Research On Resource Allocation In Reconfigurable Intelligent Surface Aided Sensing And Communication System

In recent years,with the continuous emergence and development of emerging applications such as autonomous driving,smart cities and industrial Internet of Things,communication systems are facing new challenges.These applications usually need to be highly sensing of complex environments and require the communication system to provide efficient communication services simultaneously.For this reason,joint sensing and communication(JSAC)technology came into being.JSAC technology integrates sensing and communication services in the same system at a lower cost and power consumption by sharing hardware and signal processing modules.It is one of the key technologies for the next generation of wireless communication systems.Current JSAC-related research mainly focuses on the issue of resource allocation.The core is to maximize the performance of both sensing and communication with limited radio resources or to minimize energy costs while ensuring the performance of sensing and communication.The aforementioned radio resources mainly include power,beam,and carrier,etc.However,in reality,constrained by the scarcity of radio resources,even the most optimal resource allocation algorithms cannot significantly enhance system performance.Therefore,it is crucial to improve the performance and efficiency of JSAC systems without changing the existing radio conditions.Recently,the development of reconfigurable intelligent surface(RIS)technology has provided a new approach to addressing the above issues.By controlling the phase of a large number of low-power,low-cost reflecting units,RIS can reshape and control the radio environment in a near-passive manner,enhance the coverage,spectrum efficiency,and energy efficiency of wireless communication systems.Based on this characteristic,RIS is considered as an important technology for achieving high-performance and low-energy consumption in JSAC systems.Despite significant progress in research on RIS-aided JSAC systems,there are still many issues that remain unexplored,such as the lack of resource allocation strategies with information uncertainty and the absence of methods for near-field regional position sensing.Therefore,this paper first conducts research on the near-field localization method and power optimization when line-of-sight(LOS)link is blocked to verify the advantages of RIS in assisting sensing and improving energy efficiency.Accordingly,RIS-aided JSAC resource allocation strategies are established for different information uncertainty problems.The main contents and contributions are as follows:1.For the case that the LOS link is blocked,this paper proposes an RIS-aided near-field localization and power allocation method.This paper studies the near-field localization method when the LOS link is blocked from three aspects:(1)Establishing an RIS-aided near-field localization strategy to maximize the localization performance for an area of interest and proposing RIS phase design schemes based on discrete or robust strategies;(2)Proposing a maximum likelihood-based RIS-aided near-field localization method to investigate the influence of RIS phase design methods on localization algorithms;(3)Formulating a power optimization problem to validate the effectiveness of RIS in improving energy efficiency.Simulation results confirm the superiority of RIS in assisting perception and enhancing energy efficiency.2.To solve the problems of poor performance and position parameter information uncertainty of traditional joint localization and communication(JLAC)systems,a study on robust resource allocation strategies for RIS-assisted JLAC systems was considered.Specifically,this paper first establishes a multi-user RIS-aided JLAC system and derives the closed-form Cramér-Rao lower bound as the metric for localization performance.Considering a statistical position error model,we formulate the joint optimization problem of maximizing both localization and communication performance as a bi-objective stochastic optimization problem.We develop a robust resource allocation strategy involving joint subcarrier grouping,subcarrier allocation,beam vectors,and RIS phase-shift matrices.To address this non-convex and non-linear problem,we first derive an analytical expression for the expected achievable rate and propose a novel united successive convex approximation method.In particular,to reduce the computational complexity,closed-form solutions for the beam vectors and RIS phase shifts are derived.Simulation results validate the effectiveness of the proposed method and reveal the trade-off between localization and communication services in RIS-aided JLAC systems.3.In view of the uncertainties in channel state information and target position parameter information,research on robust beamforming methods for RIS-assisted JSAC systems was carried out.This paper considers two different types of channel state information(CSI)error models: the bounded CSI error model,and the mixed bounded-moment CSI error model,and formulates the worst-case robust beamforming problem and the mixed chance-constrained and worst-case robust beamforming problem.For the worst-case robust beamforming problem,we first transform the original semi-infinite constraints into a convex linear matrix inequality form through the S-procedure.Then,we propose a successive convex approximation algorithm to obtain a suboptimal solution for the original problem.For the mixed chance-constrained and worst-case robust beamforming problem,we first consider applying the conditional value-at-risk method to handle chance constraints and then solve the semi-infinite inequality constraints caused by bounded CSI errors through a generalized S-procedure.Finally,we extend the solution method proposed in the worst-case case to solve the transformed problem.Simulation experiments verify the effectiveness of the proposed robust beamforming strategy.