Related papers: Multi-Agent Deep Reinforcement Learning for Resilience Optimization in 5G RAN

Multi-Agent Deep Reinforcement Learning for Resilience Optimization in 5G RAN

URL: http://arxiv.org/abs/2407.18066v1
Date: Thu, 25 Jul 2024 14:19:59 GMT
Title: Multi-Agent Deep Reinforcement Learning for Resilience Optimization in 5G RAN
Authors: Soumeya Kaada, Dinh-Hieu Tran, Nguyen Van Huynh, Marie-Line Alberi Morel, Sofiene Jelassi, Gerardo Rubino,
Abstract summary: This paper aims to address the problem by globally optimizing the resilience of a dense multi-cell network based on multi-agent deep reinforcement learning. Specifically, our proposed solution can dynamically tilt cell antennas and reconfigure transmit power to mitigate outages and increase both coverage and service availability.
Score: 5.3807986199066375
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Resilience is defined as the ability of a network to resist, adapt, and quickly recover from disruptions, and to continue to maintain an acceptable level of services from users' perspective. With the advent of future radio networks, including advanced 5G and upcoming 6G, critical services become integral to future networks, requiring uninterrupted service delivery for end users. Unfortunately, with the growing network complexity, user mobility and diversity, it becomes challenging to scale current resilience management techniques that rely on local optimizations to large dense network deployments. This paper aims to address this problem by globally optimizing the resilience of a dense multi-cell network based on multi-agent deep reinforcement learning. Specifically, our proposed solution can dynamically tilt cell antennas and reconfigure transmit power to mitigate outages and increase both coverage and service availability. A multi-objective optimization problem is formulated to simultaneously satisfy resiliency constraints while maximizing the service quality in the network area in order to minimize the impact of outages on neighbouring cells. Extensive simulations then demonstrate that with our proposed solution, the average service availability in terms of user throughput can be increased by up to 50-60% on average, while reaching a coverage availability of 99% in best cases.

Related papers

Edge computing service deployment and task offloading based on multi-task high-dimensional multi-objective optimization [5.64850919046892]
This study investigates service deployment and task offloading challenges in a multi-user environment. To ensure stable service provisioning, beyond considering latency, energy consumption, and cost, network reliability is also incorporated. To promote equitable usage of edge servers, load balancing is introduced as a fourth task offloading objective.
arXiv Detail & Related papers (2023-12-07T07:30:47Z)
QoS-Aware Service Prediction and Orchestration in Cloud-Network Integrated Beyond 5G [11.864695986880347]
We present a non-linear programming model that formulates the optimization problem with the aiming objective of minimizing overall cost while enhancing latency. We introduce a DDQL-based technique using RNNs to predict user behavior, empowered by a water-filling-based algorithm for service placement.
arXiv Detail & Related papers (2023-09-18T22:24:42Z)
Constrained Deployment Optimization in Integrated Access and Backhaul Networks [0.0]
We study the effect of deployment optimization on the coverage of IAB networks. We propose various millimeter wave (mmWave) blocking-aware constrained deployment optimization approaches. Our results indicate that, even with limitations on deployment optimization, network planning boosts the coverage of IAB networks considerably.
arXiv Detail & Related papers (2022-10-11T08:31:07Z)
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)
Coverage and Capacity Optimization in STAR-RISs Assisted Networks: A Machine Learning Approach [102.00221938474344]
A novel model is proposed for the coverage and capacity optimization of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) assisted networks. A loss function-based update strategy is the core point, which is able to calculate weights for both loss functions of coverage and capacity by a min-norm solver at each update. The numerical results demonstrate that the investigated update strategy outperforms the fixed weight-based MO algorithms.
arXiv Detail & Related papers (2022-04-13T13:52:22Z)
Offline Contextual Bandits for Wireless Network Optimization [107.24086150482843]
In this paper, we investigate how to learn policies that can automatically adjust the configuration parameters of every cell in the network in response to the changes in the user demand. Our solution combines existent methods for offline learning and adapts them in a principled way to overcome crucial challenges arising in this context.
arXiv Detail & Related papers (2021-11-11T11:31:20Z)
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)
Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks [17.295158818748188]
We propose a scalable and flexible algorithm for user association based on multi-agent reinforcement learning. Users act as independent agents that, based on their local observations only, learn to autonomously coordinate their actions in order to optimize the network sum-rate. Simulation results show that the proposed algorithm is able to adapt to (fast) changes of radio environment, thus providing large sum-rate gain in comparison to state-of-the-art solutions.
arXiv Detail & Related papers (2020-06-16T10:51:27Z)
Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G [53.23237216769839]
We develop a deep learning framework to approximate the optimal resource allocation policy for base stations. We find that a fully-connected neural network (NN) cannot fully guarantee the requirements due to the approximation errors and quantization errors of the numbers of subcarriers. Considering that the distribution of wireless channels and the types of services in the wireless networks are non-stationary, we apply deep transfer learning to update NNs in non-stationary wireless networks.
arXiv Detail & Related papers (2020-03-29T04:48:22Z)
Wireless Power Control via Counterfactual Optimization of Graph Neural Networks [124.89036526192268]
We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating over a single shared wireless medium. To mitigate the interference among concurrent transmissions, we leverage the network topology to create a graph neural network architecture. We then use an unsupervised primal-dual counterfactual optimization approach to learn optimal power allocation decisions.
arXiv Detail & Related papers (2020-02-17T07:54:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.