Bio inspired multi agent system for distributed power and interference management in MIMO OFDM networks

Area of Science:

• Wireless Communications
• Artificial Intelligence
• Network Optimization

Background:

• Massive MIMO-OFDM systems are crucial for high-capacity wireless networks, demanding efficient power and interference management.
• Existing resource allocation and interference control methods struggle with scalability, adaptability, and energy efficiency in dense environments.

Purpose of the Study:

• To propose a novel bio-inspired Termite Colony Optimization-based Multi-Agent System (TCO-MAS) integrated with an LSTM model.
• To enhance power allocation and interference management in massive MIMO-OFDM networks through predictive adaptability.

Main Methods:

• A Multi-Agent System (MAS) inspired by termite colony optimization was developed.
• A deep learning Long Short-Term Memory (LSTM) model was integrated for forecasting network conditions.
• Agents used pheromone-based feedback for localized optimization with reduced communication overhead.

Main Results:

• The TCO-MAS achieved a 20% higher sum rate and 15% improved energy efficiency compared to conventional algorithms.
• Evaluations included Sum Rate, Energy Efficiency, Spectral Efficiency, Latency, and Fairness Index.
• The system demonstrated superior performance under high-load conditions.

Conclusions:

• The proposed TCO-MAS offers a scalable and adaptive solution for power and interference management in ultra-dense wireless networks.
• While effective, parameter fine-tuning for pheromone adjustment is necessary for diverse scenarios.
• Further field testing is recommended to validate real-world robustness.