Performance Analysis And Optimization Of Intelligent Reflecting Surface-assisted Data And Energy Integrated Networks

From simple sensors to complex intelligent driving,Internet of Things(IoT)technology has permeated every aspect of daily life,achieving seamless integration of devices,humans,and data.With the surge in the number of IoT devices and the enhancement of their functions,traditional battery power methods face significant challenges.Although environmental energy can be utilized to charge IoT devices,the efficiency of this method is limited by the uncertainty of environmental factors.Radio frequency(RF)signal-based data and energy integrated transfer technology can provide stable and reliable power to IoT devices while transmitting data,attracting considerable attention.However,RF signal propagation is highly susceptible to multi-path fading and obstruction,limiting its application in IoT systems.As a new technology,intelligent reflecting surface(IRS)can intelligently alter the propagation paths of electromagnetic waves by adjusting the phase and amplitude responses of reflecting elements,thereby improving wireless communication and energy transfer efficiency.Therefore,combining IRS with data and energy integrated transfer technology can offer more stable and efficient energy supply for IoT devices,promoting the development and application of IoT technology in data and energy integrated networks(DEINs).Despite the numerous studies by scholars on IRS-assisted DEINs,most of these focus on formulating optimization problems to obtain optimal resource allocation schemes,often neglecting in-depth performance analysis of the system.Unlike existing research,this dissertation conducts a more detailed and thorough performance analysis of IRS-assisted DEINs and optimizes key system parameters.Specifically,the primary research content of this dissertation are as follows.1.To evaluate the capability of user equipment to power itself by harvesting RF signal energy from the environment,this dissertation proposes a novel IRS-assisted ambient energy harvesting wireless communication system framework.Unlike existing work,the proposed model simultaneously considers the power control function of the base station and the random distribution characteristics of user equipment locations.Based on the studied model,this dissertation uses moment matching and binomial expansion techniques to analyze the outage probability performance of information users and energy users.Additionally,the dissertation examines the system performance under IRS’s phase shifts misalignment for information users and in multi-information user scenarios,demonstrating the scalability of the applied analytical methods.The research results show that while the power control of the base station can effectively improve the energy efficiency of information users,it suppresses the system performance for energy users.2.For an IRS-assisted single-cell DEIN system model,a hybrid DEIN protocol based on time switching(TS)and power splitting(PS)architectures is proposed.To thoroughly discuss system performance under various conditions,the dissertation considers both the homogeneous Poisson point process for the location of IoT devices and two different phase shift models,namely random phase shift and optimal phase shift.It also considers Rayleigh and Rician fading channel conditions,as well as scenarios where the base station is equipped with multiple antennas.Based on stochastic geometry theory,the dissertation derives closed-form expressions for the uplink outage probability and average uplink throughput under these conditions and optimizes system performance using the particle swarm optimization(PSO)algorithm.Numerical simulation results reveal the impact of IoT device density and IRS reflecting element phase shifts on system performance,providing design insights for IRS deployment.3.For an IRS-aided multi-cell DEIN,this dissertation considers the impact of intercell energy leakage and co-channel interference on system performance.By characterizing the probability statistical characteristics of the system signal-to-interference-plus-noise ratio(SINR),the dissertation derives the outage probability,ergodic rate,and average symbol error probability for typical cell-edge user devices.Furthermore,by deriving the minimum number of IRS reflecting elements required for a given SINR threshold of edge user devices and a sub-optimal time allocation coefficient,the dissertation provides design insights for IRS deployment.Simulation results indicate that the negative impact of cochannel interference due to an increase in the number of cells far outweighs the positive effect of enhanced energy harvesting for edge user devices in a typical cell.4.Distinct from the previous research contents,the fourth research focuses on the analysis and optimization of IRS-assisted DEIN networks from the finite blocklength coding domain perspective.Remote devices harvest RF energy during the downlink wireless energy transfer(WET)stage and transmit information to the access point during the uplink wireless information transfer(WIT)stage.Both stages utilize short data packets for transmission.Specifically,using moment matching and linearization techniques,the dissertation addresses the difficulty in characterizing the statistical properties of the signalto-noise ratio(SNR)and derives a closed-form expression for the average packet error probability of the system.Additionally,to maximize the effective throughput under delay constraints,an efficient genetic algorithm(GA)is employed to jointly optimize the number of channel uses for downlink WET and uplink WIT phases.Numerical simulations validate the effectiveness and reliability of the theoretical analysis and optimization algorithms proposed in this dissertation.Through in-depth performance analysis and key parameter optimization,this research provides a theoretical foundation and scientific basis for the application of IRS-assisted DEIN in future 6G networks.