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

Sultan, Kiran; Alharbey, Rania A.

doi:10.1049/iet-com.2018.5038

Cited by 10 publications

(3 citation statements)

References 33 publications

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“…Classic ABC stands among the most competent EA-based algorithms, offering a simple implementation with only two control parameters, i.e., maximum generation number (MGN) and limit. It consists of three colonies of honey bees (the search agents) known as Employed Bees (EBs), Onlooker Bees (OBs) and Scout Bees (SBs) [32]. EBs and OBs are the local search agents, whereas SBs act as global search agents to extend the search process to a larger search space.…”

Section: Proposed Eabc-based Power Allocation Schemementioning

confidence: 99%

Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks

Sultan

2023

Applied Sciences

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The integration of non-orthogonal multiple access (NOMA) technology and cognitive radio networks (CRNs) promises to enhance the spectrum utilization efficiency of 5G and beyond-5G (B5G) mobile communication systems. In this article, a NOMA-based spectrum-sharing scheme is proposed for dual-hop CRNs in which a primary transmitter separated by a long distance from the primary receiver communicates via NOMA-based CRN. In this scenario, we mathematically formulate a constrained optimization problem to maximize the sum rate of all secondary users (SUs) while maintaining the total transmit power of the system. Inspired by the effectiveness of computational intelligence (CI) tools in solving non-linear optimization problems, this article proposes three CI-based solutions to the given problem aiming to guarantee quality of service (QoS) for all users. In addition, an enhanced version of the classic artificial bee colony (ABC) algorithm, referred to here as the enhanced-artificial-bee-colony (EABC)-based power allocation scheme, is proposed to overcome the limitations of classic ABC. The comparison of different CI approaches illustrates that the minimum power required by the secondary NOMA relay to satisfy the primary rate threshold of 5 bit/s/Hz is 20 mW for EABC, while ABC, PSO and GA achieve the same target at 23 mW, 27 mW and 32 mW, respectively. Thus, EABC reduces power consumption by 13.95% compared to ABC, while 29.78% and 46.15% power-saving is achieved compared to PSO and GA, respectively.

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Section: Proposed Eabc-based Power Allocation Schemementioning

confidence: 99%

Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks

Sultan

2023

Applied Sciences

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“…The processing of the signal at the antenna sensor arrays to detect the direction of the EM wave is done using multiple signal classification (MUSIC) [8]. The resolution is a measure of differentiating two energy EM waves which have equal amplitude.…”

Section: Background For Mobile Source Detectionmentioning

confidence: 99%

Conventional and Subspace Algorithms for Mobile Source Detection and Radiation Formation

Thazeen¹,

Mallikarjunaswamy²,

Siddesh³

et al. 2021

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Background/objectives: The objective of the study is to increase the resolution and radiate sharper beam towards the user in mobile communication using Smart Antenna. Methodology: The Conventional and Subspace Algorithms from the literature are studied and simulated in MATLAB so that the foundation is laid for better detection of algorithms and radiation formation. The results are explained for varying number of antenna elements and mobiles sources placed close to far. Findings: The classical direction of arrival algorithms namely CAPCON, Maximum Entropy Method, Maximum Likelihood Method are used to find the direction of mobile users based on the computation of the power spectrum. Several methods namely Least Mean Square, Griffiths Method, Variable Step Size Griffiths and Recursive Least Square are used to form the main beam for the user detected by the direction of arrival algorithms. Novelty/improvements: In order to take further the research on enhancing the resolution and having a higher convergence rate with reasonable step size, this paper presents the well-known conventional and modern algorithms from the literature. The results are simulated are well described for performing parameters.

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“…Bayesian learning is applied for the linear array to compute the value of array manifold vector, likelihood functionality has the performance parameters such as size complexity and computational complexity which are used for detection of Angle of Arrival [13]. Antenna sensor array performs the signal processing at the Base Station to detect the direction of the incoming electromagnetic wave using Multiple Signal Classification (MUSIC) algorithm [14].…”

Section: Introductionmentioning

confidence: 99%

High Resolution Detection, Estimation and Location Using GTF DoA Method for Smart Antenna System

Morab¹,

Hegde²,

Hegde³

2022

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Accommodating multiple users within the limited available bandwidth and providing the same Quality of Service (QoS) to all the users is a challenging task. Channel capacity can be increased by using the Spatial Division Multiple Access (SDMA) technique. Smart Antenna Systems are used to implement the SDMA technique in Real-Time and it also helps in finding the high-resolution Direction of Arrival (DoA) detection of the desired mobile users. In this paper novel Gaussian Triangular Factor (GTF) method is proposed for the detection of the desired mobile users from the 3-D spatial domain. This method is based on vector subspaces, which perform the triangular decomposition of the entire Eigenspace into the Lower Element Factor (LEF) and Upper Element Factor (UEF). These are then supplied for the computation of the power spectrum where peaks represent the detected locations of the desired mobile users in the 3-D spatial field. The proposed method was able to detect all the desired users, which were spaced nearer or far apart spatially, it provided high-quality detection regardless of the number of antenna elements used at the Base Station (BS). The GTF Method was able to detect all the desired users under the presence of heavy noise, fading, and interference. It was able to suppress the side lobes, back lobes, and grating lobes thus immensely improving the detection quality, detection range, system power consumption, detection efficiency, and effectiveness. The proposed GTF method was compared with several existing methods and it provided the best results for different performance parameters like Detection Error, Resolution, Time Complexity, and Disturbance Error.

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

Cited by 10 publications

References 33 publications

Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks

Computational-Intelligence-Based Spectrum-Sharing Scheme for NOMA-Based Cognitive Radio Networks

Conventional and Subspace Algorithms for Mobile Source Detection and Radiation Formation

High Resolution Detection, Estimation and Location Using GTF DoA Method for Smart Antenna System

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