Blind Joint MIMO Channel Estimation and Decoding

TL;DR

This paper introduces a blind MIMO decoding method that estimates channels and decodes signals without prior CSI, using a geometric approach suitable for small systems and practical fading scenarios.

Contribution

It presents a novel blind decoding algorithm based on minimum volume parallelepiped fitting, applicable to unknown MIMO channels with theoretical guarantees for small systems.

Findings

Algorithm achieves less than 3dB loss compared to zero-forcing with perfect CSI.

Requires small sample sizes, suitable for block-fading channels.

Works with BPSK and MPAM modulation without pilot symbols.

Abstract

We propose a method for MIMO decoding when channel state information (CSI) is unknown to both the transmitter and receiver. The proposed method requires some structure in the transmitted signal for the decoding to be effective, in particular that the underlying sources are drawn from a hypercubic space. Our proposed technique fits a minimum volume parallelepiped to the received samples. This problem can be expressed as a non-convex optimization problem that can be solved with high probability by gradient descent. Our blind decoding algorithm can be used when communicating over unknown MIMO wireless channels using either BPSK or MPAM modulation. We apply our technique to jointly estimate MIMO channel gain matrices and decode the underlying transmissions with only knowledge of the transmitted constellation and without the use of pilot symbols. Our results provide theoretical guarantees that the proposed algorithm is correct when applied to small MIMO systems. Empirical results show small sample size requirements, making this algorithm suitable for block-fading channels with coherence times typically seen in practice. Our approach has a loss of less than 3dB compared to zero-forcing with perfect CSI, imposing a similar performance penalty as space-time coding techniques without the loss of rate incurred by those techniques.