Joint Lifetime-Outage Optimization in Relay-Enabled IoT Networks - A Deep Reinforcement Learning Approach

Ali Reza Heidarpour, Mohammad Reza Heidarpour, Masoud Ardakani, Chintha Tellambura, Murat Uysal

Research output: Contribution to journalArticlepeer-review

Abstract

Network lifetime maximization in Internet of things (IoT) is of paramount importance to ensure uninterrupted data transmission and reduce the frequency of battery replacement. This letter deals with the joint lifetime-outage optimization in relay-enabled IoT networks employing a multiple relay selection (MRS) scheme. The considered MRS problem is essentially a general nonlinear 0-1 programming which is NP-hard. In this work, we use the application of the double deep Q network (DDQN) algorithm to solve the MRS problem. Our results reveal that the proposed DDQN-MRS scheme can achieve superior performance than the benchmark MRS schemes.

Original languageEnglish (US)
Pages (from-to)190-194
Number of pages5
JournalIEEE Communications Letters
Volume27
Issue number1
DOIs
StatePublished - Jan 1 2023

Keywords

  • Internet of Things
  • cooperative communication
  • deep reinforcement learning
  • lifetime
  • multiple relay selection

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Joint Lifetime-Outage Optimization in Relay-Enabled IoT Networks - A Deep Reinforcement Learning Approach'. Together they form a unique fingerprint.

Cite this