Coarse-fine spectrum sensing for Cognitive Radio for minimum sensing time

2010 ◽  
Author(s):  
Brendan Lawton ◽  
Colin C. Murphy
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Fine Spectrum ◽  
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 Spectrum Sensing for Cognitive Radio Using Genetic Algorithm

2018 ◽  
Vol 14 (09) ◽  
pp. 190 ◽  
Author(s):  
Shewangi Kochhar ◽  
Roopali Garg
Keyword(s):  
Genetic Algorithm ◽  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Optimization Technique ◽  
Primary User ◽  
Time Frame ◽  
Probability Of False Alarm ◽  
Sensing Time ◽  
Better Than

<p>Cognitive Radio has been skillful technology to improve the spectrum sensing as it enables Cognitive Radio to find Primary User (PU) and let secondary User (SU) to utilize the spectrum holes. However detection of PU leads to longer sensing time and interference. Spectrum sensing is done in specific “time frame” and it is further divided into Sensing time and transmission time. Higher the sensing time better will be detection and lesser will be the probability of false alarm. So optimization technique is highly required to address the issue of trade-off between sensing time and throughput. This paper proposed an application of Genetic Algorithm technique for spectrum sensing in cognitive radio. Here results shows that ROC curve of GA is better than PSO in terms of normalized throughput and sensing time. The parameters that are evaluated are throughput, probability of false alarm, sensing time, cost and iteration.</p>

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.

Throughput optimization for dual collaborative spectrum sensing with dynamic scheduling

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740089 ◽  
Author(s):  
Cuimei Cui ◽  
Dezhi Yang
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Optimization Algorithm ◽  
Ad Hoc ◽  
Dynamic Scheduling ◽  
Throughput Optimization ◽  
Shadow Fading ◽  
Average Throughput ◽  
Collaborative Spectrum Sensing ◽  
Sensing Time

Cognitive radio technology is envisaged to alleviate both spectrum inefficiency and spectrum scarcity problems by exploiting the existing licensed spectrum opportunistically. However, cognitive radio ad hoc networks (CRAHNs) impose unique challenges due to the high dynamic scheduling in the available spectrum, diverse quality of service (QOS) requirements, as well as hidden terminals and shadow fading issues in a harsh radio environment. To solve these problems, this paper proposes a dynamic and variable time-division multiple-access scheduling mechanism (DV-TDMA) incorporated with dual collaborative spectrum sensing scheme for CRAHNs. This study involves the cross-layered cooperation between the Physical (PHY) layer and Medium Access Control (MAC) layer under the consideration of average sensing time, sensing accuracy and the average throughput of cognitive radio users (CRs). Moreover, multiple-objective optimization algorithm is proposed to maximize the average throughput of CRs while still meeting QOS requirements on sensing time and detection error. Finally, performance evaluation is conducted through simulations, and the simulation results reveal that this optimization algorithm can significantly improve throughput and sensing accuracy and reduce average sensing time.

scholarly journals Analytical and learning-based spectrum sensing time optimisation in cognitive radio systems

2013 ◽  
Vol 7 (5) ◽  
pp. 480-489 ◽  
Author(s):  
Hossein Shokri-Ghadikolaei ◽  
Masoumeh Nasiri-Kenari ◽  
Younes Abdi
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Systems ◽  
Radio Systems ◽  
Sensing Time
Sign in / Sign up

Export Citation Format

Share Document