Medium Access using Distributed Reinforcement Learning for IoTs with Low-Complexity Wireless Transceivers

2021-04-29 · Hrishikesh Dutta, Subir Biswas

This paper proposes a distributed Reinforcement Learning (RL) based framework that can be used for synthesizing MAC layer wireless protocols in IoT networks with low-complexity wireless transceivers. The proposed framework does not rely on complex hardware capabilities such as carrier sensing and its associated algorithmic complexities that are often not supported in wireless transceivers of low-cost and low-energy IoT devices. In this framework, the access protocols are first formulated as Markov Decision Processes (MDP) and then solved using RL. A distributed and multi-Agent RL framework is used as the basis for protocol synthesis. Distributed behavior makes the nodes independently learn optimal transmission strategies without having to rely on full network level information and direct knowledge of behavior of other nodes. The nodes learn to minimize packet collisions such that optimal throughput can be attained and maintained for loading conditions that are higher than what the known benchmark protocols (such as ALOHA) for IoT devices without complex transceivers. In addition, the nodes are observed to be able to learn to act optimally in the presence of heterogeneous loading and network topological conditions. Finally, the proposed learning approach allows the wireless bandwidth to be fairly distributed among network nodes in a way that is not dependent on such heterogeneities. Via simulation experiments, the paper demonstrates the performance of the learning paradigm and its abilities to make nodes adapt their optimal transmission strategies on the fly in response to various network dynamics.

📄 PDF Abstract BibTeX arXiv:2104.14549

Code (0)

등록된 구현이 없습니다.

Tasks

reinforcement-learning Reinforcement Learning (RL)

Medium Access Control protocol for Collaborative Spectrum Learning in Wireless Networks

2021-10-25 · Tomer Boyarski, Wenbo Wang, Amir Leshem

In recent years there is a growing effort to provide learning algorithms for spectrum collaboration. In this paper we present a medium access control protocol which allows spectrum collaboration with minimal regret and h…

Scheduling

Online Distributed Evolutionary Optimization of Time Division Multiple Access Protocols

2022-04-27 · Anil Yaman, Tim Van der Lee, Giovanni Iacca

With the advent of cheap, miniaturized electronics, ubiquitous networking has reached an unprecedented level of complexity, scale and heterogeneity, becoming the core of several modern applications such as smart industry…

A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing

2021-10-05 · Akash Doshi, Srinivas Yerramalli, Lorenzo Ferrari, Taesang Yoo 외

The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access. We consider decentralized contention-based medium access for bas…

Deep Reinforcement Learning Fairness Q-Learning reinforcement-learning+2

Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle

2022-02-15 · Xiaoyu Dai, Kaoru Ota, Mianxiong Dong

As intelligent transportation systems been implemented broadly and unmanned arial vehicles (UAVs) can assist terrestrial base stations acting as multi-access edge computing (MEC) to provide a better wireless network comm…

Deep Reinforcement Learning Edge-computing Graph Attention reinforcement-learning+1

Combining Contention-Based Spectrum Access and Adaptive Modulation using Deep Reinforcement Learning

2021-09-24 · Akash Doshi, Jeffrey G. Andrews

The use of unlicensed spectrum for cellular systems to mitigate spectrum scarcity has led to the development of intelligent adaptive approaches to spectrum access that improve upon traditional carrier sensing and listen-…

Deep Reinforcement Learning Fairness reinforcement-learning Reinforcement Learning (RL)