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Cluster Content Caching: A Deep Reinforcement Learning Approach to Improve Energy Efficiency in Cell-Free Massive
Fangqing Tan1, Yuan Peng1, Qiang Liu2
1Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel, Switzerland)
|October 14, 2023
Summary
This study introduces a cache-assisted cell-free massive MIMO system to reduce energy consumption and transmission delay for micro-video applications. The proposed deep reinforcement learning approach optimizes content caching, access point clustering, and digital-to-analog converter resolution for enhanced energy efficiency.
Area of Science:
- Wireless communication networks
- Information theory
- Computer engineering
Background:
- Explosive growth of micro-video applications increases fronthaul/backhaul transmission burden and energy consumption.
- Cell-free massive MIMO (CF-mMIMO) systems offer a potential solution by utilizing access point (AP) caching.
- Optimizing energy efficiency (EE) in cache-assisted CF-mMIMO requires joint consideration of multiple factors.
Purpose of the Study:
- To establish a total energy efficiency (EE) model for a cache-assisted CF-mMIMO system.
- To propose an energy-efficient joint design for content caching, AP clustering, and low-resolution digital-to-analog converter (DAC) resolution.
- To reduce energy consumption and transmission delay in wireless networks supporting micro-video streaming.
Main Methods:
- Developed a total energy efficiency (EE) model for cache-assisted CF-mMIMO systems.
- Proposed a deep reinforcement learning (DRL) based approach for joint optimization.
- Utilized a deep deterministic policy gradient (DDPG) algorithm to optimize cache strategy, AP clustering, and DAC resolution, considering channel state information and user equipment (UE) preferences.
Main Results:
- The proposed scheme achieved a 4% higher energy efficiency compared to schemes without DAC resolution optimization.
- Significantly higher energy efficiency was observed compared to systems with only AP clustering.
- The DRL approach effectively managed content caching and DAC resolution selection.
Conclusions:
- The joint optimization of content caching, AP clustering, and DAC resolution in cache-assisted CF-mMIMO networks significantly enhances energy efficiency.
- DRL provides an effective framework for managing complex optimization problems in wireless networks.
- The proposed system effectively addresses the challenges posed by increasing data traffic from micro-video applications.
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