Research On Full-Duplex Multi-User MIMO Pre-Coding

Full-duplex(FD)communication and multi-user multiple input multiple output(MU-MIMO)are the key technologies used in next-generation mobile communications because they can effectively improve System spectrum efficiency and now they become the current research hotspot.Based on traditional cellular networks and heterogeneous networks(HetNet),this thesis analyzes the achieved sumrate of full-duplex systems and use precoding as a technical support.The main work of this thesis includes:(1)In the full-duplex cellular network,the thesis carries on the comprehensive interference analysis to the uplink base station reception and the downlink user reception.Due to the existence of self-interference at basestations,which will seriously affect the overall system capacity,this thesis based on convex optimization theory,planning a system capacity maximization problem,and the base station and uplink users to transmit power ceiling as a constraint,coding technology to effectively suppress interference and optimize system capacity.However,due to the NP-hard problem,the optimal coding matrix can not be directly solved,and the original problem is replaced by reweighted minimum mean square error(WMMSE)algorithm,use block coordinate descent(BCD)iterative algorithm to solve the problem.The whole problem is based on the Rayleigh fading channel model,considering the user-base station distribution distance factor,and under different power constraints,the system capacity analysis is carried out.The convergence of the WMMSE algorithm and the superiority of the full-duplex system are verified by the simulation results.(2)In this thesis,the capacity optimization model under full-duplex heterogeneous network is proposed.By introducing several small power base stations,the problem of cell coverage caused by traditional central base station is solved effectively.The interference factor under heterogeneous network is more complicated and needs to be Interference for effective management.The heterogeneous network of each user will be connected with all the base station,this full access to the entire return network will bring a serious overall backhaul signaling cost.In this case,a group sparse algorithm is introduced to add a cost function to the original target problem.By appropriately adjusting the penalty factor,the whole heterogeneous network presents the base station-user sparse access state,which can bring the system capacity gain,can also reduce the backhaul network signaling overhead.The results show that with the increase of the number of transmitting and receiving antennas in the base station,it can better suppress the interference and improve the system capacity.It also shows that the penalty factor can control the access sparsity of the user and the base station.The capacity of the full-duplex heterogeneous network is significantly better than that of the half-duplex system under the same number of access base stations to each user.(3)This thesis presents the Discrete Sum Rate Maximization(DSRM)algorithm,due to the limited modulation and coding schemes(MCSs),we need to choose a limited discrete rate for communication.However,this naive quantization may incur significant rate loss.The DSRM algorithm adds a discrete rate constraint to the original target problem,and the mixed integer nonlinear problem is transformed into a convex problem by the approximation algorithm.The simulation results show that the discrete rate of DSRM algorithm is better than that of the simple quantization,the effect is obvious.