Multi-objective Optimization of Cognitive Radio Networks

TL;DR

This paper introduces a novel cloud-based optimization algorithm for cognitive radio networks that improves spectrum efficiency, reduces power consumption, and minimizes human exposure, demonstrating significant advantages over traditional designs in a realistic suburban scenario.

Contribution

The paper presents a new cloud sharing-decision mechanism for multi-objective optimization in cognitive radio networks, enhancing performance metrics over traditional architectures.

Findings

Reduced power consumption by 27.5%

Decreased human exposure by 34.3%

Improved spectrum usage by 34.5%

Abstract

New generation networks, based on Cognitive Radio technology, allow dynamic allocation of the spectrum, alleviating spectrum scarcity. These networks also have a resilient potential for dynamic operation for energy saving. In this paper, we present a novel wireless network optimization algorithm for cognitive radio networks based on a cloud sharing-decision mechanism. Three Key Performance Indicators (KPIs) were optimized: spectrum usage, power consumption, and exposure of human beings. For a realistic suburban scenario in Ghent city, Belgium, we determine the optimality among the KPIs. Compared to a traditional Cognitive Radio network design, our optimization algorithm for the cloud-based architecture reduced the network power consumption by 27.5%, the average global exposure by 34.3%, and spectrum usage by 34.5% at the same time. Even for the worst optimization case, our solution performs better than the traditional architecture by 4.8% in terms of network power consumption, 7.3% in terms of spectrum usage and 4.3% in terms of global exposure.