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Joint Deep Reinforcement Learning and Unsupervised Learning for Channel Selection and Power Control in D2D Networks.

Ming Sun1, Yanhui Jin1, Shumei Wang2

  • 1College of Computer and Control Engineering, Qiqihar University, Qiqihar 161006, China.

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This study introduces a novel distributed resource allocation algorithm for device-to-device (D2D) communications. The algorithm uses deep Q-networks and unsupervised learning to reduce interference and maximize spectrum utilization in 5G networks.

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channel selectiondeep reinforcement learningdevice-to-devicepower controlunsupervised learning

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Area of Science:

  • Wireless Communication
  • Network Resource Management
  • Artificial Intelligence in Telecommunications

Background:

  • Device-to-device (D2D) communication offers a solution to spectrum scarcity in 5G networks.
  • Shared channels in D2D networks lead to significant interference, hindering capacity and spectral efficiency.
  • Existing resource allocation methods often rely on centralized control, requiring extensive network information.

Purpose of the Study:

  • To develop a distributed resource allocation algorithm for D2D networks.
  • To mitigate interference and enhance wireless spectrum utilization.
  • To improve network capacity and spectral efficiency in 5G systems.

Main Methods:

  • A deep Q-network (DQN) was employed for distributed channel allocation in dynamic environments.
  • An unsupervised learning-based deep neural network was utilized for optimized power control.
  • The proposed algorithm operates distributively, using local information for channel selection and power control.

Main Results:

  • The proposed algorithm effectively reduces interference in D2D user pairs.
  • It significantly enhances network capacity and wireless spectrum utilization.
  • Simulation results demonstrate superior performance compared to traditional centralized and distributed algorithms in terms of convergence speed and transmit sum-rate maximization.

Conclusions:

  • The integration of DQN and unsupervised learning provides an effective distributed solution for D2D resource allocation.
  • The algorithm achieves higher spectral efficiency and faster convergence than conventional methods.
  • This approach offers a scalable and efficient strategy for future wireless communication systems.