Graph Neural Networks for Channel Decoding

Cammerer, Sebastian; Hoydis, Jakob; Aoudia, Fayçal Aït; Keller, Alexander

doi:10.48550/arxiv.2207.14742

Cited by 2 publications

(2 citation statements)

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“…The importance of artificial intelligence (AI) and ML in wireless communications is growing quickly and already an integral part of 5G Release 18 [22]. AI and ML are expected to play and even more central role in 6G systems [2] and research is heavily focusing on ML-based baseband processing, ranging from atomistic (separate and independent) optimizations of neural network (NN)-based MIMO data detectors [23]- [25] or channel decoders [26], to fully NN-based transmitters and receivers (e.g., end-to-end learning methods [27]- [30]), and model-driven deep unfolding approaches [20], [21], [31]. However, existing NN-based data detectors turn out to require orders of magnitude higher complexity compared to classical algorithms that were designed by hand.…”

Section: B Related Workmentioning

confidence: 99%

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

Wiesmayr

Dick

Hoydis

et al. 2022

2022 56th Asilomar Conference on Signals, Systems, and Computers

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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 raytracer. Our results show that DUIDD outperforms classical IDD both in terms of block error rate and computational complexity.

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Section: B Related Workmentioning

confidence: 99%

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

Wiesmayr

Dick

Hoydis

et al. 2022

2022 56th Asilomar Conference on Signals, Systems, and Computers

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“…In ref. [17], a graph neural network (GNN) based architecture was proposed to learn a generalized BP algorithm and it was reported to outperform BP for BCH codes, but suffering from high computational complexity. In ref.…”

Section: Introductionmentioning

confidence: 99%

A bottom‐up design methodology of neural min‐sum decoders for LDPC codes

Luo

et al. 2022

IET Communications

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It is known the belief propagation variants of linear codes can be readily unrolled as neural networks, after assigning learnable weights on the message-passing edges. Contrary to the conventional top-down training process, where the distillation occurs in the form of pruning or sharing when downsizing model is required, a new bottom-up design methodology to augment performance of the raw min-sum decoder of LDPC codes is proposed, by introducing incrementally a few parameters in the specific positions of corresponding neural network. Then a novel postprocessing method, devised to further improve performance, can cope with decoding failures effectively. In the training process, a simplified scheme of generating training data is presented via exploiting an approximation to the targeted mixture density, and it is found the evaluation of trained parameters converges after sufficient iterations, indicating its generality with an arbitrary designated number of iterations. Lastly, an extensive simulation of three codes carried on the AWGN or Rayleigh fading channels demonstrates the design reaches a good tradeoff of low-complexity and comparable decoding performance.

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Graph Neural Networks for Channel Decoding

Cited by 2 publications

References 0 publications

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

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

A bottom‐up design methodology of neural min‐sum decoders for LDPC codes

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