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Numerous practical applications within engineering disciplines, such as telecommunications, necessitate optimizing power delivery to a connected load. This pursuit, however, entails inherent internal losses, which can either equal or exceed the power supplied to the load. The Thevenin equivalent circuit is helpful in finding the maximum power a linear circuit can deliver to a load. It is assumed in this context that the load resistance can be adjusted.
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Joint Optimization of Bandwidth and Power Allocation in Uplink Systems with Deep Reinforcement Learning.

Chongli Zhang1, Tiejun Lv1, Pingmu Huang2

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Summary

This study introduces a joint-priority-based reinforcement learning (JPRL) approach to optimize wireless resource allocation, significantly improving system throughput and reducing interference in multi-user systems.

Keywords:
joint-priority-based reinforcement learning (JPRL)multi-cell multi-user systemprioritized replay bufferthroughputuplink

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Area of Science:

  • Wireless communication systems
  • Artificial intelligence in telecommunications
  • Resource management in networks

Background:

  • Increasing numbers of users in multi-cell systems cause explosive interference, degrading communication quality.
  • Inter-cell interference is a major challenge in optimizing wireless resource utilization.
  • Existing methods struggle to balance throughput maximization with quality of service constraints.

Purpose of the Study:

  • To propose a novel approach for joint optimization of bandwidth and transmit power allocation.
  • To enhance system throughput while suppressing co-channel interference.
  • To guarantee quality of service (QoS) constraints in wireless networks.

Main Methods:

  • Developed a joint-priority-based reinforcement learning (JPRL) approach.
  • Decoupled the joint problem into bandwidth assignment and power allocation sub-problems.
  • Utilized multi-agent double deep Q network (MADDQN) for bandwidth allocation and prioritized multi-agent deep deterministic policy gradient (P-MADDPG) for power allocation.

Main Results:

  • The JPRL method demonstrated accelerated model training.
  • Achieved superior system throughput compared to alternative methods.
  • Average throughput was 10.4-15.5% higher than homogeneous-learning benchmarks and 17.3% higher than genetic algorithms.

Conclusions:

  • The proposed JPRL approach effectively optimizes wireless resource utilization.
  • JPRL significantly improves system throughput and mitigates interference.
  • This method offers a promising solution for future wireless communication systems.