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Cellular Network Power Allocation Algorithm Based on Deep Reinforcement Learning and Artificial Intelligence.

Jinghua Cao1, Xiang Zou2, Rui Xie3

  • 1Guangdong Songshan Polytechnic College, Shaoguan, Guangdong, China.

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Summary
This summary is machine-generated.

This study addresses wireless spectrum shortages by optimizing device-to-device (D2D) communication resource allocation. It proposes a new scheme to reduce interference and improve spectrum utilization in cellular networks.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Telecommunications

Background:

  • Traditional shortest path algorithms face limitations in real-world applications and problem scope.
  • Deep learning and artificial intelligence are shifting towards end-to-end systems.
  • The Internet of Things (IoT) surge exacerbates wireless spectrum scarcity and cochannel interference.

Purpose of the Study:

  • To investigate user and transmission power control selection for device-to-device (D2D) users.
  • To allocate spectrum resources efficiently in the uplink of cellular users.
  • To mitigate cochannel interference and enhance spectrum utilization in single-cell hybrid networks.

Main Methods:

  • Developing a D2D user selection scheme.
  • Implementing a transmission power control mode selection.
  • Allocating uplink spectrum resources for cellular users.

Main Results:

  • Reduced cochannel interference in cellular networks.
  • Improved spectrum utilization through efficient resource allocation.
  • Enhanced usable range of cellular networks via D2D integration.

Conclusions:

  • The proposed resource allocation scheme effectively addresses spectrum scarcity.
  • Optimized D2D communication enhances cellular network performance.
  • This approach is crucial for managing increasing data traffic in IoT environments.