DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems
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Date
2022
Publication Type
Conference Paper
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yes
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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 multiuser 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.
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published
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Book title
2022 56th Asilomar Conference on Signals, Systems, and Computers
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Pages / Article No.
181 - 188
Publisher
IEEE
Event
56th Asilomar Conference on Signals, Systems, and Computers (ACSSC 2022)
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Methods
Software
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09695 - Studer, Christoph / Studer, Christoph