Activity Detection for Grant-Free NOMA in Massive IoT Networks

• URL: http://arxiv.org/abs/2301.01274v1
• Date: Fri, 23 Dec 2022 03:44:00 GMT
• Title: Activity Detection for Grant-Free NOMA in Massive IoT Networks
• Authors: Mehrtash Mehrabi, Mostafa Mohammadkarimi and Masoud Ardakani
• Abstract summary: We propose a deep learning (DL)-based method called convolutional neural network (CNN)-activity detection (AD) Our simulations verify that our proposed CNN-AD method can achieve higher performance compared to the existing non-Bayesian greedy-based methods.
• Score: 14.43600694600554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, grant-free transmission paradigm has been introduced for massive Internet of Things (IoT) networks to save both time and bandwidth and transmit the message with low latency. In order to accurately decode the message of each device at the base station (BS), first, the active devices at each transmission frame must be identified. In this work, first we investigate the problem of activity detection as a threshold comparing problem. We show the convexity of the activity detection method through analyzing its probability of error which makes it possible to find the optimal threshold for minimizing the activity detection error. Consequently, to achieve an optimum solution, we propose a deep learning (DL)-based method called convolutional neural network (CNN)-activity detection (AD). In order to make it more practical, we consider unknown and time-varying activity rate for the IoT devices. Our simulations verify that our proposed CNN-AD method can achieve higher performance compared to the existing non-Bayesian greedy-based methods. This is while existing methods need to know the activity rate of IoT devices, while our method works for unknown and even time-varying activity rates

Related papers

• Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications [87.05763097471487]
  This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We analyze the effect of image DCT coefficients on inference accuracy and propose the channel-agnostic effectiveness encoding for offloading.
  arXiv  Detail & Related papers  (2022-11-24T20:13:17Z)
• Label-Efficient Object Detection via Region Proposal Network Pre-Training [58.50615557874024]
  We propose a simple pretext task that provides an effective pre-training for the region proposal network (RPN) In comparison with multi-stage detectors without RPN pre-training, our approach is able to consistently improve downstream task performance.
  arXiv  Detail & Related papers  (2022-11-16T16:28:18Z)
• A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks [0.0]
  Intrusion detection is one of the challenging issues in the area of network security. In this paper, a comparative analysis of conventional machine learning classification algorithms has been performed. It can be observed that Multi-Layer Perception (MLP) has dependencies between input and output and relies more on network configuration for intrusion detection.
  arXiv  Detail & Related papers  (2021-11-02T05:58:07Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Active Learning for Deep Neural Networks on Edge Devices [0.0]
  This paper formalizes a practical active learning problem for neural networks on edge devices. We propose a general task-agnostic framework to tackle this problem, which reduces it to a stream submodular property. We evaluate our approach on both classification and object detection tasks in a practical setting to simulate a real-life scenario.
  arXiv  Detail & Related papers  (2021-06-21T03:55:33Z)
• Zero-bias Deep Learning Enabled Quick and Reliable Abnormality Detection in IoT [18.474662677341012]
  This paper integrates zero-bias DNN and Quickest Event Detection algorithms. It provides a holistic framework for quick and reliable detection of both abnormalities and time-dependent abnormal events. We demonstrate the effectiveness of the framework using both massive signal records from real-world aviation communication systems and simulated data.
  arXiv  Detail & Related papers  (2021-04-08T03:31:50Z)
• An IoT-Based Framework for Remote Fall Monitoring [2.511917198008258]
  This paper presents a novel IoT-based system for fall detection that includes a sensing device transmitting data to a mobile application through a cloud-connected gateway device. The results emphasize on the significance of Continuous Wavelet Transform (CWT) as an influential feature for determining falls. For all performance metrics (accuracy, recall, precision, specificity, and F1 Score), the achieved results are higher than 95% for a dataset of small size.
  arXiv  Detail & Related papers  (2021-03-10T22:37:19Z)
• Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing [65.78881372074983]
  IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay. We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems. We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-04-15T06:13:33Z)
• BiDet: An Efficient Binarized Object Detector [96.19708396510894]
  We propose a binarized neural network learning method called BiDet for efficient object detection. Our BiDet fully utilizes the representational capacity of the binary neural networks for object detection by redundancy removal. Our method outperforms the state-of-the-art binary neural networks by a sizable margin.
  arXiv  Detail & Related papers  (2020-03-09T08:16:16Z)
• Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
  We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-01-10T05:29:17Z)

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.