可重构智能表面辅助通信系统的非线性优化及实验设计

Nonlinear optimization and experimental design of reconfigurable intelligent surface-assisted communication systems

针对可重构智能表面辅助的通信系统,提出了一种智能反射表面开关选择和功率分配的联合设计方法,以改变电磁波传播环境、提高信号传输效率、减少功率消耗。首先,在保证接收端数据传输速率达到一定门限值等多个约束条件下,建立以系统能量效率最大化为目标的多维变量联合非线性优化问题,其中涉及的优化变量包括:智能反射表面开关选择、相位调整和功率分配。在开关选择方面,采用贪婪算法选择开启起主要作用的元器件,并关掉起次要作用的智能反射表面。在功率分配方面,通过求解凸优化问题得到功率分配的闭式解。基于此,设计出一种高传输速率、低功率消耗的迭代优化算法。最后,设计仿真教学实验,通过MATLAB软件,验证了所提出的联合优化算法通过改变电磁波传播环境,有效地提高了信号传输的速率和能量效率;并分析了实验中导致算法性能发生变化的机理,这也有利于帮助学生提高科研创新能力和工程实践能力。

[Objective]The reconfigurable intelligent surface(RIS)is a key technology for 6G(the sixth generation)because it can achieve a programmable propagation environment with high energy efficiency and low hardware cost.To change an electromagnetic wave propagation environment,improve signal transmission efficiency,and reduce power consumption,an intelligent,reflective surface switch selection and power allocation design method are investigated for a RIS-assisted communication system.[Methods]A multidimensional variable joint nonlinear optimization problem is established to maximize the energy efficiency of the system and ensure that the data rate at the receiving end reaches a certain threshold and other constraints.The optimization variables involved include intelligent,reflective surface switch selection parameters and power allocation.In switch selection,a greedy algorithm selects the components playing the main role and turns off the intelligent,reflective surface playing a secondary role.In power distribution,the closed-form solution of power distribution is obtained by solving the convex optimization problem.Finally,an iterative optimization algorithm with a high transmission rate and low power consumption is designed.Meanwhile,we designed simulation experiments and verified that the proposed joint optimization algorithm can effectively improve the signal transmission data rate and energy efficiency by changing the electromagnetic wave propagation environment using MATLAB.[Results]The simulation results show that 1)the proposed distributed RIS optimization algorithm is superior to the other two comparison algorithms.Compared with the method of selecting one optimal RIS,the optimized RIS selection algorithm improved energy efficiency by 111.00%,87.31%,70.96%,63.00%,62.58%,64.09%,and 63.97%.Moreover,the energy efficiency of the system increases and stabilizes with increasing transmission power.This behavior is observed because the data rate cannot keep up with the increase in transmission power.As the maximum transmit power increases,the rate also increases.Compared with a single centralized arrangement,a distributed scheme with multiple RIS deployments in the network can provide multiple receiving signal paths and increase the data rate.2)With increasing minimum speed requirement,the energy efficiency is initially stable and later decreases,and the proposed algorithm outperforms other algorithms.This result is observed because when the rate threshold is small,it does not affect the energy efficiency.The transmission power of the base station increases with the threshold value,so the energy efficiency decreases.In addition,the power consumption of the two distributed RIS deployments is much lower than the transmit power of the centralized RIS,therefore,the energy efficiency of the distributed RIS system is much higher than that of the centralized RIS scheme.3)As the distance between RIS and users increases,energy efficiency first decreases and then increases.The RIS position must be reasonably set to maximize energy efficiency.[Conclusions]To meet the demands of high-speed and high-coverage multimedia services for future communication systems,we discuss a modeling and experimental design method for a RIS-assisted communication system.Considering the performance optimization and experimental design of this system,this paper also analyzes the mechanism behind the algorithm performance variation in the experiment.The application of this method in teaching helps improve students’abilities in scientific research innovation and engineering practice.