Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management

Yunchuan Zhang, Osvaldo Simeone, Sharu Theresa Jose, Lorenzo Maggi, Alvaro Valcarce

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Optimal resource allocation in modern communication networks calls for the optimization of objective functions that are only accessible via costly separate evaluations for each candidate solution. The conventional approach carries out the optimization of resource-allocation parameters for each system configuration, characterized, e.g., by topology and traffic statistics, using global search methods such as Bayesian optimization (BO). These methods tend to require a large number of iterations, and hence a large number of key performance indicator (KPI) evaluations. In this paper, we propose the use of meta-learning to transfer knowledge from data collected from related, but distinct, configurations in order to speed up optimization on new network configurations. Specifically, we combine meta-learning with BO, as well as with multi-armed bandit (MAB) optimization, with the latter having the potential advantage of operating directly on a discrete search space. Furthermore, we introduce novel contextual meta-BO and meta-MAB algorithms, in which transfer of knowledge across configurations occurs at the level of a mapping from graph-based contextual information to resource-allocation parameters. Experiments for the problem of open loop power control (OLPC) parameter optimization for the uplink of multi-cell multi-antenna systems provide insights into the potential benefits of meta-learning and contextual optimization.

Original languageEnglish (US)
Pages (from-to)1282-1295
Number of pages14
JournalIEEE Transactions on Cognitive Communications and Networking
Volume9
Issue number5
DOIs
StatePublished - Oct 1 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Hardware and Architecture
  • Computer Networks and Communications

Keywords

  • Bayesian optimization
  • Wireless resource allocation
  • bandit optimization
  • meta-learning
  • open loop power control

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