Model-Driven Deep Learning-Based MIMO-OFDM Detector: Design, Simulation, and Experimental Results

TL;DR

This paper introduces a deep learning-enhanced, low-complexity MIMO-OFDM detector based on unfolded iterative algorithms, achieving high spectral efficiency and robustness through simulation and real-world testing.

Contribution

It proposes a novel deep learning-tuned, conjugate gradient-based unfolded OAMP detector for MIMO-OFDM, reducing complexity while maintaining high detection performance.

Findings

Significant performance gain over traditional iterative detectors

Comparable to state-of-the-art deep learning detectors with lower complexity

Effective in real-world over-the-air tests

Abstract

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), a fundamental transmission scheme, promises high throughput and robustness against multipath fading. However, these benefits rely on the efficient detection strategy at the receiver and come at the expense of the extra bandwidth consumed by the cyclic prefix (CP). We use the iterative orthogonal approximate message passing (OAMP) algorithm in this paper as the prototype of the detector because of its remarkable potential for interference suppression. However, OAMP is computationally expensive for the matrix inversion per iteration. We replace the matrix inversion with the conjugate gradient (CG) method to reduce the complexity of OAMP. We further unfold the CG-based OAMP algorithm into a network and tune the critical parameters through deep learning (DL) to enhance detection performance. Simulation results and complexity analysis show that the proposed scheme has significant gain over other iterative detection methods and exhibits comparable performance to the state-of-the-art DL-based detector at a reduced computational cost. Furthermore, we design a highly efficient CP-free MIMO-OFDM receiver architecture to remove the CP overhead. This architecture first eliminates the intersymbol interference by buffering the previously recovered data and then detects the signal using the proposed detector. Numerical experiments demonstrate that the designed receiver offers a higher spectral efficiency than traditional receivers. Finally, over-the-air tests verify the effectiveness and robustness of the proposed scheme in realistic environments.