Hybrid PSO-GSA for energy efficient spectrum sensing in cognitive radio network

Eappen, Geoffrey; Shankar, T.

doi:10.1016/j.phycom.2020.101091

Cited by 82 publications

(57 citation statements)

References 39 publications

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“…For the comparison to be fair, all the simulations were carried out using MATLAB R2016a on an intel core i7 processor with 2.2 GHz CPU and 8 GB RAM. Tables 1 and 2 show the parameter values for PSO-HSA algorithm [21]. Table 3 shows the parameter values for PSO-GA algorithm [21].…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…Tables 1 and 2 show the parameter values for PSO-HSA algorithm [21]. Table 3 shows the parameter values for PSO-GA algorithm [21]. A three-dimensional map containing several obstacles and prohibited areas is created for implementation and subsequent comparison of different path planning methodologies.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…ICA focuses on exploitative search which is carried out by the colonies, hence the solution obtained does not lie in the global optimum region due to premature termination at local optima. Figure 7 shows the flightpath of drone obtained by using BA algorithm in a simulated obstacle-filled environment [20,21]. BA is a variation of ABC, modelled after foraging behaviour of honey bees.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

“…Revolution brings about random changes in the position of some of the countries in the search space. Hybrid bio-inspired algorithm has been successfully implemented for the wireless communication and has an upper edge over the conventional bio-inspired algorithm [18,20,21]. In this paper, hybrid PSO-HAS and PSO-GA algorithms are proposed for the UAV path planning optimization.…”

Section: Related Workmentioning

confidence: 99%

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Hybrid PSO-HSA and PSO-GA algorithm for 3D path planning in autonomous UAVs

et al. 2020

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Unmanned aerial vehicles (UAVs) are a quintessential example of automation in the field of avionics. UAVs provide a platform for performing a wide variety of tasks, but in each case the concept of path planning plays an integral role. It helps to generate a pathway free of obstacles, having minimum length leading to lesser fuel consumption, lesser traversal time and helps in steering the aircraft and its corresponding antenna power signature safely around the hostile antenna to avoid detection. To optimize path planning to incorporate all the above-mentioned constraints, this paper presents two new hybrid algorithms particle swarm optimization (PSO) with harmony search algorithm and PSO with genetic algorithm. The hybrid algorithms perform both an exploratory and exploitative search, unlike the existing algorithms which are biased, towards either an exploitative search or an exploratory search. Furthermore, the hybrid algorithms are compared to the existing optimization algorithms and in all cases the hybrid algorithms give a minimum of 7% better result against PSO with up to a 40% better result against Invasive Weed optimization algorithm for a fixed computational time, suggesting better real-time applications.

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Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Simulation Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Hybrid PSO-HSA and PSO-GA algorithm for 3D path planning in autonomous UAVs

et al. 2020

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“…At mmWave frequencies, there is high foliage loss. The power requirement for mmWave technology is relatively very high [148]. With these disadvantages of mmWave, it is difficult to establish 5G technology solely based on mmWave techniques.…”

Section: Motivational Backgroundmentioning

confidence: 99%

A Survey on Soft Computing Techniques for Spectrum Sensing in a Cognitive Radio Network

Eappen

Shankar

2020

SN COMPUT. SCI.

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The need for faster wireless connectivity is increasing rapidly in all the sectors of the technologies. Whether it is a patient monitoring system, military application, entertainment services, streaming services, or global stock markets, there is a tremendous increase in the need for enhanced wireless telecommunication services. The wireless telecommunication consumers rely on bulk data, and massive growth in the number of users has resulted in the spectrum congestion. To avoid such spectrum congestion and to satisfy the data hunger of the wireless telecommunication users, the possible solution is Cognitive Radio Network (CRN). A CRN, therefore, plays a significant role in the field of wireless communication, and an efficient spectrum sensing enhances the effectiveness of the CRN. In this paper, complete research carried out so far in the field of spectrum sensing for CRN is discussed. Different soft computing techniques (GA, PSO, ABC, ACO, FFA, FSS, Cuckoo Search, ANN, FIS, GFIS) are surveyed in this paper, along with a detailed comparative analysis between conventional and soft computing techniques for spectrum sensing. In addition to that, the challenges faced in the implementation of CRN and its requirements is also addressed. Different spectrum sensing elements and requirements are presented and road map of spectrum sensing with soft computing techniques towards 5G is discussed. Furthermore, the paper also suggests the future prospects, research challenges and open issues associated with soft computing techniques for spectrum sensing in CRN.

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