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D2D Mobile Relaying Meets NOMA-Part II: A Reinforcement Learning Perspective
Safaa Driouech1,2, Essaid Sabir1,3, Mounir Ghogho4
1NEST Research Group, LRI Lab., ENSEM, Hassan II University of Casablanca, Casablanca 20000, Morocco.
Sensors (Basel, Switzerland)
|April 3, 2021
Summary
Device-to-Device (D2D) relaying enhances mobile networks by enabling devices to intelligently choose direct or relayed connections. Decentralized reinforcement learning algorithms help devices self-organize for efficient communication, even with incomplete information.
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.
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