Joint Optimization Design Of Hybrid Beamforming And Information Freshness In C-V2X Networks

Cellular Vehicle-to-Everything(C-V2X)not only consumes a lot of bandwidth resources,but also puts forward higher requirements on data transmission rate,communication reliability,user access and other indicators.The complement millimeter wave communication and massive multiple-input multiple-output(MIMO)technology each other,effectively improves the overall channel capacity and energy efficiency of the system.Millimeter wave massive MIMO technology has become the main mean to improve data transmission rate and support high throughput service in future wireless communication systems.However,unlike low frequency bands,the cost and power consumption of millimeter wave band hardware limit the wide application of millimeter wave communication systems.In addition,age of information(AoI)is an indispensable part of intelligent transportation system,whose main function is to improve the freshness of vehicle network security information.However,the driving environment is complicated,and the channel spatial information plays an important role in Multi-user MIMO(MU-MIMO)systems.A key challenge is how to ensure that the connected vehicles update the driving environment information(DEI)in time.Therefore,we study a massive MIMO architecture with low hardware complexity and a hybrid precoding scheme with low computational complexity,and minimize AoI under limited energy constraints to maximize the system energy efficiency of C-V2 X.The main research contents are summarized as follows.1.Aiming at the high-power consumption and high hardware complexity of traditional millimeter wave massive MIMO architecture,we study a bitstream-based adaptive-connected massive MIMO(BAMM)architecture.BAMM architecture is a trade-off between high power full-connected and low performance partially connected hybrid precoding architectures.Compared with other adaptive-connected architectures,BAMM architecture allows each data stream to be computed independently in parallel and consists of fewer phase shifters and switches,further reducing power consumption in millimeter-wave MU-MIMO systems.On this basis,by combining the connectionstate matrix,the hybrid precoder and the hybrid combiner,we study a hybrid precoding and combining(HPC)scheme applicable to multi-user and multi-data streams.The successive closed forms(SCF)algorithm has been used to obtain the constant modulus of the analog precoder at convergence.In the digital precoding stage,a digital precoder and combiner are designed by performing singular value decomposition(SVD)for corresponding equivalent channel to reduce the computation.We adopt HPC scheme to maximize the energy efficiency of BAMM architecture in millimeter-wave MU-MIMO systems.In millimeter-wave MU-MIMO-OFDM system equipped with BAMM architecture,simulation results show that,with the increase of the total number of data streams,HPC scheme can achieve better energy efficiency than the traditional fullconnected architecture with some existing hybrid precoding schemes.2.In view of the problem that connected vehicles cannot obtain DEI in time and the importance of Line of Sight(Lo S)communication link to the timeliness of C-V2 X,we study that unmanned aerial vehicle(UAV)in a cellular MU-MIMO system is developed to update DEI service to ground vehicles.Under the constraint of limited airborne energy and freshness of information,a non-convex optimization problem is constructed to minimize the peak AoI of DEI(PAoDEI)and energy consumption of UAV.Then,the problem is divided into two sub-problems.One is to utilize graph theory and convex optimization technology to design UAV trajectory when satisfying the requirement of PAoDEI,so as to minimize the energy consumption of UAV.The other is to utilize BAMM architecture and HPC scheme obtained in Chapter 3 to obtain the corresponding hybrid precoding and combining matrices,so as to maximize the energy efficiency of the system.Simulation results show that the joint optimization scheme proposed in this dissertation can make better trade-off between different PAoDEI and UAV trajectory to achieve better the energy efficiency of the system.