Research On Joint Beamforming For Intelligent Reflecting Surface-aided Wireless Communications

With the drastic growth of mobile subscribers and wireless devices as well as the rapid emergence of new wireless applications such as augmented/mixed/virtual reality,industrial automation,internet of vehicles,existing 5G technologies may encounter both capacity and performance limitations in supporting the high demand for massive/ubiquitous connectivity in the forthcoming era of Internet-of-Everything(Io E).In addition,one ultimate bottleneck to achieving extremely high-capacity,ultra-reliable and low latency wireless communications lies in the random and largely uncontrollable wireless propagation environment,which causes undesired channel fading and signal distortion that are detrimental to wireless system performance.In recent years,intelligent reflecting surface(IRS),has emerged as a potential solution to solve the above problems,which has been recognized as a very promising technology for beyond-5G and 6G wireless networks.Different from conventional wireless communication techniques,IRS provide an innovative and cost-effective means of realizing the Io E by directly reshaping the wireless propagation channel to boost the received signal power at intended users and/or to suppress the interference at unintended users.To this end,this thesis focuses on the integration of IRS in wireless communication systems,and investigates how to use IRS to enhance capacity,suppress interference,and achieve ultra-reliability and low-latency communications.The main contributions of this thesis are as follows.(1)First,a joint beamforming design in IRS-aided multi-cell multiple-input single-output(MISO)networks is investigated to address the serious interference problems caused by densely deployed active nodes.The objective is to jointly optimize the coordinated transmit beamforming vectors at the multiple base stations(BSs)and the reflective beamforming vector at the IRS,to maximizing the minimum weighted received signal-to-interference-plus-noise ratio(SINR)at the users,subject to the individual maximum transmit power constraints at the BSs and the reflection constraints at the IRS.To solve the non-convex min-weighted-SINR maximization problem,an exact-alternating-optimization approach is first presented to optimize the transmit and reflective beamforming vectors in an alternating manner,in which the transmit and reflective beamforming optimization subproblems are solved exactly in each iteration by using the techniques of second-order-cone program(SOCP)and semi-definite relaxation(SDR),respectively.However,the exact-alternating-optimization approach is with high computational complexity,and may lead to compromised performance due to the uncertainty of randomization in SDR.To avoid these drawbacks,an inexact-alternatingoptimization approach is further proposed,in which the transmit and reflective beamforming optimization subproblems are solved inexactly in each iteration based on the principle of successive convex approximation(SCA).In addition,to further reduce the computational complexity,we propose another low-complexity inexact-alternating-optimization design,in which the reflective beamforming optimization subproblem is solved more inexactly.Numerical results show that the proposed system not only greatly improves the strength of the received signals of users,but also effectively suppresses the co-channel interference among multi-cell users.(2)To ensure ultra-reliability and low latency,an IRS-aided downlink ultra-reliable and low-latency communication(URLLC)system is further investigated,in which an IRS is dedicatedly deployed to assist a BS to send individual short-packet messages to multiple users.To enhance the URLLC performance,the users are divided into different groups and the messages for users in each group are encoded into a single codeword.By considering the time division multiple access(TDMA)protocol among different groups,our objective is to minimize the total latency for all users subject to their individual reliability requirements,via jointly optimizing the user grouping and blocklength allocation at the BS together with the reflective beamforming at the IRS.We solve the latency minimization problem via the alternating optimization,in which the blocklengths and the reflective beamforming are optimized by using the techniques of SCA and SDR,and the user grouping is updated by Kmeans and greedy-based methods.Numerical results show that the proposed designs significantly reduce the communication latency,as compared to various benchmark schemes,which unveil the importance of user grouping and reflective beamforming optimization for exploiting the joint encoding gain and enhancing the worst-case user performance.