Joint Channel Estimation / Data Detection in MIMO-FBMC/OQAM Systems - A Tensor-Based Approach

TL;DR

This paper introduces a tensor-based method for joint channel estimation and data detection in MIMO-FBMC/OQAM systems, addressing the challenges of self-interference and multi-antenna interference, with promising simulation results compared to CP-OFDM.

Contribution

It presents the first tensor-based approach for joint channel estimation and data detection in MIMO-FBMC/OQAM systems, especially under limited or no training data.

Findings

Tensor-based approach effectively estimates channels and detects data.

Simulation results show competitive performance with CP-OFDM.

Method addresses self-interference and multi-antenna interference challenges.

Abstract

Filter bank-based multicarrier (FBMC) systems are currently being considered as a prevalent candidate for replacing the long established cyclic prefix (CP)-based orthogonal frequency division multiplexing (CP-OFDM) in the physical layer of next generation communications systems. In particular, FBMC/OQAM has received increasing attention due to, among other features, its potential for maximum spectral efficiency. It suffers, however, from an intrinsic self-interference effect, which complicates signal processing tasks at the receiver, including synchronization, channel estimation and equalization. In a multiple-input multiple-output (MIMO) configuration, the multi-antenna interference has also to be taken into account. (Semi-)blind FBMC/OQAM receivers have been little studied so far and mainly for single-antenna systems. The problem of joint channel estimation and data detection in a MIMO-FBMC/OQAM system, given limited or no training information, is studied in this paper through a tensor-based approach in the light of the success of such techniques in OFDM applications. Simulation-based comparisons with CP-OFDM are included, for realistic transmission models.