MIMO-OFDM Optimal Decoding and Achievable Information Rates Under Imperfect Channel Estimation

TL;DR

This paper develops a robust decoding metric for MIMO-OFDM systems with imperfect channel estimates, improving error rates and outage capacity without added complexity.

Contribution

It introduces a Bayesian-based decoding metric that accounts for channel estimation errors, enhancing decoding performance in MIMO-OFDM systems.

Findings

Proposed decoder outperforms mismatched decoding in error rate

Achieves higher outage capacity in simulations

No additional complexity introduced

Abstract

Optimal decoding of bit interleaved coded modulation (BICM) MIMO-OFDM where an imperfect channel estimate is available at the receiver is investigated. First, by using a Bayesian approach involving the channel a posteriori density, we derive a practical decoding metric for general memoryless channels that is robust to the presence of channel estimation errors. Then, we evaluate the outage rates achieved by a decoder that uses our proposed metric. The performance of the proposed decoder is compared to the classical mismatched decoder and a theoretical decoder defined as the best decoder in the presence of imperfect channel estimation. Numerical results over Rayleigh block fading MIMO-OFDM channels show that the proposed decoder outperforms mismatched decoding in terms of bit error rate and outage capacity without introducing any additional complexity.