scholarly journals Collaborative Spectrum Sensing Optimisation Algorithms for Cognitive Radio Networks

2010 ◽  
Vol 2010 ◽  
pp. 1-20 ◽  
Author(s):  
Kamran Arshad ◽  
Muhammad Ali Imran ◽  
Klaus Moessner
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Fusion Rule ◽  
Decision Fusion ◽  
Soft Decision ◽  
Collaborative Spectrum Sensing ◽  
Main Challenge ◽  
Fusion Centre ◽  
Sensing Performance

The main challenge for a cognitive radio is to detect the existence of primary users reliably in order to minimise the interference to licensed communications. Hence, spectrum sensing is a most important requirement of a cognitive radio. However, due to the channel uncertainties, local observations are not reliable and collaboration among users is required. Selection of fusion rule at a common receiver has a direct impact on the overall spectrum sensing performance. In this paper, optimisation of collaborative spectrum sensing in terms of optimum decision fusion is studied for hard and soft decision combining. It is concluded that for optimum fusion, the fusion centre must incorporate signal-to-noise ratio values of cognitive users and the channel conditions. A genetic algorithm-based weighted optimisation strategy is presented for the case of soft decision combining. Numerical results show that the proposed optimised collaborative spectrum sensing schemes give better spectrum sensing performance.

scholarly journals A throughput analysis of an energy-efficient spectrum sensing scheme for the cognitive radio-based Internet of things

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Md Sipon Miah ◽  
Michael Schukat ◽  
Enda Barrett
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Time Slot ◽  
Fusion Rule ◽  
Primary User ◽  
Decision Fusion ◽  
Soft Decision ◽  
Sequential Approach ◽  
Dempster Shafer Theory ◽  
Shafer Theory

AbstractSpectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum, to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures maximum utilization of the licensed spectrum and is fundamental to building effective cognitive radio networks. In this paper, we address the issues of enhancing sensing gain, average throughput, energy consumption, and network lifetime in a cognitive radio-based Internet of things (CR-IoT) network using the non-sequential approach. As a solution, we propose a Dempster–Shafer theory-based throughput analysis of an energy-efficient spectrum sensing scheme for a heterogeneous CR-IoT network using the sequential approach, which utilizes firstly the signal-to-noise ratio (SNR) to evaluate the degree of reliability and secondly the time slot of reporting to merge as a flexible time slot of sensing to more efficiently assess spectrum sensing. Before a global decision is made on the basis of both the soft decision fusion rule like the Dempster–Shafer theory and hard decision fusion rule like the “n-out-of-k” rule at the fusion center, a flexible time slot of sensing is added to adjust its measuring result. Using the proposed Dempster–Shafer theory, evidence is aggregated during the time slot of reporting and then a global decision is made at the fusion center. In addition, the throughput of the proposed scheme using the sequential approach is analyzed based on both the soft decision fusion rule and hard decision fusion rule. Simulation results indicate that the new approach improves primary user sensing accuracy by $$13\%$$ 13 % over previous approaches, while concurrently increasing detection probability and decreasing false alarm probability. It also improves overall throughput, reduces energy consumption, prolongs expected lifetime, and reduces global error probability compared to the previous approaches under any condition [part of this paper was presented at the EuCAP2018 conference (Md. Sipon Miah et al. 2018)].

scholarly journals A Novel Semi-Soft Decision Scheme for Cooperative Spectrum Sensing in Cognitive Radio Networks

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2522 ◽  
Author(s):  
Yin Mi ◽  
Guangyue Lu ◽  
Yuxin Li ◽  
Zhiqiang Bao
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Transmission Band ◽  
Primary User ◽  
Detection Performance ◽  
Final Decision ◽  
Soft Decision ◽  
Hidden Terminal ◽  
Sensing Performance

Spectrum sensing (SS) is an essential part of cognitive radio (CR) technology, and cooperative spectrum sensing (CSS) could efficiently improve the detection performance in environments with fading and shadowing effects, solving hidden terminal problems. Hard and Soft decision detection are usually employed at the fusion center (FC) to detect the presence or absence of the primary user (PU). However, soft decision detection achieves better sensing performance than hard decision detection at the expense of the local transmission band. In this paper, we propose a tradeoff scheme between the sensing performance and band cost. The sensing strategy is designed based on three modules. Firstly, a local detection module is used to detect the PU signal by energy detection (ED) and send decision results in terms of 1-bit or 2-bit information. Secondly, and most importantly, the FC estimates the received decision data through a data reconstruction module based on the statistical distribution such that the extra thresholds are not needed. Finally, a global decision module is in charge of fusing the estimated data and making a final decision. The results from a simulation show that the detection performance of the proposed scheme outperforms that of other algorithms. Moreover, savings on the transmission band cost can be made compared with soft decision detection.

A Dynamic Threshold Optimization Algorithm for Spectrum Sensing with Energy Detection in Cognitive Radio

2012 ◽  
Vol 236-237 ◽  
pp. 917-922
Author(s):  
Wei Ran Wang ◽  
Shu Bin Wang ◽  
Xin Yan Zhao
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Optimization Algorithm ◽  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Dynamic Threshold ◽  
Threshold Optimization ◽  
Sensing Performance

In order to improve an efficiency of energy detection for a spectrum sensing in cognitive radio (CR), this paper proposes a dynamic threshold optimization algorithm. The traditional energy detection algorithm uses a fixed threshold, and can't guarantee always the optimal sensing performance in any environment. The improvement for sensing performance need to minimize the undetected probability and the probability of false alarm, and it is dissimilar for different CR users to accept these two errors. We improve the traditional energy detection algorithm, and firstly introduce a preference factor to characterize CR users’ different requirements for these two errors, then, propose a dynamic threshold optimization algorithm by minimizing integrated detection error for different signal-to-noise ratio (SNR). The simulation results show that the proposed algorithm effectively reduces the integrated spectrum sensing error, and increases the probability of detection, especially in low SNR.

Efficient Soft Decision Fusion Rule in Cooperative Spectrum Sensing

2013 ◽  
Vol 61 (8) ◽  
pp. 1931-1943 ◽  
Author(s):  
Weijia Han ◽  
Jiandong Li ◽  
Zan Li ◽  
Jiangbo Si ◽  
Yan Zhang
Keyword(s):  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Fusion Rule ◽  
Decision Fusion ◽  
Soft Decision

scholarly journals Distributed detection basis on using soft decision scheme both in local sensors and in fusion center

2021 ◽  
pp. 49-56
Author(s):  
V. I. Parfenov ◽  
V. D. Le
Keyword(s):  
Signal To Noise Ratio ◽  
Energy Parameter ◽  
Fusion Rule ◽  
Decision Fusion ◽  
Distributed Detection ◽  
Fusion Center ◽  
Soft Decision ◽  
Signal To Noise ◽  
Decision Scheme ◽  
Noise Ratio

The paper considers distributed detection problem basis on using soft decision scheme both in the local sensors and in the fusion center (FC). The algorithm for making soft decisions when receiving data from local sensors in the fusion center and its performance characteristics, which are necessary for the formation decision fusion rule, are presented. The dependencies of the total error probability on the energy parameter taking into account signal-to-noise ratio at the level of local sensors and the channel’s signal-to-noise ratio are given. The gain of the fusion rule basis on the aggregation of soft decisions in the FC when receiving data about soft local decisions, in efficiency compared to hard fusion rule.

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

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1038 ◽  
Author(s):  
Noor Gul ◽  
Muhammad Sajjad Khan ◽  
Su Min Kim ◽  
Junsu Kim ◽  
Atif Elahi ◽  
...  
Keyword(s):  
Spectrum Sensing ◽  
Cooperative 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.

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