collinearity implies linear correlation, which will lead to strong correlation between channels and violate the principle of orthogonal deployment. Therefore, we must choose carefully during the search process.

[0183] This limitation stems from the fact that collinearity implies linear correlation, which leads to strong correlation between channels and violates the principle of orthogonal deployment. Therefore, we must choose carefully during the search process.

[0184] We first select compatible combinations,T Divide into non-overlapping subsets Θ T，1 , Θ T，2 , and Θ R Divide into Θ R，1 , Θ R，2 . Then, we will T，1 With Θ R，1 Pairing to form C 1 , and Θ T，2 With Θ R，2 Pairing to form C 2 We use a 1 、a 2 、b 1 and b 2 Respectively represent Θ T，1 , Θ T，2 , Θ R，1 and θ R，2 This method is due to the fact that not every Θ T The angles in can be compared with Θ R The specific division is determined by the site conditions. Assume that we select K IRSs in total, of which C 1 There are J in it, C 2 There are KJ in , and we can get T，1 and θ R，1 By pairing elements in C2 without duplication, J unique IRS deployment angle combinations are generated. Similarly, KJ angle combinations can be obtained from C2. In this case, C 1 and C 2 The pairing results are and possible combinations. By iterating J from 0 to min(K, a 1 , a 2 , b 1 , b 2 ), we can generate all the required combinations. Finally, we can use the law of cosines to determine the specific deployment location of each IRS, thus obtaining

[0185] The present invention provides a design of power and IRS element allocation in a multi-IRS assisted MIMO system, designs a power allocation and element allocation strategy for base station MIMO multi-antennas according to user orientation under different channel conditions, and realizes maximization of channel capacity.

[0186] (2) Solve for each IRS element and power allocation

[0187] We jointly optimize the IRS element allocation and power under fixed IRS deployment and phase. The MIMO channel capacity can also be expressed as:

[0188]

[0189] Among them, p k represents the amount of transmit power allocated to the kth singular value of H, δ k represents the kth unique value of H. Under our orthogonal deployment, this means that δ k YesR k Φ k T k Furthermore, we relax the discrete value M to its continuous counterpart In order to solve the problem of coupling terms, we introduce the auxiliary variable s k and l k , and define in And k yes The square of the kth singular value of . Therefore, the optimization problem (P1) is simplified to:

[0190]

[0191] in, as well as After the above operations, the problem is still non-convex. Therefore, we use the SCA (Successive Convex Approximation) method to exist Chuhe exist Perform a first-order Taylor expansion at , and get its lower bound approximation:

[0192]

[0193] Therefore, the optimization problem of the tth iteration of SCA can be expressed as:

[0194]

[0195] Therefore, problem (P3) is a convex optimization problem in iteration t, which can be solved using CVX. After each iteration, and Update to s respectively k and The integer number of reflection elements can be reconstructed by rounding off the successive solutions to the problem. It is worth noting that the SCA-based method is very sensitive to the setting of the initial value. We propose to initialize the power allocated to each IRS to P / K and set the initial number of elements of the kth IRS to M. max / (4k). This approach helps avoid convergence to solutions that evenly distribute resources or assign all elements to a single IRS.

[0196] The present invention particularly provides a novel MIMO system channel capacity maximization solution based on multiple IRS assistance. Figure 1 The basic structure of the invention is described. Figure 2 and Figure 3 The channel capacity performance of the invention under different IRS deployment strategies is compared.

[0197] As a revolutionary technology, IRS is expected to solve the challenges of performance improvement and energy efficiency optimization in the development of wireless communication systems. For MIMO systems, IRS can realize channel reconstruction and provide sufficient multipath components to facilitate efficient spatial multiplexing. In addition, IRS can also solve the coverage problem caused by non-line-of-sight channels in communication. For multi-user systems, IRS can also play an important role. First, IRS can realize passive beamforming capabilities, which can improve the accuracy of signal coverage, align the location of the user to be served, and enhance the signal strength. In addition, IRS realizes multipath signal control through a rich number of elements, which improves the spatial freedom of the system. Finally, the passive characteristics of IRS greatly reduce the deployment cost and can be well integrated with existing communication systems. Compared with a single IRS auxiliary system, a multi-IRS auxiliary system can provide more reconfigurability and significantly improve system performance. In addition, the multi-IRS system does not have the problem of limited coverage of a single IRS, which can improve system flexibility. Therefore, using multiple IRS to improve the performance of MIMO systems is efficient and can reduce the system deployment cost, which has important implementation value in the next generation of wireless networks.

[0198] The present invention provides a novel channel capacity maximization scheme based on a multiple intelligent reflecting surface (IRS)-assisted MIMO system and a joint optimization design scheme under the system, wherein the novel system comprises a MIMO multi-antenna base station, multiple IRS reflection panels, an optimization algorithm and a target channel capacity. The core of the optimization algorithm is optimization and continuous convex approximation.

[0199] In the system, the base station and multiple IRSs jointly optimize the signal transmission design, and send power allocation and phase adjustment information to the multi-antenna array and IRS respectively to maximize the channel capacity.

[0200] Considering that the channel capacity of the system is greatly affected by IRS deployment and beamforming, the present invention takes maximizing the system channel capacity as the performance indicator, and limits the total transmit power and the total number of IRS elements to jointly optimize the design of a new multi-IRS-assisted MIMO system.

[0201] Compared with the traditional MIMO system, the multi-IRS assisted MIMO system of the present invention does not add a large number of radio frequency links and complex signal processing units, and is a design that achieves better performance at a lower cost and power consumption. Through the design of the power allocation and phase adjustment matrix of the base station and the multi-IRS, the channel capacity of the present invention is significantly improved.

[0202] The embodiment of the present invention also discloses a method for maximizing the channel capacity of a multi-IRS-assisted MIMO system, which uses a multi-IRS-assisted MIMO system and a multi-IRS system-assisted MIMO multi-antenna system to jointly perform optimization design to maximize the system channel capacity and obtain the optimal IRS deployment and beamforming solution.

[0203] The method comprises the following steps:

[0204] Power allocation steps: The base station allocates power to the MIMO multi-antenna array to control the transmit power of each data stream. The base station generates a power allocation control signal and obtains the power p of each data stream after the base station allocates power through the total power P of the base station. k .

[0205] Phase adjustment step: Multiple IRS panels are phase adjusted through an optimization algorithm to control the phase of each reflector unit. The IRS system generates a phase adjustment control signal to passively beamform the incident signal of the IRS panel by establishing the phase of all elements in the IRS phase matrix so that the reflected signal can maximize the channel capacity.

[0206] Those skilled in the art may understand this embodiment as a more specific description of Embodiment 1.

[0207] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0208] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A deployment optimization method for a high-frequency communication system assisted by multiple intelligent reflective surfaces, characterized in that: A high-frequency communication system assisted by multiple intelligent reflectors, the system comprising: a transmitter, multiple intelligent reflector systems and a receiver; the transmitter is equipped with a MIMO multi-antenna system; the intelligent reflector system assists the MIMO multi-antenna system in spatial multiplexing; the receiver receives signals from multiple intelligent reflector systems; The intelligent reflector system includes a plurality of reflective units and a controller; each reflective unit is arranged at a preset position and has no correlation with the channel; the transmitter jointly optimizes the configuration and power allocation of the reflective unit; The controller adopts a successive convex approximation (SCA) algorithm; the transmitter generates a control signal of the MIMO multi-antenna array, controls the amplitude and phase of the MIMO multi-antenna array through digital beamforming, and performs active beamforming; the controller generates a control signal of the intelligent reflector system unit, and performs passive beamforming by controlling the amplitude and phase of each reflector unit; The method comprises the following steps: Deployment steps: Deploy multiple intelligent reflective surface systems at non-collinear locations on a two-dimensional plane according to an orthogonal deployment strategy; Allocation step: Use the successive convex approximation algorithm to perform the element allocation and power optimization of the joint intelligent reflector system, transform the non-convex problem into a convex problem, and perform resource allocation; In the deployment step, all feasible deployment positions are selected and beamforming of the smart reflector system is optimized; In the allocation step, the smart reflector system elements and power are evenly allocated through an optimization algorithm.

2. The deployment optimization method of a high-frequency communication system assisted by multiple intelligent reflective surfaces according to claim 1, characterized in that: The direct link between the transmitter and the receiver becomes weak due to obstacles; the multi-intelligent reflection surface system reconstructs the channel through a single reflection path.

3. The deployment optimization method of a high-frequency communication system assisted by multiple intelligent reflective surfaces according to claim 2 is characterized in that: The MIMO multi-antenna system includes multiple transmitting antennas and receiving antennas; in a complex scenario where a base station-user link is blocked, resulting in an extremely weak signal, the system optimizes the deployment and power allocation of an intelligent reflector system.

4. The deployment optimization method of a high-frequency communication system assisted by multiple intelligent reflective surfaces according to claim 3 is characterized in that: A preset passive beamforming structure is adopted and based on orthogonality considerations, a deployment design of an intelligent reflector system is performed; the intelligent reflector system is positioned along the discrete Fourier transform direction of a transmitter and a receiver.

5. The deployment optimization method of a high-frequency communication system assisted by multiple intelligent reflective surfaces according to claim 3 is characterized in that: When the number of elements and total power of smart reflector system are limited, a successive convex approximation algorithm is used to jointly optimize the allocation of elements and power allocation of smart reflector system.

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