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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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Distributed Multi-Agent Deep Reinforcement Learning-Based Transmit Power Control in Cellular Networks.

Hun Kim1, Jaewoo So1

  • 1Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea.

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|July 12, 2025
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Summary
This summary is machine-generated.

This study introduces a novel multi-agent deep reinforcement learning (MADRL) approach with long short-term memory (LSTM) for cellular network transmit power control. The method enhances sum rate performance by enabling base stations to manage interference effectively using local data.

Keywords:
centralized training with decentralized executionmulti-agent deep reinforcement learningmulti-cell networktransmit power control

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Area of Science:

  • Wireless Communication
  • Artificial Intelligence
  • Network Engineering

Background:

  • Interference management is critical for multi-cell network performance.
  • Controlling base station downlink transmit power is essential for high data rates but becomes complex with increasing cell density.

Purpose of the Study:

  • To propose a transmit power control scheme using multi-agent deep reinforcement learning (MADRL) to maximize the sum rate in multi-cell networks.
  • To enhance MADRL by integrating a long short-term memory (LSTM) architecture for improved state retention and decision-making.

Main Methods:

  • A multi-agent deep reinforcement learning (MADRL) framework is developed for downlink transmit power control.
  • A long short-term memory (LSTM) architecture is incorporated into the MADRL agents to leverage historical state information.
  • Each base station agent utilizes only local channel state information, minimizing inter-agent communication.

Main Results:

  • The proposed MADRL-LSTM scheme demonstrates improved sum rate performance compared to existing MADRL methods.
  • The scheme effectively reduces the amount of signal messages exchanged between base station links.
  • Local channel state information utilization proves efficient for decentralized control.

Conclusions:

  • The integration of LSTM with MADRL offers a robust solution for transmit power control in dense cellular networks.
  • Decentralized control using local information is feasible and efficient, reducing communication overhead.
  • The proposed method enhances overall network capacity and user data rates through effective interference management.