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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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AI Optimization-Based Heterogeneous Approach for Green Next-Generation Communication Systems.

Haitham Khaled¹, Emad Alkhazraji¹

¹Department of Electro-Mechanical Systems Engineering Technology, Abu Dhabi Polytechnic, Abu Dhabi P.O. Box 111499, United Arab Emirates.

Sensors (Basel, Switzerland)

|August 10, 2024

Summary

This study introduces a software-defined radio (SDR)-based Long-Term Evolution Licensed Assisted Access (LTE-LAA) architecture for adaptable heterogeneous networks (HetNets). The proposed design significantly enhances throughput and reduces power consumption, paving the way for next-generation networks.

Keywords:

AI optimization cognitive radio green communication software-defined radio

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Area of Science:

Wireless Communication Networks
Network Architecture and Design
Software-Defined Networking

Background:

Traditional heterogeneous networks (HetNets) face limitations in adapting to dynamic network conditions due to hardware constraints.
Software-defined radio (SDR) technology offers a promising solution for enhancing network adaptability.

Purpose of the Study:

To introduce a novel software-defined radio (SDR)-based Long-Term Evolution Licensed Assisted Access (LTE-LAA) architecture for next-generation communication networks.
To demonstrate the feasibility of achieving high adaptability, increased throughput, and reduced power consumption in HetNets through proper design and tuning.

Main Methods:

Formulating throughput maximization and power consumption minimization as a constrained optimization problem.
Comparing proposed solutions (optimization and heuristic) against existing approaches.
Employing artificial intelligence (AI) techniques, including random forest regression, particle swarm optimization, and genetic algorithms, for multi-objective optimization.

Main Results:

The proposed SDR-based LTE-LAA architecture significantly outperforms existing approaches in terms of both power efficiency and system throughput.
AI-driven multi-objective optimization effectively balances trade-offs between throughput, power efficiency, and energy consumption.
The architecture demonstrates high-level adaptability in dynamic network environments.

Conclusions:

The developed SDR-based LTE-LAA architecture is a viable solution for next-generation HetNets, meeting critical requirements for power, throughput, and green scalability.
The study highlights the potential of SDR technology and AI in optimizing future wireless communication systems.
This research provides a foundation for more adaptive and energy-efficient wireless network designs.