Low-Latency Communications for Community Resilience Microgrids: A Reinforcement Learning Approach

Medhat Elsayed, Melike Erol-Kantarci, Burak Kantarci, Lei Wu, Jie Li

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Machine learning and artificial intelligence (AI) techniques can play a key role in resource allocation and scheduler design in wireless networks that target applications with stringent QoS requirements, such as near real-time control of community resilience microgrids (CRMs). Specifically, for integrated control and communication of multiple CRMs, a large number of microgrid devices need to coexist with traditional mobile user equipments (UEs), which are usually served with self-organized and densified wireless networks with many small cell base stations (SBSs). In such cases, rapid propagation of messages becomes challenging. This calls for a design of efficient resource allocation and user scheduling for delay minimization. In this paper, we introduce a resource allocation algorithm, namely, delay minimization Q-learning (DMQ) scheme, which learns the efficient resource allocation for both the macro cell base stations (eNB) and the SBSs using reinforcement learning at each time-to-transmit interval (TTI). Comparison with the traditional proportional fairness (PF) algorithm and an optimization-based algorithm, namely distributed iterative resource allocation (DIRA) reveals that our scheme can achieve 66% and 33% less latency, respectively. Moreover, DMQ outperforms DIRA, and PF in terms of throughput while achieving the highest fairness.

Original languageEnglish (US)
Article number8781859
Pages (from-to)1091-1099
Number of pages9
JournalIEEE Transactions on Smart Grid
Volume11
Issue number2
DOIs
StatePublished - Mar 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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