A two-threshold cooperative spectrum sensing algorithm using swarm intelligence

Mohammed, Fareeduddin; Deriche, Mohamed

doi:10.1109/comcomap.2013.6533609

Cited by 7 publications

(8 citation statements)

References 11 publications

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“…Better than traditional energy detection in hostile environment and it works well even in low SNR. Mohammed et al [113] proposed PSO-OR algorithm using two threshold energy detector for spectrum sensing. The term double threshold is used by the authors because here fusion center collects local decision and also the energy values from the SUs, PSO optimizes the decisions made by SUs and final inference is made based on local decision and the optimized value.…”

Section: B Swarm Intelligence Algorithm: Swarm Intelligencementioning

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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Section: B Swarm Intelligence Algorithm: Swarm Intelligencementioning

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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“…Here, for illustration purposes, we consider taking only two observations in the weighting process. According to the transitional probability in (13), P f,W S and P d,W S from (14) can be expanded into…”

Section: Weighted Sedmentioning

confidence: 99%

“…Similarly to the analysis for Weighted-SED scheme, this generic form can be used to formulate the probability of false alarm and detection with any number of local observations. For instance, assuming that a maximum of two observations are allowed in the second stage, P f,T S and P d,T S can be expanded as follows according to the probability of PU's transition in (13) and the probability related to the first-stage detection in (20).…”

Section: Two-stage Sedmentioning

confidence: 99%

“…For instance, in Double Threshold energy detection scheme, CR user forwards its local decision to the fusion center only when it is certain about its decision, otherwise it forwards the local observation to the fusion center [11]- [13]. In terms of detection threshold optimization, constant false-alarm rate detector is to maximize the probability of detection while probability of false alarm is fixed [9].…”

Section: Introductionmentioning

confidence: 99%

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Sequential Cooperative Spectrum Sensing Technique in Time Varying Channel

Prawatmuang

Alsusa

2014

IEEE Trans. Wireless Commun.

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Cognitive radio opportunistically accesses the spectrum while the licensed user is idle. A spectrum sensing procedure to monitor primary users' existence is therefore vital to cognitive radios. In this paper, we investigate the energy detection based sequential cooperative spectrum sensing technique in time varying channels. By utilizing past local observations from previous sensing slots, cognitive radio nodes can aggregate the current and previously received energy values to improve the detection performance. We propose the weighted sequential energy detector (SED) in which a fixed number of past observations are taken for decision making. In addition, we propose two adaptive schemes, namely the Two-Stage SED and the Differential SED, where a cognitive radio user uses previously received energy values until it detects a change in primary user's activity. The probability of false alarm and detection for the Weighted SED and Two-Stage SED schemes are derived. The results show that the proposed schemes achieve better detection performance over the conventional cooperative energy detection technique. In particular, the Two-Stage SED and Differential SED schemes can satisfy the IEEE 802.22 standard's requirement of 10% false alarm and 90% detection in scenarios that conventional techniques cannot accommodate.Index Terms-Cognitive radio, spectrum sensing, sequential spectrum sensing, adaptive spectrum sensing.

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“…This problem can be overcome by employing double threshold‐based CSS. In double threshold‐based sensing scheme, when the signal energy value lies in between the two thresholds, either the SUs transmit no information, , or they send their corresponding energy values to the FC, , or the SUs do more number of sensing rounds until they reach to any final decision . In this paper, instead of using double threshold‐based energy detector at each SU, we employ double threshold technique at the soft decision FC.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for energy‐efficient resource allocation in double threshold‐based cognitive radio network

Das

2016

Int J Communication

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Summary This paper mainly focuses on solving the energy efficiency (EE) maximization problem in double threshold‐based soft decision fusion (SDF) cooperative spectrum sensing (CSS) in the cognitive radio network (CRN). The solution to this objective problem starts with the selection of suitable secondary users (SUs) both for the spectrum sensing and data transmission. Here, energy efficiency is maximized under the constraints of interference to the primary user (PU), an acceptable outage of SUs, the transmission power of the SUs and the probability of false alarm. We propose a novel algorithm called iterative Dinkelbach method (IDM) which jointly optimizes the sensing time and transmission power allocation to the SUs. Further, Lagrangian duality theorem is employed to find the exact power assigned to the SUs. Finally, simulation results are carried out to validate the effectiveness of our proposed scheme by comparing with the other existing schemes. The performance is also analyzed for different system parameters.

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A two-threshold cooperative spectrum sensing algorithm using swarm intelligence

Cited by 7 publications

References 11 publications

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

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

Sequential Cooperative Spectrum Sensing Technique in Time Varying Channel

A novel approach for energy‐efficient resource allocation in double threshold‐based cognitive radio network

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