Adaptive Cache Policy Optimization Through Deep Reinforcement Learning in Dynamic Cellular Networks

Hakutulokset

• Aktiivinen

  RILREW: Reinforcement Learning for Real-time Wireless Scheduling and Edge Caching: Theory and Algorithm Design

  Tirkkonen, O. (Vastuullinen tutkija), Amidzade, M. (Projektin jäsen), Srinivasan, A. (Projektin jäsen), Singh, U. (Projektin jäsen), Shaikh, B. (Projektin jäsen) & Al-Tous, H. (Projektin jäsen)

  01/01/2022 → 31/12/2024

  Projekti: Academy of Finland: Other research funding

  • Cellular Network 100%
  • Long Short-Term Memory Network 100%
  • Markov Decision Process 90%
  • Deep Reinforcement Learning 89%
  • Wireless Networks 75%