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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Energy-Efficient Cooperative Transmission in Ultra-Dense Millimeter-Wave Network: Multi-Agent Q-Learning Approach.

Seung-Yeon Kim1, Haneul Ko2

  • 1Department of Computer Convergence Software, Korea University, Sejong 30019, Republic of Korea.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a multi-agent Q-learning power control scheme for ultra-dense millimeter wave networks (UDmN). The proposed method enhances signal quality and network energy efficiency by optimizing base station cooperation.

Keywords:
B5Gcooperative transmissionmillimeter wavemulti-agent Q-learningpower control

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Area of Science:

  • Wireless communication networks
  • Signal processing
  • Machine learning for networking

Background:

  • Millimeter wave (mmWave) technology offers high data rates for beyond fifth-generation networks.
  • Ultra-dense mmWave networks (UDmN) face challenges with inter-cell interference, degrading signal-to-interference-plus-noise ratio (SINR) at cell boundaries.
  • Cooperative transmission techniques like coordinated multi-point (CoMP) with joint transmission (JT) improve data rates but increase energy consumption.

Purpose of the Study:

  • To address the challenge of achieving high SINR and energy efficiency in UDmN.
  • To propose a novel power control scheme for cooperative transmissions in UDmN.
  • To balance Quality of Service (QoS) requirements with network energy consumption.

Main Methods:

  • Development of a multi-agent Q-learning-based power control scheme.
  • Definition of a reward function incorporating outage probability and energy efficiency for each base station (BS).
  • Utilization of channel state information to dynamically manage BS participation in power control.

Main Results:

  • The proposed scheme achieves optimal transmission power.
  • Significant improvements in network energy efficiency compared to conventional methods (no power control, random control).
  • Validation of enhanced overall network performance through the use of channel state information.

Conclusions:

  • The multi-agent Q-learning power control scheme effectively enhances SINR and energy efficiency in UDmN.
  • Cooperative transmission with intelligent power management is crucial for future wireless networks.
  • Channel state information plays a vital role in optimizing cooperative communication performance.