High-energy-efficiency resource allocation method in hybrid RIS assisted distributed SIMO system

Abstract

The invention discloses a high-energy-efficiency resource allocation method in a hybrid RIS assisted distributed SIMO system. According to the method, the energy efficiency of the system is maximized by jointly optimizing a receiving beam forming matrix, a hybrid RIS reflection coefficient matrix, a forward transmission quantization coefficient and user transmitting power; according to the method, an optimization scheme based on an alternating optimization method, continuous convex approximation and a fractional programming method is provided to solve the joint optimization problem, the energy-effective joint resource allocation method is obtained, effective resource allocation of the system can be achieved through the joint optimization method, and the energy efficiency of the system is improved.

Description

Technical field:

[0001] The present invention relates to a resource allocation method for a mobile communication system, and in particular to a high-energy-efficiency resource allocation method in a hybrid RIS-assisted distributed SIMO system, which belongs to the field of mobile communications. Background technology:

[0002] As one of the key technologies for future mobile communications, reconfigurable intelligent surface (RIS) technology can actively and intelligently control electromagnetic waves in space in a programmable way, forming an electromagnetic field with controllable parameters such as amplitude, phase, polarization and frequency, and improving the wireless propagation environment. In recent years, although passive RIS has attracted much attention due to its low energy consumption, high cost-effectiveness and easy deployment, its signal control ability is limited, it cannot actively enhance the signal, and it is affected by multiplicative fading, resulting in a generally weak received signal. Relatively speaking, active RIS can actively enhance the signal, but its energy consumption is high and the cost is increased. In order to overcome the shortcomings of both, researchers proposed a hybrid RIS architecture that combines the low energy consumption advantage of passive RIS and the signal enhancement capability of active RIS, aiming to optimize the signal transmission quality while reducing energy consumption and cost, thereby achieving more efficient communication in various complex environments.

[0003] In the hybrid RIS-assisted distributed SIMO system, for the resource optimization problem of maximizing energy efficiency, the receive beamforming design, RIS reflection coefficient design, user power allocation and forward quantization coefficient design generally affect each other and are closely related. There are many existing studies on the sum rate of the hybrid RIS-assisted centralized SIMO system, but there are relatively few studies on the energy efficiency of the hybrid RIS-assisted distributed SIMO system. Therefore, it is urgent to develop an optimization method to maximize energy efficiency in the hybrid RIS-assisted distributed SIMO system. Summary of the invention:

[0004] For the hybrid RIS-assisted distributed SIMO uplink communication system, in order to improve the energy efficiency in the system, the present invention proposes an effective method for jointly designing receive beamforming, hybrid RIS reflection coefficient, user transmit power and forward transmission quantization coefficient. The proposed joint optimization method can effectively realize energy-efficient resource allocation of the system.

[0005] The technical solution adopted by the present invention is: a method for high energy efficiency resource allocation in a hybrid RIS-assisted distributed SIMO system, the steps of which are as follows:

[0006] Step S1: Establish a hybrid RIS-assisted distributed SIMO uplink communication system, which includes B access points APs configured with N antennas, a hybrid RIS configured with M reflective elements, and K single-antenna users, wherein the channels between the AP, the hybrid RIS and the users obey the Rice distribution, and each AP performs beamforming processing on the received uplink signal and then transmits it back to the central processing unit CPU through the forward return link for merging and decoding;

[0007] Step S2: Establish an energy efficiency optimization problem under given constraints, and its optimization goal is to maximize the system energy efficiency The optimization variable is the user's transmit power p k , receive beamforming vector w b,k , forward quantization coefficient Q b，k , and the RIS reflection coefficient matrix Θ, the optimization constraints are the maximum transmit power of the user, the RIS reflection power constraint, the reflection amplitude constraint of the active and passive components in the RIS, the receive beamforming normalization, and the fronthaul capacity constraint; where SINR k is the signal-to-interference-to-noise ratio of user k, ζ is the inverse of the power amplifier coefficient, P act represents the total amplified power of the active components in the hybrid RIS, P static Expressed as static circuit power consumption;

[0008] Step S3: The above optimization problem belongs to a non-convex fractional programming problem. The fractional programming factor ξ is introduced, and the objective function is transformed into: The rate of user k is R k =log2(1+SINR k ); In this way, the original problem can be decomposed into the receive beamforming sub-problem, the hybrid RIS reflection coefficient design sub-problem, the user power allocation and the forward transmission quantization coefficient design sub-problem;

[0009] Step S4: For the optimization sub-problem in S3, first fix Solve the receive beamforming w b,k , then fix {w b,k , p k , Q b,k}Solve the hybrid RIS reflection coefficient matrix Θ, and finally fix {w b，k ,Θ} to solve the transmission power and forward quantization coefficient {p k , Q b，k}; Based on the alternating optimization method, generalized Rayleigh entropy, fractional programming, continuous convex approximation, and convex optimization tools are used to solve the above sub-problems, and the corresponding algorithm implementation is given to obtain {w b,k ,Θ,p k , Q b，k}'s suboptimal solution;

[0010] Step S5: Based on the obtained solution of the optimization variable, a highly energy-efficient joint resource allocation method is provided.

[0011] The present invention has the following beneficial effects: The present invention provides a high-energy-efficiency resource allocation method in a hybrid RIS-assisted distributed SIMO system, which has the advantages of high system energy efficiency and can effectively utilize communication system resources. The method makes full use of the inherent structure of the original optimization problem, first decomposes the complex joint optimization problem into a receiving beamforming sub-problem, a hybrid RIS reflection coefficient design sub-problem, and a user power allocation and forward transmission quantization coefficient design sub-problem, then uses a fractional programming method to transform the hybrid RIS reflection coefficient design sub-problem into a convex optimization problem for solution, then introduces auxiliary variables to transform the power allocation and forward transmission quantization coefficient design sub-problem into a convex optimization problem for solution, and uses the above-mentioned iterative optimization algorithm to obtain a joint resource allocation solution. Description of the drawings:

[0012] Figure 1 The present invention is a flowchart of a method for energy-efficient resource allocation in a hybrid RIS-assisted distributed SIMO system in an embodiment of the present invention.

[0013] Figure 2 The figure is a comparison chart of simulation results of the optimization scheme proposed in the embodiment of the present invention and the benchmark scheme.

[0014] Figure 3 It is a simulation curve diagram of system performance under different beamforming schemes in an embodiment of the present invention. Specific implementation method:

[0015] The present invention will be further described below in conjunction with the accompanying drawings.

[0016] 1. Analytical Method Process

[0017] The flowchart of the method for high energy efficiency resource allocation in a hybrid RIS-assisted distributed SIMO system of the present invention is as follows: Figure 1 shown.

[0018] 2. System Model

[0019] A hybrid RIS-assisted distributed SIMO system model is provided in a method for high-efficiency resource allocation in a hybrid RIS-assisted distributed SIMO system of the present invention. In the system, a hybrid RIS equipped with M reflective elements assists B APs equipped with N antennas to provide services for K single-antenna users, wherein the channels between the AP, the RIS and the user obey the Rice distribution. After each AP performs beamforming processing on the received uplink signal, it is transmitted back to the CPU through a forward return link for merging and decoding. The decoded signal can be expressed as:

[0020]

[0021] 3. Modeling and solution process of energy efficiency maximization problem of joint optimization of receive beamforming, RIS reflection coefficient, user power allocation and forward transmission quantization coefficient

[0022] In the above hybrid RIS-assisted distributed SIMO uplink communication system, the energy efficiency maximization problem is as follows:

[0023]

[0024] In view of the fractional form of the objective function in problem (2), the fractional programming factor ξ is introduced, and the objective function is transformed into:

[0025]

[0026] In the iteration of the fractional programming algorithm, the alternating optimization method and mathematical transformation are used to split the original optimization problem into the receiving beamforming sub-problem, the hybrid RIS reflection coefficient design sub-problem, the user power allocation and the forward transmission quantization coefficient design sub-problem. Based on the alternating optimization method, the beamforming matrix, the RIS reflection coefficient matrix, the user power and the forward transmission quantization coefficient are iteratively optimized. First, {p k , Θ, Q b，k Solve for W b，k , the optimization sub-problem is as follows:

[0027]

[0028] Further definition:

[0029]

[0030] According to the above generalized Rayleigh entropy form, the optimal solution is obtained as

[0031] Fixed {w b，k , p k , Q b，k}Solve Θ and optimize the subproblem as follows:

[0032]

[0033] In order to facilitate the decoupling of variables, auxiliary variables are introduced G b，i =H b diag{h i}, Convert the signal to interference and noise ratio into:

[0034]

[0035] Then introduce the auxiliary variable m k , using the fractional programming method, the original problem is transformed into solving the following convex optimization problem:

[0036]

[0037] The original problem is converted into a convex optimization problem, and the convex optimization problem solver is used to solve the hybrid RIS reflection coefficient design scheme in the current iteration;

[0038] Fixed {w b,k ,Θ}, solve {p k , Q b，k}, the optimization sub-problem is as follows:

[0039]

[0040] Introducing auxiliary variables and Perform the following variable conversions:

[0041]

[0042] The original problem is transformed into the following problem:

[0043]

[0044] The original problem is transformed into a problem about {p k , γ k ,q k , v b，k , T b,k , Q b，k}, and use standard convex optimization tools to solve the optimal power allocation scheme and the optimal forward transmission coefficient scheme for users in the current iteration.

[0045] Based on the above analysis, the resource allocation method for maximizing energy efficiency in a hybrid RIS-assisted distributed SIMO system proposed in the present invention first designs a closed-form receive beamforming based on generalized Rayleigh entropy, then solves the hybrid RIS reflection coefficient based on the fractional programming method, and finally solves the user power allocation scheme and the forward transmission quantization coefficient design scheme based on the continuous convex approximation method.

[0046] The following computer simulation is used to verify the effectiveness of the resource allocation method proposed in the present invention. The simulation parameters are set as follows: the number of APs B = 4, the number of users K = 4, the number of AP antennas N = 16, the number of RIS elements M = 32, and the thermal noise power RIS Power Gain Maximum reflected power P max =10mW, cell area side length D = 1000m.

[0047] Figure 2 The energy efficiency performance of the power allocation scheme proposed in this invention is compared with that of the genetic algorithm and the equal power allocation scheme. Figure 2 It can be observed that the system energy efficiency of the proposed power allocation scheme based on continuous convex approximation is slightly lower than that of the power allocation scheme based on genetic algorithm, which reflects the effectiveness of the proposed algorithm. max When transmitting a signal, as P max As P increases, the system energy efficiency increases first and then begins to decrease. This is because when P max When it is small, the optimized user transmission power is approximately P max , but when P max When the transmission power increases to a certain extent, the gain rate provided by the transmission power for the achievable rate will be lower than the growth rate of the system energy consumption. max Transmitting signals will lead to a decrease in system energy efficiency, but the proposed scheme keeps the energy efficiency unchanged by optimizing the transmission power.

[0048] Figure 3 The energy efficiency of distributed SIMO systems under different beamforming schemes is compared. Figure 3 It can be found that the system energy efficiency under the maximum ratio combining MRC scheme is the worst. This is because the beamforming scheme based on MRC only considers maximizing the receiving beam gain of each user, but ignores the interference between users, which seriously affects the system performance. In comparison, the beamforming scheme based on the zero forcing ZF method further eliminates the signal interference between users and improves the energy efficiency of the system. The beamforming scheme proposed in the present invention further considers the interference caused by thermal noise at AP and RIS and forward quantization error, and can well design the receiving beamforming to improve the system performance.

[0049] In summary, the method proposed in the present invention can effectively improve the energy efficiency of the hybrid RIS-assisted distributed SIMO system, and the steps of the design method are relatively simple and the effect is obvious. This fully demonstrates the effectiveness of the optimization method for maximizing energy efficiency in the hybrid RIS-assisted distributed SIMO system proposed in the present invention.

[0050] The above description is only a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements without departing from the principle of the present invention, and these improvements should also be regarded as within the protection scope of the present invention.

Claims

1. A method for high energy efficiency resource allocation in a hybrid RIS-assisted distributed single-input multiple-output SIMO system, characterized by: Here are the steps: Step S1: Establish a hybrid RIS-assisted distributed SIMO uplink communication system, which includes B access points APs configured with N antennas, a hybrid RIS configured with M reflective elements, and K single-antenna users, wherein the channels between the AP, the hybrid RIS and the users obey the Rice distribution, and each AP performs beamforming processing on the received uplink signal and transmits it back to the central processing unit CPU through the forward return link for merging and decoding; Step S2: Establish an energy efficiency optimization problem under given constraints, and its optimization goal is to maximize the system energy efficiency The optimization variable is the user's transmit power p k , receive beamforming vector w b,k , forward quantization coefficient Q b，k , and the RIS reflection coefficient matrix Θ, the optimization constraints are the maximum transmit power of the user, the RIS reflection power constraint, the reflection amplitude constraint of the active and passive components in the RIS, the receive beamforming normalization, and the fronthaul capacity constraint; where SINR k is the signal-to-interference-to-noise ratio of user k, ζ is the inverse of the power amplifier coefficient, P act represents the total amplified power of the active components in the hybrid RIS, P static Expressed as static circuit power consumption; Step S3: The above optimization problem belongs to a non-convex fractional programming problem. The fractional programming factor ξ is introduced, and the objective function is transformed into: The rate of user k is R k =log2(1+SINR k ); In this way, the original problem can be decomposed into the receive beamforming sub-problem, the hybrid RIS reflection coefficient design sub-problem, the user power allocation and the forward transmission quantization coefficient design sub-problem; Step S4: For the optimization subproblem in S3, first fix {p k , Θ, Q b，k } Solve for the receive beamforming vector w b,k , then fix {w b,k , p k , Q b,k }Solve the hybrid RIS reflection coefficient matrix Θ, and finally fix {w b,k ,Θ} to solve the transmission power and the forward quantization coefficient {p k , Q b，k }; Based on the alternating optimization method, generalized Rayleigh entropy, fractional programming, continuous convex approximation, and convex optimization tools are used to solve the above sub-problems, and the corresponding algorithm implementation is given to obtain {w b,k ,Θ,p k , Q b，k }'s suboptimal solution; Step S5: Based on the obtained solution of the optimization variable, a highly energy-efficient joint resource allocation method is provided.

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