Optimal spectrum and power allocation using evolutionary algorithms for cognitive radio networks

Anumandla, Kiran Kumar; Sabat, Samrat L.; Peesapati, Rangababu; Prabu, A.V.; Dabbakuti, J. R. K. Kumar; Rout, Ranjita

doi:10.1002/itl2.207

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…( 2) and compares it with the detection threshold which is determined by Eq. ( 3) to determine whether a major user signal is present or not (Anumandla et al, 2021;Ramírez et al, 2018;Lorincz et al, 2021;Arjoune et al, 2018):…”

Section: Energy Detection-spectrum Sensing Component For Primary User...mentioning

confidence: 99%

A Spectrum Sensing and Allocation Model for Primary User Detection and Interference Mitigation in Television Whitespaces

Notcker,

Adetiba,

Adetiba

et al. 2024

Journal of Computer Science

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Television White Space (TVWS) emerges as an encouraging solution to address the challenge of a restricted wireless communication spectrum. It denotes the frequency range spanning from 54-790 MHz and researchers have increasingly explored its propagation characteristics in recent years. Nonetheless, a notable hindrance to its effective utilization lies in the interference between primary and secondary users, as well as interference among secondary users themselves. Approaches involving spectrum sensing and resource allocation have been extensively employed independently to tackle these issues, yet they have not been integrated or utilized in combination. Hence, in this study, we formulated an architectural model that combines spectrum sensing and allocation components. This integrated model aims to detect the presence of primary users while simultaneously minimizing interference among secondary users. The spectrum sensing component utilized an energy detection model to identify primary users, mitigating interference with secondary users. Meanwhile, the spectrum allocation component employed the Particle Swarm Optimization (PSO) algorithm to determine the optimal distribution of channels among secondary users. We implemented the architectural model in a simulated TVWS network using MATLAB R2020a. Its performance was then evaluated and compared with that of matched filter and Artificial Bee Colony (ABC) algorithms, which were utilized for spectrum sensing and allocation, respectively. Based on the simulation findings, when the Signal-to-Noise Ratio (SNR) was configured at -10 dB, the detection probability for the energy detection model reached 98.23%, surpassing the matched filter's detection probability of 92.55%. With a false alarm probability of 0.51, the energy detection model exhibited a misdetection probability of 0.13%, outperforming the matched filter which had a higher misdetection probability of 2.61%. In scenarios with 10 channels and 100 secondary users, the particle swarm optimization algorithm attained a maximum throughput of 279.9 Mbps, slightly outperforming the artificial bee colony algorithm, which achieved 278.7 Mbps. In scenarios with 30 channels and 200 secondary users, the particle swarm optimization algorithm achieved throughputs of 1.575 Gbps, whereas the artificial bee colony algorithm achieved a comparable throughput of 1.571 Gbps. In the scenario where the number of channels was set to 50 and users to 300, the particle swarm optimization algorithm achieved a throughput of 3.879 Gbps, slightly surpassing the artificial bee colony algorithm, which achieved 3.864 Gbps. While the designed components consistently outperformed the matched filter and artificial bee colony algorithms across all cases, it's important to note that the model faced limitations. Specifically, it was unable to detect more than one primary user or allocate spectrum for a new incoming secondary user.

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Section: Energy Detection-spectrum Sensing Component For Primary User...mentioning

confidence: 99%

A Spectrum Sensing and Allocation Model for Primary User Detection and Interference Mitigation in Television Whitespaces

Notcker,

Adetiba,

Adetiba

et al. 2024

Journal of Computer Science

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“…This work has focused on achieving joint spectrum and power allocation in the CRNs. Multi-Objective Different Evolution (MODE) algorithm maximizes network capacity and compares with NSGA-II and MOPSO algorithms [9]. Ant Colony Optimization(ACO) algorithm is used to improve spectrum utility, and detection rate and reduce collision, detection rate, and reduced false alarm probability [10].…”

Section: Related Workmentioning

confidence: 99%

Development and Performance Evaluation of Multi-Objective Approach-Based Spectrum Allocation in Cognitive Radio Network

Brahma,

Swayamsiddha,

Panda

2023

Preprint

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For effective utilization of radio frequency, Spectrum Sensing (SS), Spectrum Decision (SD), Spectrum Allocation (SA), and Spectrum Management(SM) are some recent research directions in Cognitive Radio Networks (CRNs). Out of these sub-areas, the SA is an important task in CRN. In the past, researchers have addressed SA as a Single Objective Optimization (SOO) problem and have solved it by using bio-inspired techniques. But in reality, the SA has a multi-objective optimization problem. In the recent past, the SA problem has been addressed as a two objectives optimization problem and has been solved by Multi–Objective Bio-inspired Approach (MOBA). In the present paper, we have also considered spectral and energy efficiencies of the SA task as two independent objectives and then solved this problem using two different Multi-Objective Optimization (MOO) techniques. These two are the Multi-Objective Ant Lion Optimization Algorithm (MOALO) and the Multi-Objective Grasshopper Optimization Algorithm (MOGOA). The spectrum utilization performances achieved by the two approaches have been compared. In addition, the results obtained by the already reported Superior Population Genetic Algorithm (SPGA) based technique have also been compared. The analysis of simulation-based experimental results obtained from the three methods reveals that the proposed MOALO algorithm provides superior performance in terms of Pareto front and network capacity compared to the other two approaches. The proposed multi-objective methods can also be applied when the SA task is viewed as a three-objective optimization problem.

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“…A power mixture technique for the allocation of spectra, increasing the through-put to a CRN and satisfying the interference constraints for both users is presented in [26]. The authors of [27] discuss resource assignment in terms of spectrum utilization and net-work throughput optimization.…”

Section: Related Workmentioning

confidence: 99%

Modified Heuristic Computational Techniques for the Resource Optimization in Cognitive Radio Networks (CRNs)

et al. 2023

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With the advancement of internet technologies and multimedia applications, the spectrum scarcity problem is becoming more acute. Thus, spectral-efficient schemes with minimal interference for IoT networks are required. Device-to-device communication (D2D) technology has the potential to solve the issue of spectrum scarcity in future wireless networks. Additionally, throughput is considered a non-convex and NP-hard problem, and heuristic approaches are effective in these scenarios. This paper presents two novel heuristic approaches for throughput optimization for D2D users with quality of service (QoS)-aware wireless communication for mobile users (MU): the modified whale colony optimization algorithm (MWOA) and modified non-domination sorted genetic algorithm (MNSGA). The performance of the proposed algorithms is analyzed to show that the proposed mode selection technique efficiently fulfills the QoS requirements. Simulation results show the performance of the proposed heuristic algorithms compared to other understudied approaches.

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Optimal spectrum and power allocation using evolutionary algorithms for cognitive radio networks

Cited by 10 publications

References 14 publications

A Spectrum Sensing and Allocation Model for Primary User Detection and Interference Mitigation in Television Whitespaces

A Spectrum Sensing and Allocation Model for Primary User Detection and Interference Mitigation in Television Whitespaces

Development and Performance Evaluation of Multi-Objective Approach-Based Spectrum Allocation in Cognitive Radio Network

Modified Heuristic Computational Techniques for the Resource Optimization in Cognitive Radio Networks (CRNs)

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