Multi-Objective Modified Grey Wolf Optimization Algorithm for Efficient Spectrum Sensing in the Cognitive Radio Network

Eappen, Geoffrey; Shankar, T.

doi:10.1007/s13369-020-05084-3

Cited by 26 publications

(15 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Selecting the proper optimization technique for improving the performance of the artificial neural network (ANN) is very crucial as the entire CRN working is dependent on it . The popular swarm-based optimization scheme like particle swarm optimization (PSO), artificial bee colony (ABC) Algorithm, genetic algorithm (GA), grey wolf optimization (GWO) and ant colony algorithm (ACA) lack proper trade-off between their exploration (Global Search) and exploitation (Local Search) abilities [29,68]. The PSO lacks proper convergence ability, whereas ACA and ABC lack in exploitation [30,67].…”

Section: Related Workmentioning

confidence: 99%

Advanced squirrel algorithm‐trained neural network for efficient spectrum sensing in cognitive radio‐based air traffic control application

2021

Self Cite

View full text Add to dashboard Cite

In the current scenario, there is a drastic increase in air traffic. The air to ground communication plays a crucial role in the air traffic control system. There is a limited spectrum available for aircraft to establish a connection with the Air Traffic Controller (ATC). With air traffic growth, the available spectrum is getting more congested. This paper proposed an Advanced Squirrel Algorithm (ASA)-trained neural network (NN) for efficient spectrum sensing for cognitive radio-based air traffic control applications. ASA is a novel metaheuristic-based training algorithm for an NN. With the proposed algorithm, it is possible to dynamically allocate the unused spectrum for air to ground communication between aircraft and ATC. The quantitative analysis of the proposed ASA-NN-based spectrum sensing is done by comparing it with the existing metaheuristic-based NN training algorithms, namely, particle swarm optimization Gravitational Search Algorithm (PSOGSA), particle swarm optimization (PSO), gravitational search algorithm (GSA), and artificial bee colony (ABC). Simulation-based evaluation shows that the proposed ASA-NN is capable of efficiently detecting the spectrum holes with high convergence rate as compared to PSOGSA-, PSO-, GSA-, and ABC-based algorithms.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Section: Related Workmentioning

confidence: 99%

Advanced squirrel algorithm‐trained neural network for efficient spectrum sensing in cognitive radio‐based air traffic control application

2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Solving MST is an unconditionally constrained continuous problem, similar to the process of a whale searching for an optimal. Based on WOA, penalty method is used to deal with the minimum rate requirement constraint 1 C in problem (7). User can be equivalent to a humpback whale, and transmission power P can be equivalent to the location of search whale ( ) X t .…”

Section: Problemmentioning

confidence: 99%

“…Because of its simple structure and low computational complexity, it has been widely used to optimize resources in wireless networks, such as power allocation for spectrum and energy efficiency. In [7], MOMGWO was proposed to solve the multi-objective optimization problem in the spectrum sensing field of cognitive radio networks. In [37], a new Ant Colony System is proposed for computing the node-disjoint optimal transmission energy consumption route for coded cooperative mobile networks.…”

Section: Introductionmentioning

confidence: 99%

Whale Optimization Algorithm with Applications to Power Allocation in Interference Networks

Zhang

et al. 2021

ITC

View full text Add to dashboard Cite

Power allocation plays a pivotal role in improving the communication performance of interference-limitedwireless network (IWN). However, the optimization of power allocation is usually formulated as a mixed-integernon-linear programming (MINLP) problem, which is hard to solve. Whale optimization algorithm (WOA)has recently gained the attention of the researcher as an efficient method to solve a variety of optimizationproblems. WOA algorithm also has the disadvantages of low convergence accuracy and easy to fall into local optimum.To solve the above problems, we propose Cosine Compound Whale Optimization Algorithm (CCWOA).First of all, its unique cosine nonlinear convergence factor can balance the rate of the whole optimization processand prevent the convergence speed from being too fast. Secondly, the inertia weight and sine vector canincrease the probability of jumping out of the local optimal solution. Finally, the Archimedean spiral can reducethe risk of losing the optimal solution. A representative benchmark function is selected to test the convergencerate of CCWOA algorithm and the optimization performance of jumping out of local optimum. Compared withthe representative algorithms PFP and GAP, the optimization effect of CCWOA is almost consistent with theabove two algorithms, and even exceeds 4% - 6% in numerical value. The advantage of CCWOA is that it haslower algorithm complexity, which has a good advantage when the network computing resources are fixed. Inaddition, the optimization effect of CCWOA is higher than that of WOA, which lays a good foundation for furtherapplication of swarm intelligence optimization algorithm in network resource allocation.

show abstract

“…In the existing work, the performance analysis of spectrum sensing is done by using various schemes and protocols, e.g., multi-objective modified grey wolf optimization algorithm [19], NOMA network under power splitting based simultaneous wireless information and power transfer (SWIPT) [20], NOMA based mobile edge computing with imperfect channel state information (CSI) [21], decode-and-forward (DF) and amplify-and-forward (AF) techniques in NOMA based cognitive radio network (CRN) [22], signal segmentation [23], neural network based multilayer perception model [24], and the channel allocation scheme based on greedy algorithm [25]. Cache technology is used to even improve the performance of parameters of spectrum sensing which includes various strategies: cache capacity allocation, cache replacement, cache utilization, and cache placement [26].…”

Section: Introductionmentioning

confidence: 99%

Low Access Latency and High Throughput for Full-Duplex Cognitive Radio Using Mobility Caching Placement Strategy

Srilatha¹,

Bharathi

2022

Adv. technol. innov.

View full text Add to dashboard Cite

In a remote framework, the densely allotted radio range is not effectively utilized by an authorized primary user, which limits a secondary user to avoid disturbance to a primary user while utilizing a channel. To solve the shortage problem of accessible range, this study proposes a mobility caching placement strategy (MCPS) based sliding window scheme to optimize the latency and throughput of cognitive radio system. The performance parameters of the proposed system are analyzed using energy detection metrics by considering full-duplex communication. By the MATLAB analysis and the evaluation with and without self-interference suppression, the results demonstrate that the entrance inactivity of the MCPS based sliding window is diminished by a factor of 1.9 as contrasted with the existing full-duplex systems. With the improved latency and throughput, the proposed method can avoid the ruinous impacts of drawn-out self-residual suppression more effectively compared to the existing approaches.

show abstract

Multi-Objective Modified Grey Wolf Optimization Algorithm for Efficient Spectrum Sensing in the Cognitive Radio Network

Cited by 26 publications

References 55 publications

Advanced squirrel algorithm‐trained neural network for efficient spectrum sensing in cognitive radio‐based air traffic control application

Advanced squirrel algorithm‐trained neural network for efficient spectrum sensing in cognitive radio‐based air traffic control application

Whale Optimization Algorithm with Applications to Power Allocation in Interference Networks

Low Access Latency and High Throughput for Full-Duplex Cognitive Radio Using Mobility Caching Placement Strategy

Contact Info

Product

Resources

About