scholarly journals Robust Spectrum Sensing Demonstration Using a Low-Cost Front-End Receiver

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Daniele Borio ◽  
Emanuele Angiuli ◽  
Raimondo Giuliani ◽  
Gianmarco Baldini
Keyword(s):  
Cognitive Radio ◽  
False Alarm ◽  
Spectrum Sensing ◽  
High Frequency ◽  
Detection Probability ◽  
Low Cost ◽  
False Alarm Probability ◽  
Primary Users ◽  
Front End ◽  
Challenging Tasks

Spectrum Sensing (SS) is an important function in Cognitive Radio (CR) to detect primary users. The design of SS algorithms is one of the most challenging tasks in CR and requires innovative hardware and software solutions to enhance detection probability and minimize low false alarm probability. Although several SS algorithms have been developed in the specialized literature, limited work has been done to practically demonstrate the feasibility of this function on platforms with significant computational and hardware constraints. In this paper, SS is demonstrated using a low cost TV tuner as agile front-end for sensing a large portion of the Ultra-High Frequency (UHF) spectrum. The problems encountered and the limitations imposed by the front-end are analysed along with the solutions adopted. Finally, the spectrum sensor developed is implemented on an Android device and SS implementation is demonstrated using a smartphone.

scholarly journals A Novel Sensing Strategy Based on Energy Detector for Spectrum Sensing

2019 ◽  
Vol 9 (21) ◽  
pp. 4634 ◽  
Author(s):  
Hai Huang ◽  
Jia Zhu ◽  
Junsheng Mu
Keyword(s):  
False Alarm ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
False Alarm Probability ◽  
Energy Detector ◽  
Accuracy Improvement ◽  
Classical Scheme ◽  
Sensing Performance

Sensing strategy directly influences the sensing accuracy of a spectrum sensing scheme. As a result, the optimization of a sensing strategy appears to be of great significance for accuracy improvement in spectrum sensing. Motivated by this, a novel sensing strategy is proposed in this paper, where an improved tradeoff among detection probability, false-alarm probability and available throughput is obtained based on the energy detector. We provide the optimal sensing performance and exhibit its superiority in theory compared with the classical scheme. Finally, simulations validate the conclusions drawn in this paper.

Cepstrum-Based Spectrum Hole Search in Different Fading Scenario in Cognitive Radio Network

2017 ◽  
pp. 49-62
Author(s):  
Srijibendu Bagchi
Keyword(s):  
Cognitive Radio ◽  
False Alarm ◽  
Detection Probability ◽  
False Alarm Probability ◽  
Channel Fading ◽  
Spectral Density Estimation ◽  
Power Spectral ◽  
Detection Probabilities ◽  
Spectrum Scarcity ◽  
Spectrum Hole

Cognitive radio is now acknowledged as a potential solution to meet the spectrum scarcity problem in radio frequency range. To achieve this objective proper identification of vacant frequency band is necessary. In this article a detection methodology based on cepstrum estimation has been proposed that can be done through power spectral density estimation of the received signal. The detection has been studied under different channel fading conditions along with Gaussian noise. Two figures of merit are considered here; false alarm probability and detection probability. For a specific false alarm probability, the detection probabilities are calculated for different sample size and it has been established through numerical results that the proposed detector performs quite well in different channel impairments.

A Reliable Spectrum Sensing Strategy Based on Multiple-Antenna Technique

2013 ◽  
Vol 765-767 ◽  
pp. 2305-2308
Author(s):  
Shou Tao Lv ◽  
Ze Yang Dai ◽  
Jian Liu
Keyword(s):  
Performance Analysis ◽  
False Alarm ◽  
Closed Form ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
False Alarm Probability ◽  
Multiple Antenna ◽  
Single Antenna ◽  
Simulation Results ◽  
Present Simulation

In this paper, we propose a reliable spectrum sensing strategy based on multiple-antenna technique, called RSS-MAT, to combat the channel uncertainties. We derive the closed-form expressions of the false alarm probability and detection probability for RSS-MAT. Finally, we present simulation results to validate our performance analysis. As expected, the simulation results show that RSS-MAT outperforms the spectrum sensing strategy with single antenna.

scholarly journals Spectrum Sensing for OFDM Cognitive Radio using Matched Filter Detection

2019 ◽  
Vol 8 (2) ◽  
pp. 1443-1448
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
Orthogonal Frequency Division Multiplexing ◽  
Cross Correlation ◽  
Matched Filter ◽  
False Alarm Probability ◽  
Spectrum Allocation ◽  
Probability Of Detection ◽  
Operating Characteristics

The exponentially increasing high data rate applications demand more spectrum. Conventional spectrum allocation schemes failed in providing spectrum to meet the requirement. Cognitive radio introduces dynamic spectrum allocation. Spectrum sensing plays a very important role in cognitive radio. In this paper, OFDM with sensing algorithm based on matched filter detection is presented. An Orthogonal Frequency Division Multiplexing (OFDM) with Cyclic Prefix is used to reduce Inter Block Interference between successive symbols. The Receiver Operating Characteristics (RoC), detection probability with respect to Signal to Noise Ratio (SNR) for a given False Alarm Probability (PFA) obtained and analyzed. Simulation results shows that, this algorithm achieves probability of detection values 0.38 and 0.9 for SNR values of -20dB and -15 dB respectively at PFA=10-2 . Chen, Hou-Shin, considered Time Domain Symbol Cross-correlation between two OFDM symbols. This paper mainly focusses on improving the detection probability at low SNR values without considering cross-correlation, which requires more computations.

scholarly journals Effective Usage of Available Spectrum Using Dispersion Detection Technique

2020 ◽  
Author(s):  
Durga R ◽  
Selvaraj D
Keyword(s):  
Cognitive Radio ◽  
False Alarm ◽  
Frequency Spectrum ◽  
Spectrum Sensing ◽  
False Alarm Probability ◽  
Detection Technique ◽  
Decision Threshold ◽  
Multiple Antenna ◽  
Test Statistic ◽  
A New Technique

Spectrum sensing techniques are used for aquising the frequency spectrum in cognitive radio. From research, the efficiency of the spectrum sensing technique increases only if its complexity is increased and if its complexity is decreased then its efficiency decreases. so, a new technique is proposed in this paper based on Dispersion Detection (DD) to balance both complexity and efficiency. Using this detection technique, the false alarm probability is derived for multiple antenna using test statistic distribution. The decision threshold is derived to provide the accurate results. The derived values are verified with Monto Carlo simulation.

Sensing Confidence Level-Based Cooperative Spectrum Sensing Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 1499-1504
Author(s):  
Shi Ding Zhang ◽  
Hai Lian Wang ◽  
Jing Ping Mei
Keyword(s):  
False Alarm ◽  
Spectrum Sensing ◽  
Confidence Level ◽  
Detection Probability ◽  
Cooperative Spectrum Sensing ◽  
False Alarm Probability ◽  
Control Channel ◽  
Fusion Center ◽  
Local Spectrum ◽  
Secondary Users

Cooperative spectrum sensing is a key technology to tackle the challenges such as fading or hidden terminal problem in local spectrum sensing of cognitive radio system. Conventional cooperative method can improve the detection performance in some sense, but increase overhead of control channel. In order to reduce the overhead, a new cooperative spectrum sensing algorithm based on confidence level is proposed. In this algorithm, the maximum-eigenvalue-based detection scheme is carried out to obtain the local spectrum detection and the detection probability and false alarm probability of each secondary user are used to estimate the reliability of the sensing decision. The test statistic of the secondary users with high reliability are chosen and sent to fusion center. Then weighted factors of chosen secondary users are derived from creditability values, and the global decision is made by weighted fusion at fusion center. The simulation results show that the proposed algorithm improves the detection probability in the guarantee of the false-alarm probability close to 0 and saves half of the overhead in the control channel.

Enhanced Spectrum Sensing Algorithm Based on MME Detection and OAS Shrinkage Estimator

2019 ◽  
Vol 10 (1) ◽  
pp. 83-97
Author(s):  
Amoon Khalil ◽  
Mohiedin Wainakh
Keyword(s):  
Cognitive Radio ◽  
Covariance Matrix ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
Detection Performance ◽  
Shrinkage Estimator ◽  
Detection Window ◽  
Simulation Results

Spectrum Sensing is one of the major steps in Cognitive Radio. There are many methods to conduct Spectrum Sensing. Each method has different detection performances. In this article, the authors propose a modification of one of these methods based on MME algorithm and OAS estimator. In MME&OAS method, in each detection window, OAS estimates the covariance matrix of the signal then the MME algorithm detects the signal on the estimated matrix. In the proposed algorithm, authors assumed that there is correlation between two consecutive decisions, so authors suggest the OAS estimator depending on the last detection decision, and then detect the signal using MME algorithm. Simulation results showed enhancement in detection performance (about 2dB when detection probability is 0.9. compared to MME&OAS method).

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>

Interference Modeling and Analysis in Cognitive Radio Networks

2013 ◽  
Vol 4 (4) ◽  
pp. 1-15
Author(s):  
Yanxiao Zhao ◽  
Bighnaraj Panigrahi ◽  
Kazem Sohraby ◽  
Wei Wang
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Critical Role ◽  
Radio Networks ◽  
Secondary Users ◽  
Idle State ◽  
Modeling And Analysis ◽  
Interference Modeling ◽  
Primary Users

Cognitive radio networks (CRNs) have received considerable attention and viewed as a promising paradigm for future wireless networking. Its major difference from the traditional wireless networks is that secondary users are allowed to access the channel if they pose no harmful interference to primary users. This distinct feature of CRNs has raised an essential and challenging question, i.e., how to accurately estimate interference to the primary users from the secondary users? In addition, spectrum sensing plays a critical role in CRNs. Secondary users have to sense the channel before they transmit. A two-state sensing model is commonly used, which classifies a channel into either busy or idle state. Secondary users can only utilize a channel when it is detected to be in idle state. In this paper, we tackle the estimation of interference at the primary receiver due to concurrently active secondary users. With the spectrum sensing, secondary users are refrained from transmitting once an active user falls into their sensing range. As a result, the maximum number of simultaneously interfering secondary users is bounded, typically ranging from 1 to 4. This significant conclusion considerably simplifies interference modeling in CRNs. The authors present all the cases with possible simultaneously interfering secondary users. Moreover, the authors derive the probability for each case. Extensive simulations are conducted and results validate the effectiveness and accuracy of the proposed approach.

scholarly journals Statistical Properties of Energy Detection for Spectrum Sensing by Using Estimated Noise Variance

2019 ◽  
Vol 8 (2) ◽  
pp. 28 ◽  
Author(s):  
Xiao-Li Hu ◽  
Pin-Han Ho ◽  
Limei Peng
Keyword(s):  
False Alarm ◽  
Spectrum Sensing ◽  
False Alarm Probability ◽  
Energy Detection ◽  
Radio Spectrum ◽  
Statistical Properties ◽  
Constant False Alarm Rate ◽  
Noise Variance ◽  
Total Noise ◽  
Noise Receiver

In energy detection for cognitive radio spectrum sensing, the noise variance is usually assumed given, by which a threshold is set to guarantee a desired constant false alarm rate (CFAR) or a constant detection rate (CDR). However, in practical situations, the exact information of noise variance is generally unavailable to a certain extent due to the fact that the total noise consists of time-varying thermal noise, receiver noise, and environmental noise, etc. Hence, setting the thresholds by using an estimated noise variance may result in different false alarm probabilities from the desired ones. In this paper, we analyze the basic statistical properties of the false alarm probability by using estimated noise variance, and propose a method to obtain more suitable CFAR thresholds for energy detection. Specifically, we first come up with explicit descriptions on the expectations of the resultant probability, and then analyze the upper bounds of their variance. Based on these theoretical preparations, a new method for precisely obtaining the CFAR thresholds is proposed in order to assure that the expected false alarm probability can be as close to the predetermined as possible. All analytical results derived in this paper are testified by corresponding numerical experiments.

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