Intelligent metasurface terahertz communication multi-domain resource joint allocation method and system

Abstract

The invention provides an intelligent metasurface terahertz communication multi-domain resource joint allocation method and system, and the method comprises the steps: constructing an optimization problem P0.1 based on a target communication system, carrying out the calculation of a solution of the optimization problem P0.1, optimizing the uplink and downlink transmission power of the target communication system, carrying out the RIS beam forming, and allocating adaptive sub-bands. According to the framework provided by the invention, the RIS and the intelligent controller are additionally arranged, the structure is simple, the hardware deployment cost and the extra power overhead are relatively low, the provided optimization algorithm is simple and efficient, and the system is a novel green terahertz communication system.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communication, and specifically, relates to a method and system for jointly allocating multi-domain resources in intelligent metasurface terahertz communication. More specifically, it is a method for jointly allocating multi-domain resources in terahertz communication assisted by an intelligent metasurface. Background Art

[0002] Terahertz, i.e., THz communication, has become a promising candidate technology for the future sixth generation, i.e., 6G wireless communication due to its ultra-wide bandwidth, and theoretically can achieve a data rate of up to several trillion bits per second, i.e., Tb / s. However, THz signals face severe path loss and frequency-selective molecular absorption, which limit their transmission range. At the same time, in full-duplex enabled terahertz communication, severe self-interference hinders the transmission efficiency of the system. Intelligent metasurface, i.e., RIS, provides a promising solution to address these challenges. By using a large number of passive reflection elements, RIS can direct the signal to any direction, thereby enhancing the signal strength and making it a practical solution for THz full-duplex communication.

[0003] Although there have been many studies, most RIS beamforming schemes perform poorly in the case of THz full-duplex systems, especially in practical THz communication systems. This is because the frequency-selective channel characteristics of the THz band are not considered in the research work, and no adaptive sub-band resource allocation scheme has been proposed for this.

[0004] Therefore, how to consider the above problems in an RIS-assisted terahertz communication system and jointly optimize the designs such as power, bandwidth, and RIS beamforming is an important topic. Finding an efficient resource allocation method that can significantly improve the performance of the THz communication system is particularly crucial for the next-generation network.

[0005] Patent document CN117997402A discloses a multi-user uplink communication method and device assisted by a multi-layer intelligent metasurface. The scheme includes proposing an uplink communication system model for a multi-layer RIS-assisted user terminal transmitter for the Internet of Things multi-user uplink communication scenario to improve the uplink data transmission ability of Internet of Things terminals; then, on this basis, with the goal of maximizing the achievable sum rate of all users, a joint optimization model of user transmit power, multi-layer RIS phase shift, and receive beamforming is established under the premise of power consumption limitation and guaranteeing user service quality to significantly improve the sum rate of the multi-user uplink communication system; finally, the model is decomposed into three sub-problems, and an alternating optimization algorithm is used for solution to respectively obtain the optimal solutions of the power allocation vector, the phase shift matrix of the multi-layer RIS, and the receive beamforming matrix. This scheme does not consider the frequency-selective channel characteristics of the THz band, and no adaptive sub-band resource allocation scheme has been proposed for this. This problem urgently needs to be solved. Summary of the Invention

[0006] Aiming at the defects in the prior art, the purpose of the present invention is to provide an intelligent metasurface terahertz communication multi-domain resource joint allocation method and system.

[0007] An intelligent metasurface-assisted terahertz communication multi-domain resource allocation method provided by the present invention includes:

[0008] Based on the target communication system, an optimization problem P0.1 is constructed, the solution of the optimization problem P0.1 is calculated, and then the uplink and downlink transmission powers, RIS beamforming, and allocation of adaptive sub-bands of the target communication system are optimized.

[0009] Preferably, the target communication system includes: a base station, a RIS system, and a user;

[0010] The base station receives the signal of the user, and the RIS system generates a passive beamforming control signal to serve the user; when performing beamforming, the RIS system constructs an optimization problem P0.1 and optimizes and solves all optimization variables to obtain the solution of the optimization problem P0.1;

[0011] The adaptive sub-band is a sub-band of the bandwidth.

[0012] Preferably, the mathematical expression of the optimization problem P0.1 is:

[0013]

[0014] Preferably, the objective function of the optimization problem P0.1 is replaced with a first-order Taylor expansion expression, and a new equality constraint penalty term is introduced into the objective function of the optimization problem P0.1 to construct a sub-problem of the optimization variable f, a sub-problem of the optimization variable B, and a sub-problem of the optimization variable and then iteratively solve the three sub-problems to obtain the values of the optimization variable f, the optimization variable B, and the optimization variable , that is, the solution of the entire optimization problem P0.1 is obtained;

[0015] The mathematical expression of the new equality constraint penalty term is:

[0016]

[0017] where, Ξ(f s ,B s ) represents the new equality constraint penalty term; f s represents the center frequency of the s-th sub-band;

[0018] The sub-problem of the optimization variable f, the mathematical expression is:

[0019]

[0020] Then, by using the software for solving convex optimization problems, solve the sub-problem of the optimization variable f to obtain the optimization variable f;

[0021] The sub-problem of the optimization variable B has the following mathematical expression:

[0022]

[0023] where P2 represents the sub-problem of the optimization variable B;

[0024] The sub-problem of the optimization variable has the following mathematical expression:

[0025]

[0026] where P3 represents the sub-problem of the optimization variable ;

[0027] Alternately iterate the sub-problem of the optimization variable f, the sub-problem of the optimization variable B, and the sub-problem of the optimization variable to obtain a sub-optimal solution of the optimization problem P0.1 as the solution of the optimization problem P0.1.

[0028] Preferably, based on the solution of the optimization problem P0.1, through the passive beamforming of the RIS panel of the RIS system, control the phase of the reflection unit of each RIS panel to optimize the transmission power of the base station and the user on different sub-bands.

[0029] An intelligent metasurface terahertz communication multi-domain resource joint allocation system provided by the present invention includes:

[0030] Problem construction module: Based on the target communication system, construct the optimization problem P0.1;

[0031] Solution calculation module: Calculate the solution of the optimization problem P0.1;

[0032] Optimization module: Optimize the uplink and downlink transmission power, RIS beamforming, and allocate adaptive sub-bands of the target communication system.

[0033] Preferably, the target communication system includes: a base station, a RIS system, and a user;

[0034] The base station receives the signal of the user, and the RIS system generates a passive beamforming control signal to serve the user; when performing beamforming, the RIS system constructs the optimization problem P0.1 and optimizes and solves all optimization variables to obtain the solution of the optimization problem P0.1;

[0035] The adaptive sub-band is a sub-band of the bandwidth.

[0036] Preferably, in the problem construction module, the mathematical expression of the optimization problem P0.1 is:

[0037]

[0038] Preferably, in the solution module, the objective function of the optimization problem P0.1 is replaced with a first-order Taylor expansion expression, and a new equality constraint penalty term is introduced into the objective function of the optimization problem P0.1 to construct a sub-problem of the optimization variable f and a sub-problem of the optimization variable B. Then, the values of the optimization variable f and the optimization variable B are obtained by solving, and finally the remaining optimization variables are solved, that is obtain the solutions to all the optimization problems P0.1;

[0039] The mathematical expression of the new equality constraint penalty term is:

[0040]

[0041] where, Ξ(f s , B s ) represents the new equality constraint penalty term; f s represents the center frequency of the s-th sub-band;

[0042] The sub-problem of the optimization variable f, the mathematical expression is:

[0043]

[0044] Then, through the solution software for convex optimization problems, the sub-problem of the optimization variable f is solved to obtain the optimization variable f; the sub-problem of the optimization variable B, the mathematical expression is:

[0045]

[0046] where, P2 represents the sub-problem of the optimization variable B;

[0047] The sub-problem of the optimization variable , the mathematical expression is:

[0048]

[0049] where, P3 represents the sub-problem of the optimization variable ;

[0050] Alternately iterate the sub-problem of the optimization variable f, the sub-problem of the optimization variable B, and the sub-problem of the optimization variable to obtain a sub-optimal solution of the optimization problem P0.1 as the solution of the optimization problem P0.1.

[0051] Preferably, in the optimization module, based on the solution of the optimization problem P0.1, the phase of the reflection unit of each RIS panel is controlled by the passive beamforming of the RIS panel of the RIS system, and the transmission power of the base station and the user on different sub-bands is optimized.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] 1. The present invention realizes the joint allocation of multi-domain resources in the power domain, frequency spectrum domain and spatial domain in the terahertz communication system. Specifically, the joint design realizes power allocation, passive beamforming and adaptive sub-band allocation, improving the transmission efficiency of the terahertz system.

[0054] 2. The present invention solves the problems of large path loss, obvious molecular absorption effect in the terahertz system, and serious self-interference in the full-duplex system; compared with the traditional resource allocation methods, namely the equal-bandwidth sub-band allocation method and the fixed sub-band allocation method, when the number of RIS units is 500, the spectral efficiency is increased by 13% and 42% respectively.

[0055] 3. The architecture proposed in the present invention adds an RIS and an intelligent controller with a simple structure, low hardware deployment cost and low additional power overhead. The proposed optimization algorithm is simple and efficient, and it is a new type of green terahertz communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objects and advantages of the present invention will become more apparent:

[0057] Figure 1 Schematic diagram of the RIS-assisted terahertz communication system provided by the present invention;

[0058] Figure 2 Method comparison diagram showing the change with the base station transmission power provided by the present invention;

[0059] Figure 3 Method comparison diagram showing the change with the number of RIS units provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0060] The present invention will be described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those of ordinary skill in the art can make several changes and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0061] The present invention utilizes a RIS system to assist in the information transmission of a terahertz communication system. By jointly allocating multi-domain resources in the power domain, spectral domain, and spatial domain, the transmission efficiency of the terahertz communication system is effectively improved.

[0062] An embodiment of the present invention discloses a method for jointly allocating multi-domain resources in a RIS-assisted terahertz communication system, as Figure 1 shown, including a terahertz full-duplex system, a RIS panel, an FPGA intelligent controller, and a full-duplex user group.

[0063] The RIS system assists the terahertz full-duplex system in information transmission. Considering the severe path loss in the terahertz band, the frequency-dependent molecular absorption effect, and the self-interference obstacle in the full-duplex system, by jointly allocating multi-domain resources in the power domain, spectral domain, and spatial domain, specifically, including optimizing the uplink and downlink transmission power, RIS beamforming, and adaptive sub-band allocation, the transmission efficiency of the terahertz system is improved.

[0064] The terahertz full-duplex base station transmits signals to users through the downlink channel and simultaneously receives signals from users through the uplink channel;

[0065] The RIS system generates a passive beamforming control signal. By establishing the amplitude and phase of all elements in the RIS coefficient matrix Θ, passive beamforming is performed on the incident signal of the RIS panel, so that the reflected signal can better serve the user group;

[0066] The terahertz full-duplex user receives signals from the base station through the downlink channel and simultaneously transmits signals to the base station through the uplink channel.

[0067] The method for jointly allocating multi-domain resources in a RIS-assisted terahertz communication system: By constructing an optimization problem with the maximum-minimum rate as the performance index and the maximum transmit power, the reflection ability of the reflection unit, adaptive sub-band allocation, and the maximum total bandwidth as constraints, the present invention realizes the resource allocation method for this new type of RIS-assisted terahertz communication system.

[0068] The design of the method for jointly allocating multi-domain resources in a RIS-assisted terahertz communication system: The constructed maximum-minimum rate optimization problem is a non-convex optimization problem, and the global optimal solution cannot be directly obtained. In the following, the present invention is based on methods such as the penalty function method, the gradient descent method, and the successive convex approximation algorithm, and alternately optimizes all optimization variables to obtain a sub-optimal solution with relatively high quality.

[0069] In response to the requirements of next-generation wireless networks for transmission efficiency and cost reduction, the present invention provides a design for a resource allocation method for a new type of RIS-assisted terahertz communication system.

[0070] The RIS-assisted terahertz communication system constructed according to the present invention, the base station and user power allocation, RIS beamforming, and adaptive sub-band allocation scheme.

[0071] The base station and the user need to consider the power allocation when transmitting signals. When the RIS assists in transmitting signals, it needs to control the phase of each reflection unit to achieve passive transmission beamforming. The system bandwidth allocation needs to consider the severe path loss in the terahertz band, the frequency-dependent molecular absorption effect, and the self-interference obstacle in the full-duplex system.

[0072] The RIS-assisted terahertz communication multi-domain resource joint allocation method provided by the present invention includes a base station, an RIS system, and a user;

[0073] The base station is a terahertz full-duplex system;

[0074] The user is a terahertz full-duplex user;

[0075] The RIS system assists the terahertz full-duplex system in information transmission. Considering the severe path loss in the terahertz band, the frequency-dependent molecular absorption effect, and the self-interference obstacle in the full-duplex system, by jointly allocating multi-domain resources in the power domain, frequency domain, and spatial domain, specifically, including optimizing the uplink and downlink transmission power, RIS beamforming, and adaptive sub-band allocation, the transmission efficiency of the terahertz system is improved.

[0076] Preferably, the RIS system includes an RIS panel and an FPGA intelligent controller;

[0077] The RIS panel includes a plurality of reflection units;

[0078] The RIS panel performs passive beamforming through the FPGA intelligent controller to control the phase of each reflection unit.

[0079] Preferably, the base station includes a terahertz full-duplex system;

[0080] First, construct an RIS-assisted terahertz communication system. The base station includes a terahertz full-duplex base station, and the user group includes terahertz full-duplex users;

[0081] The terahertz full-duplex base station transmits signals to the user through the downlink channel and simultaneously receives signals from the user through the uplink channel;

[0082] The RIS system generates a passive beamforming control signal. By establishing the amplitude and phase of all elements in the RIS coefficient matrix Θ, passive beamforming is performed on the incident signal of the RIS panel, so that the reflected signal can better serve the user group;

[0083] The terahertz full-duplex user receives signals from the base station through the downlink channel and transmits signals to the base station through the uplink channel simultaneously.

[0084] Specifically, the RIS system is a passive RIS-assisted system;

[0085] When the RIS system assists the terahertz full-duplex system in beamforming, an optimization problem is constructed with constraints of maximizing the minimum rate, maximum transmit power, reflection ability of reflection units, adaptive sub-band allocation, and maximum total bandwidth;

[0086] The constructed optimization problem of maximizing the minimum rate is a non-convex optimization problem. Based on methods such as semidefinite relaxation and successive convex approximation, a two-layer penalty function method is proposed to optimize and solve all optimization variables.

[0087] Specifically, the terahertz full-duplex system has 1 transmit antenna and 1 receive antenna, and the RIS panel has N = N x ×N y reflection units, denoted by , serving K full-duplex users, denoted by , and each user also has 1 transmit antenna and 1 receive antenna; The system adopts a multi-carrier information transmission scheme with a total bandwidth of B max , and the entire transmission bandwidth is divided into S sub-bands, denoted by , and the bandwidth of each sub-band is denoted as B s , represented by B = {B1, …, B S}.

[0088] To avoid inter-band interference, a fixed guard bandwidth B g is introduced between each sub-band, and the center frequency of each sub-band is denoted as f s , represented by f = {f1, …, f S}; then f s is expressed as Through resource allocation, each sub-band is allocated to at most one user for uplink and downlink signal transmission. Then, the uplink signal-to-noise ratio of the k-th user on the s-th sub-band is expressed as:

[0089]

[0090] where is the signal power that the user expects to receive, q k,s is the transmission power of the k-th user on the s-th sub-band; Γp s |g s | 2 is the self-interference at the base station side, Γ is the self-interference cancellation coefficient, p s is the transmission power of the base station on the s-th sub-band; B s$N_0$ is the noise interference; represents the uplink channel between the base station and the RIS; (·) H represents the conjugate transpose operator of a matrix or vector, and $\Theta$ represents the matrix when the RIS-assisted signal beamforming is performed; represents the uplink channel between the user and the RIS, is expressed as:

[0091]

[0092] where, represents a vector with dimension $N\times1$, $e$ represents the natural constant; $d$ r is the distance between the base station and the RIS, represents the channel path gain between the base station and the RIS, is the azimuth angle of the emission angle of the RIS, represents the elevation angle of the emission angle of the RIS, $c$ represents the speed of light; $k(f)$ is the molecular absorption effect related to the frequency, and is expressed as:

[0093]

[0094] where, $\sigma_1,\sigma_2,\sigma_3$ are the curve fitting parameters; $a(·)$ represents the uniform planar array steering vector. is expressed as:

[0095]

[0096] where, $d$ k is the distance between the $k$-th user and the RIS, is the azimuth angle of the arrival angle of the RIS, represents the elevation angle of the arrival angle of the RIS.

[0097] $g$ s is expressed as:

[0098]

[0099] where, $g$ s represents the self-interference channel from the base station transmitting antenna to the base station receiving antenna; is the path gain of this self-interference channel, $d_0$ is the distance between the base station transmitting antenna and the receiving antenna, $\kappa$ λ represents the wave number;

[0100] The matrix $\Theta$ when the RIS-assisted signal beamforming is performed, is expressed as:

[0101]

[0102] where, $\theta$ n represents the phase of the $n$-th element when the RIS-assisted signal beamforming is performed; $v$H is the vector representation of the RIS coefficient matrix Θ, and diag(·) represents the operator that converts a vector into a diagonal matrix;

[0103] The downlink signal-to-noise ratio of the k-th user on the s-th sub-band is expressed as:

[0104]

[0105] where is the signal power that the user expects to receive; Γq k,s |h k,s | 2 is the self-interference at the user side; represents the downlink channel between the user and the RIS; represents the downlink channel between the base station and the RIS, h k,s represents the self-interference channel from the transmitting antenna to the receiving antenna of the k-th user; represents:

[0106]

[0107] is expressed as

[0108]

[0109] where j represents the imaginary unit;

[0110] is the path gain of this self-interference channel, d 0,k is the distance from the transmitting antenna to the receiving antenna of the k-th user. Then the optimization problem P0 is expressed as:

[0111] P0: max B,v,p,q,α,τ τ

[0112]

[0113] where α = {α k,s} is the binary sub-band allocation indication parameter, and α k,s = 1 means that the s-th sub-band is allocated to user k, and the uplink rate expression is The downlink rate expression is:

[0114]

[0115] P b represents the maximum transmit power of the base station, and P k represents the maximum transmit power of the k-th user.

[0116] The optimization problem is a non-convex optimization problem with respect to variables B, v, p, q, α, and τ, and the two-layer penalty function method is used for alternating optimization and solution.

[0117] Preferably, the resource allocation method for RIS-assisted terahertz communication is characterized in that it jointly optimizes the uplink and downlink transmission powers of the base station and the user, RIS beamforming, and adaptive sub-band allocation to maximize the minimum rate; the following is a detailed explanation of the two-layer penalty function method;

[0118] In other words, the present invention provides a resource allocation method for jointly optimizing the uplink and downlink transmission powers of the base station and the user, RIS beamforming, and adaptive sub-band allocation to maximize the minimum rate; the following is a detailed explanation of the two-layer penalty function method

[0119] Outer layer: Update the penalty factor

[0120] Introduce new variables and After that, the Big-M method is adopted; then the fourth constraint in the original problem P0 can be transformed into Furthermore, the uplink and downlink rate formulas are equivalently transformed into and Next, the second constraint in the original problem P0 is transformed into the following two constraints: and Since the second constraint causes the feasible region to be non-connected, for the feasibility of actual calculation, it is incorporated into the objective function as a penalty term, and then the original problem P0 is transformed into

[0121]

[0122] where B represents the set of sub-bandwidths, i.e., the first optimization variable; v represents the vector representation of the RIS coefficient matrix Θ, i.e., the second optimization variable; p represents the set of base station transmission powers, i.e., the third optimization variable; q represents the set of user transmission powers, i.e., the fourth optimization variable; represents the set of base station power variables introduced by the Big-M method i.e., the fifth optimization variable; represents the set of user power variables introduced by the Big-M method i.e., the sixth optimization variable; α represents the binary sub-band allocation indication parameter α k,s i.e., the seventh optimization variable; τ represents the weighted minimum rate, i.e., the eighth optimization variable; μ ≥ 0 is used as a penalty parameter for punishing the violation of the constraint violation;

[0123] ω i,k represents the weighting coefficient; S represents the number of sub-bands of the transmission bandwidth; Denotes the uplink and downlink rates. When \(i = d\), it represents the downlink rate; when \(i = u\), it represents the uplink rate; Denotes for all;

[0124] Denotes the user quantity data set; \(K\) represents the maximum number of users; \(\alpha\) k,s Denotes the binary sub - band allocation indication parameter, where \(k\) represents the ordinal number of the user; \(s\) represents the ordinal number of the sub - band; \(B\) s Denotes the bandwidth of the sub - band; \(B\) max Denotes the total bandwidth; \(f\) end Denotes the highest frequency; \(f\) start Denotes the lowest frequency; \(B\) g Denotes the fixed guard bandwidth introduced between sub - bands; \([v]\) n Denotes the \(n\)th element in \(v\); \(P\) b Denotes the maximum transmission power of the base station, \(P\) k Denotes the maximum transmission power of the \(k\)th user; \(p\) s Denotes the transmission power of the base station of the target communication system on the \(s\)th sub - band; Denotes the base - station power variable introduced by the Big - M method; Denotes the user power variable introduced by the Big - M method.

[0125] In other words, where \(\mu\geq0\) is a penalty parameter used to penalize the violation of the constraints The proposed algorithm gradually increases \(\mu\) until the penalty term is less than a threshold. Since the existence of the penalty term makes the objective function of problem P0.1 a non - concave function, we use the method of successive convex approximation and replace it with its first - order Taylor expansion expression Since the rate expression in problem P0.1 is very complex for the optimization variable \(B\), it is split into two optimization variables \(B\) and \(f\), and the new equality constraint introduced thereby is also introduced as a penalty term into the objective function of problem P0.1.

[0126] Inner layer: Alternately iterate the variables

[0127] Solve the optimization variable \(f\):

[0128] Let and \(V = vv\) H , then Is re - expressed as It is observed that For is a convex function, and its lower bound is obtained through the first - order Taylor expansion:

[0129]

[0130] Among them,

[0131]

[0132] denotes the (n x , n y )-th term, then can be expressed as:

[0133] Similarly, define as the lower bound of the uplink cascaded channel.

[0134] Note that and are both non-convex and non-concave functions with respect to f s . Construct their approximate functions through their second-order Taylor expansions as:

[0135]

[0136] Among them,

[0137] Among them, is 's first derivative and 's second derivative. Select a positive real number such that it satisfies then it can be constructed as

[0138] For the self-interference channel expression of the k-th user, transform it into where and Similarly, the self-interference channel expression of the base station can be transformed into where and Next, introduce two sets of slack variables and satisfying the following constraints:

[0139]

[0140] Then transform the downlink rate expression into, and further construct its lower bound as:

[0141]

[0142] Among them, denotes definition; similarly, the lower bound of the uplink rate expression is constructed as:

[0143]

[0144] Convex constraint Introduce two new sets of variables and Satisfy the following constraints

[0145]

[0146] This non-convex constraint is transformed into:

[0147]

[0148] First, re-express it as

[0149] Furthermore, introduce a set of slack variables Satisfy the following constraints:

[0150]

[0151] Through the method of successive convex approximation, the above constraints can be transformed into:

[0152]

[0153] Furthermore, introduce and Satisfy the following constraints:

[0154]

[0155] Adopt the method of successive convex approximation to handle the above non-convex constraints. Similarly, use the method of successive convex approximation to handle the non-convex constraint Up to this point, the sub-problem of optimizing variable f is transformed into:

[0156]

[0157] Among them, P1 represents the sub-problem of optimizing variable f; y, u, w, z, t, r all represent the introduced slack variables, all of which are sets; f represents the set of center frequencies of sub-bands; represents the lower bound of the uplink and downlink rates. When i is d, it represents the lower bound of the downlink rate. When i is u, it represents the lower bound of the uplink rate; σ1 represents a curve fitting parameter; σ2 represents another curve fitting parameter; σ3 represents yet another curve fitting parameter; u s represents the elements in u; represents u s a feasible solution of; w s represents the elements in w; t k,s represents the elements in t; r k,s represents the elements in r; y k,s represents the elements in y; c represents the speed of light; d k represents the distance between the k-th user and the RIS; d r represents the distance between the base station and the RIS;

[0158] represents a feasible solution of, where i is d or u; and both represent elements in z; represents the downlink equivalent channel; represents the uplink equivalent channel; N0 represents the noise density; represents the lower bound of the downlink cascaded channel, constructed by second-order Taylor expansion; represents the lower bound of the uplink cascaded channel, constructed by second-order Taylor expansion;

[0159] Then, through a convex optimization problem-solving software, such as the MATLAB cvx tool, solve the sub-problem of the optimization variable f to obtain the optimization variable f;

[0160] Solve the optimization variable B:

[0161] The sub-problem of the optimization variable B is

[0162]

[0163] The optimal B can be obtained by solving through a convex optimization problem-solving software.

[0164] Solve the optimization variable

[0165] The optimization variable The sub-problem is:

[0166]

[0167] where P3 represents the sub-problem of the optimization variable ;

[0168] Alternately iterate the sub-problem of the optimization variable f, the sub-problem of the optimization variable B, and the sub-problem of the optimization variable to obtain a sub-optimal solution of the optimization problem P0.1 as the solution of the optimization problem P0.1.

[0169] This sub-problem can be solved by Algorithm 1 in the following literature: [1] C. Qiu et al., “Intelligent reflecting surface empowered self-interference cancellation in full-duplex systems,” IEEE Trans. Commun., vol. 72, no. 5, pp. 2945-2958, May 2024.

[0170] The present invention particularly provides a method for joint allocation of multi-domain resources in a RIS-assisted terahertz communication system. Figure 1 The basic structural composition of the invention is described. Figure 2 and Figure 3 The performance gain of the invention is compared.

[0171] As a revolutionary technology, RIS (Reconfigurable Intelligent Surface) is expected to address the challenges of performance improvement and deployment cost in the development of terahertz communication systems. For communication systems, RIS can achieve channel reconstruction, providing sufficient multipath components for accurate beamforming. In addition, RIS can also solve the problems of severe transmission attenuation and frequency-selective molecular absorption effects in terahertz communication. First, RIS can achieve the ability of passive beamforming, which can increase the information transmission range, align the user location, and enhance the signal strength. In addition, the passive characteristics of RIS greatly reduce the deployment cost and can be well integrated with future terahertz communication systems. Therefore, using RIS to improve the performance of terahertz full-duplex systems is efficient and can reduce the deployment cost of individual base stations and network formation, which has important implementation value in the next-generation wireless network.

[0172] The present invention provides a novel RIS-assisted terahertz communication system multi-domain resource joint allocation scheme. The novel system includes a terahertz full-duplex base station, RIS, an FPGA intelligent controller, and a full-duplex user. The full name of FPGA is Field Programmable Gate Array, and its Chinese translation is Field Programmable Logic Gate Array.

[0173] In the system, the RIS system assists the terahertz full-duplex system in information transmission, alleviating the severe path loss and self-interference problems in the terahertz full-duplex system. By jointly allocating multi-domain resources in the power domain, frequency domain, and spatial domain, specifically, including optimizing the uplink and downlink transmission power, RIS beamforming, and adaptive sub-band allocation, the transmission efficiency of the terahertz system is improved.

[0174] The present invention designs a multi-domain resource allocation scheme for a novel RIS-assisted terahertz full-duplex system with the maximum-minimum rate as the performance metric and by restricting the maximum transmit power, the reflection ability of reflection units, and the total bandwidth constraint.

[0175] Compared with the traditional terahertz system, the RIS system-assisted terahertz system in the present invention does not add a large number of additional radio frequency links and complex signal processing units, and is a design that can achieve better performance at lower cost and power consumption. Through the design of an efficient resource allocation scheme, the minimum rate performance in this system is significantly improved.

[0176] For the described double-layer penalty function algorithm, the outer-layer algorithm gradually reduces the penalty factor μ until the penalty term is less than a pre-defined threshold, and the inner-layer algorithm uses the gradient descent method to alternately iterate and optimize the variables.

[0177] Preferably, a method for joint multi-domain resource allocation in RIS-assisted terahertz communication is applied to an RIS-assisted terahertz communication system, jointly optimizing the uplink and downlink transmission power, RIS beamforming, and adaptive sub-band allocation to improve the transmission efficiency of the terahertz system, that is, to improve the terahertz full-duplex communication efficiency.

[0178] The method for joint multi-domain resource allocation in RIS-assisted terahertz communication includes the following steps:

[0179] Step of updating the penalty factor in the outer layer: By continuously increasing the value of the penalty factor, forcing the penalty term to approach zero, thereby satisfying the constraint.

[0180] Step of iterative gradient descent method in the inner layer:

[0181] Step of optimizing variable f: Based on continuous convex approximation methods such as quadratic Taylor expansion, optimize variable f.

[0182] Step of optimizing variable B: Optimize variable B through a convex optimization solver.

[0183] Optimizing variable Step: The RIS panel performs passive beamforming through an FPGA intelligent controller, controls the phase of each reflection unit, and optimizes the transmission power of the base station and users on different sub-bands.

[0184] Preferably, in the step of updating the penalty factor in the outer layer of the method for joint multi-domain resource allocation in RIS-assisted terahertz communication, an exclusive strategy allocation of adaptive sub-bands is achieved by adjusting the penalty factor.

[0185] In the inner-layer gradient descent method iteration step, the RIS system generates a passive beamforming control signal. By establishing the phases of all elements in the RIS coefficient matrix Θ, passive beamforming is performed on the incident signals of the RIS panel to enhance the signal strength received by the user. At the same time, the transmission powers of the base station and the user are optimized to achieve the maximum efficiency transmission performance.

[0186] The present invention also provides an intelligent metasurface terahertz communication multi-domain resource joint allocation system. The intelligent metasurface terahertz communication multi-domain resource joint allocation system can be implemented by executing the process steps of the intelligent metasurface terahertz communication multi-domain resource joint allocation method. That is, those skilled in the art can understand the intelligent metasurface terahertz communication multi-domain resource joint allocation method as a preferred implementation manner of the intelligent metasurface terahertz communication multi-domain resource joint allocation system.

[0187] An intelligent metasurface terahertz communication multi-domain resource joint allocation system according to the present invention includes:

[0188] A problem construction module: Based on the target communication system, construct an optimization problem P0.1; A solution calculation module: Calculate the solution of the optimization problem P0.1; An optimization module: Optimize the uplink and downlink transmission powers, RIS beamforming, and allocate adaptive sub-bands of the target communication system.

[0189] Those skilled in the art know that in addition to implementing the system and its various devices, modules, and units provided by the present invention in the form of pure computer-readable program code, the method steps can be logically programmed to enable the system and its various devices, modules, and units provided by the present invention to be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers, etc., to achieve the same functions. Therefore, the system and its various devices, modules, and units provided by the present invention can be regarded as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be regarded as the structures within the hardware component; the devices, modules, and units for implementing various functions can also be regarded as both software modules for implementing the method and the structures within the hardware component.

[0190] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present application.

[0191] The specific embodiments of the present invention have been described above. 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 do not affect the essence of the present invention. Without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for joint allocation of multi-domain resources in intelligent metasurface terahertz communication, characterized in that, Including: Based on the target communication system, construct the optimization problem P0.1, calculate the solution of the optimization problem P0.1, and then optimize the uplink and downlink transmission power, RIS beamforming, and allocate adaptive sub-bands of the target communication system.

2. The intelligent metasurface terahertz communication multi-domain resource joint allocation method according to claim 1, wherein The target communication system includes: a base station, a RIS system, and a user. The base station receives the signal of the user, and the RIS system generates a passive beamforming control signal to serve the user. When performing beamforming, the RIS system constructs the optimization problem P0.1 and optimizes and solves all optimization variables to obtain the solution of the optimization problem P0.

1. The adaptive sub-band is a sub-band of the bandwidth.

3. The intelligent metasurface terahertz communication multi-domain resource joint allocation method according to claim 2, characterized in that The mathematical expression of the optimization problem P0.1 is: Among them, B represents the set of bandwidths of sub-bands, i.e., the first optimization variable; v represents the vector representation of the RIS coefficient matrix Θ, i.e., the second optimization variable; p represents the set of base station transmission powers, i.e., the third optimization variable; q represents the set of user transmission powers, i.e., the fourth optimization variable; represents the base station power variable introduced by the Big-M method set, i.e., the fifth optimization variable; represents the user power variable introduced by the Big-M method set, i.e., the sixth optimization variable; α represents the binary sub-band allocation indication parameter α k,s set, i.e., the seventh optimization variable; τ represents the weighted minimum rate, i.e., the eighth optimization variable; μ ≥ 0 is used as a penalty parameter for penalizing constraint violations; ω i,k represents a weighting coefficient; S represents the number of sub-bands of the transmission bandwidth; represents the uplink and downlink rate. When i is d, it represents the downlink rate. When i is u, it represents the uplink rate; represents for all; represents the user quantity data set; K represents the maximum number of users; α k,s represents the binary sub-band allocation indication parameter, where k represents the ordinal number of the user; s represents the ordinal number of the sub-band; B s represents the bandwidth of the sub-band; B max represents the total bandwidth; f end represents the highest frequency; f start represents the lowest frequency; B g represents the fixed protection bandwidth introduced between sub-bands; [v] n represents the nth element in v; P b represents the maximum transmit power of the base station, P k represents the maximum transmit power of the kth user; p s represents the transmit power of the base station of the target communication system on the s-th sub-band; represents the base station power variable introduced by the Big-M method; represents the user power variable introduced by the Big-M method.

4. The intelligent metasurface terahertz communication multi-domain resource joint allocation method according to claim 3, characterized in that Replace the objective function of the optimization problem P0.1 with a first-order Taylor expansion expression, and introduce a new equality constraint penalty term into the objective function of the optimization problem P0.1 to construct sub-problems for the optimization variable f, sub-problems for the optimization variable B, and sub-problems for the optimization variable to iteratively solve the three sub-problems to obtain the values of the optimization variable f, the optimization variable B, and the optimization variable , that is, to obtain the solution of the entire optimization problem P0.1; The mathematical expression of the new equality constraint penalty term is: where, Ξ(f s , B s ) represents the new equality constraint penalty term; f s represents the center frequency of the s-th sub-band; The sub-problem of the optimization variable f, the mathematical expression is: Among them, P1 represents the sub-problem of the optimization variable f; y, u, w, z, t, and r all represent the introduced slack variables, all of which are sets; f represents the set of center frequencies of the sub-bands. represents the lower bound of the uplink and downlink rates. When i is d, it represents the lower bound of the downlink rate, and when i is u, it represents the lower bound of the uplink rate; σ1 represents a curve fitting parameter; σ2 represents another curve fitting parameter; σ3 represents yet another curve fitting parameter; u s represents the element in u. represents u s a feasible solution of; w s represents the element in w; t k,s represents the element in t; r k,s represents the element in r; y k,s represents the element in y; c represents the speed of light. d k represents the distance between the k-th user and the RIS; d r represents the distance between the base station and the RIS. denote feasible solutions, where i is d or u; and both denote elements in z; denotes the downlink equivalent channel; denotes the uplink equivalent channel; N0 denotes the noise density; denotes the lower bound of the downlink cascaded channel, constructed by second-order Taylor expansion; denotes the lower bound of the uplink cascaded channel, constructed by second-order Taylor expansion; Then, through the convex optimization problem solving software, solve the sub-problem of the optimization variable f to obtain the optimization variable f. The sub-problem of the optimization variable B, the mathematical expression is: Where P2 represents the sub-problem of the optimization variable B. The optimization variable has a sub-problem with the mathematical expression: where P3 represents the sub-problem of the optimization variable ; Sub-problems for alternately iteratively optimizing variable f, sub-problems for optimizing variable B, and sub-problems for optimizing variable , and obtaining a sub-optimal solution of optimization problem P0.1 as the solution of optimization problem P0.

1.

5. The intelligent metasurface terahertz communication multi-domain resource joint allocation method according to claim 2, wherein Based on the solution of the optimization problem P0.1, through the passive beamforming of the RIS panel of the RIS system, control the phase of the reflection unit of each RIS panel, and optimize the transmission power of the base station and the user on different sub-bands.

6. An intelligent metasurface terahertz communication multi-domain resource joint allocation system, characterized in that, Including: Problem construction module: Based on the target communication system, construct the optimization problem P0.

1. Solution calculation module: Calculate the solution of the optimization problem P0.

1. Optimization module: Optimize the uplink and downlink transmission power, RIS beamforming, and allocate adaptive sub-bands of the target communication system.

7. The intelligent metasurface terahertz communication multi-domain resource joint allocation system according to claim 6, wherein The target communication system includes: a base station, a RIS system, and a user. The base station receives the signal of the user, and the RIS system generates a passive beamforming control signal to serve the user. When performing beamforming, the RIS system constructs the optimization problem P0.1 and optimizes and solves all optimization variables to obtain the solution of the optimization problem P0.

1. The adaptive sub-band is a sub-band of the bandwidth.

8. The intelligent metasurface terahertz communication multi-domain resource joint allocation system according to claim 6, characterized in that, In the problem construction module, the mathematical expression of the optimization problem P0.1 is: Among them, B represents the set of bandwidths of sub - frequency bands, i.e., the first optimization variable; v represents the vector representation of the RIS coefficient matrix Θ, i.e., the second optimization variable; p represents the set of base - station transmit powers, i.e., the third optimization variable; q represents the set of user transmission powers, i.e., the fourth optimization variable; represents the base - station power variables introduced by the Big - M method set, i.e., the fifth optimization variable; represents the user power variables introduced by the Big - M method set, i.e., the sixth optimization variable; α represents the binary sub - frequency - band allocation indication parameter α k,s set, i.e., the seventh optimization variable; τ represents the weighted minimum rate, i.e., the eighth optimization variable; μ≥0 is used as a penalty parameter for penalizing constraint violations; ω i,k represents a weighting coefficient; S represents the number of sub-bands of the transmission bandwidth; represents the uplink and downlink rates. When i is d, it represents the downlink rate. When i is u, it represents the uplink rate; represents for all; represents the user quantity data set; K represents the maximum number of users; α k,s represents the binary sub - band allocation indication parameter, where k represents the ordinal number of the user; s represents the ordinal number of the sub - band; B s represents the bandwidth of the sub - band; B max represents the total bandwidth; f end represents the highest frequency; f start represents the lowest frequency; B g represents the fixed protection bandwidth introduced between sub - bands; [v] n represents the nth element in v; P b represents the maximum transmit power of the base station, P k represents the maximum transmit power of the kth user; p s represents the transmission power of the base station of the target communication system on the s - th sub - band; represents the base station power variable introduced by the Big - M method; represents the user power variable introduced by the Big - M method.

9. The intelligent metasurface terahertz communication multi-domain resource joint allocation system according to claim 8, characterized in that, In the solution module, the objective function of the optimization problem P0.1 is replaced with a first-order Taylor expansion expression, and a new equality constraint penalty term is introduced into the objective function of the optimization problem P0.

1. Sub-problems of the optimization variable f and the optimization variable B are constructed, and then the values of the optimization variable f and the optimization variable B are obtained by solving. Finally, the remaining optimization variables are solved, that is the solutions to all the optimization problems P0.1 are obtained; The mathematical expression of the new equality constraint penalty term is: Among them, Ξ(f s , B s ) represents the new equality constraint penalty term; f s represents the center frequency of the sth sub-band; The sub-problem of the optimization variable f, the mathematical expression is: Among them, P1 represents the sub-problem of the optimization variable f; y, u, w, z, t, and r all represent the introduced slack variables, that is, sets; f represents the set of center frequencies of sub-bands. represents the lower bound of the uplink and downlink rates. When i is d, it represents the lower bound of the downlink rate. When i is u, it represents the lower bound of the uplink rate; σ1 represents a curve fitting parameter; σ2 represents another curve fitting parameter; σ3 represents yet another curve fitting parameter; u s represents the element in u; represents u s a feasible solution of; w s represents the element in w; t k,s represents the element in t; r k,s represents the element in r; y k,s represents the element in y; c represents the speed of light; d k represents the distance between the k-th user and the RIS; d r represents the distance between the base station and the RIS; denote feasible solutions, where i is d or u; and both denote elements in z; denote the downlink equivalent channel; denote the uplink equivalent channel; N0 denotes the noise density; denote the second-order Taylor expansion of the lower bound of the downlink cascaded channel; denote the second-order Taylor expansion of the lower bound of the uplink cascaded channel; Then, through the convex optimization problem solving software, solve the sub-problem of the optimization variable f to obtain the optimization variable f. The sub-problem of the optimization variable B, the mathematical expression is: Where P2 represents the sub-problem of the optimization variable B. The optimization variable of the sub-problem, and the mathematical expression is: where P3 represents the sub-problem of the optimization variable ; Sub-problems of alternately iteratively optimizing variable f, sub-problems of optimizing variable B, and sub-problems of optimizing variable , to obtain a sub-optimal solution of optimization problem P0.1 as the solution of optimization problem P0.

1.

10. The intelligent metasurface terahertz communication multi-domain resource joint allocation system according to claim 7, characterized in that In the optimization module, based on the solution of the optimization problem P0.1, through the passive beamforming of the RIS panel of the RIS system, control the phase of the reflection unit of each RIS panel, and optimize the transmission power of the base station and the user on different sub-bands.

Images