Compressed sensing channel estimation for STBC‐SM based hybrid MIMO‐OFDM system for visible light communication

Abstract

Summary This paper presents the idea of sparse channel estimation using compressed sensing (CS) method for space–time block coding (STBC), and spatially multiplexing (SM) derived hybrid multiple‐input multiple‐output (MIMO) Asymmetrically clipped optical‐orthogonal frequency division multiplexing (ACO‐OFDM) optical wireless communication system. This hybrid system accounts multiplexing gain of SM and diversity gain of STBC technique. We present a new variant of sparsity adaptive matching pursuit (SaMP) algorithm called dynamic step‐size SaMP (DSS‐SaMP) algorithm. It makes use of the inherent and implicit structure of SaMP, along with dynamic adaptivity of step‐size feature which is compatible with the energy of the input signal, thus the name dynamic step size. Existing CS‐based recovery algorithms like orthogonal matching pursuit, SaMP, adaptive step‐size SaMP, and proposed DSS‐SaMP were compared for hybrid MIMO‐ACO‐OFDM visible light communication system. The performance analysis is demonstrated through simulation results with respect to bit error rate, symbol error rate, mean square error, computational complexity, and peak‐to‐average power ratio. Simulation results show that the proposed technique gives improved performance and lesser computational complexity in comparison with conventional estimation algorithms.

Sparse Channel Estimation for Visible Light Optical OFDM Systems Relying on Bayesian Learning

“…Niaz et al [25] reported a self-aware step size sparsity adaptive matching pursuit (SS-SAMP) technique for CE in an ACO-OFDM system, that offers superior performance in terms of BER and minimum mean square error (MMSE) compared to the conventional CE approaches of VLC systems. In [37], Manur and Ali extended the concept of [25] to MIMO-ACO-OFDM systems using the dynamic step-size sparsity adaptive matching pursuit (DSS-SAMP) technique for both spatially multiplexed (SM) and for spacetime block coded (STBC) VLC systems. A key drawback of the proposed techniques of [25] and [37] is that their performance depends on the choice of thresholds utilized, which must be empirically adjusted for best results.…”

“…In [37], Manur and Ali extended the concept of [25] to MIMO-ACO-OFDM systems using the dynamic step-size sparsity adaptive matching pursuit (DSS-SAMP) technique for both spatially multiplexed (SM) and for spacetime block coded (STBC) VLC systems. A key drawback of the proposed techniques of [25] and [37] is that their performance depends on the choice of thresholds utilized, which must be empirically adjusted for best results. Furthermore, the performance of their proposed algorithms is dependent on the choice of the measurement matrix.…”