Energy-Efficient Collaborative Base Station Control in Massive MIMO Cellular Networks

This repository is associated with the publication titled "Energy-Efficient Collaborative Base Station Control in Massive MIMO Cellular Networks: A Multi-Agent Reinforcement Learning Approach". This work provides a Multi-Agent Reinforcement Learning (MARL) approach to minimize the total energy consumption of multiple massive MIMO base stations (BSs) in a multi-cell network, while maintaining overall quality-of-service.

The strategy involves making decisions on multi-level advanced sleep modes, antenna switching, and user association for the base stations. By modelling the problem as a decentralized partially observable Markov decision process (DEC-POMDP), we propose a multi-agent proximal policy optimization (MAPPO) algorithm to obtain a collaborative BS control policy.

Overview

This solution has been shown to significantly improve network energy efficiency, adaptively switch the BSs into different depths of sleep, reduce inter-cell interference, and maintain a high quality-of-service (QoS).

Features

The key features of the project include:

• Simulating a 5G network environment using real-world mobile traffic patterns.
• Implementing a multi-agent proximal policy optimization (MAPPO) algorithm for collaborative base station control.
• Ensuring that the algorithm results in significant energy savings compared to baseline solutions, without compromising on QoS.

Environment Configuration

The configuration of the simulation environment is as follows:

Traffic Model

The traffic generator uses real-world data and contains arrival rates for each time slot (of 20 mins) and each application category.

Action Space

• Switch Antennas: Options include -4, 0, +4.
• Switch Sleep Mode: Options include active (0), SM1 (1ms activation delay) (1), SM2 (10ms activation delay) (2), and SM3 (100ms activation delay) (3).
• Switch Connection Mode: Options include disconnecting all users (0), keeping current connections but refusing new connections (1), and accepting new connections (2).

State Space

The state of the environment is defined by:

• Total power consumption.
• User statistics.
• Actual and required sum rates.
• State of the base stations, which includes:
  • Power consumption.
  • Number of active antennas.
  • Connection mode.
  • Sleep mode.
  • Next sleep mode.
  • Remaining wake-up time.
  • History of traffic rates.
  • Associated user statistics.

Reward

The reward function is a combination of

• Weighted sum of drop rates in each application category.
• Total ower consumption.

Notes

• The agents make a decision every 20ms.
• When a base station is in SM1 and a new user arrives, it will wake up automatically.

Contributions and Feedback

Feel free to provide feedback or contribute to this project. You can either fork this repository or open a new issue if you find a bug or have a feature request.