PulseTrendingJournal ClubResearchersJournalsExplore
Instagram
HomeTrendingJournal ClubExplore
Synapse
⌘+K
Synapse
October 6, 2025International Journal of Electronics and Communication EngineeringOpen Access

Enhancing Multi-Cell Dynamic TDD with Multi-Agent Deep Reinforcement Learning

View Full Paper
Ask AI
Bookmark
Share

Discussion

Loading...

Member takes

Overview

This approach reduces cross-link interference and enhances traffic management in multi-cell 5G systems.

Key Points

  • The multi-agent deep reinforcement learning framework effectively determines TDD patterns and improves traffic handling.
  • Each decentralized agent independently optimizes TDD configurations, reducing control latency and signaling overhead.
  • By exchanging messages, agents monitor buffer states and adapt to traffic variations, minimizing cross-link interference.
  • Performance may decline in high-interference scenarios, emphasizing the need for further practical deployment studies.

Cite This Study

A 2025 study studied this question.

synapsesocial.com/papers/68e3d4b4c5e2a5a458845fdfhttps://doi.org/10.14445/23488549/ijece-v12i9p106
View Full Paper
Ask AI
Bookmark
Share