Blind Joint Channel Estimation and Data Detection for Precoded Multi-Layered Space-Frequency MIMO Schemes

Due to the scarcity of the electromagnetic spectrum, multidimensional signaling schemes that take into account several signal dimensions such as space, time, frequency and constellation, are good candidates for increasing the data rate and/or improving the link reliability in future communication systems. This work addresses the problem of joint channel estimation and data detection in precoded multi-layered space-frequency codes (MLSFC) in multiple input multiple output (MIMO) systems based on orthogonal frequency division multiplexing (OFDM). First, we consider a modified (precoded) MLSFC transmit structure that consists in extending the constellation rotation across multiple OFDM symbols. By recasting the received signal as trilinear model, we propose a low-complexity blind receiver based on the least squares Khatri–Rao factorization (LSKRF). Our proposed LSKRF receiver is a closed-form solution and it outperforms the classical alternating least squares (ALS) receiver while being less complex since no iteration is need. Our results attest to the benefits of the proposed receiver in a variety of MLSFC schemes in comparison with the non-blind zero-forcing-based MLSFC receiver that assumes perfect channel knowledge.