Research On Symbol-Level Precoding For Wireless Communication Assisted By Intelligent Reflecting Surface

The new generation of mobile communication networks faces challenges such as high hardware costs,limited coverage areas,and severe device interference.Intelligent Reflecting Surfaces(IRS),as a low-power,low-cost,and programmable electromagnetic surface,can intelligently regulate the wireless environment and effectively solve the above bottlenecks,opening up a new paradigm for communication networks,which has received widespread attention from both academia and industry.In wireless communication systems,interference is generally considered a detrimental factor that impairs system performance.However,by optimizing the design of Symbol-Level Precoding(SLP),harmful interference well be converted into useful signal power,and interference thus becomes a beneficial factor to effectively improve the energy efficiency of the communication system.Nevertheless,the joint optimization design of IRS and SLP has not been studied in depth.Therefore,this thesis takes the IRS-assisted wireless communication system with SLP as the research object,fully utilizing the potential advantages of IRS and SLP,and thoroughly studying the pre-coding joint optimization design problem with the goal of enhancing system efficiency.The main contribution is summarized as follows:In the context of interference exploitation problem in a multi-user multi-input single-output downlink system,considering a wireless communication system with continuous phase-shift IRS assistance.A joint optimization problem is studied,with the objective of maximizing the minimum signal-to-noise ratio of the receiving users.To solve the non-convex objective function,a two-stage algorithm is proposed,which optimizes the IRS reflection phase-shift matrix and the base station precoder using a semi-definite relaxation algorithm and a convex optimization solver,respectively.Simulation results show that the proposed algorithm effectively reduces the bit error rate compared to the linear precoding scheme,and demonstrate significant advantages of the joint optimization algorithm in improving system performance.In the context of joint beamforming optimization design with discrete phase shift for an IRS,we establish an optimization problem that minimizes the total transmit power of a base station subject to user quality-of-service requirements and discrete reflection coefficient constraints of the IRS.Due to the increased difficulty of solving the non-convex optimization problem with discrete phase shifts of the IRS,a low-complexity algorithm is proposed to optimize the base station precoder and the IRS discrete phase shift matrix separately.Firstly,based on the performance enhancement analysis of SLP,the discrete phase optimization is transformed into a maximization problem of the user received signal-to-noise ratio,and the optimal discrete IRS beamforming matrix is obtained by using a grouping ordering algorithm.Then,based on the optimal phase shift,the transmission precoding is solved by utilizing the Lagrange duality and gradient descent algorithm.Simulation results show that the proposed joint optimization algorithm effectively reduces the total transmit power of the base station,and the performance improvement trend of the system decreases as the quantization resolution increases,demonstrating the existence of an optimal trade-off between system performance and complexity.