Research On The Performance Optimization Algorithms Of Wireless Relay System Based On Non-orthogona Multiple Access

With the rapid development of wireless communication technology and the popularization of intelligent devices,the shortage of spectrum resources and energy consumption are becoming increasingly prominent.As a key technology to effectively improve system throughput,reliability and coverage,relay technology plays an important part in wireless communication systems.Furthermore,non-orthogonal multiple access(NOMA)has become a research hotspot in recent years due to its potential to greatly improve the spectral efficiency and support massive connectivity.Besides,simultaneous wireless information and power transfer(SWIPT)can provide sustainable energy supply for energy-constrained systems.At the same time,the continuous emergence of new intelligent applications requires large energy and computing resources.In the case of limited energy and computing capacity of intelligent devices,how to effectively improve the quality of service(QoS)and information transmission rate of wireless communication systems has become a key issue to be solved urgently.Mobile edge computing(MEC)provides a new method to solve this problem,which can complement the advantages of NOMA.Based on the above analysis,aiming at the problems of limited spectrum resources,energy and computing capacity,this paper takes the NOMA-based wireless relay system as research object,combined with full duplex(FD)two-way relay,multiple input multiple output(MIMO),SWIPT,intelligent reflecting surface(IRS)and MEC technology,and studies the performance optimization problems of energy harvesting maximization,energy consumption minimization and computation rate maximization.The main research contents includes:For the designed NOMA-based SWIPT assisted multiple input single output(MISO)FD two-way relay system model,an optimization algorithm for maximizing harvested energy is proposed,where the relay is equipped with a power splitting(PS)receiver.Under QoS and power constraints,a joint optimization problem of transmit beamforming matrix and PS ratio to maximize the harvested energy is formulated.For the established multivariable coupled nonconvex optimization problem,a bilevel programming(BLP)-based algorithm is proposed.Because of the high complexity of BLP algorithm,a successive convex approximation(SCA)based iterative algorithm is proposed,which can effectively reduce the computational complexity.The simulation results verify the effectiveness of the proposed scheme.For the designed NOMA-based IRS assisted MEC system model,an optimization algorithm for minimizing energy consumption is proposed,where the IRS is deployed to replace the traditional active relay to assist the computing offloading of devices.Subjecting to the constraints of time delay,power and IRS phase,a joint optimization problem of transmission power,offloading partitions,transmission time and IRS phase shift to minimize energy consumption is formulated.For the established multivariable coupled nonconvex optimization problem,in order to reduce the computational complexity,the closed form optimal solution of the transmission power is derived,and the alternative optimization is introduced to decouple the optimization problem into two different optimization subproblems.Due to the multivariable coupling in the offloading optimization subproblem,a SCA-based iterative algorithm is proposed.For the non-convexity of IRS phase matrix optimization subproblem,an eigenvalue approximation-based semidefinite relaxation(SDR)algorithm is proposed.The simulation results verify the effectiveness of the proposed scheme.For the designed NOMA-based IRS assisted MIMO-MEC system,an optimization algorithm for maximizing computation rate is proposed,where the access point(AP)is equipped with multiple antennas and a direct communication link is added.Besides,the receive beamforming matrix is optimized to further improve the system offloading performance.Considering the constraints of energy,transmission power and signal-tonoise ratio,a computation rate maximization problem is formulated to jointly optimize the receive beamforming matrix,CPU frequency,transmission power and IRS phase matrix.For the established multivariable coupled nonlinear nonconvex optimization problem,an idea of alternating optimization is introduced to decouple the optimization problem into four optimization subproblems.A closed form algorithm is proposed to optimize the receive beamforming matrix and CPU frequency respectively,which reduces the computational complexity.Considering the complexity of the transmission power optimization and IRS phase optimization subproblems,a SCA-based iterative algorithm and a gaussian randomization-based SDR iterative algorithm are proposed.The simulation results verify the effectiveness of the proposed scheme.The research contents of this paper provides a feasible scheme and design reference for future communication systems.In the next step,further research should be considered under the conditions of optimizing user decoding order and imperfect channel state information.