Low-complexity blind maximum-likelihood detection for SIMO systems with general constellations

Abstract

The demand for high data rate reliable communications poses great challenges to the next generation wireless systems in highly dynamic mobile environments. In this paper, we investigate the joint maximum-likelihood (ML) channel estimation and signal detection problem for single-input multiple-output (SIMO) wireless systems with general modulation constellations and propose an efficient sequential decoder for finding the exact joint ML solution. Unlike other known methods, the new decoder can even efficiently find the joint ML solution under high spectral efficiency non-constant modulus modulation constellations. In particular, the new algorithm does not need such preprocessing steps as Cholesky or QR decomposition in the traditional sphere decoders for joint ML channel estimation and data detection. The elimination of such preprocessing not only reduces the number of floating point computations, but also will potentially lead to smaller size and power consumption in VLSI implementations while providing better numerical stability.