DRF Codes: Deep SNR-Robust Feedback Codes

• URL: http://arxiv.org/abs/2112.11789v1
• Date: Wed, 22 Dec 2021 10:47:25 GMT
• Title: DRF Codes: Deep SNR-Robust Feedback Codes
• Authors: Mahdi Boloursaz Mashhadi, Deniz Gunduz, Alberto Perotti, and Branislav Popovic
• Abstract summary: We present a new deep-neural-network (DNN) based error correction code for fading channels with output feedback, called deep SNR-robust feedback (DRF) code. We show that the DRF codes significantly outperform state-of-the-art in terms of both the SNR-robustness and the error rate in additive white Gaussian noise (AWGN) channel with feedback.
• Score: 2.6074034431152344
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present a new deep-neural-network (DNN) based error correction code for fading channels with output feedback, called deep SNR-robust feedback (DRF) code. At the encoder, parity symbols are generated by a long short term memory (LSTM) network based on the message as well as the past forward channel outputs observed by the transmitter in a noisy fashion. The decoder uses a bi-directional LSTM architecture along with a signal to noise ratio (SNR)-aware attention NN to decode the message. The proposed code overcomes two major shortcomings of the previously proposed DNN-based codes over channels with passive output feedback: (i) the SNR-aware attention mechanism at the decoder enables reliable application of the same trained NN over a wide range of SNR values; (ii) curriculum training with batch-size scheduling is used to speed up and stabilize training while improving the SNR-robustness of the resulting code. We show that the DRF codes significantly outperform state-of-the-art in terms of both the SNR-robustness and the error rate in additive white Gaussian noise (AWGN) channel with feedback. In fading channels with perfect phase compensation at the receiver, DRF codes learn to efficiently exploit knowledge of the instantaneous fading amplitude (which is available to the encoder through feedback) to reduce the overhead and complexity associated with channel estimation at the decoder. Finally, we show the effectiveness of DRF codes in multicast channels with feedback, where linear feedback codes are known to be strictly suboptimal.

Related papers

• Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems [74.52117784544758]
  This paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix. The entire encoder-decoder network is utilized for channel compression. Our method outperforms state-of-the-art channel estimation and feedback techniques in joint tasks.
  arXiv  Detail & Related papers  (2023-06-08T06:15:17Z)
• Robust Non-Linear Feedback Coding via Power-Constrained Deep Learning [7.941112438865385]
  We develop a new family of non-linear feedback codes that greatly enhance robustness to channel noise. Our autoencoder-based architecture is designed to learn codes based on consecutive blocks of bits. We show that our scheme outperforms state-of-the-art feedback codes by wide margins over practical forward and feedback noise regimes.
  arXiv  Detail & Related papers  (2023-04-25T22:21:26Z)
• A Scalable Graph Neural Network Decoder for Short Block Codes [49.25571364253986]
  We propose a novel decoding algorithm for short block codes based on an edge-weighted graph neural network (EW-GNN) The EW-GNN decoder operates on the Tanner graph with an iterative message-passing structure. We show that the EW-GNN decoder outperforms the BP and deep-learning-based BP methods in terms of the decoding error rate.
  arXiv  Detail & Related papers  (2022-11-13T17:13:12Z)
• Feedback is Good, Active Feedback is Better: Block Attention Active Feedback Codes [13.766611137136168]
  We show that GBAF codes can also be used for channels with active feedback. We implement a pair of transformer architectures, at the transmitter and the receiver, which interact with each other sequentially. We achieve a new state-of-the-art BLER performance, especially in the low SNR regime.
  arXiv  Detail & Related papers  (2022-11-03T11:44:06Z)
• Denoising Diffusion Error Correction Codes [92.10654749898927]
  Recently, neural decoders have demonstrated their advantage over classical decoding techniques. Recent state-of-the-art neural decoders suffer from high complexity and lack the important iterative scheme characteristic of many legacy decoders. We propose to employ denoising diffusion models for the soft decoding of linear codes at arbitrary block lengths.
  arXiv  Detail & Related papers  (2022-09-16T11:00:50Z)
• Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding [94.40747235081466]
  We propose an end-to-end deep learning-based joint transceiver design algorithm for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. We develop a DNN architecture that maps the received pilots into feedback bits at the receiver, and then further maps the feedback bits into the hybrid precoder at the transmitter.
  arXiv  Detail & Related papers  (2021-10-22T20:49:02Z)
• Deep Extended Feedback Codes [9.112162560071937]
  The encoder in the DEF architecture transmits an information message followed by a sequence of parity symbols. DEF codes generalize Deepcode in several ways to provide better error correction capability. Performance evaluations show that DEF codes have better performance compared to other DNN-based codes for channels with feedback.
  arXiv  Detail & Related papers  (2021-05-04T08:41:14Z)
• Model-Driven Deep Learning Based Channel Estimation and Feedback for Millimeter-Wave Massive Hybrid MIMO Systems [61.78590389147475]
  This paper proposes a model-driven deep learning (MDDL)-based channel estimation and feedback scheme for millimeter-wave (mmWave) systems. To reduce the uplink pilot overhead for estimating the high-dimensional channels from a limited number of radio frequency (RF) chains, we propose to jointly train the phase shift network and the channel estimator as an auto-encoder. Numerical results show that the proposed MDDL-based channel estimation and feedback scheme outperforms the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-04-22T13:34:53Z)
• Doubly Residual Neural Decoder: Towards Low-Complexity High-Performance Channel Decoding [19.48350605321212]
  Deep neural networks have been successfully applied in channel coding to improve the decoding performance. We propose doubly residual neural (DRN) decoder. DRN enables significant decoding performance improvement while maintaining low complexity.
  arXiv  Detail & Related papers  (2021-02-08T01:48:16Z)
• Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces [99.34306447202546]
  This paper proposes a deep denoising neural network assisted compressive channel estimation for mmWave IRS systems. We first introduce a hybrid passive/active IRS architecture, where very few receive chains are employed to estimate the uplink user-to-IRS channels. The complete channel matrix can be reconstructed from the limited measurements based on compressive sensing.
  arXiv  Detail & Related papers  (2020-06-03T12:18:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.