Deep Learning Based Equalizer for MIMO-OFDM Systems with Insufficient Cyclic Prefix

TL;DR

This paper introduces a deep learning-based equalizer for MIMO-OFDM systems with insufficient cyclic prefix, effectively mitigating inter-carrier and inter-symbol interference caused by multipath delay spread exceeding CP length.

Contribution

It proposes a novel deep learning equalizer that approximates maximum likelihood detection and a joint detection scheme for improved performance in challenging multipath conditions.

Findings

Significant performance improvement over traditional schemes.

Effective mitigation of ICI and ISI in multipath channels.

Enhanced detection accuracy with the proposed deep learning approach.

Abstract

In this paper, we study the equalization design for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with insufficient cyclic prefix (CP). In particular, the signal detection performance is severely impaired by inter-carrier interference (ICI) and inter-symbol interference (ISI) when the multipath delay spread exceeding the length of CP. To tackle this problem, a deep learning-based equalizer is proposed for approximating the maximum likelihood detection. Inspired by the dependency between the adjacent subcarriers, a computationally efficient joint detection scheme is developed. Employing the proposed equalizer, an iterative receiver is also constructed and the detection performance is evaluated through simulations over measured multipath channels. Our results reveal that the proposed receiver can achieve significant performance improvement compared to two traditional baseline schemes.