Research On Distributed Precoding In IMT-Advanced

Coordinated Multiple Points transmission is one of the key technologies to achieve the performance of IMT-Advanced. In a multicell coordination systems, the interference can be transformed into useful signal by properly designing the coordinated precoder for the multiple base stations with full or partial channel state information (CSI) sharing among different base stations. This is an attractive way of handing intercell interference and improving coverage, cell-edge throughput and system efficiency. Unfortunately, the demands on backhaul capacity and computational power scale rapidly with the number of cells, which make the approach unsuitable for the practical systems. In this paper, we consider distributed network MIMO where the coordinated base stations share knowledge of the data symbols but have local CSI only, thereby not only reducing the feedback load on the uplink and avoiding cell to cell CSI exchange, but also allowing the scalability of multicell cooperation to large and dense networks.Based on the multicell MIMO distributed principle, without sharing data information among base stations, we analyze the Distributive Bargaining Solution precoding algorithm and its two implementations: Orthogonal Bases Increment and Zero-Forcing Increment, which can achieve higher system performance than the the Egoistic precoding and Altruistic precoding.With data shared among coordinated base stations, we analyze the characterization of the Pareto boundary and propose DVSINR (Distributed Virtual SINR) based on precoding DMRT (Maximum Ratio Transmission), DZF (Distributed Zero-Forcing) precoding. Power allocation schemes based on instantaneous CSI, statistical CSI and the joint instantaneous and statistical CSI are analyzed to maximize the total system capacity. The simulation results demonstrate that DVSINR precoding has excellent character of the DMRT at low SNRs and the DZF precoding at high SNRs, and the performance of DVSINR precoding is close to that of centralized optimal precoding, but it greatly reduces the feedback amount of information and computing complexity.In view of the average bit error rate (BER) of the system, we study the optimal centralized power allocation algorithm, and propose two suboptimal power allocation algorithms with per base station power constraint. The numerical results show that the proposed suboptimal schemes achieve a performance very close to the optimal but with lower computational complexity. Moreover, the performance of the proposed power allocation schemes is close to the one obtained considering TPC(Total Power Constraint) over a supercell.