Distributed Deep Joint Source-Channel Coding over a Multiple Access Channel

• URL: http://arxiv.org/abs/2211.09920v1
• Date: Thu, 17 Nov 2022 22:36:03 GMT
• Title: Distributed Deep Joint Source-Channel Coding over a Multiple Access Channel
• Authors: Selim F. Yilmaz, Can Karamanli, Deniz Gunduz
• Abstract summary: We consider distributed image transmission over a noisy multiple access channel (MAC) using deep joint source-channel coding (DeepJSCC) We introduce a novel joint image compression and transmission scheme, where the devices send their compressed image representations in a non-orthogonal manner. We show significant improvements in terms of the quality of the reconstructed images compared to transmission employing current DeepJSCC approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider distributed image transmission over a noisy multiple access channel (MAC) using deep joint source-channel coding (DeepJSCC). It is known that Shannon's separation theorem holds when transmitting independent sources over a MAC in the asymptotic infinite block length regime. However, we are interested in the practical finite block length regime, in which case separate source and channel coding is known to be suboptimal. We introduce a novel joint image compression and transmission scheme, where the devices send their compressed image representations in a non-orthogonal manner. While non-orthogonal multiple access (NOMA) is known to achieve the capacity region, to the best of our knowledge, non-orthogonal joint source channel coding (JSCC) scheme for practical systems has not been studied before. Through extensive experiments, we show significant improvements in terms of the quality of the reconstructed images compared to orthogonal transmission employing current DeepJSCC approaches particularly for low bandwidth ratios. We publicly share source code to facilitate further research and reproducibility.

Related papers

• Distributed Deep Joint Source-Channel Coding with Decoder-Only Side Information [6.411633100057159]
  We consider low-latency image transmission over a noisy wireless channel when correlated side information is present only at the receiver side. We propose a novel neural network architecture that incorporates the decoder-only side information at multiple stages at the receiver side.
  arXiv  Detail & Related papers  (2023-10-06T15:17:45Z)
• High Perceptual Quality Wireless Image Delivery with Denoising Diffusion Models [10.763194436114194]
  We consider the image transmission problem over a noisy wireless channel via deep learning-based joint source-channel coding (DeepJSCC) We introduce a novel scheme that utilizes the range-null space decomposition of the target image. We demonstrate significant improvements in distortion and perceptual quality of reconstructed images compared to standard DeepJSCC and the state-of-the-art generative learning-based method.
  arXiv  Detail & Related papers  (2023-09-27T16:30:59Z)
• 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)
• 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)
• DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding [6.55705721360334]
  We show that DeepJSCC-Q can achieve similar performance to prior works that allow any complex valued channel input. DeepJSCC-Q preserves the graceful degradation of image quality in unpredictable channel conditions.
  arXiv  Detail & Related papers  (2022-06-16T11:43:50Z)
• DeepJSCC-Q: Channel Input Constrained Deep Joint Source-Channel Coding [5.046831208137847]
  DeepJSCC-Q is an end-to-end optimized joint source-channel coding scheme for wireless image transmission. It preserves the graceful degradation of image quality observed in prior work when channel conditions worsen.
  arXiv  Detail & Related papers  (2021-11-25T11:59:17Z)
• 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)
• Deep Multimodal Fusion by Channel Exchanging [87.40768169300898]
  This paper proposes a parameter-free multimodal fusion framework that dynamically exchanges channels between sub-networks of different modalities. The validity of such exchanging process is also guaranteed by sharing convolutional filters yet keeping separate BN layers across modalities, which, as an add-on benefit, allows our multimodal architecture to be almost as compact as a unimodal network.
  arXiv  Detail & Related papers  (2020-11-10T09:53:20Z)
• Bandwidth-Agile Image Transmission with Deep Joint Source-Channel Coding [7.081604594416339]
  We consider the scenario in which images are transmitted progressively in layers over time or frequency. DeepJSCC-$l$ is an innovative solution that uses convolutional autoencoders. DeepJSCC-$l$ has comparable performance with state of the art digital progressive transmission schemes in the challenging low signal-to-noise ratio (SNR) and small bandwidth regimes.
  arXiv  Detail & Related papers  (2020-09-26T00:11:50Z)
• Modeling Lost Information in Lossy Image Compression [72.69327382643549]
  Lossy image compression is one of the most commonly used operators for digital images. We propose a novel invertible framework called Invertible Lossy Compression (ILC) to largely mitigate the information loss problem.
  arXiv  Detail & Related papers  (2020-06-22T04:04:56Z)
• Decentralized Learning for Channel Allocation in IoT Networks over Unlicensed Bandwidth as a Contextual Multi-player Multi-armed Bandit Game [134.88020946767404]
  We study a decentralized channel allocation problem in an ad-hoc Internet of Things network underlaying on the spectrum licensed to a primary cellular network. Our study maps this problem into a contextual multi-player, multi-armed bandit game, and proposes a purely decentralized, three-stage policy learning algorithm through trial-and-error.
  arXiv  Detail & Related papers  (2020-03-30T10:05:35Z)

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.