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Area of Science:

  • Mobile Communications
  • Network Engineering
  • Artificial Intelligence

Background:

  • Device-to-Device (D2D) relaying is crucial for enhancing mobile network performance.
  • Decentralized decision-making in D2D systems can paradoxically decrease performance.
  • Self-organizing systems are needed to maintain efficiency in D2D communications.

Purpose of the Study:

  • To propose a self-organized system where devices decide between direct or D2D relay connections.
  • To analyze the performance of this system using a biform game framework.
  • To enable devices to learn optimal strategies in a distributed manner.

Main Methods:

  • A biform game framework was used to analyze system performance under pure and mixed strategies.
  • Two reinforcement learning (RL) algorithms were employed for decentralized strategy learning.
  • Simulations were conducted to evaluate performance under varying channel conditions.

Main Results:

  • Decentralized RL algorithms facilitate device self-organization and satisfactory performance.
  • The proposed system effectively handles incomplete information and uncertainties.
  • D2D relaying significantly improves system performance, as demonstrated through simulations.

Conclusions:

  • D2D relaying is vital for improving mobile network efficiency.
  • Decentralized RL is a key enabler for self-organizing D2D communication systems.
  • The proposed learning schemes demonstrate robustness under different channel fading conditions.