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access icon free ULA-based near-field source localisation in cognitive femtocell network: a comparative study of genetic algorithm hybridised with pattern search and swarm intelligence

  • Author(s): Kiran Sultan 1  and  R. A. Alharbey 2
    • View affiliations
  • Source: Volume 13, Issue 12, 30 July 2019, p. 1753 – 1761
    DOI:  10.1049/iet-com.2018.5038 , Print ISSN 1751-8628, Online ISSN 1751-8636
© The Institution of Engineering and Technology
Received 13/02/2018, Accepted 05/04/2019, Revised 20/03/2019, Published 10/04/2019

The rapidly proliferating wireless technology brings an overwhelming increase in wireless applications at a continuous pace. In this framework, a major proportion of overall voice and data traffic originates from indoor users, directing the research world towards the deployment of low-power, low-cost, and low-range femtocell networks in the existing macrocells. However, this development will result in cross-tier interference as well as interference to the primary or licensed users. Cognitive femtocell networks smartly address this challenge through their intelligent sensing and decision-making capabilities. This study proposes a joint spectrum sensing technique, which involves individual sensing by multiple cognitive femtocell base stations (CFBSs), each equipped with a uniform linear array (ULA) of passive sensors. The local observations of CFBSs are then evaluated at the fusion centre to make final decision applying majority rule. In addition to the detection of a number of active primary femtocell networks (PFNs), the proposed near-field source localisation technique provides four-dimensional parameter estimation of each detected PFN signal, i.e. amplitude, angle-of-arrival, frequency, and range. Finally, the proposal is supported by three different implementations, i.e. Hybrid Genetic Algorithm, Particle Swarm Optimization, and Artificial Bee Colony. It also outsmarts an existing single-ULA based spectrum sensing technique in the literature.

Inspec keywords: genetic algorithms; cognitive radio; particle swarm optimisation; direction-of-arrival estimation; femtocellular radio

Other keywords: genetic algorithm; pattern search; near-field source localisation; cognitive femtocell network; individual sensing; continuous pace; swarm intelligence; near-field source localisation technique; low-range femtocell networks; decision-making capabilities; wireless technology; primary users; multiple cognitive femtocell base stations; wireless applications; final decision applying majority rule; cross-tier interference; data traffic originates; joint spectrum sensing technique; intelligent sensing; indoor users; CFBSs; active primary femtocell networks; voice; single-ULA-based spectrum sensing technique

Subjects: Optimisation techniques; Mobile radio systems; Signal processing and detection

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