Optimal spectrum sensing time considering spectrum handoff due to false alarm in cognitive radio networks

2009 ◽  
Vol 13 (12) ◽  
pp. 899-901 ◽  
Author(s):  
D.-J. Lee ◽  
M.-S. Jang
Keyword(s):  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Spectrum Handoff ◽  
Sensing Time

scholarly journals Efficient Approximations for Optimization of N-Out-of-K Rule for Heterogeneous Cognitive Radio Networks

2021 ◽  
Vol 11 (7) ◽  
pp. 3083
Author(s):  
Youheng Tan ◽  
Xiaojun Jing
Keyword(s):  
Cognitive Radio ◽  
Computational Complexity ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Closed Form Expression ◽  
Sampling Point ◽  
Mutual Cooperation ◽  
Communication Time ◽  
Sensing Time

Spectrum sensing (SS) has attracted much attention due to its important role in the improvement of spectrum efficiency. However, the limited sensing time leads to an insufficient sampling point due to the tradeoff between sensing time and communication time. Although the sensing performance of cooperative spectrum sensing (CSS) is greatly improved by mutual cooperation between cognitive nodes, it is at the expense of computational complexity. In this paper, efficient approximations of the N-out-of-K rule-based CSS scheme under heterogeneous cognitive radio networks are provided to obtain the closed-form expression of the sensing threshold at the fusion center (FC), where the false alarm probability and its corresponding detection probability are approximated by the Poisson distribution. The computational complexity required to obtain the optimal sensing threshold at the FC has greatly decreased and theoretical derivations state that the approximation error is negligible. The simulations validate the effectiveness of the proposed scheme.

scholarly journals Traffic Based Optimization of Spectrum Sensing in Cognitive Radio Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Changhua Yao ◽  
Qihui Wu ◽  
Linfang Zhou
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Concave Function ◽  
Radio Networks ◽  
Data Traffic ◽  
Time Value ◽  
Achievable Throughput ◽  
Special Case ◽  
Sensing Time

We propose a more practical spectrum sensing optimization problem in cognitive radio networks (CRN), by considering the data traffic of second user (SU). Compared with most existing work, we do not assume that SU always has packets to transmit; instead, we use the actual data transmitted per second rather than the channel capacity as the achievable throughput, to reformulate the Sensing-Throughput Tradeoff problem. We mathematically analyze the problem of optimal sensing time to maximize the achievable throughput, based on the data traffic of SU. Our model is more general because the traditional Sensing-Throughput Tradeoff model can be seen as a special case of our model. We also prove that the throughput is a concave function of sensing time and there is only one optimal sensing time value which is determined by the data traffic. Simulation results show that the proposed approach outperforms existing methods.

scholarly journals DOUBLE THRESHOLD SPECTRUM SENSING WITH OPTIMIZATION OF SAMPLES IN COGNITIVE RADIO NETWORKS

2021 ◽  
Vol 22 (2) ◽  
pp. 161-167
Author(s):  
Chilakala Sudhamani
Keyword(s):  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Primary User ◽  
Radio Networks ◽  
Probability Of Detection ◽  
Secondary User ◽  
Correct Detection ◽  
Propagation Time

In cognitive radio networks spectrum sensing plays a vital role to identify the presence or absence of the primary user. In conventional spectrum sensing one secondary user will make a final decision regarding the availability of licensed spectrum. But Secondary user fail to make a correct detection about the presence of the primary user if he is in fading environment and it causes interference to the licensed users. Therefore to avoid interference to the licensed users and to make correct detection, number of samples is increased, Which increases the probability of detection. In this paper the optimization of samples is proposed to reduce the system overhead and also to increase the propagation time. Simulation results show the optimized value of samples for a given probability of false alarm and also the variation of probability of detection with optimized samples and false alarm is shown in the results. ABSTRAK: Dalam rangkaian radio kognitif, penginderaan spektrum memainkan peranan penting untuk mengenal pasti kehadiran atau ketiadaan pengguna utama. Dalam penginderaan spektrum konvensional, seorang pengguna sekunder akan membuat keputusan akhir mengenai ketersediaan spektrum berlesen. Tetapi pengguna Sekunder gagal membuat pengesanan yang betul mengenai kehadiran pengguna utama jika dia berada dalam persekitaran yang pudar dan menyebabkan gangguan kepada pengguna yang berlesen. Oleh itu untuk mengelakkan gangguan kepada pengguna berlesen dan membuat pengesanan yang betul, jumlah sampel meningkat, yang meningkatkan kemungkinan pengesanan. Dalam makalah ini pengoptimuman sampel dicadangkan untuk mengurangi overhead sistem dan juga untuk meningkatkan waktu penyebaran. Hasil simulasi menunjukkan nilai sampel yang dioptimumkan untuk kebarangkalian penggera palsu dan juga variasi kebarangkalian pengesanan dengan sampel yang dioptimumkan dan penggera palsu ditunjukkan dalam hasil.

scholarly journals Performance Analysis of Cooperative Spectrum Sensing In Different Case Scenarios

2019 ◽  
Vol 8 (10) ◽  
pp. 3869-3872
Keyword(s):  
Cognitive Radio ◽  
Performance Analysis ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Cooperative Relay

Cooperative relay based spectrum sensing techniques are primarily available techniques in the field of research in cognitive radio networks. Even such techniques are available there is need to consider fundamental effects on spectrum sensing with various combination of scenarios that lead to false alarm detection. In this paper we have compared the three cases of cooperative spectrum sensing to analyze the effects and to form the direction of further research expectations in the field of cooperative spectrum sensing

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