Study On Precoding Design And Power Control In MIMO Two-Way Relaying System

Traditional wireless one-way relaying network usually works on the half-duplexmode, promoting system performance as well as making a loss of timeslot resource.By using Two-Way Relaying, the system can make a further improvement ofbandwidth efficiency when assure the coverage of network. MIMO effectively makesgood use of spatial resource to dramatically enhance system capacity withoutincreasing extra transmission power. Signal Alignment, as a new type of signalprocessing, align two data stream from different users into the same subspacedimensionality, enabling relay nodes handle and forward the compounded signal, aswell as subchannel allocation. This paper focuses mainly on the transmission problemof MIMO Two-Way Relaying system, making a research on channel precoding aswell as power control problem. The main works is described as follows:（1）Discuss Multiple Access Broadcast two-way relaying model and analyze thetransmission of Amplify Forward Two-Way Relaying system. At last, introduceMIMO to make an analysis of system performance in AF MIMO Two-Way Relayingnetwork.（2）Using Generalized Singular Value Decomposition and Matrix Permutation,this paper proposes a novel channel diagonalization precoding design scheme inMIMO Two-Way Relaying system. On the basis of diagonalization, this schemealigns all subchannel, which reduces the difficulty of the subsequent optimization.（3）Based on the design scheme above, when restricted to the total power, wepropose two-step power solution to formulate a closed-form power allocationexpression, which simplifies the complexity of power allocation to obtain a betterAverage Sum Rate and system capacity.Simulation results demonstrate that proposed GSVD-based precoding and powercontrol scheme significantly improves the system sum rate while retaining thediagonalized channel structure. Compared with the equal power allocation scheme,the proposed scheme achieves better system performance while approaching theiterative optimization power scheme with less computational complexity.