Capacity Analysis and Structured Sparse Detection of Generalized Media-based Modulation

Abstract

Media-based modulation (MBM) is an attractive channel modulation scheme with rate, performance, and hardware advantages. In this paper, we are concerned with two important aspects of MBM. The first one is on the capacity of MBM and the other is on low-complexity detection of high-rate MBM signals using structured spare recovery techniques. We derive closed-form expression for the capacity of generalized MBM (GMBM). The main idea in the capacity analysis is to recognize that MBM uses two alphabets to convey information, namely, the source alphabet (e.g., QAM/PSK) and the channel alphabet (fade coefficients). This observation allows us to show that the capacity is achieved when the mutual information is maximized over the source-channel product alphabet. We then propose a greedy structured sparse recovery algorithm for the detection of GMBM signal vectors. The proposed algorithm is a two-stage algorithm in which support recovery is done in greedy manner first, followed by data detection in the nonzero positions. Simulation results show that the proposed algorithm achieves good performance at low complexity even when the system is highly underdetermined.