Sparse Zero Correlation Zone Arrays for Training Design in Spatial Modulation Systems

TL;DR

This paper introduces sparse zero correlation zone (SZCZ) arrays as a new training matrix design for spatial modulation systems, offering improved channel estimation by leveraging arrays with zero auto- and cross-correlation zones.

Contribution

The paper proposes a novel class of 2D arrays called SZCZ arrays for training in SM systems, with constructions based on 2D RGBFs that achieve larger ZCZ widths and controllable sparsity.

Findings

SZCZ arrays provide larger ZCZ widths than existing schemes.

Simulation shows superior channel estimation in frequency-selective channels.

SZCZ arrays enhance delay tolerance in multipath environments.

Abstract

This paper presents a novel training matrix design for spatial modulation (SM) systems, by introducing a new class of two-dimensional (2D) arrays called sparse zero correlation zone (SZCZ) arrays. An SZCZ array is characterized by a majority of zero entries and exhibits the zero periodic auto- and cross-correlation zone properties across any two rows. With these unique properties, we show that SZCZ arrays can be effectively used as training matrices for SM systems. Additionally, direct constructions of SZCZ arrays with large ZCZ widths and controllable sparsity levels based on 2D restricted generalized Boolean functions (RGBFs) are proposed. Compared with existing training schemes, the proposed SZCZ-based training matrices have larger ZCZ widths, thereby offering greater tolerance for delay spread in multipath channels. Simulation results demonstrate that the proposed SZCZ-based training design exhibits superior channel estimation performance over frequency-selective fading channels compared to existing alternatives.