Analysis of Spectrum Sensing in Cognitive Radio
01 Oct 2016-pp 1-4
TL;DR: The optimal Bayesian Detector with QAM modulation is deduced and analyzed in terms of probability of false alarm and detection over over AWGN channel and results show that the Bayesiandetector with 8-QAM modulation has optimum performance over BayesianDetector with8-PSK modulation.
Abstract: In recent times, the popularity of Cognitive Radio has been increased because of its spectrum sensing characteristics. Using CR, the spectrum is utilized efficiently and maintain efficient communication when the primary user is not using the spectrum. In this paper, Bayesian detector is used for detecting the presence of primary user's signal. This approach is optimal when PU is not using the spectrum. The optimal Bayesian Detector with QAM modulation is deduced and analyzed in terms of probability of false alarm and detection over over AWGN channel. Simulation results show that the Bayesian Detector with 8-QAM modulation has optimum performance over Bayesian Detector with 8-PSK modulation.
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21 Apr 2022
TL;DR: Deep learning classifier namely Neural Network a Multilayer Perceptron (MLP) and machine learning approaches such as Gradient Boosting (GB), Support Vector Machine (SVM), Logistic Regression (L_R), K-nearest Neighbor (KNN) and Bagging algorithm are proposed in this paper.
Abstract: Cognitive Radio (CR) network is established for spectrum utilization. This technology allows unlicensed users to share the spectrum with licensed users. In order to perform such a process, the spectrum needs to be periodically scanned in order to find the voids in the white (licensed) spectrum. Automatic spectrum sensing approaches are proposed in this paper. Deep learning classifier namely Neural Network a Multilayer Perceptron (MLP) and machine learning approaches such as Gradient Boosting (GB), Support Vector Machine (SVM), Logistic Regression (L_R), K-nearest Neighbor (KNN) and Bagging algorithm. SVM-based spectrum sensing is outperformed with 94.01 % spectrum sensing accuracy was achieved using this technique.
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TL;DR: In this paper , a framework and feasibility study for deploying broadband internet services using Television White Space (TVWS) technology in Ugbawka, a rural area in Enugu state, Nigeria is presented.
Abstract: As a result of the switchover from analogue to digital transmission, Television White Space (TVWS) presented itself as good opportunity to supplement the existing licensed spectrum to ease the spectrum scarcity. Rural communities were usually not connected due to poor returns to the internet providers to provide broadband access to the areas. This research has prepared the framework and feasibility study for deploying broadband internet services using Television White Space (TVWS) technology in Ugbawka, a rural area in Enugu state, Nigeria. In this work, a Network Ping was run on five websites using three major internet service providers as backhaul to establish facts of poor or even non-existent internet services. Using Ping Plotter 5 Pro, accessing ieee.com using MTN yielded a Round Trip (RT) average of 846.433 ms, with a loss of 83.8% packet over 10mins count. Radio Frequency (RF) Explorer and Carlson transceiver, Customer Premise Equipment (CPE) were used for field trials to determine availability of TVWS Frequencies. A database app was developed by writing some codes in the Basic for Android (B4A) Intermediate Development Environment (IDE). Empirical outdoor propagation model was developed with a 2.15 pathloss exponent while the indoor propagation model gave a pathloss exponent of 3.47 . An algorithm was developed, titled, TVWS Optimization Quadrature Amplitude Algorithm, (TOQA), where throughput of this project performed better by giving 60Mbps and 70 Mbps at Signal-Noise-Ratio (SNR) of 5dB while the conventional algorithm gave 30Mbps and 25 Mbps at same SNR value. The Bit Error Rate was lower than the conventional models used, giving the TOQA values of 10-3 at SNR of 5 dB and 10-6 at SNR of 30 dB while the conventional method gave 10-1 and 10-3 respectively at the same SNR values. With an Average of 56.2% network latency recorded in Ugbawka, TVWS will make a great impact on Internet connectivity if deployed.
References
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TL;DR: Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks: radio-scene analysis, channel-state estimation and predictive modeling, and the emergent behavior of cognitive radio.
Abstract: Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.
12,172 citations
"Analysis of Spectrum Sensing in Cog..." refers background in this paper
...Secondary Users (SU) senses the spectrum and instantly moves its signal into unused channels avoiding the used channels for the purpose of providing reliable communication and efficient utilization of the radio spectrum [3]....
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TL;DR: With RKRL, cognitive radio agents may actively manipulate the protocol stack to adapt known etiquettes to better satisfy the user's needs and transforms radio nodes from blind executors of predefined protocols to radio-domain-aware intelligent agents that search out ways to deliver the services the user wants even if that user does not know how to obtain them.
Abstract: Software radios are emerging as platforms for multiband multimode personal communications systems. Radio etiquette is the set of RF bands, air interfaces, protocols, and spatial and temporal patterns that moderate the use of the radio spectrum. Cognitive radio extends the software radio with radio-domain model-based reasoning about such etiquettes. Cognitive radio enhances the flexibility of personal services through a radio knowledge representation language. This language represents knowledge of radio etiquette, devices, software modules, propagation, networks, user needs, and application scenarios in a way that supports automated reasoning about the needs of the user. This empowers software radios to conduct expressive negotiations among peers about the use of radio spectrum across fluents of space, time, and user context. With RKRL, cognitive radio agents may actively manipulate the protocol stack to adapt known etiquettes to better satisfy the user's needs. This transforms radio nodes from blind executors of predefined protocols to radio-domain-aware intelligent agents that search out ways to deliver the services the user wants even if that user does not know how to obtain them. Software radio provides an ideal platform for the realization of cognitive radio.
9,238 citations
"Analysis of Spectrum Sensing in Cog..." refers background in this paper
...Cognitive Radio (CR) is an inadvisable solution to avoid the congestion by efficient usage of the frequency bands that are not used by licensed users [1, 2]....
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01 Jan 1976
1,104 citations
"Analysis of Spectrum Sensing in Cog..." refers background or methods in this paper
...for = 0 The expected posterior cost is minimized based on Bayesian decision rule [8] and is defined as...
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...variance Optimal BD is used to determine the likelihood ratio and the decision made by comparing the likelihood ratio with the threshold [8]....
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...true decision be ‘0’ (Assuming Probability of error minimum) [8], then the threshold Є is given by Є = ( ) ( ) (10)...
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01 Jan 2002
TL;DR: The concept of a random variable (RV) was introduced by as discussed by the authors, and continuous and discrete RVs were used to estimate the mean and variance of the expected value/mean and variance, moments and characteristic functions.
Abstract: Probability theory: Review of Set theory; introduction to probability, axioms of probability; joint and conditional probability; Bayes theorem. Random Variables: The concept of a random variable (RV); continuous and discrete RVs; probability distribution and density functions, properties; some standard examples; Functions of an RV, distribution and densities of functions of an RV, examples; expected value/mean and variance; moments and characteristic functions; two RVs: joint distribution and density functions; correlation, covariance, orthogonality and independence; conditional distribution and density functions. Elements of Estimation theory: Estimation of mean and variance; Chebyshev inequality; Parameter Estimation, Properties of Estimators; Cramer-Rao bound. Stochastic Processes: Introduction, Statistics of stochastic processes, correlation and covariance; Stationarity; Autocorrelation, Power density spectrum, and Wiener Khinchin Theorem; Linear Systems with stochastic inputs.
95 citations
"Analysis of Spectrum Sensing in Cog..." refers methods in this paper
...and can be transformed to and by jacobian method [9] as ( , ) = |J| p , (14)...
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TL;DR: The simulations have shown that Bayesian detector has a performance similar to the energy detector in low SNR regime, but has better performance in highSNR regime in terms of spectrum utilization and secondary users' throughput.
Abstract: With the prior knowledge that the primary user is highly likely idle and the primary signals are digitally modulated, we propose an optimal Bayesian detector for spectrum sensing to achieve higher spectrum utilization in cognitive radio networks. We derive the optimal detector structure for MPSK modulated primary signals with known order over AWGN channels and give its corresponding suboptimal detectors in both low and high SNR (Signal-to-Noise Ratio) regimes. Through approximations, it is found that, in low SNR regime, for MPSK (M>2) signals, the suboptimal detector is the energy detector, while for BPSK signals the suboptimal detector is the energy detection on the real part. In high SNR regime, it is shown that, for BPSK signals, the test statistic is the sum of signal magnitudes, but uses the real part of the phase-shifted signals as the input. We provide the performance analysis of the suboptimal detectors in terms of probabilities of detection and false alarm, and selection of detection threshold and number of samples. The simulations have shown that Bayesian detector has a performance similar to the energy detector in low SNR regime, but has better performance in high SNR regime in terms of spectrum utilization and secondary users' throughput.
36 citations
"Analysis of Spectrum Sensing in Cog..." refers methods in this paper
...Spectrum Sensing (SS) in CR using BD has been developed with BPSK and MPSK as primary signals [5, 6]....
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