Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2206.05881v1
• Date: Mon, 13 Jun 2022 02:19:20 GMT
• Title: Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach
• Authors: Lingling Zhang, Yanxiang Jiang, Fu-Chun Zheng, Mehdi Bennis, and Xiaohu You
• Abstract summary: fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs)
• Score: 67.06539298956854
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs). Due to the limited resource of F-APs, it is important to design an efficient task offloading scheme. In this paper, by considering time-varying network environment, a dynamic computation offloading and resource allocation problem in F-RANs is formulated to minimize the task execution delay and energy consumption of MDs. To solve the problem, a federated deep reinforcement learning (DRL) based algorithm is proposed, where the deep deterministic policy gradient (DDPG) algorithm performs computation offloading and resource allocation in each F-AP. Federated learning is exploited to train the DDPG agents in order to decrease the computing complexity of training process and protect the user privacy. Simulation results show that the proposed federated DDPG algorithm can achieve lower task execution delay and energy consumption of MDs more quickly compared with the other existing strategies.

Related papers

• DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach [16.139481340656552]
  This paper presents a joint approach that combines multiple-agent reinforcement learning (MARL) and generative diffusion models (GDM) In the second stage, we introduce a novel DNN task assignment algorithm, termed GDM-MADDPG, which utilizes the reverse denoising process of GDM to replace the actor network in multi-agent deep deterministic policy gradient (MADDPG) Simulation results indicate that our algorithm performs favorably compared to benchmarks in terms of path planning, Age of Information (AoI), energy consumption, and task load balancing.
  arXiv  Detail & Related papers  (2024-11-13T02:41:02Z)
• AI-based Radio and Computing Resource Allocation and Path Planning in NOMA NTNs: AoI Minimization under CSI Uncertainty [23.29963717212139]
  We develop a hierarchical aerial computing framework composed of high altitude platform (HAP) and unmanned aerial vehicles (UAVs) It is shown that task scheduling significantly reduces the average AoI. It is shown that power allocation has a marginal effect on the average AoI compared to using full transmission power for all users.
  arXiv  Detail & Related papers  (2023-05-01T11:52:15Z)
• Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet [48.96004919910818]
  We propose a multi-agent deep reinforcement learning (MADRL) based power control scheme for the HetNet. To promote cooperation among agents, we develop a penalty-based Q learning (PQL) algorithm for MADRL systems. In this way, an agent's policy can be learned by other agents more easily, resulting in a more efficient collaboration process.
  arXiv  Detail & Related papers  (2022-12-15T17:01:56Z)
• Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control [37.54493447920386]
  We propose a learning-based dynamic time-frequency division duplexing (D-TFDD) scheme to meet asymmetric and heterogeneous traffic demands. We formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP) In order to jointly optimize the global resources in a decentralized manner, we propose a federated reinforcement learning (RL) algorithm named Wolpertinger deep deterministic policy gradient (FWDDPG) algorithm.
  arXiv  Detail & Related papers  (2022-11-04T07:39:21Z)
• 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)
• 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 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)
• Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks [12.599009485247283]
  We propose a federated deep reinforcement learning framework to solve a multi-objective optimization problem. To enhance the learning speed of IoT devices (agents), we incorporate federated learning (FDL) at the end of each episode. Our numerical results demonstrate the efficacy of our proposed federated DDQN framework in terms of learning speed.
  arXiv  Detail & Related papers  (2021-04-02T18:41:59Z)
• Edge Intelligence for Energy-efficient Computation Offloading and Resource Allocation in 5G Beyond [7.953533529450216]
  5G beyond is an end-edge-cloud orchestrated network that can exploit heterogeneous capabilities of the end devices, edge servers, and the cloud. In multi user wireless networks, diverse application requirements and the possibility of various radio access modes for communication among devices make it challenging to design an optimal computation offloading scheme. Deep Reinforcement Learning (DRL) is an emerging technique to address such an issue with limited and less accurate network information.
  arXiv  Detail & Related papers  (2020-11-17T05:51:03Z)
• A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks [108.57859531628264]
  A joint task, spectrum, and transmit power allocation problem is investigated for a wireless network. The proposed algorithm can reduce the number of iterations needed for convergence and the maximal delay among all users by up to 18% and 11.1% compared to the standard Q-learning algorithm.
  arXiv  Detail & Related papers  (2020-07-20T13:46:42Z)

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.