# A Tensor-based Approach to Joint Channel Estimation / Data Detection in Flexible Multicarrier MIMO Systems

## TL;DR

This paper introduces a tensor-based method for joint channel estimation and data detection in flexible MIMO FBMC systems, addressing challenges posed by their complex structure and multi-antenna interference, and demonstrating improved performance over traditional methods.

## Contribution

It proposes a novel tensor-based framework that unifies and extends existing joint estimation and detection techniques for MIMO FBMC systems.

## Key findings

- Tensor approach encompasses existing methods as special cases
- Simulation results show performance gains over training-only methods
- Addresses challenges of self interference and multi-antenna interference in FBMC

## Abstract

Filter bank-based multicarrier (FBMC) systems have attracted increasing attention recently in view of their many advantages over the classical cyclic prefix (CP)-based orthogonal frequency division multiplexing (CP-OFDM) modulation. However, their more advanced structure (resulting in, for example, self interference) 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 receivers, of increasing interest in (massive) MIMO systems, have been little studied so far for FBMC and mainly for the single-antenna case only. The design of such receivers for flexible MIMO FBMC systems, unifying a number of existing FBMC schemes, is considered in this paper through a tensor-based approach, which is shown to encompass existing joint channel estimation and data detection approaches as special cases, adding to their understanding and paving the way to further developments. Simulation-based results are included, for realistic transmission models, demonstrating the estimation and detection performance gains from the adoption of these receivers over their training only-based counterparts.