Spectrum Sensing In Cognitive Radio Research Articles

Spectrum Sensing for Cognitive Radios With Unknown Noise Variance and Time-variant Fading Channels

The unknown noise variance and time-variant fading channels make the spectrum sensing design a challenging task for cognitive radios. Most existing sensing methods suffer from the information uncertainty and can hardly acquire promising performances in the adverse situations. To address this challenge, in this paper, we first formulate a dynamic state-space model for spectrum sensing, in which the unknown noise variance and time-variant flat fading channels are all taken into considerations. The dynamic behaviors of both primary user states and fading channels are characterized by two discrete state Markov chains. Based on this model, a novel spectrum sensing scheme is designed to recursively estimate the occupancy state of primary users, by estimating the time-variant fading channel gain and noise parameters jointly. The joint estimation is primarily premised on a maximum a posteriori probability criterion and the marginal particle filtering schemes. Simulation results are provided to demonstrate the advantages of our proposed method, which can significantly improve the sensing performance over time-variant flat fading channels, even with unknown noise variance.

Spectrum Sensing in Cognitive Radio Using Combined Sequential Energy Detector and Cyclostationary Feature Detector

In the following research, we derive a detector which is based on sequential probability ratio test (SPRT) and it uses Energy Detector (ED) which is followed by Cyclostationary Feature Detector (CFD). ED is a blind sensing technique and it is easy to implement while conceptually simple. However, it is highly affected by interference and noise uncertainties. Therefore, CFD is applied for fine sensing as research has shown that Cyclostationary Feature Detector is more suitable than the energy detection when noise uncertainties are unknown. Our method is novel in trying to derive a sequential Energy Detector and combine it with Cyclostationary Feature Detector for low SNR region where average sample number (ASN) as a random variable may take very high value to achieve a desired performance level for sequential Energy Detector. For this sequential Energy Detector is terminated after it reaches certain cut-off sample number, making it truncated sequential Energy Detector.

Multiband Cooperative Spectrum Sensing for Cognitive Radio in the Presence of Malicious Users

We propose a cooperative multiband spectrum sensing scheme for a cognitive radio network operating in the presence of malicious users. More precisely, we investigate the optimality of cooperative spectrum sensing with the aim of optimizing the detection performance and then maximize the aggregate achievable throughput given a bound on the aggregate interference imposed on the primary network. Comparative simulation results validate the superiority of our proposed method in terms of detection accuracy compared to conventional defense techniques used to combat the presence of malicious attackers.

Comparatative Analysis of Energy Detection Spectrum Sensing of Cognitive Radio Under Wireless Environment Using SEAMCAT

In the recent years, the Cognitive Radio technology imposed itself as a good solution to enhance the utilization of unused spectrum and globalized the radio environment for different band users that utilize or require different techniques for transmission. In this paper, the energy detection spectrum sensing technique that is used to detect the presence of unknown deterministic signal is studied under the non-time dispersive fading environment using the Hata propagation model for picocell communication systems. The different aspects of non-time dispersive fading regions over energy detection spectrum sensing and impact of changing a detection threshold of the secondary user Cognitive Radio on interference at primary user for non-cooperative spectrum access have been studied in the terms of probability of interference. The entire Comparatative Analysis of Spectrum Sensing in Cognitive radio has been carried out with the aid of SEAMCAT software platform.

Current Trends and Research Challenges in Spectrum-Sensing for Cognitive Radios

The ever increasing demand of wireless communication systems has led to search of suitable spectrum bands for transmission of data. The research in the past has revealed that radio spectrum is under-utilized in most of the scenarios. This prompted the scientist to seek a solution to utilize the spectrum efficiently. Cognitive Radios provided an answer to the problem by sensing the idle (licensed) bands and allowing (secondary) users to transmit in these idle spaces. Spectrum sensing forms the main block of cognition cycle. This paper reviews the current trends in research in the domain of spectrum sensing. The author describes the type of channel being modelled, diversity combining schemes used, optimal algorithms applied at fusion centre, spectrum sensing techniques employed. Further, the research challenges are discussed. It is presented that various attributes like sensing time, throughput, rate reliability, optimum cooperative users, sensing frequency etc. needs to be addressed. A trade-off needs to be established to optimize two opposing parameters like sensing and throughput.

A New Wireless Network Scheme for Spectrum Sensing in Cognitive Radio

Spectrum sensing task is known to be one of the most important functionalities of a cognitive radio for dynamic spectrum access networks to be aware of the radio environment. Licensed user (primary user) detection has gained a great deal of interest recently on spectrum sensing strategies whereas unlicensed user (secondary user) detection must be a part of sensing with equal proportion of interest. This paper proposes a novel scheme that enables an efficient and robust spectrum sensing for both primary and secondary users detection. The proposed scheme is based on intentionally adding known noise at the sensing side. Then the sensing detector becomes insensitive to signal and noise types, and thus noise uncertainty problem is diminished. After sensing, the added noise is removed without affecting existing transmission on the channel. Although there have been several spectrum sensing methods, the proposed scheme is the optimal solution for spectrum sensing, to the best of our knowledge.DOI: http://dx.doi.org/10.5755/j01.eee.21.6.13769

A New Wireless Network Scheme for Spectrum Sensing in Cognitive Radio

Spectrum sensing task is known to be one of the most important functionalities of a cognitive radio for dynamic spectrum access networks to be aware of the radio environment. Licensed user (primary user) detection has gained a great deal of interest recently on spectrum sensing strategies whereas unlicensed user (secondary user) detection must be a part of sensing with equal proportion of interest. This paper proposes a novel scheme that enables an efficient and robust spectrum sensing for both primary and secondary users detection. The proposed scheme is based on intentionally adding known noise at the sensing side. Then the sensing detector becomes insensitive to signal and noise types, and thus noise uncertainty problem is diminished. After sensing, the added noise is removed without affecting existing transmission on the channel. Although there have been several spectrum sensing methods, the proposed scheme is the optimal solution for spectrum sensing, to the best of our knowledge. DOI: http://dx.doi.org/10.5755/j01.eee.21.6.13769

Fusion Rule for Cooperative Spectrum Sensing in Cognitive Radio

The increasing demands for wireless communication in all fields of life indicate a promising commercial potential. Cognitive radio (CR) is introduced as the radio that can replace the present smart phones in the future. Cooperative spectrum sensing is considered as an important step toward coexistence of CRs with primary users. Spectrum sensing needs to be reliable at anywhere at any time. In this paper, we put forward a scenario where cooperative spectrum sensing may not be reliable due to the specialty of the region. We also propose a fusion rule to make a reliable decision fusion at those regions. We have analyzed the performance of this fusion rule under Rayleigh fading channel and shadowing. Two separate simulation setups have been used to model these scenarios. Performance of the proposed fusion rule is compared with other fusion rules in the literature. The feature of adjustable weights according to the position of the CRs has enabled this fusion rule to perform better in the scenarios considered. With the feature of adaptability, this rule can work well in all scenarios related to cooperative spectrum sensing.

Source Number Detection and Realization of ROC for Spectrum Sensing in Cognitive Radio

Background/Objectives: The pre-assigned spectrum allocation policy that has been adopted by various wireless technologies according to the standards of Federal Communications Commission (FCC), in Cognitive radio, the whole available spectrum is grouped into many different bands and the usage of bands by different applications leads to the existence of spectrum hole(s). Methods/Statistical Analysis: High sampling rate is required for cognitive transceiver spectrum sensing applications, multiple analog front-end circuitry, high resolution of Analog to Digital Converters having large and dynamic range, and fast signal processors. Calculating the interference temperature or noise variance over intended Narrow Band (NB) transmission signals is a part of spectrum sensing. Findings: The main issue related to cognitive radio applications is to sense the vacant spectrum holes and simultaneously accommodate the unlicensed secondary users in that. For cognitive radio implementation, spectrum sensing is the foremost concern. The paper presents the realization of Receiver Operating Characteristic (ROC) for implementation of proposed source number detection for existing adaptive energy detection scheme. The realization is mapped using comprehensive Monte Carlo simulations that are quantified for detection performance as a received signal to noise ratio. Application/Improvements: As per the application point of view, different combinations of the sensing techniques are required for different requirements. For example, in case of quick and coarse scan of broad frequency range, the sensing method can of energy detection type. The present white spaces can further be discovered accurately using feature detection method.

Spectrum Sensing for Cognitive Radio Using Hybrid Matched Filter Single Cycle Cyclostationary Feature Detector

Spectrum sensing is an important task in cognitive radio (CR). Matched filter technique one of the techniques employed for spectrum sensing in cognitive radio which faces the challenge of low frequency offset tolerance in very low SNR environments. Hybrid matched filter architecture is used to improve this frequency offset tolerance. But, overall even such kind of architecture results in coarse detection. In very low SNR environments, where primary user is highly mobile, the multipath profile results in unknown phase of signal. Such kind of signal cannot even be detected by Hybrid Matched Filter. In this paper we propose combination of Hybrid Matched Filter and Single Cycle Cyclostationary Feature Detector to enhance the detection of such architecture. This results in both high frequency offset tolerance as well as fine detection of signal with unknown phase in very low SNR environments. Simulation results show significant improvement in the probability of detection and false alarm of the proposed scheme over Hybrid Matched Filter.

Wideband Spectrum Sensing for Cognitive Radios in Weakly Correlated Non-Gaussian Noise

Wideband signals can be subjected to various types of non-Gaussian and impulsive noise. Such non-Gaussian noise may cause degradation in the detection performance of conventional wideband signal detection methods, such as the periodogram-based approach. In addition, the signal detection probability can be further reduced due to possible correlation among the noise samples. Under such non-Gaussian noise conditions, we formulate a wideband signal detection method for cognitive radios (CRs) based on a locally-optimal (LO) Neyman-Pearson (NP) detector by assuming a weakly correlated noise model with known parameters. The corresponding decision statistic is expressed in the frequency-domain, allowing to detect the spectral activities within the sensed band of interest. The proposed detector is shown to reduce the impact of correlated non-Gaussian noise on the detection performance. We compute the receiver operating characteristic (ROC) of the proposed wideband LO-NP detector and show its superior performance, compared to existing wideband detectors under correlated noise conditions.

Energy Detection Based Spectrum Sensing for Cognitive Radios Over Time-Frequency Doubly Selective Fading Channels

Cognitive radios may operate in practice under various adverse environments. For typical mobile and short-range scenarios, wireless links may tend to be time and frequency selective, i.e., the multipath propagations with time-varying fading coefficients will be inevitable. To cope with the encountered doubly-selective channels, in this paper we present a new spectrum sensing algorithm for distributed applications. First, a dynamic discrete state-space model is established to characterize sensing process, where the occupancy state of primary band and the time-varying multipath channel are treated as two hidden states, while the summed energy is adopted as the observed output. With this new paradigm, spectrum sensing is realized by acquiring primary states and time-dependent multipath channel jointly. For the formulated problem, unfortunately, Bayesian statistical inference may be impractical due to the absence of likelihoods and involved non-stationary distributions. To remedy this problem, an iterative algorithm is further designed by resorting to sequential importance sampling techniques; thus, the dynamic non-Gaussian multipath channel and primary states are estimated recursively. Another critical challenge, e.g., the noise uncertainty, is also considered, which may be incorporated conveniently into this sensing diagram and, furthermore, addressed effectively by the designed algorithm. Simulations validate the proposed algorithm. While classical schemes fail to deal with doubly selective channels, the new sensing scheme can exploit the underlying channel memory and operate well, which provides a great promise to realistic applications.

An Energy Approximation Model Based on Restricted Isometry Property in Compressive Spectrum Sensing for Cognitive Radio

Compressive Sensing (CS) has proved to be an effective technique in terms of spectrum sensing of Cognitive Radio (CR). However the signal reconstruction is costly and unnecessary. Through analysis of Restricted Isometry Property (RIP) of CS, it is feasible to detect the primary user directly through the compressed signal. In this paper, a novel Energy Approximation model based on Compressive Spectrum Sensing (EA-CSS) in cognitive radios is proposed. In contrast with the traditional compressive spectrum sensing scheme, this method does not need to reconstruct the original signal and has lower complexity algorithms at the same time. In the modeling, the observed signal energy and the original signal energy is nearly equal. The cooperative sensing based on this model is also improved and shows more robust to the noise compare with the common cooperative sensing scheme. Simulation results show that the proposed techniques have a reliable detection performance with small number of samples.

Impact of Primary User Duty Cycle in Generalized Fading Channels on Spectrum Sensing in Cognitive Radio

Spectrum sensing is the key technology in cognitive radio for the efficient use of radio spectrum. The implementation of energy detection based spectrum sensing is done with the assumption of constant activity of the primary user. Practically, often the scenario occurs when primary user transmits signal in the frequency band of interest only for a fraction of the observation period. The paper aims at analyzing the degradation in performance of energy detection based sensors in the case of generalized κ-μ and α-μ fading channels. The theoretical results indicate low reliable performance in the case of fading channel due to primary user duty cycle variation at low signal-to-noise ratios.

A Survey on Energy Detection for Spectrum Sensing in Cognitive Radio

Cognitive radio () is an emerging and fascinating technology for the opportunistic use of underutilised spectrum that promises to change the future technological trends forever, if employed properly. One of the major challenges in CR network is spectrum sensing that is to detect the presence of licenced primary transmissions. In this paper, a survey of various spectrum sensing methodologies is presented.

Detection of Signals With Observations in Multiple Subbands: A Scheme of Wideband Spectrum Sensing for Cognitive Radio With Multiple Antennas

We address detection schemes of spectrum sensing for cognitive radio with multiple receive antennas operating over a wideband channel composed of a multitude of subbands. By taking the observations in all subbands into consideration in the likelihood functions for sensing a subband, the test statistics of the proposed schemes are functions of the sample covariance matrix in the subband under consideration and that in the subband exhibiting the lowest power spectral density. The false alarm and detection probabilities of the proposed schemes are analyzed theoretically and confirmed via simulations when the numbers of observations are the same for all the subbands. It is shown through computer simulations that the proposed schemes can provide considerable performance gains over conventional schemes for wideband spectrum sensing when the observations are spatially correlated and temporally independent/dependent.

Adaptive Decode-and-Forward Protocol Based Cooperative Spectrum Sensing in Cognitive Radio with Interference at the Secondary Users

In this paper a novel multiple-user cooperative spectrum sensing scheme (MCSS) based on hybrid relay is proposed to achieve the spatial diversity gain in detection of the primary user (PU) in a cognitive radio (CR) network. A practically important case where co-channel interference signals are present at the network is considered for the analysis. Closed-form expressions of detection probability $$(\hbox {P}_{\mathrm{d}})$$ ( P d ) and false alarm probability $$(\alpha )$$ ( ? ) for the proposed adaptive decode-and-forward based multiple-user cooperative spectrum sensing scheme (ADF-MCSS) using energy detector over Rayleigh fading sensing channels is derived in presence of co-channel interference at the secondary user which is far away from the PU. Further we extend the concept of two user amplify-and-forward (AF) and decode-and-forward (DF) cooperative spectrum sensing schemes in multiple-user scenario (i.e. AF-MCSS and DF-MCSS) over Rayleigh fading channels when the secondary user (which is far away from PU) is affected by interference. Closed-form expressions of AF-MCSS and DF-MCSS schemes over a Rayleigh fading channels are also evaluated and compared with that of proposed ADF-MCSS in presence of interference signals at the secondary user. Further the performance analysis of AF-MCSS, DF-MCSS and ADF-MCSS schemes are compared with the existing non-cooperative spectrum sensing schemes in presence of interference at the secondary user. Our analysis is validated by numerical and simulation results for multiple-user CR network. The impact of number of cooperative relays, SNR in sensing channel, energy of interference signal, false alarm on detection probability in proposed ADF, AF and DF schemes is shown.

Dynamic Double Energy Thresholds Detection for Cooperative Spectrum Sensing in Cognitive Radio

This paper suggests a cooperative spectrum sensing using dynamic double thresholds energy detection and adaptive grid search to obtain the highest probability detection. The proposed double thresholds are adaptive to noise fluctuation. A new fusion method combined of weighting and voting is used in cooperative sensing. Further, in order to obtain the best sensing performance, we firstly use adaptive grid search to find the optimal double thresholds. Simulation results show that the proposed method has excellent robustness to noise fluctuation and good sensing performance even under low signal to noise ratio (SNR).

An Algorithm for Spectrum Sensing in Cognitive Radio under Noise Uncertainty

With increasing demand of new wireless applications and increasing number of wireless user’s problem of spectrum scarcity arises. Cognitive Radio technology supports dynamic spectrum access to address spectrum scarcity problem. Reliability of cognitive operation entirely depends upon how effectively task of spectrum sensing has been performed. Spectrum sensing is a process of discovering voids in spectrum which can be allocated to cognitive users opportunistically. There exists number of traditional spectrum sensing methods in literature for constant noise floor. Practically, noise spectrum density is uncertain under which performance of spectrum sensing scheme degrades. In this paper a new spectrum sensing adaptive algorithm considering noise uncertainty has been proposed. Simulation results of proposed scheme shows a constant detection probability has been achieved under noise uncertainty.

Spectrum Occupancy Information in Support of Adaptive Spectrum Sensing for Cognitive Radio

Cognitive radio (CR) is an emerging solution to spectrum scarcity currently faced worldwide as CR can change its operating parameters like frequency and bandwidth according to the radio environment and provides efficient spectrum utilisation. Many measurement campaigns conducted around the world showed that the licensed spectrum is not efficiently utilized which led to the need of implementation of dynamic spectrum access (DSA). Statistical information about current frequency usage and its use for developing new algorithms and protocols for CR network is the research area that still requires much more effort. In this paper we discuss the comparison of measurement results obtained from two measurement campaigns conducted in Pune at same location but with different total bandwidths considered. Further to implement CR the best suited channels are obtained from the data collected by using adaptive sensing methods, reference, duty cycle, periodicity and Markov method. The results of spectrum sensing methods show that spectrum occupancy information play a vital role in sensing best suited channels for cognitive radio.