Research On Performance Enhancement Technologies For High-Speed Railway Mm Wave Communications

As the most sustainable transportation mode with large volume and high velocity,high-speed railways(HSR)act as national key facilities and basic industries,and play an irreplaceable role supporting the economic and social development and national security.A reliable train-to-ground communication system is an important guarantee for train safety operation and can provide reliable mobile data transmission for user equipments onboard the train.Emerging onboard services spring up such as passenger-oriented broadband services,multimedia scheduling,and Internet-of-Things for Railways,calling for explosive mobile data demand in contradiction with spectrum scarcity.Improving the customer experience and providing broadband mobile communications are getting increasingly urgent in developing smart rail.In line of this,millimeter wave(mm Wave)frequency band with abundant spectrum resources can improve system capacity indicating higher data-rate transmission and better user experience(such as enjoying video streaming).However,mm Wave communications in HSR networks is faced with severe challenges such as fast time-varying channel,blockage-sensitive,and frequent cell handover,brought by particular features of typical HSR scenarios including high-velocity,complicated environment,and intensive user access.To tackle above challenges thus supporting reliable broadband train-to-ground communications,this dissertation focuses on the performance enhancement technologies for high-speed railway mm Wave communications.General scenarios including with/without blockage events,perfect channel state information(CSI)or statistical CSI,single-cell or multi-cell cases are considered.By incorporating advanced technologies including hybrid beamforming(HBF),Intelligent Reflecting Surface(IRS),and mobile edge caching(MEC),this dissertation studies the beamforming design,anti-blockage strategy,and adaptive video streaming scheme,to enhance the capacity,reliability and user experience.Main innovations and contributions include:1)To boost the system capacity,the dissertation incorporates the HBF structure in HSR mm Wave communications.Considering a Multi-User Multi-Input Multi-Output(MU-MIMO)downlink communication network,an optimization problem is formulated to maximize the system capacity by jointly optimizing transceiver beamforming matrices,subject to the transmit power and predefined beamforming codebook constraints.First,in blockage-free scenarios,we propose a two-stage HBF algorithm with low-complexity by utilizing an weighted mean square error minimization algorithm and an orthogonal matching pursuit(OMP)method.Second,in blocked scenarios,we design an anti-blockage scheme by adaptively invoking the proposed two-stage algorithm.Theoretical analyses and simulation results demonstrate the significant improvement of the system capacity reaching up to Gbps data rate(69.6 % improvement in comparison with analog beamforming),with low-complexity.Meanwhile,by intelligently invoking the two-stage algorithm according to the blockage degree,the modified algorithm is proved to reduce the system outage probability significantly(about 20 %).2)In view of the susceptibility of mm Wave to blockage,Intelligent Reflecting Surface(IRS)are deployed to enhance HSR mm Wave communications with additional reflecting link,to boost the system capacity and reliability.To jointly optimize the IRS phase shift matrix and beamforming design,an outage probability minimization and an ergodic capacity maximization problem are formulated respectively,assuming that only statistical CSI is available.First,we derive a closed-form expression of the outage probability based on probability theory.Second,an algorithm is proposed to minimize the outage probability,by exploiting the Quadratically Constrained Quadratic Programming(QCQP)and the generalized eigenvalue-eigenvector.Third,when focusing on the ergodic capacity,an upper bound and the Jensen approximation analysis are derived.Monte Carlo simulation results validate the close agreement with the theoretical analysis.Besides,simulation results demonstrate that the proposed algorithm with statistical CSI is highly close to that with perfect CSI,indicating the proposed algorithm can deal with the channel uncertainty in general high-mobility cases.Moreover,simulation results also demonstrate the IRS-assisted system can achieve lower outage probability(around a factor of 13.2)and improves the ergodic capacity(up to a factor of 6.61)with lower transmit power budget(as illustrated as about 12 d Bm).3)To improve user experience,a mobile edge caching(MEC)assisted HSR mm Wave communication system is proposed to deliver adaptive video streaming.A multicell coordinated caching optimization problem is formulated to maximize the long-term statistical average quality of experience(Qo E)under the constraints of the buffer stability and limited cache storage,by jointly designing the cache storage allocation and video quality assignment.First,to better characterize user experience,comprehensive actors affecting the perceptual Qo E incorporated in adaptive video streaming to evaluate user satisfaction.Second,to deal with challenges including dynamic channel environments,varying service requests,buffer stability and longterm statistical average,a distributed online algorithm based on the Lyapunov optimization theory is proposed to solve the dynamic stochastic optimization problem.Theoretical analyses and simulation results validate the system stability in terms of the buffer status,with video quality and video fluency balanced.Meanwhile,the proposed algorithm improves the passenger Qo E more than twice in comparison with traditional system without IRS assisted.