Neuromorphic Integrated Sensing and Communications

• URL: http://arxiv.org/abs/2209.11891v1
• Date: Sat, 24 Sep 2022 00:23:25 GMT
• Title: Neuromorphic Integrated Sensing and Communications
• Authors: Jiechen Chen, Nicolas Skatchkovsky, and Osvaldo Simeone
• Abstract summary: We introduce neuromorphic integrated sensing and communications (N-ISAC), a novel solution that enables efficient online data decoding and radar sensing. N-ISAC leverages a common IR waveform for the dual purpose of conveying digital information and of detecting the presence or absence of a radar target. A spiking neural network (SNN) is deployed at the receiver to decode digital data and detect the radar target using directly the received signal.
• Score: 28.475406916976247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neuromorphic computing is an emerging technology that support event-driven data processing for applications requiring efficient online inference and/or control. Recent work has introduced the concept of neuromorphic communications, whereby neuromorphic computing is integrated with impulse radio (IR) transmission to implement low-energy and low-latency remote inference in wireless IoT networks. In this paper, we introduce neuromorphic integrated sensing and communications (N-ISAC), a novel solution that enables efficient online data decoding and radar sensing. N-ISAC leverages a common IR waveform for the dual purpose of conveying digital information and of detecting the presence or absence of a radar target. A spiking neural network (SNN) is deployed at the receiver to decode digital data and detect the radar target using directly the received signal. The SNN operation is optimized by balancing performance metric for data communications and radar sensing, highlighting synergies and trade-offs between the two applications.

Related papers

• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  In neuromorphic computing, spiking neural networks (SNNs) perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have demonstrated that embedding a few bits of payload in each spike exchanged between the spiking neurons can further enhance inference accuracy. This paper investigates a wireless neuromorphic split computing architecture employing multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning [97.99077847606624]
  This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making.
  arXiv  Detail & Related papers  (2024-04-02T10:19:04Z)
• Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN [63.90647197249949]
  In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. In this paper, we present a cooperative radio map estimation approach enabled by the generative adversarial network (GAN)
  arXiv  Detail & Related papers  (2024-02-05T05:01:28Z)
• Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing [74.12670841657038]
  Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. Data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. We propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
  arXiv  Detail & Related papers  (2023-12-08T13:50:30Z)
• Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference [32.0035037154674]
  This paper proposes an end-to-end design for a neuromorphic wireless Internet-of-Things system. Each sensing device is equipped with a neuromorphic sensor, a spiking neural network (SNN), and an impulse radio transmitter with multiple antennas. Pilots, encoding SNNs, decoding SNN, and hypernetwork are jointly trained across multiple channel realizations.
  arXiv  Detail & Related papers  (2022-06-13T11:13:39Z)
• Integrating Sensing and Communication in Cellular Networks via NR Sidelink [7.42576783544779]
  We discuss a common issue related to sidelink-based RF-sensing, which is its angle and rotation dependence. We propose a graph based encoder to capture propose-temporal features of the data and four approaches for multi-angle learning.
  arXiv  Detail & Related papers  (2021-09-15T12:41:31Z)
• Rethinking the Tradeoff in Integrated Sensing and Communication: Recognition Accuracy versus Communication Rate [21.149708253108788]
  Integrated sensing and communication (ISAC) is a promising technology to improve the band-utilization efficiency. There exists a tradeoff between the sensing and communication performance. This paper formulates and solves a multi-objective optimization problem which simultaneously maximizes the recognition accuracy and the communication data rate.
  arXiv  Detail & Related papers  (2021-07-20T17:00:35Z)
• Complex-valued Convolutional Neural Networks for Enhanced Radar Signal Denoising and Interference Mitigation [73.0103413636673]
  We propose the use of Complex-Valued Convolutional Neural Networks (CVCNNs) to address the issue of mutual interference between radar sensors. CVCNNs increase data efficiency, speeds up network training and substantially improves the conservation of phase information during interference removal.
  arXiv  Detail & Related papers  (2021-04-29T10:06:29Z)
• Spiking Neural Networks -- Part III: Neuromorphic Communications [38.518936229794214]
  The presence of more and more wirelessly connected devices is driving efforts to export advances in machine learning. Implementing machine learning models for learning and inference on battery-powered devices that are connected via bandwidth-constrained channels remains challenging. This paper explores two ways in which Spiking Neural Networks (SNNs) can help address these open problems.
  arXiv  Detail & Related papers  (2020-10-27T11:52:35Z)
• End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence [38.518936229794214]
  We introduce a novel "all-spike" low-power solution for remote wireless inference that is based on neuromorphic sensing, Impulse Radio (IR), and Spiking Neural Networks (SNNs) We introduce an end-to-end training procedure that treats the cascade of encoder, channel, and decoder as a probabilistic SNN-based autoencoder that implements Joint Source-Channel Coding (JSCC) The experiments confirm that the proposed end-to-end neuromorphic edge architecture provides a promising framework for efficient and low-latency remote sensing, communication, and inference.
  arXiv  Detail & Related papers  (2020-09-03T09:10:16Z)
• Temporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Driving [65.36115045035903]
  We propose an approach to address the object recognition problem directly with raw temporal pulses utilizing the spiking neural network (SNN) Being evaluated on various datasets, our proposed method has shown comparable performance as the state-of-the-art methods, while achieving remarkable time efficiency.
  arXiv  Detail & Related papers  (2020-01-24T22:58:55Z)

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.