Neural Network-Based Bandit: A Medium Access Control for the IIoT Alarm Scenario
- URL: http://arxiv.org/abs/2407.16877v1
- Date: Tue, 23 Jul 2024 22:57:23 GMT
- Title: Neural Network-Based Bandit: A Medium Access Control for the IIoT Alarm Scenario
- Authors: Prasoon Raghuwanshi, Onel Luis Alcaraz López, Neelesh B. Mehta, Hirley Alves, Matti Latva-aho,
- Abstract summary: We propose a deep reinforcement learning based distributed Random Access scheme, entitled Neural Network-Based Bandit (NNBB) for the IIoT alarm scenario.
In such a scenario, the devices may detect a common critical event, and the goal is to ensure the alarm information is delivered successfully from at least one device.
Our simulation results show that as the number of devices in the network increases, so does the performance gain of the NNBB compared to the Multi-Armed Bandit (MAB) RA benchmark.
- Score: 28.65053609286692
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Efficient Random Access (RA) is critical for enabling reliable communication in Industrial Internet of Things (IIoT) networks. Herein, we propose a deep reinforcement learning based distributed RA scheme, entitled Neural Network-Based Bandit (NNBB), for the IIoT alarm scenario. In such a scenario, the devices may detect a common critical event, and the goal is to ensure the alarm information is delivered successfully from at least one device. The proposed NNBB scheme is implemented at each device, where it trains itself online and establishes implicit inter-device coordination to achieve the common goal. Devices can transmit simultaneously on multiple orthogonal channels and each possible transmission pattern constitutes a possible action for the NNBB, which uses a deep neural network to determine the action. Our simulation results show that as the number of devices in the network increases, so does the performance gain of the NNBB compared to the Multi-Armed Bandit (MAB) RA benchmark. For instance, NNBB experiences a 7% success rate drop when there are four channels and the number of devices increases from 10 to 60, while MAB faces a 25% drop.
Related papers
- Deep Attention Recognition for Attack Identification in 5G UAV
scenarios: Novel Architecture and End-to-End Evaluation [3.3253720226707992]
Despite the robust security features inherent in the 5G framework, attackers will still discover ways to disrupt 5G unmanned aerial vehicle (UAV) operations.
We propose Deep Attention Recognition (DAtR) as a solution to identify attacks based on a small deep network embedded in authenticated UAVs.
arXiv Detail & Related papers (2023-03-03T17:10:35Z) - Signal Detection in MIMO Systems with Hardware Imperfections: Message
Passing on Neural Networks [101.59367762974371]
In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments.
It is difficult to train a deep neural network (DNN) with limited pilot signals, hindering its practical applications.
We design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm.
arXiv Detail & Related papers (2022-10-08T04:32:58Z) - Deep Learning-Based Synchronization for Uplink NB-IoT [72.86843435313048]
We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT)
The introduced NN architecture leverages residual convolutional networks as well as knowledge of the preamble structure of the 5G New Radio (5G NR) specifications.
arXiv Detail & Related papers (2022-05-22T12:16:43Z) - Switching in the Rain: Predictive Wireless x-haul Network
Reconfiguration [17.891837432766764]
Wireless x-haul networks rely on microwave and millimeter-wave links between 4G and/or 5G base-stations to support ultra-high data rate and ultra-low latency.
precipitation may cause severe signal attenuation, which significantly degrades the network performance.
We develop a Predictive Network Reconfiguration framework that uses historical data to predict the future condition of each link and then prepares the network ahead of time for imminent disturbances.
arXiv Detail & Related papers (2022-03-07T13:40:38Z) - Learning-Based UAV Trajectory Optimization with Collision Avoidance and
Connectivity Constraints [0.0]
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks.
In this paper, we reformulate the multi-UAV trajectory optimization problem with collision avoidance and wireless connectivity constraints.
We propose a decentralized deep reinforcement learning approach to solve the problem.
arXiv Detail & Related papers (2021-04-03T22:22:20Z) - Deep Reinforcement Learning for Dynamic Spectrum Sharing of LTE and NR [10.210703513367864]
A proactive dynamic spectrum sharing scheme between 4G and 5G systems is proposed.
A deep reinforcement learning algorithm based on Monte Carlo Tree Search is proposed.
Results show that the proposed scheme is able to take actions while accounting for future states instead of being greedy in each subframe.
arXiv Detail & Related papers (2021-02-22T16:56:51Z) - Adversarial Attacks on Deep Learning Based Power Allocation in a Massive
MIMO Network [62.77129284830945]
We show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network.
We benchmark the performance of these attacks and show that with a small perturbation in the input of the neural network (NN), the white-box attacks can result in infeasible solutions up to 86%.
arXiv Detail & Related papers (2021-01-28T16:18:19Z) - Enabling certification of verification-agnostic networks via
memory-efficient semidefinite programming [97.40955121478716]
We propose a first-order dual SDP algorithm that requires memory only linear in the total number of network activations.
We significantly improve L-inf verified robust accuracy from 1% to 88% and 6% to 40% respectively.
We also demonstrate tight verification of a quadratic stability specification for the decoder of a variational autoencoder.
arXiv Detail & Related papers (2020-10-22T12:32:29Z) - Neural Networks and Value at Risk [59.85784504799224]
We perform Monte-Carlo simulations of asset returns for Value at Risk threshold estimation.
Using equity markets and long term bonds as test assets, we investigate neural networks.
We find our networks when fed with substantially less data to perform significantly worse.
arXiv Detail & Related papers (2020-05-04T17:41:59Z) - Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio [101.84651388520584]
This paper presents a new framework named network adjustment, which considers network accuracy as a function of FLOPs.
Experiments on standard image classification datasets and a wide range of base networks demonstrate the effectiveness of our approach.
arXiv Detail & Related papers (2020-04-06T15:51:00Z)
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.