A reconfigurable metasurface-assisted cross-domain opportunistic precoding method

Abstract

This invention discloses a reconfigurable metasurface-assisted cross-domain opportunistic precoding method. Under different user QoS constraints, it addresses the joint optimization problem of maximizing spectral efficiency. The objective problem is divided into two suboptimal problems, which are solved separately. Then, based on the solutions to the suboptimal problems, a joint iterative algorithm is used to approximate the optimal solution of the original problem. Compared with traditional communication system transmission schemes, the proposed system can reduce SE and BER performance losses even with incomplete CSI, achieving efficient transmission.

Description

Technical Field

[0001] This invention belongs to the field of data transmission technology, specifically relating to a reconfigurable metasurface-assisted cross-domain opportunistic precoding method. Background Technology

[0002] Currently, cross-domain communication technologies play a crucial role in modern wireless communication to improve system performance, increase transmission rates, and enhance interference management. Multiple-input multiple-output (MIMO) technology is an effective solution for providing additional cross-domain spatial dimensions, and resource allocation methods can significantly improve spatial and spectral utilization in MIMO cross-domain networks. Therefore, MIMO is applied to non-orthogonal multiple access (NOMA) systems to increase achievable user rates. Most current research focuses on preprocessing, such as combining transmit antenna selection and beamforming in MIMO-NOMA systems, proposing a penalized dual decomposition to achieve maximum sum rate; and jointly framing user clustering, downlink beamforming, and power allocation. A massive MIMO antenna scheme is proposed, providing closed-form expressions for the spectral efficiency and bit error rate for all users.

[0003] However, traditional NONA systems require perfect channel state information (CSI) and channel estimation, leading to high complexity in both the transmitter and receiver. To overcome the limitations of perfect CSI, opportunistic beamforming (OBF) was proposed, where a set of random weights is used to preprocess the transmitted signal. Due to multi-user diversity gain, the OBF-NONA system achieves spectral efficiency (SE) approximately equal to that of traditional systems. Furthermore, considering the improvement in SE and overcoming communication link barriers, intelligent transmitter surfaces (RIS) have been incorporated into the NONA system. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention provides a reconfigurable metasurface-assisted cross-domain opportunistic precoding method. Under different user QoS constraints, it addresses the joint optimization problem of maximizing spectral efficiency. The objective problem is divided into two suboptimal problems, which are solved separately. Then, based on the solutions to the suboptimal problems, a joint iterative algorithm is used to approximate the optimal solution of the original problem. Compared with traditional communication system transmission schemes, this invention can reduce SE and BER performance losses even with incomplete CSI, achieving efficient transmission.

[0005] The technical solution adopted by this invention to solve its technical problem is as follows:

[0006] Step 1: In a reconfigurable metasurface-assisted cross-domain system, there is a base station (BS), a smart transmitter surface (RIS), and U, where U ≥ 3 users; the base station (BS) is equipped with N T N TThere are ≥2 transmitting antennas, and each user has N. R N R ≥2 receiving antennas; Smart Transmitter Surface (RIS) involves N I N I There are ≥2 reconfigurable smart surfaces arranged on a plane; U users are randomly located and each user can independently process the received signal; downlink transmission includes two aspects, namely pilot signal transmission and user signal transmission.

[0007] Step 2: Base station (BS), intelligent transmitter surface (RIS), and user application opportunity preprocessing (OP); replace the processing matrix calculated using channel state information (CSI) with a randomly generated matrix, and then use multi-user opportunity diversity to compensate for performance loss;

[0008] The pilot signal is denoted as x. p And satisfy

[0009] The generated w represents the opportunity weight vector, the opportunity weight vector w m for:

[0010]

[0011] Where 1≤n t ≤N T ,random variable satisfy and Let ||w|| represent amplitude and phase, and ||w|| 2 =1;

[0012] Transmitted pilot data s p Represented as:

[0013] s p =wx p

[0014] The base station (BS), the intelligent transmitter surface (RIS), and the users use flat fading channel pulses, and the channel matrix is ​​represented as follows:

[0015]

[0016] in, This indicates the channel pulse from the transmitting antenna to the user receiving antenna;

[0017] Similar to the above formula, the channel matrix from the base station to the RIS and from the RIS to the user is represented as follows:

[0018]

[0019]

[0020] Step 3: Analyze the joint signal-to-noise ratio;

[0021] The matrix form of the reflected signal adjustment is as follows:

[0022]

[0023] Where the phase shift parameter satisfy

[0024] The pilot signal received by the user is represented as:

[0025]

[0026] Where z (u) The white noise parameter is expressed as:

[0027] By applying the MRC criterion and combining the results, the pilot signal was optimized to obtain:

[0028]

[0029] Therefore, the joint signal-to-noise ratio is obtained as follows:

[0030]

[0031] Step 4: Analyze the signal-to-noise ratio of signal transmission between users;

[0032] Because multi-user multiplexing is provided, NOMA is introduced, and the superimposed transmission signals of the users are as follows:

[0033]

[0034] Base station (BS) and RIS, as well as user application opportunity preprocessing (OP), use randomly generated matrices instead of processing matrices calculated using CSI, and then employ multi-user opportunity diversity to compensate for performance loss; p v The user power is represented by the required signal denoted as x. v and satisfy

[0035] By using continuous interference cancellation and applying the Cauchy-Schwarz inequality to the previous signal-to-noise ratio, the signal-to-noise ratios for different users are obtained:

[0036]

[0037]

[0038] Step 5: Analyze and compare the probability density function and cumulative distribution function of direct connection and reconfigurable metasurface-assisted connection;

[0039] For this system, the direct connection equivalent channel for users is represented as:

[0040]

[0041] The channel impulse response is expressed as: in and This represents the in-phase phase component and the quadrature phase component.

[0042] After simplifying the direct-connection equivalent channel, it is represented as:

[0043]

[0044] The CDF and PDF obtained through direct connection can be represented as:

[0045]

[0046]

[0047] in,

[0048] The user's reconfigurable metasurface-assisted equivalent channel is represented as:

[0049]

[0050] Similarly, the CDF and PDF obtained with the assistance of reconfigurable metasurfaces are represented as follows:

[0051]

[0052]

[0053] Step 6: Theoretical analysis of spectral efficiency and bit error rate performance;

[0054] The spectral efficiency is determined by the tail of the equivalent channel, and therefore means:

[0055]

[0056]

[0057] in, O(.) represents a higher-order infinitesimal;

[0058] When U is large enough, the spectral efficiency is expressed as:

[0059]

[0060] Regarding the bit error rate, the determination region is represented as follows:

[0061]

[0062] Considering the ideal situation and ignoring error propagation, the bit error rate is:

[0063]

[0064] Step 7: Power allocation and user scheduling using the maximum SE criterion;

[0065] Maximum SE standard for power allocation and user schedules:

[0066]

[0067] The restrictions are:

[0068]

[0069]

[0070] l5=P △ -(P u′ -P u )≤0,

[0071]

[0072]

[0073] in, This indicates the acceptable bit error rate for the user. It is the minimum demand rate of the user, P △ It is the minimum power difference, P Σ Indicates the total power of the system. Indicates a group of transmitted user pairs;

[0074] Step 8: Divide the optimization problem in Step 7 into two suboptimal problems; simplify the power allocation problem in the power domain using the Karush-Kuhn-Tucker formula;

[0075]

[0076]

[0077] Step 9: In the user domain, use a traversal algorithm to select all possible combinations of users in turn, and find the user pairing scheme that maximizes the above function;

[0078]

[0079]

[0080] Step 10: Solve the two suboptimal problems using a joint iterative algorithm.

[0081] Preferably, step 10 specifically includes:

[0082] The joint iterative algorithm consists of four steps: a. Input and initialization; b. Finding the global solution using iterative algorithms in the power domain, time-frequency domain, and user domain, and calculating the sum and spectral efficiency; c. Comparing the sum and spectral efficiency at the current iteration number with the previously calculated sum and spectral efficiency; d. Outputting the maximum value of the objective function and the corresponding parameters.

[0083] The beneficial effects of this invention are as follows:

[0084] Compared with traditional communication system transmission schemes, this invention, even with incomplete CSI, proposes a system that reduces SE and BER performance losses, achieving efficient transmission. Attached Figure Description

[0085] Figure 1 This invention provides a reconfigurable metasurface-assisted cross-domain system model.

[0086] Figure 2 This is a graph showing the trend of spectral efficiency and the number of RIS components under different signal-to-noise ratios in an embodiment of the present invention.

[0087] Figure 3 This is a graph showing the trend of bit error rate as a function of signal-to-noise ratio for different users in an embodiment of the present invention.

[0088] Figure 4 This is a comparison chart of the spectral efficiency of the opportunity processing scheme and the coherent processing scheme in an embodiment of the present invention. Detailed Implementation

[0089] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0090] To overcome the limitations of perfect CSI, opportunistic beamforming (OBF) is proposed, which uses a set of random weights to preprocess the transmitted signal. Furthermore, to ensure the performance of the wireless communication system, the power domain and time-frequency domain are jointly combined using cross-domain communication techniques. In addition, RIS (Reconfigurational Signaling System) shows great potential for intelligently customizing and reconfiguring the radio propagation environment in a cost-effective manner. Therefore, this invention addresses a reconfigurable metasurface-assisted cross-domain opportunistic precoding scheme to achieve efficient transmission in the power domain, user domain, and time-frequency domain, and proposes a joint optimization problem to maximize the sum and spectral efficiency (SE).

[0091] This invention proposes a joint optimization problem that maximizes spectral efficiency while satisfying different user QoS constraints. The objective problem is divided into two suboptimal problems, and the suboptimal problems are solved separately. Then, based on the solutions of the suboptimal problems, a joint iterative algorithm is used to approximate the optimal solution of the original problem.

[0092] Step 1: As Figure 1 As shown, in the reconfigurable metasurface-assisted cross-domain system of this invention, a base station BS, a RIS, and U (U≥3) users are involved. The base station is equipped with N... T N T ≥2) Transmitting antennas, each user has N R (N R ≥2) Receiving antenna. RIS involves N I (N I ≥2) reconfigurable smart surfaces are arranged on a plane. Assume U users are randomly located, and each user can independently process the received signal. Downlink transmission includes two aspects: pilot signal transmission and user signal transmission.

[0093] Step 2: Opportunistic preprocessing (OP) is applied to the base station (BS), RIS, and users. The processing matrix calculated using CSI is replaced with a randomly generated matrix, and then multi-user opportunity diversity is used to compensate for performance loss; where the pilot signal is denoted as x. p And satisfy The resulting w represents the opportunity weight vector.

[0094] Opportunity weight vector w m for:

[0095]

[0096] Where 1≤n t ≤N T ,random variable satisfy and Let ||w|| represent amplitude and phase, and ||w|| 2 =1. Transmitted pilot data s p It can be represented as:

[0097] s p =wx p

[0098] Furthermore, flat fading channel pulses are used between the base station (BS), the RIS, and the users. The channel matrix can be represented as:

[0099]

[0100] in, This indicates the channel pulse from the transmitting antenna to the user receiving antenna;

[0101] Similar to the above formula, the channel matrix from the base station to the RIS and from the RIS to the user can be expressed as:

[0102]

[0103]

[0104] Step 3: Analyze the joint signal-to-noise ratio.

[0105] Because a RIS was introduced into the system, the matrix form of the reflected signal adjustment was used to improve transmission quality.

[0106]

[0107] Where the phase shift parameter satisfy

[0108] The pilot signal received by the user can be represented as: Where z (u) The white noise parameter is expressed as:

[0109] By applying the MRC criterion and combining the results, the pilot signal can be optimized to obtain:

[0110]

[0111] Therefore, the joint signal-to-noise ratio can be obtained as follows:

[0112]

[0113] Step 4: Analyze the signal-to-noise ratio of signal transmission between users.

[0114] Because multi-user multiplexing is provided, NOMA is introduced, and the superimposed transmission signals of the users are as follows:

[0115]

[0116] Opportunistic preprocessing (OP) was applied to the base station (BS), RIS, and users. The processing matrix calculated using CSI was replaced with a randomly generated matrix, and then multi-user opportunistic diversity was used to compensate for the performance loss. Where p v The user power is represented by the required signal denoted as x. v and satisfy

[0117] By using continuous interference cancellation and applying the Cauchy-Schwarz inequality to the previous signal-to-noise ratio, the signal-to-noise ratios for different users can be obtained:

[0118]

[0119]

[0120] Step 5: Analyze and compare the probability density function and cumulative distribution function of direct connection and reconfigurable metasurface assisted connection.

[0121] For this system, the direct connection equivalent channel for users is represented as:

[0122]

[0123] The channel impulse response is expressed as: in and This represents the in-phase phase component and the quadrature phase component.

[0124] After simplifying the direct-connection equivalent channel, it can be expressed as:

[0125]

[0126] The CDF and PDF obtained through direct connection can be represented as:

[0127]

[0128]

[0129] in,

[0130] The user's reconfigurable metasurface-assisted equivalent channel is represented as:

[0131]

[0132] Similarly, the CDF and PDF obtained with the assistance of reconfigurable metasurfaces are represented as follows:

[0133]

[0134]

[0135] Step 6: Theoretical analysis of spectral efficiency and bit error rate performance.

[0136] Due to opportunistic processing and multi-user selection, the spectral efficiency of this invention is determined by the tail of the equivalent channel. Therefore, it can be represented as...

[0137]

[0138]

[0139] in, O(.) represents a higher-order infinitesimal.

[0140] When U is large enough, the spectral efficiency is expressed as:

[0141]

[0142] Regarding the bit error rate, the determining region can be represented as:

[0143]

[0144] Considering the ideal situation and ignoring error propagation, the bit error rate is:

[0145]

[0146] Step 7: To distinguish signals from different users and achieve multi-user multiplexing, this invention proposes a power allocation and user scheduling method using the maximum SE criterion. The maximum SE criterion for power allocation and user scheduling is as follows:

[0147]

[0148] The restrictions are:

[0149]

[0150]

[0151] l5=P △ -(P u′ -P u )≤0,

[0152]

[0153]

[0154] in, This indicates the acceptable bit error rate for the user. It is the minimum demand rate of the user, P △ It is the minimum power difference, P Σ Indicates the total power of the system. This represents a group of user pairs being transmitted.

[0155] Step 8: Divide the optimization problem in Step 7 into two suboptimal problems. First, discuss the power allocation problem in the power domain, and simplify it using the Karush-Kuhn-Tucker (KKT) formula.

[0156]

[0157]

[0158] Step 9: In the user domain, use a traversal algorithm to select all possible combinations of users in turn, and find the user pairing scheme that maximizes the above function.

[0159]

[0160]

[0161] Step 10: Solve the two suboptimal problems using a joint iterative algorithm.

[0162] The joint iterative algorithm consists of four steps: a. input and initialization; b. finding the global solution using iterative algorithms in the power domain, time-frequency domain, and user domain, and calculating the sum and spectral efficiency; c. comparing the sum and spectral efficiency at the current iteration number with the previously calculated sum and spectral efficiency; d. outputting the maximum value of the objective function and the corresponding parameters.

[0163] Step 11: Perform numerical simulations on the reconfigurable metasurface-assisted transdomain system and obtain simulation results.

[0164] To reveal the trends of spectral efficiency and the number of RIS components under different signal-to-noise ratios, such as Figure 2 As shown, with the increase of the number of reflecting elements, the number of feasible paths in the RIS-assisted link increases, and the equivalent channel gain becomes larger. This reveals the trend of bit error rate variation with signal-to-noise ratio for different users, such as... Figure 3 As shown, when the number of waiting users is high, the users have a lower bit error rate. The larger the number of waiting users, the greater the multi-user diversity gain introduced. Finally, the spectral efficiency of opportunistic processing and coherent processing schemes is compared, as shown... Figure 4 As shown, the coherent mechanism transmits the signal with perfect CSI after channel estimation. Instantaneous signal-to-noise ratio feedback is required. Therefore, the proposed scheme has a significant advantage for scenarios where establishing a feedback link is difficult.

Claims

1. A reconfigurable metasurface-assisted cross-domain opportunistic precoding method, characterized in that, Includes the following steps: Step 1: In a reconfigurable metasurface-assisted cross-domain system, there is a base station (BS), a smart transmitter surface (RIS), and U, where U ≥ 3 users; the base station (BS) is equipped with N T N T There are ≥2 transmitting antennas, and each user has N R N R ≥2 receiving antennas; Smart emitter plane RIS involves N I N I There are ≥2 reconfigurable smart surfaces arranged on a plane; U users are randomly located and each user can independently process the received signal; downlink transmission includes two aspects, namely pilot signal transmission and user signal transmission. Step 2: Base station (BS), intelligent transmitter surface (RIS), and user application opportunity preprocessing (OP); replace the processing matrix calculated using channel state information (CSI) with a randomly generated matrix, and then use multi-user opportunity diversity to compensate for performance loss; The pilot signal is denoted as x. p And satisfy The resulting w represents the opportunity weight vector, expressed as: Where 1≤n t ≤N T random variable w nt satisfy and Let ||w|| represent amplitude and phase, and ||w|| 2 =1; Transmitted pilot data s p Represented as: s p =wx p The base station (BS), the intelligent transmitter surface (RIS), and the users use flat fading channel pulses, and the channel matrix is ​​represented as follows: in, This indicates the channel pulse from the transmitting antenna to the user receiving antenna; Similar to the above formula, the channel matrix from the base station to the RIS and from the RIS to the user is represented as follows: Step 3: Analyze the joint signal-to-noise ratio; The matrix form of the reflected signal adjustment is as follows: Where the phase shift parameter satisfy The pilot signal received by the user is represented as: Where z (u) The white noise parameter is expressed as: By applying the MRC criterion and combining the results, the pilot signal was optimized to obtain: Therefore, the joint signal-to-noise ratio is obtained as follows: Step 4: Analyze the signal-to-noise ratio of signal transmission between users; Because multi-user multiplexing is provided, NOMA is introduced, and the superimposed transmission signals of the users are as follows: Base station (BS) and RIS, as well as user application opportunity preprocessing (OP), use randomly generated matrices instead of processing matrices calculated using CSI, and then employ multi-user opportunity diversity to compensate for performance loss; p v The user power is represented by the required signal denoted as x. v and satisfy By using continuous interference cancellation and applying the Cauchy-Schwarz inequality to the previous signal-to-noise ratio, the signal-to-noise ratios for different users are obtained: Step 5: Analyze and compare the probability density function and cumulative distribution function of direct connection and reconfigurable metasurface-assisted connection; For this system, the direct connection equivalent channel for users is represented as: The channel impulse response is expressed as: in and This represents in-phase and quadrature phase components; After simplifying the direct-connection equivalent channel, it is represented as: The CDF and PDF obtained through direct connection are represented as follows: in, The user's reconfigurable metasurface-assisted equivalent channel is represented as: Similarly, the CDF and PDF obtained with the assistance of reconfigurable metasurfaces are represented as follows: Step 6: Theoretical analysis of spectral efficiency and bit error rate performance; The spectral efficiency is determined by the tail of the equivalent channel, and therefore means: in, O(·) represents a higher-order infinitesimal; When U is large enough, the spectral efficiency is expressed as: Regarding the bit error rate, the determination region is represented as follows: Considering the ideal situation and ignoring error propagation, the bit error rate is: Step 7: Power allocation and user scheduling using the maximum SE criterion; Maximum SE standard for power allocation and user schedules: The restrictions are: in, This indicates the acceptable bit error rate for the user. It is the minimum demand rate of the user, P △ It is the minimum power difference. P Σ Indicates the total power of the system. Indicates a group of transmitted user pairs; Step 8: Divide the optimization problem in Step 7 into two suboptimal problems; simplify the power allocation problem in the power domain using the Karush-Kuhn-Tucker formula; Step 9: In the user domain, use a traversal algorithm to select all possible combinations of users in turn, and find the user pairing scheme that maximizes the above function; Step 10: Solve the two suboptimal problems using a joint iterative algorithm.

2. The reconfigurable metasurface-assisted cross-domain opportunistic precoding method according to claim 1, characterized in that, Step 10 specifically involves: The joint iterative algorithm consists of four steps: a. Input and initialization; b. Finding the global solution using iterative algorithms in the power domain, time-frequency domain, and user domain, and calculating the sum and spectral efficiency; c. Comparing the sum and spectral efficiency at the current iteration number with the previously calculated sum and spectral efficiency; d. Outputting the maximum value of the objective function and the corresponding parameters.

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