A Study of Cognitive Radio Sensing Techniques for Optimum Spectrum Utilization

Decision Fusion ◽

Vital Role ◽

Ratio Method ◽

Spectrum Utilization ◽

Secondary Users ◽

Fusion Centre

Background: The spectrum scarcity plays a vital role in wireless communications. We are in the situation to use it through an efficient methodology. Objective: To identify the holes in the spectrum through an efficient spectrum sensing and to allocate the bands to the unlicensed users (Secondary Users- SU). Methods: It has been proposed to make a comparative study among the existing spectrum sensing methods based on the following parameters such as Probability detection (Pd) measurement, Algorithm, Decision fusion method, Network model. Results: Comparative study has been made to find the pros and cons of the existing techniques with their limitations and the field of application. CSS (Cooperative Spectrum Sensing) technique significantly consumes less energy and reporting time to FC (Fusion Centre) since it utilizes LLR (Log Likelihood Ratio) Method to find the Probability of detection using Chair-varshney rule in local sensing with parallel report approach in the cluster based network. Conclusion: Through the study and the comparison of parameters in literature, it is found that CSS provide better detection. Therefore, this technique can be considered as an efficient technique to find the holes and to share the frequency with SU’s.

A Novel Weighted-Cooperative Spectrum Sensing Scheme Using Clustering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.195-196.277 ◽

2012 ◽

Vol 195-196 ◽

pp. 277-282

Author(s):

Hui Heng Liu ◽

Wei Chen

Keyword(s):

Spectrum Sensing ◽

Cluster Head ◽

Fusion Center ◽

Secondary Users ◽

Probability Of Error ◽

Radio System ◽

Simulation Results ◽

Observation Results

This paper proposes a novel weighted-cooperative spectrum sensing scheme using clustering for cognitive radio system. We firstly classify the secondary users into a few clusters according to several existent methods, and then use cluster-head to collect the observation results come from different secondary users in the same cluster and make a cluster-decision. Considering the different distances between the clusters and the fusion center, different weightings are used to weight the cluster-decisions before combining. The simulation results show that our proposed method improve the probability of detection and reduce the probability of error.

Fuzzy-based reliable and secure cooperative spectrum sensing for the smart grid

International Journal of ADVANCED AND APPLIED SCIENCES ◽

10.21833/ijaas.2021.02.013 ◽

2021 ◽

Vol 8 (2) ◽

pp. 92-100

Author(s):

Laila Nassef ◽

Reemah Alhebshi ◽

Keyword(s):

Cognitive Radio ◽

Smart Grid ◽

Spectrum Sensing ◽

Fusion Center ◽

Secondary Users ◽

Sensing Data ◽

Spectrum Detection ◽

Fuzzy Fusion

Cognitive radio is a promising technology to solve the spectrum scarcity problem caused by inefficient utilization of radio spectrum bands. It allows secondary users to opportunistically access the underutilized spectrum bands assigned to licensed primary users. The local individual spectrum detection is inefficient, and cooperative spectrum sensing is employed to enhance spectrum detection accuracy. However, cooperative spectrum sensing opens up opportunities for new types of security attacks related to the cognitive cycle. One of these attacks is the spectrum sensing data falsification attack, where malicious secondary users send falsified sensing reports about spectrum availability to mislead the fusion center. This internal attack cannot be prevented using traditional cryptography mechanisms. To the best of our knowledge, none of the previous work has considered both unreliable communication environments and the spectrum sensing data falsification attack for cognitive radio based smart grid applications. This paper proposes a fuzzy inference system based on four conflicting descriptors. An attack model is formulated to determine the probability of detection for both honest and malicious secondary users. It considers four independent malicious secondary users’ attacking strategies of always yes, always no, random, and opposite attacks. The performance of the proposed fuzzy fusion system is simulated and compared with the conventional fusion rules of AND, OR, Majority, and the reliable fuzzy fusion that does not consider the secondary user’s sensing reputation. The results indicate that incorporating sensing reputation in the fusion center has enhanced the accuracy of spectrum detection and have prevented malicious secondary users from participating in the spectrum detection fusion

Implementation of Spectrum Sensing for Maximum Throughput & Better Probability of Detection using Energy Detector

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9339.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 3332-3340

Keyword(s):

Spectrum Sensing ◽

Software Defined Radio ◽

Channel Allocation ◽

Radio Resource Management ◽

Energy Detector ◽

Spectrum Utilization ◽

Radio Resource ◽

Maximum Throughput ◽

Secondary Users

In Radio Resource Management, channel allocation protocols plays a crucial role in ensuring that Cognitive Radio(CR) technology achieves more efficiency in radio resource utilization. CR is an improved Software Defined Radio (SDR) that consequently distinguishes the encompassing Radio Frequency (RF) at that point quickens and after that intelligently obliges its working parameters to the foundation of a system to fulfill client characterized needs. CR innovation manages the unlicensed (auxiliary) user to utilize the authorized range briefly when authorized (essential) user doesn't utilize the range. Initially, Maximum Throughput allocation protocol is aimed to achieve optimal throughput and to compare with existing protocols and then introducing another protocol and then prove whether it will give best outperformance than maximum throughput or not. Performance comparisons of different parameters for various secondary users (SUs) were carried out to compare the different protocols comprehensively. In the same way to implement spectrum sensing for increasing of probability of detection by using energy detector under different conditions. increasing of the Maximum Throughput and Probability of Detection leads to efficient spectrum utilization by reuse of the channels results in many network applications like telecommunications, television broadcasting, ect.

International Journal of Electrical and Electronic Engineering & Telecommunications ◽

Energy-Efficient Cluster-Based Cooperative Spectrum Sensing in a Multiple Antenna Cognitive Radio Network

10.18178/ijeetc.10.3.176-185 ◽

2021 ◽

pp. 176-185

Author(s):

Samson I. Ojo ◽

◽

Zachaeus K. Adeyemo ◽

Damilare O. Akande ◽

Ayobami O. Fawole

Keyword(s):

Cognitive Radio ◽

Spectrum Sensing ◽

Energy Efficient ◽

Cluster Head ◽

Fusion Rule ◽

Multiple Antenna ◽

Secondary Users ◽

Sensing Time

Spectrum Hole Detection (SHD) is a major operation in a Cognitive Radio (CR) network to identify empty spectrum for maximum utilization. However, SHD is often affected by multipath effects resulting in interference. The existing techniques used to address these problems are faced by poor detection rate, long sensing time and bandwidth inefficiency. Hence, this paper proposes a cluster-based Energy-Efficient Multiple Antenna Cooperative Spectrum Sensing (EEMACSS) for SHD in CR networks using Energy Detector (ED) with a modified combiner. Multiple secondary users are used to carry out local sensing using ED in multiple antenna configurations. The local sensing results are combined at the cluster head using majority fusion rule to determine the sensing results at each cluster. The sensing results from individual cluster are combined to determine the global sensing result using OR fusion rule. The proposed EEMACSS is evaluated using Probability of Detection (PD), Sensing Time (ST) and Spectral Efficiency (SE) by comparing with existing techniques. The results reveal that the proposed technique shows better performance.

Boosted Trees Algorithm as Reliable Spectrum Sensing Scheme in the Presence of Malicious Users

Electronics ◽

10.3390/electronics9061038 ◽

2020 ◽

Vol 9 (6) ◽

pp. 1038 ◽

Cited By ~ 1

Author(s):

Noor Gul ◽

Muhammad Sajjad Khan ◽

Su Min Kim ◽

Junsu Kim ◽

Atif Elahi ◽

...

Keyword(s):

Spectrum Sensing ◽

Decision Fusion ◽

Machine Learning Techniques ◽

Soft Decision ◽

Secondary Users ◽

Sensing Data ◽

Learning Techniques ◽

Higher Weights ◽

Sensing Performance

Cooperative spectrum sensing (CSS) has the ability to accurately identify the activities of the primary users (PUs). As the secondary users’ (SUs) sensing performance is disturbed in the fading and shadowing environment, therefore the CSS is a suitable choice to achieve better sensing results compared to individual sensing. One of the problems in the CSS occurs due to the participation of malicious users (MUs) that report false sensing data to the fusion center (FC) to misguide the FC’s decision about the PUs’ activity. Out of the different categories of MUs, Always Yes (AY), Always No (AN), Always Opposite (AO) and Random Opposite (RO) are of high interest these days in the literature. Recently, high sensing performance for the CSS can be achieved using machine learning techniques. In this paper, boosted trees algorithm (BTA) has been proposed for obtaining reliable identification of the PU channel, where the SUs can access the PU channel opportunistically with minimum disturbances to the licensee. The proposed BTA mitigates the spectrum sensing data falsification (SSDF) effects of the AY, AN, AO and RO categories of the MUs. BTA is an ensemble method for solving spectrum sensing problems using different classifiers. It boosts the performance of some weak classifiers in the combination by giving higher weights to the weak classifiers’ sensing decisions. Simulation results verify the performance improvement by the proposed algorithm compared to the existing techniques such as genetic algorithm soft decision fusion (GASDF), particle swarm optimization soft decision fusion (PSOSDF), maximum gain combination soft decision fusion (MGCSDF) and count hard decision fusion (CHDF). The experimental setup is conducted at different levels of the signal-to-noise ratios (SNRs), total number of cooperative users and sensing samples that show minimum error probability results for the proposed scheme.

A Genetic Algorithm-Based Soft Decision Fusion Scheme in Cognitive IoT Networks with Malicious Users

Wireless Communications and Mobile Computing ◽

10.1155/2020/2509081 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Muhammad Sajjad Khan ◽

Noor Gul ◽

Junsu Kim ◽

Ijaz Mansoor Qureshi ◽

Su Min Kim

Keyword(s):

Genetic Algorithm ◽

Spectrum Sensing ◽

Dynamic Spectrum Access ◽

Decision Fusion ◽

Weighting Coefficient ◽

Soft Decision ◽

Secondary Users ◽

Coefficient Vector ◽

Fusion Scheme

Internet of Things (IoT) is a new challenging paradigm for connecting a variety of heterogeneous networks. Since its introduction, many researchers have been studying how to efficiently exploit and manage spectrum resource for IoT applications. An explosive increase in the number of IoT devices accelerates towards the future-connected society but yields a high system complexity. Cognitive radio (CR) technology is also a promising candidate for future wireless communications. CR via dynamic spectrum access provides opportunities to secondary users (SUs) to access licensed spectrum bands without interfering primary users by performing spectrum sensing before accessing available spectrum bands. However, multipath effects can degrade the sensing capability of an individual SU. Therefore, for more precise sensing, it is helpful to exploit multiple collaborative sensing users. The main problem in cooperative spectrum sensing is the presence of inaccurate sensing information received from the multipath-affected SUs and malicious users at a fusion center (FC). In this paper, we propose a genetic algorithm-based soft decision fusion scheme to determine the optimum weighting coefficient vector against SUs’ sensing information. The weighting coefficient vector is further utilized in a soft decision rule at FC in order to make a global decision. Through extensive simulations, the effectiveness of the proposed scheme is evaluated compared with other conventional schemes.

An SETM Algorithm for Combating SSDF Attack in Cognitive Radio Networks

Wireless Communications and Mobile Computing ◽

10.1155/2020/9047809 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

S. Tephillah ◽

J. Martin Leo Manickam

Keyword(s):

Cognitive Radio ◽

Spectrum Sensing ◽

Trust Management ◽

Radio Networks ◽

Second Phase ◽

Common Channel ◽

Secondary Users ◽

Management Algorithm ◽

Different Types

Security is a pending challenge in cooperative spectrum sensing (CSS) as it employs a common channel and a controller. Spectrum sensing data falsification (SSDF) attacks are challenging as different types of attackers use them. To address this issue, the sifting and evaluation trust management algorithm (SETM) is proposed. The necessity of computing the trust for all the secondary users (SUs) is eliminated based on the use of the first phase of the algorithm. The second phase is executed to differentiate the random attacker and the genuine SUs. This reduces the computation and overhead costs. Simulations and complexity analyses have been performed to prove the efficiency and appropriateness of the proposed algorithm for combating SSDF attacks.

Optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01977-5 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Haiyan Ye ◽

Jiabao Jiang

Keyword(s):

Cognitive Radio ◽

Spectrum Sensing ◽

Cognitive Radio Networks ◽

Radio Networks ◽

Spectrum Utilization ◽

Channel Characteristics ◽

Optimal Linear ◽

Static Division ◽

Cooperative Nodes

AbstractThe lack of spectrum resources restricts the development of wireless communication applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability.