Multi-user Wireless Image Semantic Transmission over MIMO Multiple Access Channels

• URL: http://arxiv.org/abs/2504.07969v1
• Date: Tue, 18 Mar 2025 12:41:38 GMT
• Title: Multi-user Wireless Image Semantic Transmission over MIMO Multiple Access Channels
• Authors: Bingyan Xie, Yongpeng Wu, Feng Shu, Jiangzhou Wang, Wenjun Zhang,
• Abstract summary: It incorporates CSI as the side information into both the semantic encoders and decoders to generate a proper feature mask map.<n> Numerical results verify the superiority of proposed MU-LCFSC compared to DeepJSCC-NOMA over 3 dB in terms of PSNR.
• Score: 31.74978416736268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper focuses on a typical uplink transmission scenario over multiple-input multiple-output multiple access channel (MIMO-MAC) and thus propose a multi-user learnable CSI fusion semantic communication (MU-LCFSC) framework. It incorporates CSI as the side information into both the semantic encoders and decoders to generate a proper feature mask map in order to produce a more robust attention weight distribution. Especially for the decoding end, a cooperative successive interference cancellation procedure is conducted along with a cooperative mask ratio generator, which flexibly controls the mask elements of feature mask maps. Numerical results verify the superiority of proposed MU-LCFSC compared to DeepJSCC-NOMA over 3 dB in terms of PSNR.

Related papers

• Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding [0.25602836891933073]
  We introduce a machine learning-aided wireless image transmission method that merges compression and channel coding using a multi-view autoencoder.<n>Our method scales up to 16 users and beyond, with only a 0.6% increase in the number of trainable parameters compared to a single-user model.
  arXiv  Detail & Related papers  (2025-03-23T12:27:20Z)
• Multimodal-Aware Fusion Network for Referring Remote Sensing Image Segmentation [7.992331117310217]
  Referring remote sensing image segmentation (RRSIS) is a novel visual task in remote sensing images segmentation.<n>We design a multimodal-aware fusion network (MAFN) to achieve fine-grained alignment and fusion between the two modalities.
  arXiv  Detail & Related papers  (2025-03-14T08:31:21Z)
• Multi-Task Semantic Communication With Graph Attention-Based Feature Correlation Extraction [69.24689059980035]
  This paper presents a new graph attention inter-block (GAI) module to the encoder/transmitter of a multi-task semantic communication system.<n>We interpret the outputs of the intermediate feature extraction blocks of the encoder as the nodes of a graph to capture the correlations of the intermediate features.<n> Experiments demonstrate that the proposed model surpasses the most competitive and publicly available models by 11.4% on the CityScapes 2Task dataset.
  arXiv  Detail & Related papers  (2025-01-02T04:38:01Z)
• CF-CGN: Channel Fingerprints Extrapolation for Multi-band Massive MIMO Transmission based on Cycle-Consistent Generative Networks [26.720410416586677]
  Multi-band massive multiple-input multiple-output (MIMO) communication can promote the cooperation of licensed and unlicensed spectra.<n>Channel fingerprints (CF) are used to assist channel state information (CSI) acquisition and reduce computational complexity.<n>We propose CF-CGN to extrapolate CF for multi-band massive MIMO transmission where licensed and unlicensed spectra cooperate to provide ubiquitous connectivity.
  arXiv  Detail & Related papers  (2024-12-30T11:52:39Z)
• Robust Image Semantic Coding with Learnable CSI Fusion Masking over MIMO Fading Channels [44.15994739018646]
  Existing semantic communication frameworks mainly consider single-input single-output Gaussian channels or Rayleigh fading channels. We propose a learnable CSI fusion semantic communication framework, where CSI is treated as side information by the semantic extractor. Experiment results testify the superiority of LCFSC over traditional schemes and state-of-the-art Swin Transformer-based semantic communication frameworks.
  arXiv  Detail & Related papers  (2024-05-30T08:23:17Z)
• OTSeg: Multi-prompt Sinkhorn Attention for Zero-Shot Semantic Segmentation [57.84148140637513]
  Multi-Prompts Sinkhorn Attention (MPSA) effectively replaces cross-attention mechanisms within Transformer framework in multimodal settings. OTSeg achieves state-of-the-art (SOTA) performance with significant gains on Zero-Shot Semantic (ZS3) tasks.
  arXiv  Detail & Related papers  (2024-03-21T07:15:37Z)
• Communication-Efficient Framework for Distributed Image Semantic Wireless Transmission [68.69108124451263]
  Federated learning-based semantic communication (FLSC) framework for multi-task distributed image transmission with IoT devices. Each link is composed of a hierarchical vision transformer (HVT)-based extractor and a task-adaptive translator. Channel state information-based multiple-input multiple-output transmission module designed to combat channel fading and noise.
  arXiv  Detail & Related papers  (2023-08-07T16:32:14Z)
• 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)
• Multi-view Multi-label Anomaly Network Traffic Classification based on MLP-Mixer Neural Network [55.21501819988941]
  Existing network traffic classification based on convolutional neural networks (CNNs) often emphasizes local patterns of traffic data while ignoring global information associations. We propose an end-to-end network traffic classification method.
  arXiv  Detail & Related papers  (2022-10-30T01:52:05Z)
• Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification [12.670741983987943]
  In frequency division duplex (FDD) multiple-input multiple-output (MIMO) wireless communications, limited channel state information (CSI) feedback is a central tool. To achieve a given CSI quality, the CSI quantization codebook size has to grow exponentially with the number of antennas.
  arXiv  Detail & Related papers  (2020-09-28T18:19:02Z)

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.