Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks

• URL: http://arxiv.org/abs/2208.08039v1
• Date: Wed, 17 Aug 2022 03:00:24 GMT
• Title: Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks
• Authors: Alexandros-Apostolos A. Boulogeorgos, Edwin Yaqub, Rachana Desai, Tachporn Sanguanpuak, Nikos Katzouris, Fotis Lazarakis, Angeliki Alexiou and Marco Di Renzo
• Abstract summary: 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.
• Score: 76.89730672544216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. However, due to the directional nature and the line-of-sight demand of THz links, as well as the ultra-dense deployment of THz networks, a number of challenges that the medium access control (MAC) layer needs to face are created. In more detail, the need of rethinking user association and resource allocation strategies by incorporating artificial intelligence (AI) capable of providing "real-time" solutions in complex and frequently changing environments becomes evident. Moreover, to satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. Motivated by this, this article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management, while maximizing systems' reliability through blockage minimization. In more detail, a fast and centralized joint user association, radio resource allocation, and blockage avoidance by means of a novel metaheuristic-machine learning framework is documented, that maximizes the THz networks performance, while minimizing the association latency by approximately three orders of magnitude. To support, within the access point (AP) coverage area, mobility management and blockage avoidance, a deep reinforcement learning (DRL) approach for beam-selection is discussed. Finally, to support user mobility between coverage areas of neighbor APs, a proactive hand-over mechanism based on AI-assisted fast channel prediction is~reported.

Related papers

• Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach [61.74489383629319]
  Massive random access of devices in the emerging Open Radio Access Network (O-RAN) brings great challenge to the access control and management. reinforcement-learning (RL)-assisted scheme of closed-loop access control is proposed to preserve sparsity of access requests. Deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces.
  arXiv  Detail & Related papers  (2023-03-05T12:25:49Z)
• Federated Meta-Learning for Traffic Steering in O-RAN [1.400970992993106]
  We propose an algorithm for RAT allocation based on federated meta-learning (FML) We have designed a simulation environment which contains LTE and 5G NR service technologies.
  arXiv  Detail & Related papers  (2022-09-13T10:39:41Z)
• Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN [11.464582983164991]
  New open radio access network (O-RAN) with distinguishing features such as flexible design, disaggregated virtual and programmable components, and intelligent closed-loop control was developed. O-RAN slicing is being investigated as a critical strategy for ensuring network quality of service (QoS) in the face of changing circumstances. This paper introduces a novel framework able to manage the network slices through provisioned resources intelligently.
  arXiv  Detail & Related papers  (2022-08-30T17:00:53Z)
• Learning Resilient Radio Resource Management Policies with Graph Neural Networks [124.89036526192268]
  We formulate a resilient radio resource management problem with per-user minimum-capacity constraints. We show that we can parameterize the user selection and power control policies using a finite set of parameters. Thanks to such adaptation, our proposed method achieves a superior tradeoff between the average rate and the 5th percentile rate.
  arXiv  Detail & Related papers  (2022-03-07T19:40:39Z)
• Intelligent Blockage Prediction and Proactive Handover for Seamless Connectivity in Vision-Aided 5G/6G UDNs [8.437758224218648]
  Mobility management is a critical issue in ultra-dense networks (UDNs) We propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely handover (HO) Results show that our BLK detection and PHO algorithm achieves 40% improvement in maintaining user connectivity and the required quality of experience (QoE)
  arXiv  Detail & Related papers  (2022-02-21T16:21:49Z)
• Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks [82.02891936174221]
  Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can coordinate over a wireless network is a promising approach. In this paper, a novel semantic-aware CDRL method is proposed to enable a group of untrained agents with semantically-linked DRL tasks to collaborate efficiently across a resource-constrained wireless cellular network.
  arXiv  Detail & Related papers  (2021-11-23T18:24:47Z)
• Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services [10.718353079920007]
  This paper presents a novel architecture that leverages Deep Reinforcement Learning at the edge of the network in order to address Radio Access Network Slicing and Radio Resource Management. The effectiveness of our proposal against baseline methodologies is investigated through computer simulation, by considering an autonomous-driving use-case.
  arXiv  Detail & Related papers  (2021-03-18T14:18:34Z)
• Towards AIOps in Edge Computing Environments [60.27785717687999]
  This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
  arXiv  Detail & Related papers  (2021-02-12T09:33:00Z)
• Deep Reinforcement Learning for Adaptive Network Slicing in 5G for Intelligent Vehicular Systems and Smart Cities [19.723551683930776]
  We develop a network slicing model based on a cluster of fog nodes (FNs) coordinated with an edge controller (EC) For each service request in a cluster, the EC decides which FN to execute the task, locally serve the request at the edge, or to reject the task and refer it to the cloud. We propose a deep reinforcement learning (DRL) solution to adaptively learn the optimal slicing policy.
  arXiv  Detail & Related papers  (2020-10-19T23:30:08Z)
• Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning [58.44609538048923]
  Radio Frequency powered Cognitive Radio Networks (RF-CRN) are likely to be the eyes and ears of upcoming modern networks such as Internet of Things (IoT) To be considered autonomous, the RF-powered network entities need to make decisions locally to maximize the network throughput under the uncertainty of any network environment. In this paper, deep reinforcement learning is proposed to overcome the shortcomings and allow a wireless gateway to derive an optimal policy to maximize network throughput.
  arXiv  Detail & Related papers  (2020-07-07T01:49:07Z)

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.