| With the rapid development of wireless communication technologies in the past 30 years,the number of users and devices accessing the network has increased exponentially and the expectations for communication data rate,latency,and quality keep increasing.Therefore,with the full maturity of the 4th generation(4G)mobile communication system and the gradual commercializing of the 5th generation(5G)mobile communication system on a large scale,the research on the next-generation communication system,namely the 6th generation(6G)related technologies have been on the track.This paper studies the intelligent reflective surface(IRS)-aided multi-antenna system technologies in next-generation communication systems.As one of the main technologies for the multi-antenna system,multiple-input multiple-output(MIMO)can improve the capacity and performance of communication systems by utilizing spatial diversity,multiplexing gain,etc.This technology has been deployed in existing commercial communication systems and will play a critical role in future communication scenarios.The IRS is equipped with a large number of passive reflection elements,which can adjust the phase and amplitude of the elements in a digitally controllable way,to reflect the incident signal to a specific angle to achieve the purpose of modifying the signal propagation environment macroscopically.With this feature,the IRS can greatly improve the system’s spectral efficiency(SE)as well as energy efficiency(EE)by establishing an additional link between the transceivers.Therefore,the IRS can provide all-around and considerable performance improvements for future 6G communication systems,including enhanced coverage,efficient communication,ultra-reliable communication,and high QoS.Some literature has studied these two technologies,however,in the IRS-multiple-input single-output(MISO)system,the direct link between the user and the base station is not considered in the research of EE maximization,and it is difficult to ensure that all sub-problems obtain optimal solutions when optimizing these problems.On the other hand,there is also a lack of exploration on uplink communication scenarios.When the existing researches study the latency minimization problem of IRS-mobile edge computing(MEC)system,the consideration of non-orthogonal multiple access(NOMA)and wireless power transfer(WPT)technologies is neglected.Furthermore,the optimal open-loop precoding matrix design and the IRS phase shift optimization for minimizing maximum pair-wise error probability(PEP)in a point-topoint IRS-MIMO system under correlated channels are still blank.In response to the above problems,this paper specifically discusses transceivers’ beamforming design,phase shift matrix optimization,power allocation,user ordering,hybrid multiple access protocol,the precoder design in the correlated channel,optimization algorithm design,and other issues in different IRS-assisted systems.The main contributions of this paper are as follows:1.For an IRS-MISO system with a direct link,the definition of the system EE is given.The optimization framework jointly optimizes the transmit beamforming vector,the IRS phase shift matrix,and the transmit power under the constraint of the user’s QoS,the maximum transmit power,the IRS modulo-one limit,and the transmit beamforming vector power limit.Since the optimization problem is non-convex,we optimize the variables separately to find the optimal solutions for the three sub-problems.A low-complexity iterative optimization scheme is used to combine the three sub-problems to obtain a suboptimal joint optimization solution.In the simulation part,we verify the rapid convergence of the proposed iterative algorithm.Afterward,we demonstrate that the performance of our proposed scheme is improved by about 70% compared to that without IRS in terms of EE.2.For the multi-user uplink IRS-MIMO system with direct link,NOMA is adopted and the EE maximization problem is proposed under the constraints of users’ QoS,receive beamforming vector power limit,transmit power limit,and IRS phase shift matrix modulo-one limit.An initial-channel-gain-based user ordering scheme is proposed.After separately optimizing the three sub-problems of active beamforming,IRS phase shift matrix,and power control at the base station,an iterative algorithm of alternating optimization with low complexity is proposed.In the performance evaluation,the fast convergence of the proposed algorithm is verified,and the performance of the proposed algorithm is compared with the orthogonal multiple access(OMA)scheme and the scheme without IRS.Simulations show that the proposed scheme can achieve very significant performance gains in terms of EE performance.3.The MEC is introduced into an IRS-MIMO system and WPT-enabled Internet-of-Things(IoT)devices are deployed in this system.The communication process between the IoT device and the access point(AP)is divided into two stages: energy harvesting and data offloading.A NOMA-frequency division multiple access(FDMA)based timing protocol is proposed.The protocol effectively utilizes the advantages of these two multiple access technologies.On this basis,the total system latency minimization problem with transmission power limit,beamforming power limit,and IRS phase shift matrix modulolimitation constraint is proposed,where the IoT device transmit power,IRS phase shift matrix,transmit and receive beamforming vectors,and devices clustering are jointly optimized.In order to solve the multi-variable non-convex optimization problem,the alternative optimization scheme is adopted,and the above variables are optimized separately.Then,an initial-channel-gain and length-of-offloading-bits-based clustering scheme is proposed.In the simulation part,the rapid convergence of the proposed algorithm is verified.The time consumption of the proposed clustering scheme,random clustering,and exhaustive search are compared.The simulation results reveal that the proposed clustering scheme can be close to exhaustive in terms of latency performance,while it has the lowest time complexity.In the latency comparison among different schemes,the proposed scheme outperforms all benchmark schemes.4.The point-to-point IRS-MIMO system under a correlated channel is modeled.Under the condition of a high signal-to-noise ratio(SNR),the approximate expression of PEP is given.On this basis,we purpose the joint optimizing framework.By jointly optimizing the IRS phase shift matrix,the transmission vector difference,and the precoding matrix,the form of the optimal vector difference and the closed-form solution of the optimal precoding matrix are obtained while the suboptimal solution is obtained by optimizing the precoding matrix,which is close to the optimal one.On this basis,we further propose the optimization scheme for the IRS phase shift matrix.Simulation results demonstrate the accuracy of the proposed search algorithm and its superiority in PEP performance.The joint optimization scheme for open-loop systems can achieve a 20 dB performance gain compared to existing literature schemes,while a maximum 12 dB performance gain can be achieved by the proposed optimizing scheme for the IRS phase shift matrix. |
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