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

Eappen, Geoffrey; Shankar, T.

doi:10.1007/s42979-020-00372-z

Cited by 19 publications

(8 citation statements)

References 131 publications

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“…Hereby, a review of several surveys in the field of probabilistic-based spectrum sensing, is presented to both describe their relevance, and to outline the areas in which this paper expands the discussion. The authors in [21] review algorithms which, based on the standard sensing techniques, determine the overall decision on spectrum occupancy in the span of a CR network. Main attention is given on neural network, genetic, particle swarm optimization and other similar algorithms.…”

Section: Motivationmentioning

confidence: 99%

Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

et al. 2021

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With the advent of Sixth Generation (6G) telecommunication systems already envisioned, increased effort is made to further develop current communication technologies, so they can be incorporated together with the novel ones, to deliver uninterrupted and satisfactory service for any application in every location on the ground, underwater, in the air, or in space. One such technology is Cognitive Radio (CR) which has received much attention due to its potential for increase of utilization, especially in the bands below 6 GHz. The main enabler for CR is spectrum sensing because it provides the opportunity for dynamic assessment of the radio environment to identify unused channels. This functionality has been the object of many research works for that very reason. In spite of this, the provision of accurate and fast spectrum characterization in time, frequency and space has proven to be a non-trivial task. This paper presents a detailed review of probabilistic spectrum sensing methods classified by the feature they extract from the received signal samples, to provide accurate detection of the primary user (PU) signal. The main design characteristics (such as probability of detection, robustness to noise and fading, signal/noise model assumptions, and computational complexity), strengths and weaknesses for each type are also summarized. Based on current concepts for 6G networks and applications, a framework for human-centric cognition-based wireless access is presented, which specifies the role of spectrum sensing-based CR in future networks.INDEX TERMS 6G, cognitive radio, feature detection, human-centric wireless access, Internet of Things, probabilistic spectrum sensing, wireless communications.

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Section: Motivationmentioning

confidence: 99%

Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

et al. 2021

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“…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]. The GA tends to get stuck to the local best solution instead of finding the global best [31].…”

Section: Related Workmentioning

confidence: 99%

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

2021

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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.

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“…To calculate the derivative, the first step is to expand formula (11) to express all the terms related to t i in detail. According to formula (1),…”

Section: Su Sensing Time Gamementioning

confidence: 99%

“…Due to the signal-to-noise ratio g . 0, it can be seen from the image of the function f (g) = 1 2 À 1 2 e Àg that when g . 0, f (g) 2 (0, 0:5).…”

Section: Su Sensing Time Gamementioning

confidence: 99%

“…With the vigorous advances in the mobile Internet and the Internet of Things, people's demand for spectrum resources is even more urgent. 1 Traditional radio spectrum allocation is a fixed allocation strategy, which prevents the spectrum resources from being fully utilized. Cognitive radio technology is a spectrum sharing technology which has a significant role in improving spectrum utilization.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Smart contract-based secure cooperative spectrum sensing algorithm

Zhu

2021

International Journal of Distributed Sensor Networks

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Spectrum sensing is the key technology of cognitive radio. In this article, we apply blockchain technology in spectrum sensing process and propose a related algorithm based on reputation. The algorithm builds a system model based on smart contract in blockchain and applies blockchain asymmetric encryption algorithm and digital signature technology in the process of secondary users’ transmitting local judgments to the secondary user base station. The algorithm can resist spectrum sensing data falsification (SSDF) attack launched by malicious users. This article comprehensively considers the channel error rate, detection probability, secondary user base station budget and remaining energy of the secondary users (SUs) and then establishes the SU’s utility function as well as the game model. By solving the Nash equilibrium, the SU determines whether it uploads sensing data. Finally, the SU base station selects registered SUs by calculating and updating their reputation, obtaining the final judgment by voting rule. With simulations, we prove that the algorithm proposed in this article increases the accuracy and security of spectrum sensing and can effectively resist SSDF attack.

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

Cited by 19 publications

References 131 publications

Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

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

Smart contract-based secure cooperative spectrum sensing algorithm

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