Distributed Deep Joint Source-Channel Coding with Decoder-Only Side Information

• URL: http://arxiv.org/abs/2310.04311v2
• Date: Tue, 27 Feb 2024 18:10:02 GMT
• Title: Distributed Deep Joint Source-Channel Coding with Decoder-Only Side Information
• Authors: Selim F. Yilmaz, Ezgi Ozyilkan, Deniz Gunduz, Elza Erkip
• Abstract summary: 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.
• Score: 6.411633100057159
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We consider low-latency image transmission over a noisy wireless channel when correlated side information is present only at the receiver side (the Wyner-Ziv scenario). In particular, we are interested in developing practical schemes using a data-driven joint source-channel coding (JSCC) approach, which has been previously shown to outperform conventional separation-based approaches in the practical finite blocklength regimes, and to provide graceful degradation with channel quality. We propose a novel neural network architecture that incorporates the decoder-only side information at multiple stages at the receiver side. Our results demonstrate that the proposed method succeeds in integrating the side information, yielding improved performance at all channel conditions in terms of the various quality measures considered here, especially at low channel signal-to-noise ratios (SNRs) and small bandwidth ratios (BRs). We have made the source code of the proposed method public to enable further research, and the reproducibility of the results.

Related papers

• 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)
• Distributed Deep Joint Source-Channel Coding over a Multiple Access Channel [0.0]
  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.
  arXiv  Detail & Related papers  (2022-11-17T22:36:03Z)
• Distributed Image Transmission using Deep Joint Source-Channel Coding [8.316711745589354]
  We study the problem of deep joint source-channel coding (D-JSCC) for correlated image sources. We propose a deep neural networks solution that includes lightweight edge encoders and a powerful center decoder. Our results show the impressive improvement of reconstruction quality in both links by exploiting the noisy representations of the other link.
  arXiv  Detail & Related papers  (2022-01-25T14:25:26Z)
• 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)
• CAnet: Uplink-aided Downlink Channel Acquisition in FDD Massive MIMO using Deep Learning [51.72869237847767]
  In frequency-division duplexing systems, the downlink channel state information (CSI) acquisition scheme leads to high training and feedback overheads. We propose an uplink-aided downlink channel acquisition framework using deep learning to reduce these overheads.
  arXiv  Detail & Related papers  (2021-01-12T10:12:28Z)
• FedRec: Federated Learning of Universal Receivers over Fading Channels [92.15358738530037]
  We propose a neural network-based symbol detection technique for downlink fading channels. Multiple users collaborate to jointly learn a universal data-driven detector, hence the name FedRec. The performance of the resulting receiver is shown to approach the MAP performance in diverse channel conditions without requiring knowledge of the fading statistics.
  arXiv  Detail & Related papers  (2020-11-14T11:29:55Z)
• 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)
• Operation-Aware Soft Channel Pruning using Differentiable Masks [51.04085547997066]
  We propose a data-driven algorithm, which compresses deep neural networks in a differentiable way by exploiting the characteristics of operations. We perform extensive experiments and achieve outstanding performance in terms of the accuracy of output networks.
  arXiv  Detail & Related papers  (2020-07-08T07:44:00Z)
• 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.