Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approach

• URL: http://arxiv.org/abs/2211.12201v1
• Date: Tue, 22 Nov 2022 11:50:05 GMT
• Title: Distributed Resource Allocation for URLLC in IIoT Scenarios: A Multi-Armed Bandit Approach
• Authors: Francesco Pase, Marco Giordani, Giampaolo Cuozzo, Sara Cavallero, Joseph Eichinger, Roberto Verdone, Michele Zorzi
• Abstract summary: This paper addresses the problem of enabling inter-machine Ultra-Reliable Low-Latency Communication (URLLC) in future 6G Industrial Internet of Things (IIoT) networks. We study a distributed, user-centric scheme based on machine learning in which User Equipments autonomously select their uplink radio resources. Using simulation, we demonstrate that a Multi-Armed Bandit (MAB) approach represents a desirable solution to allocate resources with URLLC in mind.
• Score: 17.24490186427519
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper addresses the problem of enabling inter-machine Ultra-Reliable Low-Latency Communication (URLLC) in future 6G Industrial Internet of Things (IIoT) networks. As far as the Radio Access Network (RAN) is concerned, centralized pre-configured resource allocation requires scheduling grants to be disseminated to the User Equipments (UEs) before uplink transmissions, which is not efficient for URLLC, especially in case of flexible/unpredictable traffic. To alleviate this burden, we study a distributed, user-centric scheme based on machine learning in which UEs autonomously select their uplink radio resources without the need to wait for scheduling grants or preconfiguration of connections. Using simulation, we demonstrate that a Multi-Armed Bandit (MAB) approach represents a desirable solution to allocate resources with URLLC in mind in an IIoT environment, in case of both periodic and aperiodic traffic, even considering highly populated networks and aggressive traffic.

Related papers

• A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT [16.167107624956294]
  Industrial Internet of Things (IIoT) networks will provide Ultra-Reliable Low-Latency Communication (URLLC) to support critical processes. Standard protocols for allocating wireless resources may not optimize the latency-reliability trade-off, especially for uplink communication.
  arXiv  Detail & Related papers  (2023-11-21T12:22:04Z)
• Intelligent Load Balancing and Resource Allocation in O-RAN: A Multi-Agent Multi-Armed Bandit Approach [4.834203844100679]
  We propose a multi-agent multi-armed bandit for load balancing and resource allocation (mmLBRA) scheme. We also present the mmLBRA-LB and mmLBRA-RA sub-schemes that can operate independently in non-realtime RAN intelligent controller (Non-RT RIC) and near-RT RIC, respectively.
  arXiv  Detail & Related papers  (2023-03-25T04:42:30Z)
• 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)
• Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction [72.59079526765487]
  The dynamic scheduling of ultra-reliable and low-latency traffic (URLLC) in the uplink can significantly enhance the efficiency of coexisting services. The main challenge is posed by the uncertainty in the process of URLLC packet generation. We introduce a novel scheduler for URLLC packets that provides formal guarantees on reliability and latency irrespective of the quality of the URLLC traffic predictor.
  arXiv  Detail & Related papers  (2023-02-15T14:09:55Z)
• Device Selection for the Coexistence of URLLC and Distributed Learning Services [12.093278114651524]
  We investigate a mixed service scenario where distributed AI workflow and ultra-reliable low latency communication (URLLC) services run concurrently over a network. We propose a risk-based formulation for device selection to minimize the AI training delays during its convergence period. We transform it into a deep reinforcement learning problem and address it via a framework based on soft actor-critic algorithm.
  arXiv  Detail & Related papers  (2022-12-22T15:36:15Z)
• 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)
• AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles [61.21359293642559]
  The dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies. We consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it.
  arXiv  Detail & Related papers  (2022-03-05T10:54:05Z)
• Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning [55.198301429316125]
  Machine learning (ML) is a key technique for big-data-driven modelling and analysis of massive Internet of Things (IoT) based intelligent and ubiquitous computing. For fast-increasing applications and data amounts, distributed learning is a promising emerging paradigm since it is often impractical or inefficient to share/aggregate data. This paper studies the problem of training an ML model over decentralized systems, where data are distributed over many user devices.
  arXiv  Detail & Related papers  (2022-02-07T15:04:15Z)
• Federated Learning over Wireless IoT Networks with Optimized Communication and Resources [98.18365881575805]
  Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention. It is of interest to investigate fast responding and accurate FL schemes over wireless systems. We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
  arXiv  Detail & Related papers  (2021-10-22T13:25:57Z)
• Coordinates-based Resource Allocation Through Supervised Machine Learning [14.014514995022182]
  We propose a coordinates-based resource allocation scheme using supervised machine learning techniques. The proposed scheme performs consistently well with realistic-system simulation, requiring only 4 s of training time.
  arXiv  Detail & Related papers  (2020-05-13T18:33:23Z)

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.