Sparsity Enhanced Decision Feedback Equalization

TL;DR

This paper introduces a sparsity-based thresholding algorithm for decision feedback equalization in single-carrier systems, improving convergence speed and bit error rate performance with low computational complexity.

Contribution

It proposes a novel sparsity-enhanced DFE algorithm with a theoretical framework and convex relaxation for initial solution, applicable to various wireless systems.

Findings

Significant bit error rate improvement over MMSE

Fast convergence due to multiple symbol feedback

Low computational complexity

Abstract

For single-carrier systems with frequency domain equalization, decision feedback equalization (DFE) performs better than linear equalization and has much lower computational complexity than sequence maximum likelihood detection. The main challenge in DFE is the feedback symbol selection rule. In this paper, we give a theoretical framework for a simple, sparsity based thresholding algorithm. We feed back multiple symbols in each iteration, so the algorithm converges fast and has a low computational cost. We show how the initial solution can be obtained via convex relaxation instead of linear equalization, and illustrate the impact that the choice of the initial solution has on the bit error rate performance of our algorithm. The algorithm is applicable in several existing wireless communication systems (SC-FDMA, MC-CDMA, MIMO-OFDM). Numerical results illustrate significant performance improvement in terms of bit error rate compared to the MMSE solution.