Cyclostationary Detection Research Articles

Performance evaluation of FRESH filter based spectrum sensing for cyclostationary signals

This paper considers the problem of spectrum sensing of cyclostationary signals for cognitive radios. It has been reported earlier using simulation results that FRESH filtering a signal, prior to spectrum sensing, may result in gains of more than 5 dB over the standard energy and cyclostationary detectors. This paper develops a quasi-analytical theory of spectrum sensing based on FRESH filtering. It is shown that significant performance gains are achievable in both energy detection and cyclostationarity detection via FRESH filtering of the received signal prior to the detection step. The aforementioned approach may be shown to reduce the number of samples required to achieve a given detection performance by more than 90% in practice, thereby reducing the sensing time in a cognitive radio system. It is also shown that the FRESH filtering before energy detection may reduce the effects of SNR walls caused due to noise uncertainty. The validity of all the derived observations is verified via simulations.

Efficient Hardware Architecture for Cyclostationary Detector

Objectives:Cognitive radio is evolved for utilising the unused spread spectrum effectively in wireless communication. The foremost concept is sensing the holes (white spaces) in the frequency spectrum allotted and it facilitates a way that how effectively and efficiently the bandwidth is used.Methods/Analysis:There are various methods available for sensing the spectrum and one such a sensing method is cyclostationary detection. The method of cyclostationary feature mainly focuses on detecting whether the primary user is present or absent. By using cyclic cross-periodogram matrix, the calculation of threshold of a signal is carried out to find the existence of noise or signal. Findings:The difficulty in evaluating the targeted threshold is evaded by training an artificial neural network by extracted cyclostationary feature vectors which are obtained by FFT accumulation method. Novelty /Improvement:This paper proposes hardware architecture for cyclostationary detection.

Enriched the Spectrum Sensing Performance of Estimated SNR based Detector in Cognitive Radio Networks

Spectrum sensing is one of the vital functions, plays an important role in cognitive radio networks (CRN). This paper discuss to enrich the sensing performance of propose estimated signal to noise ratio (SNR) based detector. Estimated SNR detector consists two detectors, energy detector (ED) & ED with ADT detector, out of these two detectors only one will perform sensing operation at a time. Which detector will be work, selection will be done on condition between estimated SNR value (Se) and threshold (γ). Results show that proposed ESNR_ADT scheme outperforms the cyclostationary based sensing method by 30.5 % at – 10 dB SNR. It is also shown that the proposed scheme takes smaller detection time period as compare to cyclostationary detection in the order of 5.2 ms at 20 dB SNR. Furthermore, the scheme was associated with cooperative spectrum sensing (CSS) where each CRs work together in order to take final decision. Finally, it was analyzed that proposed ESNR_ADT sensing scheme with CSS improves detection performance in the order of 0.9 at low SNR value of – 18.5 dB approximately.

Reliable spectrum sensing scheme based on dual detector with double-threshold for IEEE 802.22 WRAN

In this paper, a reliable spectrum sensing (SS) scheme using dual detectors for IEEE 802.22 WRAN has been presented. In dual or double detectors, first one detector is energy detector (ED) with fixed threshold (FT), while second one is ED with adaptive double threshold (ADT). Numerical results shows that proposed reliable scheme improves detection performance and outperforms the cyclostationary based sensing method and adaptive SS by 12.9% and 15% at −8 dB signal-to-noise ratio (SNR), respectively. It also shows that the proposed scheme takes smaller detection time period than cyclostationary detection and adaptive SS scheme in the order of 5 ms and 0.8 ms at −20 dB SNR, respectively. Moreover, the scheme analyzes in conjunction with cooperative spectrum sensing (CSS) where each CRs work together in order to take final or global decision. It is observed that the proposed sensing scheme with CSS achieves spectrum detection performance in the order of 0.9 for SNR value of as low as −17.5 dB when numbers of CRs are ten.

The effects of non-linearity in spectrum sensing receivers

This paper analyses the effects of receiver non-linearity on the performance of the most commonly utilized signal detectors in cognitive radio systems. The analysis covers both self-modulation products of a single orthogonal frequency-division multiplexing (OFDM) signal and intermodulation(IM)products of two OFDM signals, and also their contribution to the probability of false detections. As a result, this work presents the linearity requirements for the spectrum sensor receiver front-end as a function of the sensitivity of the signal detector. Furthermore, we show that the cyclostationary feature detectors are more robust than the energy detectors against IM products of multiple interferers. Theoretical results are verified in measurements with a cyclostationary feature detector using digital video broadcasting – terrestrial (DVB-T) signals as an example.

Higher Order Statistical Approach for Performance Evaluation of different Spectrum Sensing Techniques in Cognitive Radio Network

radio is an intelligent wireless technology that increases the spectrum efficiency for its usage in applications. CR enriches wireless technology by utilizing the spectrum holes in order to provide high order quality service to users and to minimize the interference that can occur in the network. The work that has to be proposed we will uses two Spectrum Sensing techniques for Cognitive radio network which include Cyclostationary detection and Energy detection techniques. In this paper, the parameter used for Cyclostationary signal is Spectral Correlation function(SCF). The detection capability of this (SCF) with different windows is used to check the periodicity of the signal using different-different windows. Due to the periodicity of the baseband signal, SCF would be able to detect the primary user signal at very low SNR. We also analyzed in our proposed work that capability of periodicity of the signal of SCF is not only limited to noise affected signal, it also able to detect the attenuated signal. We have also simulated Energy detection over MIMO fading channel as it models both Rician fading channel and Rayleigh fading channel. The performance in terms of Bit error rate by providing low probability of false alarm and high probability of detection is analyzed. The Statistical test based comparison is made between the two sensing techniques to evaluate the performance in terms of signal to noise ratio. In the proposed work An extensive set of simulations have been conducted in MATLAB . Keywordsradio, Energy Detection, Cyclostationary Detection

Spectrum Sensing Under RF Non-Linearities: Performance Analysis and DSP-Enhanced Receivers

Intermodulation products arise as a result of low noise amplifier (LNA) and mixer non-linearities in wideband receivers. In the presence of strong blockers, the intermodulation distortion can deteriorate the spectrum sensing performance by causing false alarms and degrading the detection probability. We theoretically analyze the impact of third-order non-linearities on the detection and false alarm probabilities for both energy detectors and cyclostationary detectors under front-end LNA non-linearities. We show that degradation of the detection performance due to nonlinearities of both energy and cyclostationary detection is strongly dependent on the modulation type of the blockers. We then propose two DSP-enhanced receiver architectures to compensate the impact of nonlinearities. The first approach is a post-processing technique which compensates for nonlinearities effect on the test statistic by adapting the sensing time and detection threshold. The second approach is a pre-processing method that compensates by correcting received samples prior to computing the test statistic. This approach is based on adaptively estimating the intermodulation distortion, weighting it by a scalar constant and subtracting it from the subband of interest. We propose a method to adaptively compute the optimal weighting coefficient and show that it depends on the power and modulation of the blockers. Our results show that the pre-processing sample-based compensation method is more effective and that clear dynamic range extension can be obtained by using intermodulation compensation without resorting to increasing the sensing time. We also study the impact of uncertainties about the knowledge or estimates for nonlinearity parameters.

Analysis of Cyclostationary CR Detector for OFDM signals

The cyclostationary based spectrum sensing algorithm is especially suitable for detection of orthogonal frequency division multiplexing (OFDM) signals that exhibit strong periodic correlation due to insertion of cyclic prefix between OFDM blocks.Normally, the cyclic prefix is used to remove the Intersymbol interference (ISI) and intercarrier interference (ICI) of the OFDM signal and so it eliminates a need for complex equalizer. In this paper, simulation results demonstrate the normalized spectrum correlation function of the primary OFDM signal used in cognitive radio systems with different values of cyclic frequency. Two techniques are assumed to be used as PAPR reduction of OFDM transmitted signal. The results show that only one of them has no apparent effect on the correlation performance of the cyclostationary detector. Finally, it is found that a reduction of 30% in the implementation complexity can be taken by suitable choice of detector cyclic frequency.

Cyclostationary Detection Using DVB-T Signal in a Cognitive Radio System

Due to fixed available spectrum and increase in wireless applications it is necessary to use a technology which can detect the unused spectrum so that spectrum efficiency can be improved. The device which is used known as cognitive radio (CR). Cognitive radio is an intelligent device which can easily sense the available channel in an available spectrum, for spectrum sensing it basically uses three techniques Energy Detection, Matched Filter Detection and Cyclostationary Detection. In this paper spectrum detection is done by using Cyclostationary detection which provide better results at low SNR in comparison to other techniques, DVB-T signal is used for finding Cyclostationary spectrum and detection of primary signal at different SNR values.

Cyclostationary Detection Based Spectrum Sensing for Cognitive Radio Networks

In this paper, cyclostationary detection Based spectrum sensing is considered for cognitive radio networks. We first summarize the existing first-order and second-order cyclostationary detection algorithms, which can be considered as a brief tutorial on detection theory of the cyclostationary signals. Based on this, we propose a cooperative spectrum sensing method for a cognitive radio networks with multiple terminals and one fusion center. It is shown that the proposed method have reliable performance even in low signal-to-noise ratio (SNR) region. It is also found that the increasing number of secondary users (SUs) can result in improved detection performance, especially at low SNR. Simulation results are then provided to corroborate the proposed studies. Index Terms—Cognitive radio, cooperative spectrum sensing, cyclostationary detection.

Analysis of Cyclostationary CR Detector for OFDM signals

The cyclostationary based spectrum sensing algorithm is especially suitable for detection of orthogonal frequency division multiplexing (OFDM) signals that exhibit strong periodic correlation due to insertion of cyclic prefix between OFDM blocks.Normally, the cyclic prefix is used to remove the Intersymbol interference (ISI) and intercarrier interference (ICI) of the OFDM signal and so it eliminates a need for complex equalizer. In this paper, simulation results demonstrate the normalized spectrum correlation function of the primary OFDM signal used in cognitive radio systems with different values of cyclic frequency. Two techniques are assumed to be used as PAPR reduction of OFDM transmitted signal. The results show that only one of them has no apparent effect on the correlation performance of the cyclostationary detector. Finally, it is found that a reduction of 30% in the implementation complexity can be taken by suitable choice of detector cyclic frequency.

Performance Analysis of Linear Cooperative Cyclostationary Spectrum Sensing Over Nakagami-<inline-formula> <tex-math notation="TeX">$m$</tex-math></inline-formula> Fading Channels

This paper deals with performance evaluation of linear combination-based cooperative cyclostationary spectrum sensing (CCSS) when both sensing and reporting links are characterized as independent (and not necessarily identically distributed) Nakagami-m fading channels. To do so, we propose a soft-decision fusion rule for performing CCSS in cognitive radio networks. In essence, we formulate the false alarm (FA) probability of a cooperative detector over Nakagami channels. Based on the Pade approximation method, we derive new approximated closed-form expressions for the FA probability. Furthermore, a more accurate expression for the special case of Rayleigh fading is derived. Through numerical simulations, it is shown that the proposed CCSS scheme significantly enhances the secondary network performance in terms of global detection probability metric, as compared with the commonly used equal-gain combining (EGC) method. In addition, we show that the employed cyclostationary detector provides the highest detection probability compared with the other alternatives.

Novel Compressed Sensing Algorithm Based on Modulation Classification and Symbol Rate Recognition

In the complex and changing radio environment, how to achieve the fast and accurate spectrum sensing over an ultra-wide bandwidth is a big challenge. A novel compressed sensing algorithm based on modulation classification and symbol rate recognition is proposed by using the minimal sampling rate to detect spectrum holes. It is more efficient than the Nyquist sampling rate and traditional compressed sampling rate, which requires the reconstruction of the original signal. Simulation results show that it can further decrease the compressed sampling rate depending on the relation between compression ratio with modulation scheme and symbol rate. Furthermore, in order to improve the accuracy of modulation classification and symbol rate recognition, a compressed sensing and wavelet transform (CS-WT) feature detector is proposed to perform wideband detection in low SNR condition. Simulation results show that CS-WT feature detector can effectively reduce the noise introduced by CS process. Given the false alarm of 0.05 and the detection probability of 0.9, the detection probability of proposed CS-WT feature detection algorithm can be improved 4 dB compared to traditional cyclostationary detection. Therefore, the overall sampling rate can be dramatically reduced without spectrum detection performance deterioration compared to the conventional static sampling algorithm.

Editoral: Special Issue on Photonic and RF Communications Systems

Editoral: Special Issue on Photonic and RF Communications Systems

Spectrum Sensing for Cognitive Radios Based on Space-Time FRESH Filtering

In this paper, we consider the problem of spectrum sensing of cyclostationary signals for cognitive radios. It is shown that the detection performance in this case may be improved by enhancing the cyclostationary features of the signal of interest. The optimal filter for cyclostationary features is revisited, following which an adaptive space-time structure exploiting the spatial, temporal and spectral coherence of a cyclostationary signal incident upon an antenna array is proposed for enhancing the signal. A low complexity adaptation algorithm for this structure is also proposed. The performance of this detector is evaluated and compared with standard spectrum sensing methods. Simulation results show that a suitable space-time FRESH filtering configuration may be used to yield gains of up to 10 dB over both the standard energy detector and the traditional cyclostationary detector.

A Review On Spectrum Sensing For Cognitive Radio Networks

Cognitive radio is the emerging technology for supporting dynamic spectrum access. To detect the presence of the primary users in a licensed spectrum is a fundamental problem for cognitive radio. In cognitive radio networks, the performance of the spectrum sensing depends on the sensing time and the fusion scheme. These schemes are used when cooperative sensing is applied. In this paper, spectrum sensing techniques such as energy detection, matched filtering detection, cyclostationary detection and waveform based sensing methods are discussed. Energy detection is most commonly used in local spectrum sensing. This paper gives the overview about comparison between different methods. It helps to find the better method of spectrum sensing.

COMPRESSED MEASUREMENTS BASED CYCLOSTATIONARY DETECTOR FOR WIDEBAND COGNITIVE RADIOS

Cyclostationary feature detection is one of the most powerful spectrum sensing techniquesused for cognitive radio (CR) systems. This is because of its robustness against noiseuncertainties. However, this technique needs high sampling rates, which is limited by thestate-of the-art analog to digital converters (ADCs), especially in wideband regime.Comressive sensing (CS) was used by many researchers for solving this problem viasub-Nyquist sampling rates. However CS solves the high sampling rate problem, but it doesnot reduce complexity considerably. This is because spectrum sensing is performed in threesteps: sensing compressed measurements, then reconstructing the Nyquist rate signal, andfinally performing cyclostationary detection (CD) on the reconstructed signal. In this paperwe suggest performing CD directly on the compressed measurements skipping thereconstruction step which is the most complex step in CS. This can be realized by designingthe sensing matrix with constraints different from those used in the conventional CS. Resultsshow that performance is improved relative to applying CD on the Nyquist rate signal. Thisis in addition to reduction in receiver complexity resulting from reducing sampling rates. Adetection probability of 78.7% can be achieved with only 7% of samples used by theconventional cyclostationary detection technique that achieves a detection probability of32.7%.

Sensing performance of efficient cyclostationary detector with multiple antennas in multipath fading and lognormal shadowing environments

Spectrum sensing is a key technical challenge for cognitive radio (CR). It is well known that multicycle cyclostation-arity (MC) detection is a powerful method for spectrum sensing. However, a conventional MC detector is difficult to implement because of its high computational complexity. This paper considers reducing computational complexity by simplifying the test statistic of a conventional MC detector. On the basis of this simplification process, an improved MC detector is proposed. Compared with the conventional detector, the proposed detector has low-computational complexity and high-accuracy sensing performance. Subsequently, the sensing performance is further investigated for the cases of Rayleigh, Nakagami-m, Rician, and Rayleigh fading and lognormal shadowing channels. Furthermore, square-law combining (SLC) is introduced to improve the detection capability in fading and shadowing environments. The corresponding closed-form expressions of average detection probability are derived for each case by the moment generation function (MGF) and contour integral approaches. Finally, illustrative and analytical results show the efficiency and reliability of the proposed detector and the improvement in sensing performance by SLC in multipath fading and lognormal shadowing environments.

Analysis of Improved Cyclostationary Detector with Multiple Antennas over Fading Channels

A comprehensive performance analysis of the multi-cycle cyclostationary (MC) detection-based spectrum sensing over fading channels with multiple independent and correlated antennas is developed. We first proposed an improved MC detector, aiming to reduce the computational complexity of the conventional one. Compared with conventional MC detector, the proposed method is low-computational complexity and high-accuracy on sensing performance. Based on the proposed MC detector, for the multiple independent antennas case, the average detection probability by employing square-law combining (SLC) is derived for several fading channels such as Nakagami, Rayleigh and Rician by using the moment generation function (MGF) approach. For multiple correlated antenna case, with Nakagami fading and SLC scheme, expressions of detection probability are derived by the same approach as it in the independent antennas case. Special cases of a linear array of 2 and 4 arbitrarily correlated antennas are treated. Finally, illustrative and analytical results show that the reliability of our proposed MC detector and the degradation of sensing performance over correlation and fading.

Sensing Performance of Advanced LTE Cognitive Femtocells

The deployment of femtocells in a LTE advanced system can extend the system coverage to include indoors. Unfortunately, an electromagnetic interference may occur among the femto users and the macro users in the case of co-channel deployment. A cognitive radio can be used to mitigate the interference among the femtocells and the macrocells. It is applied by allowing the femto users to be handled as secondary users of any other existing network. In this paper, the performance of the LTE advanced Femtocell is studied by using different detectors such as energy detector, cyclostationary detector, and matched filter detector, which is not clarified until now. Moreover, the analysis of these detectors is made. Comparisons among these detectors are carried out. Different wireless channel models, Additive White Gaussian Noise (AWGN) and fading channels, are implemented to verify the operation of the proposed LTE advanced Femtocell. Simulation results show that there is a tradeoff between a false alarm probability and the signal to noise ratio value of any detector to have a certain performance. Moreover, the performance of the cyclostationary detector and the matched filter detector is better than the energy detector especially at low signal to noise ratio values. Unfortunately, the cyclostationary detector performance is not satisfying when the fading channels are employed.