RSCNet: Dynamic CSI Compression for Cloud-based WiFi Sensing

• URL: http://arxiv.org/abs/2402.04888v2
• Date: Mon, 20 May 2024 15:48:38 GMT
• Title: RSCNet: Dynamic CSI Compression for Cloud-based WiFi Sensing
• Authors: Borna Barahimi, Hakam Singh, Hina Tabassum, Omer Waqar, Mohammad Omer,
• Abstract summary: This paper develops a novel Real-time Sensing and Compression Network (RSCNet) which enables sensing with compressed CSI. RSCNet balances the trade-off between CSI compression and sensing precision, thus streamlining real-time cloud-based WiFi sensing with reduced communication costs. Numerical findings demonstrate the gains of RSCNet over the existing benchmarks like SenseFi, showcasing a sensing accuracy of 97.4% with minimal CSI reconstruction error.
• Score: 9.34104644481967
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: WiFi-enabled Internet-of-Things (IoT) devices are evolving from mere communication devices to sensing instruments, leveraging Channel State Information (CSI) extraction capabilities. Nevertheless, resource-constrained IoT devices and the intricacies of deep neural networks necessitate transmitting CSI to cloud servers for sensing. Although feasible, this leads to considerable communication overhead. In this context, this paper develops a novel Real-time Sensing and Compression Network (RSCNet) which enables sensing with compressed CSI; thereby reducing the communication overheads. RSCNet facilitates optimization across CSI windows composed of a few CSI frames. Once transmitted to cloud servers, it employs Long Short-Term Memory (LSTM) units to harness data from prior windows, thus bolstering both the sensing accuracy and CSI reconstruction. RSCNet adeptly balances the trade-off between CSI compression and sensing precision, thus streamlining real-time cloud-based WiFi sensing with reduced communication costs. Numerical findings demonstrate the gains of RSCNet over the existing benchmarks like SenseFi, showcasing a sensing accuracy of 97.4% with minimal CSI reconstruction error. Numerical results also show a computational analysis of the proposed RSCNet as a function of the number of CSI frames.

Related papers

• RN-Net: Reservoir Nodes-Enabled Neuromorphic Vision Sensing Network [7.112892720740359]
  Event-based cameras are inspired by spiking and asynchronous spike representation of the biological visual system. We propose a neural network architecture, based on simple convolution layers integrated with dynamic temporal encoding for local and global reservoirs. RN-Net achieves the highest accuracy of 99.2% for DV128 Gesture reported to date, and one of the highest accuracy of 67.5% for DVS Lip dataset at a much smaller network size.
  arXiv  Detail & Related papers  (2023-03-19T21:20:45Z)
• Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations [16.819111460629397]
  This work aims to shed light on the impact of Wi-Fi 6 features on the sensing performance and to create a benchmark for future research on Wi-Fi sensing. We perform an extensive CSI data collection campaign involving 3 individuals, 3 environments, and 12 activities, using Wi-Fi 6 signals. An anonymized ground truth obtained through video recording accompanies our 80-GB dataset, which contains almost two hours of CSI data from three collectors.
  arXiv  Detail & Related papers  (2023-02-02T10:21:00Z)
• Attention-based Feature Compression for CNN Inference Offloading in Edge Computing [93.67044879636093]
  This paper studies the computational offloading of CNN inference in device-edge co-inference systems. We propose a novel autoencoder-based CNN architecture (AECNN) for effective feature extraction at end-device. Experiments show that AECNN can compress the intermediate data by more than 256x with only about 4% accuracy loss.
  arXiv  Detail & Related papers  (2022-11-24T18:10:01Z)
• 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)
• SAR Despeckling Using Overcomplete Convolutional Networks [53.99620005035804]
  despeckling is an important problem in remote sensing as speckle degrades SAR images. Recent studies show that convolutional neural networks(CNNs) outperform classical despeckling methods. This study employs an overcomplete CNN architecture to focus on learning low-level features by restricting the receptive field. We show that the proposed network improves despeckling performance compared to recent despeckling methods on synthetic and real SAR images.
  arXiv  Detail & Related papers  (2022-05-31T15:55:37Z)
• 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)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• Learning Frequency-aware Dynamic Network for Efficient Super-Resolution [56.98668484450857]
  This paper explores a novel frequency-aware dynamic network for dividing the input into multiple parts according to its coefficients in the discrete cosine transform (DCT) domain. In practice, the high-frequency part will be processed using expensive operations and the lower-frequency part is assigned with cheap operations to relieve the computation burden. Experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures.
  arXiv  Detail & Related papers  (2021-03-15T12:54:26Z)
• CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback [7.63185216082836]
  This paper presents a novel neural network CLNet tailored for CSI feedback problem based on the intrinsic properties of CSI. The experiment result shows that CLNet outperforms the state-of-the-art method by average accuracy improvement of 5.41% in both outdoor and indoor scenarios.
  arXiv  Detail & Related papers  (2021-02-15T12:16:11Z)
• Aggregated Network for Massive MIMO CSI Feedback [18.04633171156304]
  ACRNet is designed to boost the feedback performance with network aggregation and parametric RuLU activation. Experiments show that ACRNet outperforms loads of previous state-of-the-art feedback networks without any extra information.
  arXiv  Detail & Related papers  (2021-01-17T08:19:40Z)

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.