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D2D Mobile Relaying Meets NOMA -Part I:A Biform Game Analysis.

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Device-to-Device (D2D) relaying enhances mobile network performance by enabling intelligent device-level decisions. Reinforcement learning algorithms help devices self-organize, improving individual throughput and overall system efficiency.

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Area of Science:

  • Mobile Communications
  • Wireless Networking
  • Game Theory

Background:

  • Device-to-Device (D2D) relaying is crucial for enhancing mobile network performance.
  • Decentralized decision-making in D2D networks can lead to performance degradation.
  • Self-organizing systems are needed for efficient D2D communication.

Purpose of the Study:

  • To analyze the performance of D2D relaying in mobile networks.
  • To model device behavior using game theory and predict system performance.
  • To implement Reinforcement Learning (RL) for distributed self-organization in D2D networks.

Main Methods:

  • Derivation of outage probability for cellular and D2D links.
  • Biform game analysis to determine pure and mixed Nash equilibria.
  • Implementation of two RL algorithms for distributed strategy learning.

Main Results:

  • D2D relaying improves per-device throughput by offloading the network.
  • The study provides a framework to analyze and predict system performance.
  • RL algorithms enable devices to learn equilibrium strategies in a distributed manner.

Conclusions:

  • Decentralized D2D relaying, optimized by RL, enhances mobile network efficiency.
  • Game theory provides valuable insights into device behavior and system performance.
  • Self-organization through RL is a viable approach for future mobile networks.