Research On Effective Capacity Analysis And Optimization Of Reconfigurable Intelligent Surface-aided NOMA Systems

With the rapid development of Internet of everything(Io E),the future mobile communication system will face great challenges in the spectral efficiency,energy efficiency,and deployment cost due to the proliferation of wireless devices.Fortunately,the combination of non-orthogonal multiple access(NOMA)and reconfigurable intelligent surface(RIS)is considered as a promising solution to solve the above challenges.Simultaneously,more stringent demands for low-latency and high-reliability performance of wireless networks are proposed by real-time applications such as autonomous driving and virtual reality.To meet these demands,the effective capacity(EC)theory based on the cross-layer model is used in various wireless networks to guarantee the user’s latency quality of service(Qo S).Hence,the investigation of the RIS-NOMA network’s EC is a key to providing highly reliable and lowlatency services for RIS-NOMA users.Based on a downlink model of two-user RIS-NOMA network,the EC performance analysis and resource allocation optimization are investigated in this thesis.The main contributions are summarized as follows:1.The closed-form expressions of the user’s ECs are derived and the asymptotic analysis is provided.Firstly,considering the downlink model of the two-user RIS-NOMA network,the probability density functions(PDF)of the Rayleigh-Nakagami-m channel are used to derive the closed-form expressions of strong-user’s EC and weak-user’s EC respectively.Secondly,in order to reveal the relationships between the effective capacity and various system parameters intuitively,the asymptotic ECs in high SNR and low SNR regimes are also derived,revealing the impact on the ECs with the number of reflecting elements,delay Qo S exponent and base station transmit power.Finally,the above theoretical results are verified by Monte Carlo simulation.The obtained theoretical results can provide useful insights for the design and optimization of the RIS aided NOMA networks.2.In order to guarantee the fairness of NOMA users,a joint optimization algorithm of user power allocation and RIS phase-shift is proposed for the maximization-minimum EC in the RIS aided two-user downlink NOMA.Firstly,due to the non-convexity of the objective function,stochastic successive convex approximation(SSCA)is introduced in this thesis to construct surrogate functions of the objective function.Secondly,semidefinite relaxation(SDR)based on Gaussian randomization is used to obtain high-quality initial points.The numerical results show that the proposed joint optimization has a certain performance gain compared to the classical power allocation algorithms such as fixed power allocation and fractional power allocation under random RIS phase-shift,and the gain of the proposed joint optimization is more significant when the line of sight(Lo S)component exists in RIS cascaded channels.