Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks

• URL: http://arxiv.org/abs/2011.13634v3
• Date: Thu, 31 Mar 2022 10:34:22 GMT
• Title: Deep Reinforcement Learning for Resource Constrained Multiclass Scheduling in Wireless Networks
• Authors: Apostolos Avranas (EURECOM), Marios Kountouris (EURECOM), Philippe Ciblat (T\'el\'ecom Paris)
• Abstract summary: In our setup, the available limited bandwidth resources are allocated in order to serve randomly arriving service demands. We propose a distributional Deep Deterministic Policy Gradient (DDPG) algorithm combined with Deep Sets to tackle the problem. Our proposed algorithm is tested on both synthetic and real data, showing consistent gains against state-of-the-art conventional methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The problem of resource constrained scheduling in a dynamic and heterogeneous wireless setting is considered here. In our setup, the available limited bandwidth resources are allocated in order to serve randomly arriving service demands, which in turn belong to different classes in terms of payload data requirement, delay tolerance, and importance/priority. In addition to heterogeneous traffic, another major challenge stems from random service rates due to time-varying wireless communication channels. Various approaches for scheduling and resource allocation can be used, ranging from simple greedy heuristics and constrained optimization to combinatorics. Those methods are tailored to specific network or application configuration and are usually suboptimal. To this purpose, we resort to deep reinforcement learning (DRL) and propose a distributional Deep Deterministic Policy Gradient (DDPG) algorithm combined with Deep Sets to tackle the aforementioned problem. Furthermore, we present a novel way to use a Dueling Network, which leads to further performance improvement. Our proposed algorithm is tested on both synthetic and real data, showing consistent gains against state-of-the-art conventional methods from combinatorics, optimization, and scheduling metrics.

Related papers

• Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning [69.00997996453842]
  We propose a deep Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for virtual network embedding. We show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue.
  arXiv  Detail & Related papers  (2024-06-25T07:42:30Z)
• Multi Agent DeepRL based Joint Power and Subchannel Allocation in IAB networks [0.0]
  Integrated Access and Backhauling (IRL) is a viable approach for meeting the unprecedented need for higher data rates of future generations. In this paper, we show how we can use Deep Q-Learning Network to handle problems with huge action spaces associated with fractional nodes.
  arXiv  Detail & Related papers  (2023-08-31T21:30:25Z)
• Dynamic Scheduling for Federated Edge Learning with Streaming Data [56.91063444859008]
  We consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints. Due to limited communication resources and latency requirements, only a subset of devices is scheduled for participating in the local training process in every iteration.
  arXiv  Detail & Related papers  (2023-05-02T07:41:16Z)
• 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)
• Learning Resilient Radio Resource Management Policies with Graph Neural Networks [124.89036526192268]
  We formulate a resilient radio resource management problem with per-user minimum-capacity constraints. We show that we can parameterize the user selection and power control policies using a finite set of parameters. Thanks to such adaptation, our proposed method achieves a superior tradeoff between the average rate and the 5th percentile rate.
  arXiv  Detail & Related papers  (2022-03-07T19:40:39Z)
• 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)
• A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement [0.7885276250519428]
  We introduce a hybrid placement solution based on Deep Reinforcement Learning (DRL) and a dedicated optimization based on the Power of Two Choices principle. The proposed Heuristically-Assisted DRL (HA-DRL) allows to accelerate the learning process and gain in resource usage when compared against other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-05-14T10:04:17Z)
• Better than the Best: Gradient-based Improper Reinforcement Learning for Network Scheduling [60.48359567964899]
  We consider the problem of scheduling in constrained queueing networks with a view to minimizing packet delay. We use a policy gradient based reinforcement learning algorithm that produces a scheduler that performs better than the available atomic policies.
  arXiv  Detail & Related papers  (2021-05-01T10:18:34Z)
• Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning [40.5603189901241]
  We investigate a radio access network (RAN) slicing problem for Internet of vehicles (IoV) services with different quality of service (QoS) requirements. A dynamic RAN slicing framework is presented to dynamically allocate radio spectrum and computing resource. We show that the RAWS effectively reduces the system cost while satisfying requirements with a high probability, as compared with benchmarks.
  arXiv  Detail & Related papers  (2020-12-03T15:08:38Z)
• Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications [119.81868223344173]
  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.
  arXiv  Detail & Related papers  (2020-07-27T17:38:51Z)

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.