Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications

• URL: http://arxiv.org/abs/2007.13709v2
• Date: Sat, 17 Apr 2021 18:27:32 GMT
• Title: Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications
• Authors: Zhan Gao and Mark Eisen and Alejandro Ribeiro
• Abstract summary: The paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) communications. Under this framework, we propose two algorithms that solve FSO resource allocation problems.
• Score: 119.81868223344173
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper investigates the general problem of resource allocation for mitigating channel fading effects in Free Space Optical (FSO) communications. The resource allocation problem is modeled as the constrained stochastic optimization framework, which covers a variety of FSO scenarios involving power adaptation, relay selection and their joint allocation. Under this framework, we propose two algorithms that solve FSO resource allocation problems. We first present the Stochastic Dual Gradient (SDG) algorithm that is shown to solve the problem exactly by exploiting the strong duality but whose implementation necessarily requires explicit and accurate system models. As an alternative we present the Primal-Dual Deep Learning (PDDL) algorithm based on the SDG algorithm, which parametrizes the resource allocation policy with Deep Neural Networks (DNNs) and optimizes the latter via a primal-dual method. The parametrized resource allocation problem incurs only a small loss of optimality due to the strong representational power of DNNs, and can be moreover implemented without knowledge of system models. A wide set of numerical experiments are performed to corroborate the proposed algorithms in FSO resource allocation problems. We demonstrate their superior performance and computational efficiency compared to the baseline methods in both continuous power allocation and binary relay selection settings.

Related papers

• Joint Optimization of Resource Allocation and Data Selection for Fast and Cost-Efficient Federated Edge Learning [9.460012379815423]
  Deploying learning at the wireless edge introduces FEEL (FEEL) We propose an efficient system, a federated joint resource allocation and data selection. The superiority of our proposed scheme of joint resource allocation and data selection is validated.
  arXiv  Detail & Related papers  (2024-07-03T08:03:59Z)
• DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
  We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
  arXiv  Detail & Related papers  (2024-06-11T06:31:03Z)
• Two-Stage ML-Guided Decision Rules for Sequential Decision Making under Uncertainty [55.06411438416805]
  Sequential Decision Making under Uncertainty (SDMU) is ubiquitous in many domains such as energy, finance, and supply chains. Some SDMU are naturally modeled as Multistage Problems (MSPs) but the resulting optimizations are notoriously challenging from a computational standpoint. This paper introduces a novel approach Two-Stage General Decision Rules (TS-GDR) to generalize the policy space beyond linear functions. The effectiveness of TS-GDR is demonstrated through an instantiation using Deep Recurrent Neural Networks named Two-Stage Deep Decision Rules (TS-LDR)
  arXiv  Detail & Related papers  (2024-05-23T18:19:47Z)
• Federated Reinforcement Learning for Resource Allocation in V2X Networks [46.6256432514037]
  Resource allocation significantly impacts the performance of vehicle-to-everything (V2X) networks. Most existing algorithms for resource allocation are based on optimization or machine learning. In this paper, we explore resource allocation in a V2X network under the framework of federated reinforcement learning.
  arXiv  Detail & Related papers  (2023-10-15T15:26:54Z)
• Multi-Resource Allocation for On-Device Distributed Federated Learning Systems [79.02994855744848]
  This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL) system. Each mobile device in the system engages the model training process within the specified area and allocates its computation and communication resources for deriving and uploading parameters, respectively.
  arXiv  Detail & Related papers  (2022-11-01T14:16:05Z)
• Graph Reinforcement Learning for Radio Resource Allocation [13.290246410488727]
  We resort to graph reinforcement learning for exploiting two kinds of relational priors inherent in many problems in wireless communications. To design graph reinforcement learning framework systematically, we first conceive a method to transform state matrix into state graph. We then propose a general method for graph neural networks to satisfy desirable permutation properties.
  arXiv  Detail & Related papers  (2022-03-08T08:02:54Z)
• Evolutionary Optimization for Proactive and Dynamic Computing Resource Allocation in Open Radio Access Network [4.9711284100869815]
  Intelligent techniques are urged to achieve automatic allocation of the computing resource in Open Radio Access Network (O-RAN) Existing problem formulation to solve this resource allocation problem is unsuitable as it defines the capacity utility of resource in an inappropriate way. New formulation that better describes the problem is proposed.
  arXiv  Detail & Related papers  (2022-01-12T08:52:04Z)
• Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks [119.81868223344173]
  This paper investigates the optimal resource allocation in free space optical (FSO) fronthaul networks. We consider the graph neural network (GNN) for the policy parameterization to exploit the FSO network structure. The primal-dual learning algorithm is developed to train the GNN in a model-free manner, where the knowledge of system models is not required.
  arXiv  Detail & Related papers  (2020-06-26T14:20:48Z)

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.