Spectrum sensing in cognitive femtocell network based on near‐field source localisation using genetic algorithm

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

“…In this section, the simulation results are presented along with the discussion on the observations. All parameters to be estimated are taken from [20] for comparison. The carrier frequency and each channel's bandwidth is 2 GHz and 10 MHz, respectively.…”

“…The tabular values to illustrate the closeness between theunknown actual data and the best estimates generated by PSO, ABC, andGA‐PS are presented in Tables 2and 3. The number of active PFsignals and their corresponding parameters to be estimated are setaccording to [20] for comparison. The significance of employing multiple ULAs is obvious from the resultswhich outsmart the single ULA scheme proposed in [20].…”

“…The work done by Aisha et al . [20] for detection and parameters’ estimation of active primary femtocell networks (PFNs) using a single uniform linear array (ULA) has been extended in this study by applying our acknowledged detection and estimation technique [21]. The basic algorithm for detection of a number of active PFNs through ULA is kept the same as previously proposed [22], because of its proven high accuracy.…”

“…Assuming that there is no multipath fading, the main features of this proposal are highlighted below: Avoids the hidden‐node problem by using omni‐directional sensors and by applying the majority rule of decision fusion at the FC. Smartly differentiates two signals incident with same AoA but different carrier frequencies. Ensures the accuracy of results even in the cases of variation of array parameters, and accomplishes this by taking a sufficient number of snapshots. Observes the system's response against variation of signal‐to‐noise ratio (SNR) and the number of sensors in the ULA. Provides three different implementations of the proposed detection and estimation scheme, first based on genetic algorithm hybridised with pattern search (GA‐PS), second based on particle swarm optimisation (PSO) and third employs artificial bee colony (ABC). Their results have been compared with [20] to validate their effectiveness and to find out the best of all implementations for optimising the problem in‐hand. Furthermore, the three algorithms have been compared in terms of their convergence behaviour, since longer convergence time indicates high‐computational complexity and processing energy consumption, thus resulting in an inefficient real‐time implementation [24].…”

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

“…An emerging trend in literature for reducing interference in CRN is cognitive femtocells. 153,154 Basically, femtocells are low-power, low-cost, compact BSs deployed to boost cellular capacity and coverage inside small offices and enterprise network. 117 Femtocells with cognitive capabilities have been found to enhance opportunistic spectrum utilization with minimal transmit power as well as interference to incumbent PUs.…”