DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems

TL;DR

DUIDD introduces a deep-unfolded, interleaved detection and decoding approach that enhances MIMO wireless system performance by reducing complexity and error rates through optimized iterative processing.

Contribution

It proposes a novel deep-unfolded interleaved detection and decoding framework that improves efficiency and accuracy over traditional IDD methods in MIMO systems.

Findings

DUIDD outperforms classical IDD in block error rate.

DUIDD reduces computational complexity.

DUIDD accelerates convergence of detection and decoding.

Abstract

Iterative detection and decoding (IDD) is known to achieve near-capacity performance in multi-antenna wireless systems. We propose deep-unfolded interleaved detection and decoding (DUIDD), a new paradigm that reduces the complexity of IDD while achieving even lower error rates. DUIDD interleaves the inner stages of the data detector and channel decoder, which expedites convergence and reduces complexity. Furthermore, DUIDD applies deep unfolding to automatically optimize algorithmic hyperparameters, soft-information exchange, message damping, and state forwarding. We demonstrate the efficacy of DUIDD using NVIDIA's Sionna link-level simulator in a 5G-near multi-user MIMO-OFDM wireless system with a novel low-complexity soft-input soft-output data detector, an optimized low-density parity-check decoder, and channel vectors from a commercial ray-tracer. Our results show that DUIDD outperforms classical IDD both in terms of block error rate and computational complexity.