Spectrum Sensing Technique Research Articles

Optimal and Suboptimal Multi Antenna Spectrum Sensing Techniques with Master Node Cooperation for Cognitive Radio Systems

In this paper, we consider the primary user detection problem in cognitive radio systems by using multi antenna at the cognitive radio receiver. An optimal linear combiner multi antenna based spectrum sensing technique is proposed using the multitaper spectrum estimation method. A suboptimal square law combiner multi antenna based technique, using the multitaper method, is also proposed. The decision statistics’ probability density functions of the proposed techniques are derived theoretically. Probabilities of detection and false alarm formulae are presented using the Neyman Pearson criterion. Both proposed techniques are derived when energy detector is used. Based on our results, we found that the general likelihood ratio detector1 (GLRD1) and the blind GLRD that are proposed in the literature, require signal to noise ratios SNRs=7.5 and 9.6 dB, respectively to achieve a probability of detection of 99.99% at false alarm 1% with additive white Gaussian noise (AWGN) using 4 antennas and 16 samples for sensing. In our proposed optimal and suboptimal techniques, the required SNRs are found as 12 and 7.5 dB, respectively to achieve the same probabilities in the same conditions. Of course, this result gives an indication that even GLRD multi antenna based spectrum sensing techniques are blind in their philosophy, but that comes at the expense of their performance. Simulation results that confirm the theoretical work are also presented. An AWGN and Rayleigh flat fading environments are examined in the results. Finally, a new concept of cooperative spectrum sensing, the master node, is introduced.

Cooperative Spectrum Sensing Techniques with Temporal Dispersive Reporting Channels

Cooperative approaches have been proposed as an effective way to improve the spectrum sensing accuracy. Generally, cooperative spectrum sensing techniques require two successive stages: sensing and reporting. The reporting channels are usually assumed ideal. In this paper, we remove this assumption and we investigate the effects of reporting channels affected by temporal dispersion on cooperative spectrum sensing. To this aim, we propose two fusion schemes: a Widely Linear scheme and a Linear one. For both the schemes, closed-form expressions of the detection and the false alarm probabilities are derived. The performance are also evaluated numerically, and the results show that the Widely Linear detector outperforms the Linear one in operative conditions of practical interest. Moreover, for the sake of completeness, a theoretical comparison of the proposed detectors is carried out for reporting channels affected by multipath frequency non-selective fading. Surprisingly, the analysis proves that the two detectors perform exactly the same under this assumption. Therefore, there is not anymore advantage in using the Widely Linear scheme, which exhibits higher, although limited, computational complexity. The theoretical analysis is validated numerically.

A Cooperative Spectrum Sensing Technique with Dynamic Frequency Boundary Detection and Information-Entropy-Fusion for Primary User Detection

The invention of cognitive radio concept is aimed to overcome the spectral scarcity issues of emerging radio systems by exploiting under-utilization of licensed spectrum. As the cognitive users (secondary users) are only allowed to use a licensed spectrum in the absence of its rightful owner, the ability to accurately sense the presence of the rightful owners (primary users) is highly essential. The traditional way of having individual secondary users perform their own spectrum sensing is vulnerable to the presence of noise and shadowing of propagation channel. Cooperative spectrum sensing emerges as an attractive alternative that exploits the inherent geospatial diversity of multiple cognitive radios to enhance the robustness of sensing accuracy. Two specific issues in cooperative spectrum sensing are discussed in this paper. The first issue is on dynamic detection of primary user’s bands. A dynamic band clustering (DBC) algorithm that uses K-means clustering technique is proposed in this paper. The proposed algorithm reduces the number of erroneous narrow subbands resulting from spurious noise. This in turn minimizes the number of subbands to be detected and hence the overall sensing time. The second issue is on reducing the overheads required to facilitate fusion center operation. A novel entropy-based maximal ratio combining for decision-fusion center is also proposed in this paper. Based on extensive simulation studies, the proposed fusion technique is shown to be comparable to conventional information-fusion techniques. The performance offered by the proposed technique is achieved with significant reduction in the bandwidth overheads.

Wavelet Based Spectrum Sensing Techniques for Cognitive Radio - A Survey

Spectrum scarcity is one of the major issues faced in Wireless communication technology. Efficient spectrum utilization is of utmost importance to alleviate the problem of interference and reduced data rates. Cognitive Radios adapt themselves according to the available spectrum and thereby enhance transmission and reception of data, without affecting adjacent band users. The pre-requisite for such an objective is the precise calculation of spectrum boundaries. Many methods have been suggested and revised from time to time. The Wavelet Edge Detection is one of the most widely used Spectrum Sensing techniques. This technique observes the spatial distribution of spectral data at multiple resolutions. The aim of this paper is to familiarize the reader with the mathematics behind the application of wavelets for edge detection, which is made use of for spectrum sensing applications. The several variants of this scheme which was originally formulated by Mallat et al are discussed and the inherent flaws or complexities are pointed out. The importance of choosing a suitable wavelet system is explained. We then proceed further and present an adaptive algorithm which chooses a suitable wavelet system by analyzing the nature of the spectrum. The slope of the Power Spectral Density is used as an index to distinguish between sharp and blunt peaks. Sparse spectra with conspicuous peaks utilize Haar wavelet system whereas dense spectra with subtle peaks use Gaussian Wavelet System. Multi-scale sums are used since they produce more accurate results than multi-scale products. The simulations are carried out in the FM frequency band (88-108 MHz) using MATLAB.

A Low-Complexity Cyclostationary-Based Spectrum Sensing for UWB and WiMAX Coexistence With Noise Uncertainty

This paper focuses on a cognitive radio approach based on a noncooperative spectrum-sensing technique that is suitable for applications in detection and avoidance (DAA) schemes to allow coexistence between ultrawideband multiband (secondary user) and Worldwide Interoperability for Microwave Access (WiMAX; primary user) systems and, hence, improve spectral efficiency. In particular, the cyclostationary property of WiMAX signals due to the cyclic prefix is exploited here. The spectrum-sensing method devised in this paper is a statistical test that relies on cyclostationarity, and it belongs to the class of the single-cycle detector. The performance of the proposed detector, in terms of primary-user detection probability, is derived by computer simulations. The results obtained highlight that the proposed approach enables efficient downlink detection under conditions of low signal-to-noise ratio (SNR) and noise uncertainty. The implementation complexity of the proposed method is also discussed here to highlight advantages with respect to previously proposed alternatives. Finally, an analytical approach is presented to derive the false-alarm probability to allow the implementation of a constant false-alarm rate detector. In addition, the use of an efficient interference-mitigation scheme is also considered to improve the bit-error-rate performance.

A Novel Spectrum-Sensing Technique in Cognitive Radio Based on Stochastic Resonance

We propose a novel spectrum-sensing method for cognitive radio (CR) based on stochastic resonance (SR). The spectral power of primary users (PUs) can be amplified, and the signal-to-noise ratio (SNR) of a received signal can be increased using SR. This ensures that the detection probability of the proposed approach is higher than that of the traditional energy detector. The detection probability of the proposed method is also theoretically derived under a constant false-alarm rate (CFAR). Performance analyses and computer simulation results show that the effectiveness of the proposed SR-based spectrum-sensing approach, particularly under low SNR circumstances, is better than that of the traditional energy-detection method. The computational complexity of the proposed approach can also be guaranteed to be comparable with the traditional spectrum-sensing methods.

Cognitive Radio Network Tomography

The cognitive radio network (CRN), as a promising technique in future wireless communication networks, shall execute some critical functionalities to enhance existing wireless networks, such as network reconfigurability to adaptively select networks (e.g., in IEEE P1900.4 and ETSI-RSS), spectrum opportunity utilization for transmissions over opportunistic links to enhance spectrum efficiency (e.g., in IEEE 802.22), and further cooperative relays among cognitive radios (CRs) and nodes of coexisting multiradio systems, including heterogeneous primary systems. To support multilink operations and networking functions in CRN, traditional spectrum sensing is not enough, and we thus develop CRN tomography to meet the general needs of CRN operations at both the link and network levels. Borrowing the concept from medical/Internet tomography via statistical inferring, we establish the framework and methodology of CRN tomography that can be passive monitoring or active probing defined over link- or network-level parameter inference. Generally speaking, conventional CR spectrum-sensing techniques belong to the category of the passive link-level monitoring. Multiple-system sensing and identification can be considered as a sort of passive network-level CRN tomography. We further propose active link-level CRN tomography by examining the radio resource for transmissions. Finally, CRN tomography using active network-level probing is illustrated by the estimation of successful packet-transmission probability in network operations. This paper initiates explorations of CRN tomography obtaining the required parameters at the link and network levels for successful CRN operations.

Autocorrelation-Based Spectrum Sensing for Cognitive Radios

We propose a new spectrum-sensing technique based on the sample autocorrelation of the received signal. We assume that the received signal is oversampled and allow for frequency offset between the local oscillator and the carrier of the primary signal. We evaluate the performance of this algorithm for both additive white Gaussian noise (AWGN) and Rayleigh-fading channels and study its sensitivity to carrier frequency offset. Simulation results are presented to verify the accuracy of the approximation assumptions in our analysis. The performance of the proposed algorithm is also compared with those from the energy detector, the covariance detector, and the cyclic-autocorrelation detector. The results show that our algorithm outperforms the covariance detector and the cyclic autocorrelation detector. It also outperforms the energy detector in the presence of noise power uncertainty or in the case of unknown primary signal bandwidth. Finally, we investigate three diversity combining techniques, namely 1) equal gain combining, 2) selective combining and 3) equal gain correlation combining. Our simulations show that for detection probabilities of interest (e.g., > 0.9), a system with a four-branch diversity achieves a signal-to-noise ratio (SNR) gain of more than 5 dB over the no-diversity system that uses the same number of received signal samples.

Addressing feasibility of cognitive radios

In this article we present a feasibility study of spectrum-sensing techniques using a research testbed platform for exploration and demonstration of cognitive radio systems. Our cognitive radio testbed is particularly suited for the development of physical and network layer functionalities and their experimental characterization in realistic wireless scenarios. Advanced testbed capabilities include real-time high-speed signal processing and protocol implementation, and support for multiple networks interaction and multiple antennas operation. This testbed is used for an experimental study of a set of prominent candidate techniques proposed in the literature for implementation of spectrum sensing functionality. We first consider three physical layer signal processing approaches based on energy, pilot, and feature detection. Our experimental results show that theoretical performance of these approaches for spectrum sensing cannot be achieved for the detection of very weak signals using a practical sensing receiver implementation. Also, we explain why these limitations exist by identifying sources of errors and provide basis for the design of robust spectrum sensing techniques. Next, we consider spectrum sensing techniques that use multiple measurements to improve sensing reliability. Our experimental study demonstrates practically achievable gains by exploiting multipath channel diversity through multiple antenna processing, and spatial diversity using user cooperation in a typical indoor environment.