DeepMPR: Enhancing Opportunistic Routing in Wireless Networks through Multi-Agent Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2306.09637v1
• Date: Fri, 16 Jun 2023 05:53:42 GMT
• Title: DeepMPR: Enhancing Opportunistic Routing in Wireless Networks through Multi-Agent Deep Reinforcement Learning
• Authors: Saeed Kaviani, Bo Ryu, Ejaz Ahmed, Deokseong Kim, Jae Kim, Carrie Spiker, Blake Harnden
• Abstract summary: Opportunistic routing relies on the broadcast capability of wireless networks. To reduce the cost of broadcast, multicast routing schemes use the connected dominating set (CDS) or multi-point relaying (MPR) set to decrease the network overhead. Common MPR selection algorithms are, rely on coordination between nodes, need high computational power for large networks, and are difficult to tune for network uncertainties. In this paper, we use multi-agent deep reinforcement learning to design a novel MPR multicast routing technique, DeepMPR, which is outperforming the OLSR MPR selection algorithm.
• Score: 0.5818726765408144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Opportunistic routing relies on the broadcast capability of wireless networks. It brings higher reliability and robustness in highly dynamic and/or severe environments such as mobile or vehicular ad-hoc networks (MANETs/VANETs). To reduce the cost of broadcast, multicast routing schemes use the connected dominating set (CDS) or multi-point relaying (MPR) set to decrease the network overhead and hence, their selection algorithms are critical. Common MPR selection algorithms are heuristic, rely on coordination between nodes, need high computational power for large networks, and are difficult to tune for network uncertainties. In this paper, we use multi-agent deep reinforcement learning to design a novel MPR multicast routing technique, DeepMPR, which is outperforming the OLSR MPR selection algorithm while it does not require MPR announcement messages from the neighbors. Our evaluation results demonstrate the performance gains of our trained DeepMPR multicast forwarding policy compared to other popular techniques.

Related papers

• Robust Network Slicing: Multi-Agent Policies, Adversarial Attacks, and Defensive Strategies [11.308544280789016]
  We present a multi-agent deep reinforcement learning (deep RL) framework for network slicing in a dynamic environment with multiple base stations and users. We develop a deep RL based jammer with limited prior information and limited power budget. We show that the proposed jammer can significantly reduce the victims' performance without direct feedback or prior knowledge on the network slicing policies.
  arXiv  Detail & Related papers  (2023-11-19T03:07:29Z)
• Intelligent multicast routing method based on multi-agent deep reinforcement learning in SDWN [4.521033397097599]
  Multicast communication technology is widely applied in wireless environments with a high device density. This paper proposes a new multicast routing method based on multiagent deep reinforcement learning (MADRL-MR) in a software-defined wireless networking (SDWN) environment. Simulation experiments show that MADRL-MR outperforms existing algorithms in terms of throughput, delay, packet loss rate, etc.
  arXiv  Detail & Related papers  (2023-05-12T14:05:03Z)
• Multi-Agent Reinforcement Learning for Network Routing in Integrated Access Backhaul Networks [0.0]
  We aim to maximize packet arrival ratio while minimizing their latency in IAB networks. To solve this problem, we develop a multi-agent partially observed Markov decision process (POMD) We show that A2C outperforms other reinforcement learning algorithms, leading to increased network efficiency and reduced selfish agent behavior.
  arXiv  Detail & Related papers  (2023-05-12T13:03:26Z)
• Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning [60.94661435297309]
  The scale of mobile networks makes it challenging to optimize antenna parameters using manual intervention or hand-engineered strategies. We propose a new multi-agent reinforcement learning algorithm to optimize mobile network configurations globally. We empirically demonstrate the performance of the algorithm on an antenna tilt tuning problem and a joint tilt and power control problem in a simulated environment.
  arXiv  Detail & Related papers  (2023-01-20T17:06:34Z)
• State-Augmented Learnable Algorithms for Resource Management in Wireless Networks [124.89036526192268]
  We propose a state-augmented algorithm for solving resource management problems in wireless networks. We show that the proposed algorithm leads to feasible and near-optimal RRM decisions.
  arXiv  Detail & Related papers  (2022-07-05T18:02:54Z)
• Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces [56.35676570414731]
  The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments. RISs offer a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces.
  arXiv  Detail & Related papers  (2022-05-08T06:21:33Z)
• Hierarchical Multi-Agent DRL-Based Framework for Joint Multi-RAT Assignment and Dynamic Resource Allocation in Next-Generation HetNets [21.637440368520487]
  This paper considers the problem of cost-aware downlink sum-rate via joint optimal radio access technologies (RATs) assignment and power allocation in next-generation wireless networks (HetNets) We propose a hierarchical multi-agent deep reinforcement learning (DRL) framework, called DeepRAT, to solve it efficiently and learn system dynamics. In particular, the DeepRAT framework decomposes the problem into two main stages; the RATs-EDs assignment stage, which implements a single-agent Deep Q Network algorithm, and the power allocation stage, which utilizes a multi-agent Deep Deterministic Policy Gradient
  arXiv  Detail & Related papers  (2022-02-28T09:49:44Z)
• 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)
• Optimal Power Allocation for Rate Splitting Communications with Deep Reinforcement Learning [61.91604046990993]
  This letter introduces a novel framework to optimize the power allocation for users in a Rate Splitting Multiple Access network. In the network, messages intended for users are split into different parts that are a single common part and respective private parts.
  arXiv  Detail & Related papers  (2021-07-01T06:32:49Z)
• 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.