Study On Semi-blind Interference Alignment In The Noise Environment

Interference Alignment (IA) has become a promising direction in the wire-less network research field. Blind Interference Alignment (BIA), originated from IA, arouses wild attentions because that it can obtain great performance improvements without relying on the channel state information at the transmit-ter. How to increase the performance of BIA in practical scenario has important theoretical value and application prospect.This thesis began with the analysis of the noise accumulation procedure in BIA, mainly focusing on the techniques of improving the channel capacity,making the following contributions:First, focusing on the performance deterioration caused by the noise accu-mulations at the user side in BIA, this thesis proposed a Grouped Semi-Blind Interference Alignment (Gro-SBIA). To reflect the performance deterioration addressed by the noise accumulation, the average E-EDS (Effective Encoded Data Stream) was defined to evaluate the performance of the Semi-Blind Inter-ference Alignment (SBIA) in the noise environment. The Signal-to-Noise Ratio(SNR) reduced factor was defined to quantify the noise accumulation degree of the user. Gro-SBIA separated users into groups to lower the SNR reduced fac-tors. Based on the users' received SNR, Gro-SBIA adjusted the users- residing groups, maximizing the average E-EDS for improving the channel capacity.Simulation results demonstrate the performance gain achieved by Gro-SBIA and the rationality of the metric "average E-EDS".Second, aiming to reduce the performance gaps between users caused by noise accumulations in BIA, this thesis proposed a Fairly Grouped Semi-Blind Interference Alignment (FGro-SBIA). The definition of fairness between users in FGro-SBIA was proposed to measure the conflicts between individual perfor-mances and the overall performance. FGro-SBIA separated users into groups,which not only lower the SNR reduced factors, but also allocate an arbitrary number of E-EDS to the user. Based on the users' received SNR, FGro-SBIA maximized the average E-EDS by adjusting the user grouping scheme and the transmission resource allocation scheme, improving the channel capacity with the fairness constraints. Simulation results demonstrate the performance gain achieved by FGro-SBIA, which scarifies limited overall performance for greatly improving the fairness performance between the users .Besides, to resolve the low efficiency of the transmission power allocation in BIA in the noise environment, this thesis proposed a SBIA power allocation algorithm. After analyzing the impact of the power allocation scheme on the performance of BIA, this thesis proposed a retrospective gradient descent power allocation algorithm based on the channel statistics information, to improve the channel capacity with individual performance guarantee. The proposed algo-rithm guaranteed the final SNR of the users higher than a preset SNR threshold,to avoid the performance reduction caused by the BIA scheme. Simulation re-sults demonstrate the performance gain achieved by the proposed algorithm.In addition, focusing on the difficulty of extending BIA into multi-cell network, this thesis presented a Multi-cell Grouped Semi-Blind Interference Alignment (MGro-SBIA). To apply Gro-SBIA into the multi-cell network, this thesis analyzed the relationship between Gro-SBIA and MGro-SBIA, and also analyzed the impact of users' access and users' grouping scheme on the multi-cell network performance. MGro-SBIA can jointly optimize users' access and users' grouping scheme, maximizing the average E-EDS and improving the channel capacity.Finally, the conclusion of this thesis was presented, and the future work of the SBIA was envisioned in theory and practice.