Active RIS-Assisted URLLC NOMA-Based 5G Network with FBL under Jamming Attacks

• URL: http://arxiv.org/abs/2501.13231v1
• Date: Wed, 22 Jan 2025 21:31:21 GMT
• Title: Active RIS-Assisted URLLC NOMA-Based 5G Network with FBL under Jamming Attacks
• Authors: Ghazal Asemian, Mohammadreza Amini, Burak Kantarci,
• Abstract summary: We tackle the challenge of jamming attacks in Ultra-Reliable Low Communication (URLLC) networks under Finite Blocklength (FBL) conditions.<n>We introduce an innovative approach that employs ReReliable Intelligent Surfaces (RIS) with active elements to enhance energy efficiency.<n>Our results indicate that increasing the number of RIS elements from 4 to 400 can improve signal-to-jamming-plus-noise ratio (SJNR) by 13.64%.
• Score: 5.715528540446773
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we tackle the challenge of jamming attacks in Ultra-Reliable Low Latency Communication (URLLC) within Non-Orthogonal Multiple Access (NOMA)-based 5G networks under Finite Blocklength (FBL) conditions. We introduce an innovative approach that employs Reconfigurable Intelligent Surfaces (RIS) with active elements to enhance energy efficiency while ensuring reliability and meeting latency requirements. Our approach incorporates the traffic model, making it practical for real-world scenarios with dynamic traffic loads. We thoroughly analyze the impact of blocklength and packet arrival rate on network performance metrics and investigate the optimal amplitude value and number of RIS elements. Our results indicate that increasing the number of RIS elements from 4 to 400 can improve signal-to-jamming-plus-noise ratio (SJNR) by 13.64\%. Additionally, optimizing blocklength and packet arrival rate can achieve a 31.68% improvement in energy efficiency and reduced latency. These findings underscore the importance of optimized settings for effective jamming mitigation.

Related papers

• Multi-Agent Reinforcement Learning for Sample-Efficient Deep Neural Network Mapping [54.65536245955678]
  We present a decentralized multi-agent reinforcement learning (MARL) framework designed to overcome the challenge of sample inefficiency.<n>We introduce an agent clustering algorithm that assigns similar mapping parameters to the same agents based on correlation analysis.<n> Experimental results show our MARL approach improves sample efficiency by 30-300x over standard single-agent RL.
  arXiv  Detail & Related papers  (2025-07-22T05:51:07Z)
• Meta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks [1.2940734305933084]
  Dynamic allocation of spectrum in 5G / 6G networks is critical to efficient resource utilization.<n>Applying traditional deep reinforcement learning (DRL) is often infeasible due to its immense sample complexity.<n>We propose a meta-learning framework that enables agents to learn a robust initial policy and rapidly adapt to new wireless scenarios.
  arXiv  Detail & Related papers  (2025-07-13T21:29:39Z)
• A Multi-Armed Bandit Framework for Online Optimisation in Green Integrated Terrestrial and Non-Terrestrial Networks [5.6405054075554055]
  Integrated terrestrial and non-terrestrial network (TN-NTN) architectures offer a promising solution for expanding coverage and improving capacity for the network.<n>We propose a novel online framework for integrated TN-NTN architectures, built on a multi-armed bandit (MAB) formulation and leveraging the Bandit-feedback Constrained Online Mirror Descent (BCOMD) algorithm.<n>Our approach adaptively optimises key system parameters--including bandwidth allocation, user equipment (UE) association, and macro base station (MBS) shutdown--to balance network capacity and energy efficiency in real time.
  arXiv  Detail & Related papers  (2025-06-10T22:06:15Z)
• Deep Reinforcement Learning-Based User Association in Hybrid LiFi/WiFi Indoor Networks [1.086544864007391]
  Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) indoor networks has been envisioned as a promising technology to alleviate the ever-increasing data rate demand in indoor scenarios. The hybrid LiFi/WiFi indoor networks can leverage the advantages of fast data transmission from LiFi and wider coverage of the spectrum compared to standalone networks. The objective of the paper is to design a new user-access point association algorithm for the hybrid networks.
  arXiv  Detail & Related papers  (2025-03-03T18:33:11Z)
• DT-DDNN: A Physical Layer Security Attack Detector in 5G RF Domain for CAVs [11.15939066175832]
  jamming attacks pose substantial risks to the 5G network. This work presents a novel deep learning-based technique for detecting jammers in CAV networks. Results show that the proposed method achieves 96.4% detection rate in extra low jamming power.
  arXiv  Detail & Related papers  (2024-03-05T04:29:31Z)
• Bypassing a Reactive Jammer via NOMA-Based Transmissions in Critical Missions [13.784352398504343]
  Wireless networks can be vulnerable to radio jamming attacks. This paper analyzes the effect of a reactive jammer on reliability, average transmission delay, and the effective sum rate (ESR) It is shown that by adjusting the allocated transmit power to UEs by gNB, the gNB can bypass the jammer effect to fulfill the 0.99999 reliability and the latency of 5ms without the need for packet re-transmission.
  arXiv  Detail & Related papers  (2024-01-18T21:51:57Z)
• Unlocking Metasurface Practicality for B5G Networks: AI-assisted RIS Planning [33.98674736140333]
  We present a first-of-its-kind deep reinforcement learning (DRL) solution, dubbed as DRISA, which trains a DRL agent and, in turn, obtains san optimal RIS deployment. Our benchmarks showcase better coverage, i.e., 10-dB increase in minimum signal-to-noise ratio (SNR) at lower computational time (up to -25 percent) while improving scalability towards denser network deployments.
  arXiv  Detail & Related papers  (2023-10-16T12:14:42Z)
• Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks [52.96907334080273]
  As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions. We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
  arXiv  Detail & Related papers  (2023-09-16T11:37:23Z)
• Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition [14.18507067281377]
  This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided blockage Dynamic Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model.
  arXiv  Detail & Related papers  (2023-06-22T10:10:25Z)
• Lyapunov-Driven Deep Reinforcement Learning for Edge Inference Empowered by Reconfigurable Intelligent Surfaces [30.1512069754603]
  We propose a novel algorithm for energy-efficient, low-latency, accurate inference at the wireless edge. We consider a scenario where new data are continuously generated/collected by a set of devices and are handled through a dynamic queueing system.
  arXiv  Detail & Related papers  (2023-05-18T12:46:42Z)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)
• 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)
• Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications [64.1076645382049]
  Combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency. In this paper, we study the spectrum, energy, and time resource management for deterministic-CR-NOMA IoT systems.
  arXiv  Detail & Related papers  (2021-09-17T08:55:48Z)
• On Topology Optimization and Routing in Integrated Access and Backhaul Networks: A Genetic Algorithm-based Approach [70.85399600288737]
  We study the problem of topology optimization and routing in IAB networks. We develop efficient genetic algorithm-based schemes for both IAB node placement and non-IAB backhaul link distribution. We discuss the main challenges for enabling mesh-based IAB networks.
  arXiv  Detail & Related papers  (2021-02-14T21:52:05Z)
• Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications [54.610318402371185]
  Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. We minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences.
  arXiv  Detail & Related papers  (2020-05-25T01:42:55Z)

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.