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Updated: Jul 7, 2026

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

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

Published on: September 8, 2023

Borrowing channel assignment strategies based on heuristic techniques for cellular systems.

H G Sandalidis¹, P P Stavroulakis, J Rodriguez-Tellez

¹Telecommunication Systems Institute of Crete, 73133 Chania, Crete, Greece.

IEEE Transactions on Neural Networks

|February 7, 2008

Summary

This summary is machine-generated.

This study compares three heuristic borrowing channel assignment (BCA) techniques for cellular systems. Results show these computational intelligence methods offer efficient channel allocation under various traffic loads.

Related Experiment Videos

Last Updated: Jul 7, 2026

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

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

Published on: September 8, 2023

Area of Science:

Computer Science
Telecommunications Engineering
Operations Research

Background:

Cellular systems require increasingly efficient channel allocation techniques.
Borrowing Channel Assignment (BCA) offers a balance between fixed and dynamic allocation.
Existing BCA methods need further optimization for performance.

Purpose of the Study:

To evaluate three heuristic BCA techniques using computational intelligence.
To analyze the performance of BCA under uniform and nonuniform traffic conditions.
To compare these heuristics against established channel allocation methods.

Main Methods:

Formulating BCA as a combinatorial optimization problem for an ideal cellular model.
Implementing and testing heuristics based on Hopfield neural networks.
Applying a Combinatorial Evolution Strategy (CES) for channel assignment.
Developing a hybrid heuristic combining neural networks and CES.

Main Results:

Simulation results demonstrate the effectiveness of the evaluated BCA heuristics.
Performance comparisons were made under diverse traffic load scenarios.
The hybrid heuristic showed promising results in optimizing channel allocation.

Conclusions:

The investigated computational intelligence heuristics provide efficient solutions for BCA.
These methods are suitable for optimizing channel allocation in cellular networks.
Further research can explore advanced hybrid approaches for dynamic channel management.