Genetic algorithm based sensing and channel allocation in cognitive ad-hoc networks

Shrestha, Anish Prasad; Won, Jong-Min; Yoo, Sang‐Jo; Seo, Myunghwan; Cho, Hyung-Weon

doi:10.1109/ictc.2016.7763447

Cited by 9 publications

(5 citation statements)

References 5 publications

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“…To solve the problem of spectrum access in dynamic networks, many methods can implement dynamic spectrum access, including competition algorithms (such as ALOHA [46] and CSMA [47] ), reservation algorithms (such as FPRP [48] and NbIA [49] ), and the queuing algorithms (such as DLPS [50] ). With the development of evolutionary algorithms, including GA [51,52] , PSO [53] , and biological foraging algorithms [54] , researchers are increasingly trying to integrate their use in dynamic spectrum access.…”

Section: Dynamic Spectrum Access Technologymentioning

confidence: 99%

Intelligent cognitive spectrum collaboration: Convergence of spectrum sensing, spectrum access, and coding technology

Cai

2020

Intell. and Converged Netw.

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For a future scenario where everything is connected, cognitive technology can be used for spectrum sensing and access, and emerging coding technologies can be used to address the erasure of packets caused by dynamic spectrum access and realize cognitive spectrum collaboration among users in mass connection scenarios. Machine learning technologies are being increasingly used in the implementation of smart networks. In this paper, after an overview of several key technologies in the cognitive spectrum collaboration, a joint optimization algorithm of dynamic spectrum access and coding is proposed and implemented using reinforcement learning, and the effectiveness of the algorithm is verified by simulations, thus providing a feasible research direction for the realization of cognitive spectrum collaboration.

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Section: Dynamic Spectrum Access Technologymentioning

confidence: 99%

Intelligent cognitive spectrum collaboration: Convergence of spectrum sensing, spectrum access, and coding technology

Cai

2020

Intell. and Converged Netw.

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“…Based on simulation results, it can be inferred that GA-based technique for spectrum sensing weight optimization has improved the overall sensing performance in terms of low value for probability of missed detection. In [127], authors proposed Genetic Algorithm(GA) based optimization scheme for frequency hoping CRN performing joint out of band spectrum sensing and channel allocation. Proposed GA-based technique comprises an algorithm dealing with sensing and data transmission utility.…”

Section: Spectrum Sensing Optimizationmentioning

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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“…For maximizing the throughput for SUs avoiding the collision trade‐off between sensing accuracy and transmission of data is described in Zhang et al and Al‐Doseri . For maximizing the utilities and many multiobjective functions, scientists took help of metaheuristic algorithms like genetic algorithm (GA) in Balieiro et al, Kamran and Moessner, and Khalid and Anpalagan . To solve the problem of channel allocation, game theory technique has been proposed.…”

Section: Introductionmentioning

confidence: 99%

Guided joint spectrum sensing and resource allocation using a novel random walk grey wolf optimization for frequency hopping cognitive radio networks

Karthikeyan

Srividhya

Kundu

2019

Int J Communication

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Summary Effective use of unused licensed spectrum by secondary nodes without causing any harmful interference to the primary users is one of the challenging task, and in this paper, a detailed description of joint spectrum sensing and resource allocation in cognitive radio (CR) networks is discussed. The CR system utility is maximized by joint spectrum sensing and channel resource allocation scheme based on random walk grey wolf optimization (RW‐GWO) algorithm for frequency hopping cognitive radio‐based networks. In this paper, the nodes will sense the presence of primary users in the same channel and move to another channel in the guidance of a guide node (GN) if primary signal is detected in the channel. In order to achieve collision‐free communication, primary signals and secondary signals use the channels strategically and the mechanism is described in this paper. RW‐GWO is used to derive the optimum sensing and data transmission schedules. It selects the sensing nodes to sense the spectrum and other nodes take part in transmitting the data. GNs not only guide the nodes to hop to the next channel if the primary channel is detected, but also distribute the nodes in different available secondary hopping channels. Simulation results show that reliable spectrum sensing and efficient channel allocation can be achieved in our proposed algorithm.

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Genetic algorithm based sensing and channel allocation in cognitive ad-hoc networks

Cited by 9 publications

References 5 publications

Intelligent cognitive spectrum collaboration: Convergence of spectrum sensing, spectrum access, and coding technology

Intelligent cognitive spectrum collaboration: Convergence of spectrum sensing, spectrum access, and coding technology

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

Guided joint spectrum sensing and resource allocation using a novel random walk grey wolf optimization for frequency hopping cognitive radio networks

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