Heterogeneous Semi-Blind Interference Alignment in Finite-SNR Networks With Fairness Consideration
Qing Yang, Ting Jiang, Norman C. Beaulieu, Jingjing Wang, Chunxiao Jiang, Shahid Mumtaz, Zheng Zhou
Abstract
Standard blind interference alignment (sBIA) suffers from noise accumulation which severely deteriorates received signal-to-noise ratio (SNR) and significantly reduces transmission rate. A noise accumulation factor is proposed to describe the loss between the user's received SNR, and the final post processing SNR which determines the performance of the decoding of the encoded data streams (EDSs). A heterogeneous semi-BIA (H-SBIA) framework where users with different noise accumulation factors can be flexibly allocated effective EDSs (E-EDSs) is constructed. Relying on the H-SBIA framework, a heuristic H-SBIA algorithm is designed for maximizing the overall E-EDSs considering both fairness and coherence time constraints. Extensive simulations demonstrate that H-SBIA produces great fairness performance improvement at a limited cost in the achievable sum rate. The Jain's fairness index is about 2.2 times greater than that for SNR-SBIA proposed in previous work, at the cost of sacrificing 10% of the achievable sum rate.