Online Radio Pattern Optimization Based on Dual Reinforcement-Learning Approach for 5G URLLC Networks

Ali Abdelmawgood Ali Ali Esswie, Klaus Ingemann Pedersen, Preben E. Mogensen

Research output: Contribution to journalJournal articleResearchpeer-review

3 Citations (Scopus)
32 Downloads (Pure)


The fifth generation (5G) radio access technology is designed to support highly delay-sensitive applications, i.e., ultra-reliable and low-latency communications (URLLC). For dynamic time division duplex (TDD) systems, the real-time optimization of the radio pattern selection becomes of a vital significance in achieving decent URLLC outage latency. In this study, a dual reinforcement machine learning (RML) approach is developed for online pattern optimization in 5G new radio TDD deployments. The proposed solution seeks to minimizing the maximum URLLC tail latency, i.e., min-max problem, by introducing nested RML instances. The directional and real-time traffic statistics are monitored and given to the primary RML layer to estimate the sufficient number of downlink (DL) and uplink (UL) symbols across the upcoming radio pattern. The secondary RML sub-networks determine the DL and UL symbol structure which best minimizes the URLLC outage latency. The proposed solution is evaluated by extensive and highly-detailed system level simulations, where our results demonstrate a considerable URLLC outage latency improvement with the proposed scheme, compared to the state-of-the-art dynamic-TDD proposals.
Original languageEnglish
Article number9145539
JournalIEEE Access
Pages (from-to)132922-132936
Number of pages15
Publication statusPublished - Jul 2020


  • 5G new radio
  • Dynamic-TDD
  • Q-learning
  • cross link interference (CLI)
  • machine learning
  • reinforcement learning


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