CSI Compression using Channel Charting

• URL: http://arxiv.org/abs/2501.01431v1
• Date: Mon, 16 Dec 2024 08:30:53 GMT
• Title: CSI Compression using Channel Charting
• Authors: Baptiste Chatelier, Vincent Corlay, Matthieu Crussière, Luc Le Magoarou,
• Abstract summary: Multi-antenna communication systems in frequency division duplex (FDD) require channel state information (CSI) reporting from mobile users to the base station (BS)<n>To mitigate the overhead associated with CSI reporting, compressed CSI techniques have been proposed with the idea of recovering the original CSI at the BS from its compressed version sent by the mobile users.<n>Channel charting is an unsupervised dimensionality reduction method that consists in building a radio-environment map from CSIs.
• Score: 6.067275317776295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reaping the benefits of multi-antenna communication systems in frequency division duplex (FDD) requires channel state information (CSI) reporting from mobile users to the base station (BS). Over the last decades, the amount of CSI to be collected has become very challenging owing to the dramatic increase of the number of antennas at BSs. To mitigate the overhead associated with CSI reporting, compressed CSI techniques have been proposed with the idea of recovering the original CSI at the BS from its compressed version sent by the mobile users. Channel charting is an unsupervised dimensionality reduction method that consists in building a radio-environment map from CSIs. Such a method can be considered in the context of the CSI compression problem, since a chart location is, by definition, a low-dimensional representation of the CSI. In this paper, the performance of channel charting for a task-based CSI compression application is studied. A comparison of the proposed method against baselines on realistic synthetic data is proposed, showing promising results.

Related papers

• Study on Downlink CSI compression: Are Neural Networks the Only Solution? [1.0305984157213843]
  Massive Multi Input Multi Output (MIMO) systems enable higher data rates in the downlink (DL) The higher DL data rates are achieved by effective implementation of spatial multiplexing and beamforming. For Frequency Division Duplexing (FDD) systems, the DL channel state information (CSI) has to be transmitted by User Equipment (UE) to the gNB. To address the overhead issue, AI/ML methods using auto-encoders have been investigated, where an encoder neural network model at the UE compresses the CSI and a decoder neural network model at the gNB reconstructs it.
  arXiv  Detail & Related papers  (2025-02-10T08:00:14Z)
• 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)
• Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising [94.09442506816724]
  Blind-spot networks (BSN) have been prevalent neural architectures in self-supervised image denoising (SSID)<n>We build a Transformer-based Blind-Spot Network (TBSN) which shows strong local fitting and global perspective abilities.
  arXiv  Detail & Related papers  (2024-04-11T15:39:10Z)
• A Low-Overhead Incorporation-Extrapolation based Few-Shot CSI Feedback Framework for Massive MIMO Systems [45.22132581755417]
  Accurate channel state information (CSI) is essential for downlink precoding in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. However, obtaining CSI through feedback from the user equipment (UE) becomes challenging with the increasing scale of antennas and subcarriers. Deep learning-based methods have emerged for compressing CSI but these methods require substantial collected samples. Existing deep learning methods also suffer from dramatically growing feedback overhead owing to their focus on full-dimensional CSI feedback. We propose a low-overhead-Extrapolation based Few-Shot CSI
  arXiv  Detail & Related papers  (2023-12-07T06:01:47Z)
• 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)
• Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO [56.022764337221325]
  Reconfigurable intelligent surface (RIS) can significantly enhance the service coverage of Tera-Hertz massive multiple-input multiple-output (MIMO) communication systems. However, obtaining accurate high-dimensional channel state information (CSI) with limited pilot and feedback signaling overhead is challenging. This paper proposes a deep learning (DL)-based rate-splitting multiple access scheme for RIS-aided Tera-Hertz multi-user multiple access systems.
  arXiv  Detail & Related papers  (2022-09-18T03:07:37Z)
• Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems [77.0986534024972]
  Deep learning (DL)-based CSI feedback refers to CSI compression and reconstruction by a DL-based autoencoder and can greatly reduce feedback overhead. The focus is on novel neural network architectures and utilization of communication expert knowledge to improve CSI feedback accuracy.
  arXiv  Detail & Related papers  (2022-06-29T03:28:57Z)
• Deep Learning for 1-Bit Compressed Sensing-based Superimposed CSI Feedback [2.6831842796906393]
  This paper proposes a deep learning scheme to improve the 1-bit compressed sensing-based superimposed CSI feedback. The proposed scheme improves the recovery accuracy of the UL-US and downlink CSI with lower processing delay.
  arXiv  Detail & Related papers  (2022-03-13T09:33:53Z)
• Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback [9.959844922120524]
  Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to achieve spatial diversity and multiplexing gains. In this paper, we propose a deep learning (DL)-based CSI compression scheme, called DeepCMC, composed of convolutional layers followed by quantization and entropy coding blocks. DeepCMC is trained to minimize a weighted rate-distortion cost, which enables a trade-off between the CSI quality and its feedback overhead.
  arXiv  Detail & Related papers  (2020-03-07T12:33:31Z)
• DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection [98.43451011898212]
  In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging. We propose a data-driven implementation of the iterative soft interference cancellation (SIC) algorithm which we refer to as DeepSIC. DeepSIC learns to carry out joint detection from a limited set of training samples without requiring the channel to be linear.
  arXiv  Detail & Related papers  (2020-02-08T18:31:00Z)

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.