As one functionality of the wireless distributed network (WDN) enabling flexible wireless networks, it is supposed that a dynamic spectrum access is applied to OFDM systems for superior radio resource management. As a basic technology for such WDN, our study deals with the OFDM signal detection based on its cyclostationary feature. Previous relevant studies mainly relied on software simulations based on the Monte Carlo method. This paper analytically clarifies the relationship between the design parameters of the detector and its detection performance. The detection performance is formulated by using multiple design parameters including the transfer function of the receive filter. A hardware experiment with radio frequency (RF) signals is also carried out by using the detector consisting of an RF unit and FPGA. Thereby, it is verified that the detection characteristics represented by the false-alarm and non-detection probabilities calculated by the analytical formula agree well with those obtained by the hardware experiment. Our analysis and experiment results are useful for the parameter design of the signal detector to satisfy required performance criteria.

This paper presents a computationally efficient cyclostationarity detection based spectrum sensing technique in cognitive radio. Traditionally, several cyclostationarity detection based spectrum sensing techniques with a low computational complexity have been presented, e.g., peak detector (PD), maximum cyclic autocorrelation selection (MCAS), and so on. PD can be affected by noise uncertainty because it requires a noise floor estimation, whereas MCAS does not require the estimation. Furthermore, the computational complexity of MCAS is greater than that of PD because MCAS must compute some statistics for signal detection instead of the estimation unnecessary whereas PD must compute only one statistic. In the presented MCAS based techniques, only one statistic must be computed. The presented technique obtains other necessary statistics from the procedure that computes the statistic. Therefore, the computational complexity of the presented is almost the same as that of PD, and it does not require the noise floor estimation for threshold. Numerical examples are shown to validate the effectiveness of the presented technique.

This paper presents a weighted diversity combining technique for the cyclostationarity detection based spectrum sensing of orthogonal frequency division multiplexing signals in cognitive radio. In cognitive radio systems, secondary users must detect the desired signal in an extremely low signal-to-noise ratio (SNR) environment. In such an environment, multiple antenna techniques (space diversity) such as maximum ratio combining are not effective because the energy of the target signal is also extremely weak, and it is difficult to synchronize some received signals. The cyclic autocorrelation function (CAF) is used for traditional cyclostationarity detection based spectrum sensing. In the presented technique, the CAFs of the received signals are combined, while the received signals themselves are combined with general space diversity techniques. In this paper, the value of the CAF at peak and non-peak cyclic frequencies are computed, and we attempt to improve the sensing performance by using different weights for each CAF value. The results were compared with those from conventional methods and showed that the presented technique can improve the spectrum sensing performance.

Cognitive radio (CR) is an important technology to provide high-efficiency data communication for the IoT (Internet of Things) era. Signal detection is a key technology of CR to detect communication opportunities. Energy detection (ED) is a signal detection method that does not have high computational complexity. It, however, can only estimate the presence or absence of signal(s) in the observed band. Cyclostationarity detection (CS) is an alternative signal detection method. This method detects some signal features like periodicity. It can estimate the symbol rate of a signal if present. It, however, incurs high computational complexity. In addition, it cannot estimate the symbol rate precisely in the case of single carrier signal with a low Roll-Off factor (ROF). This paper proposes a method to estimate coarsely a signal's bandwidth and carrier frequency from its power spectrum with lower computational complexity than the CS. The proposed method can estimate the bandwidth and carrier frequency of even a low ROF signal. This paper evaluates the proposed method's performance by numerical simulations. The numerical results show that in all cases the proposed coarse bandwidth and carrier frequency estimation is almost comparable to the performance of CS with lower computational complexity and even outperforms in the case of single carrier signal with a low ROF. The proposed method is generally effective for unidentified classification of the signal i.e. single carrier, OFDM etc.

The evolution of wireless communication systems leads to Dynamic Spectrum Allocation for Cognitive Radio, which requires reliable spectrum sensing techniques. Among the spectrum sensing methods proposed in the literature, those that exploit cyclostationary characteristics of radio signals are particularly suitable for communication environments with low signal-to-noise ratios, or with non-stationary noise. However, such methods have high computational complexity that directly raises the power consumption of devices which often have very stringent low-power requirements. We propose a strategy for cyclostationary spectrum sensing with reduced energy consumption. This strategy is based on the principle that p processors working at slower frequencies consume less power than a single processor for the same execution time. We devise a strict relation between the energy savings and common parallel system metrics. The results of simulations show that our strategy promises very significant savings in actual devices.

Automatic modulation classification (AMC) is an important function of radio surveillance systems in order to identify unknown signals. Many previous works on AMC have utilized signal cyclostationarity, particularly spectral correlation density (SCD), but many of them fail to address several implementation issues, such as the assumption of perfect knowledge of the symbol rate. In this paper, we discuss several practical issues, e.g. cyclic frequency mismatch, which may affect the SCD, and propose compensation techniques to overcome those issues. We also propose a novel feature extraction technique from the SCD, which utilizes the SCD of not only the original received signal, but also the squared received signal. A symbol rate estimation technique which complements the feature extraction is also proposed. Finally, the classification performance of the system is evaluated through Monte Carlo simulations using a wide variety of modulated signals, and simulation results show that the proposed technique can estimate the symbol rate and classify modulation with a probability of above 0.9 down to SNRs of 5 dB.

An important and widely considered signal identification technique for cognitive radios is cyclostationarity-based feature detection because this method does not require time and frequency synchronization and prior information except for information concerning cyclic autocorrelation features of target signals. This paper presents the development and experimental evaluation of cyclostationarity-based signal identification equipment. A spatial channel emulator is used in conjunction with the equipment that provides an environment to evaluate realistic spectrum sharing scenarios. The results reveal the effectiveness of the cyclostationarity-based signal identification methodology in realistic spectrum sharing scenarios, especially in terms of the capability to identify weak signals.

Spectrum sensing, a key technical challenge in cognitive radios (CR) technology, is a technique that enables the spectrum of licensed systems to be accessed without causing undue interference. It is well known that cyclostationarity detectors have great advantages over energy detectors in terms of the robustness to noise uncertainty that significantly degrades the performance as well as the capability to distinguish the signal of interest from the other interferences and noise. The generalized likelihood ratio test (GLRT) is a recognized sensing technique that utilizes the inherent cyclostationarity of the signal and has been intensively studied. However, no comprehensive evaluation on its performance enhancement has been published to date. Moreover high computational complexity is still a significant problem for its realization. This paper proposes a maximum ratio combining multi-cyclic detector which uses multiple cyclic frequencies for performance enhancement with reduced computational complexity. An orthogonal frequency-division multiplexing (OFDM) signal based on the ISDB-T (integrated services digital broadcasting terrestrial), a Japanese digital television broadcasting standard, was used in the evaluation assuming this as a primary system in WRAN (wireless regional area network) applications like IEEE 802.22.

The cyclic autocorrelation of common digital modulation is researched, and the relationship between the cyclic autocorrelation and the delay, corresponding to the symbol rate, is deduced, then a searching algorithm for the symbol rate is proposed. Theoretical analyses and simulation results show that this method has less computation complexity and is also quite accurate. The estimation result is almost immune to the stationary noise. It's of practical value to modulation recognition and blind demodulation.

In this paper, a simple blind algorithm for a beamforming antenna is proposed. This algorithm exploits the property of cyclostationary signals whose cyclic autocorrelation function depends on delay as well as frequency. The cost function is the mean square error between the delay product of the beamformer output and a complex exponential. Exploiting the delay greatly reduces the possibility of capturing undesired signals. Through analysis of the minima of the non-quadratic cost function, conditions to extract a single signal are derived. Application of this algorithm to code-division multiple-access systems is considered, and it is shown through simulation that the desired signal can be extracted by appropriately choosing the delay as well as the frequency.

Carrier frequency and symbol timing errors may greatly degrade the performance of the orthogonal frequency division mulitplexing (OFDM) system, especially in multipath environment. In this paper, we explore the cyclostationarity of OFDM signals, which only relies on second order statistics, to estimate the synchronization offset. First, a coarse carrier frequency offset estimator for multipath environment is developed using the second order statistics of the received OFDM signal. It has a wide capture range though not accurate. Second, we introduce a new synchronization algorithm based on cyclostationarity and matched filter theories, which can get the maximal estimation SNR in multipath environment. Both estimators utilize channel state information to achieve better estimation performance and are non-pilot aided. They can be combined to form a whole OFDM synchronizer for multipath environment. Finally, simulations confirm the performance of the estimation algorithm.

In this paper, we consider the problem of estimating the time-varying directions-of-arrival (DOAs) of coherent narrowband cyclostationary signals impinging on a uniform linear array (ULA). By exploiting the cyclostationarity of most communication signals, we investigate a new computationally efficient subspace-based direction estimation method without eigendecomposition and spatial smoothing (SS) processes. The proposed method uses the inherently temporal property of incident signals and a subarray scheme to decorrelate the signal coherency and to suppress the noise and interfering signals, while the null subspace is obtained from the resulting cyclic correlation matrix through a linear operation. Then an on-line implementation of this method is presented for tracking the DOAs of slowly moving coherent signals. The proposed algorithm is computationally simple and has a good tracking performance. The effectiveness of the proposed method is verified through numerical examples.

The effect of subarray size (equal to the order of the prediction model plus one) on the estimation performance of a previously proposed forward-backward linear prediction (FBLP) based cyclic method is investigated. This method incorporates an overdetermined FBLP model with a subarray scheme and is used to estimate the directions-of-arrival (DOAs) of coherent cyclostationary signals impinging on a uniform linear array (ULA) from the corresponding polynomial or spectrum formed by the prediction coefficients. However, the decorrelation is obtained at the expense of a reduced working array aperture, as it is with the spatial smoothing (SS) technique. In this paper, an analytical expression of the mean-squared-error (MSE) of the spectral peak position is derived using the linear approximation for higher signal-to-noise ratio (SNR). Then the subarray size that minimizes this approximate MSE is identified. The effect of subarray size on the DOA estimation is demonstrated and the theoretical analysis is substantiated through numerical examples.

To improve the resolution capability of the directions-of-arrival (DOA) estimation, some subspace-based methods have recently been developed by exploiting the specific signal properties (e.g. non-Gaussian property and cyclostationarity) of communication signals. However, these methods perform poorly as the ordinary subspace-based methods in multipath propagation situations, which are often encountered in mobile communication systems because of various reflections. In this paper, we investigate the direction estimation of coherent signals by jointly utilizing the merits of higher-order statistics and cyclostationarity to enhance the performance of DOA estimation and to effectively reject interference and noise. For estimating the DOA of narrow-band coherent signals impinging on a uniform linear array, a new higher-order cyclostationarity based approach is proposed by incorporating a subarray scheme into a linear prediction technique. This method can improve the resolution capability and alleviate the difficulty of choosing the optimal lag parameter. It is shown numerically that the proposed method is superior to the conventional ones.

In a variety of communication systems, the multipath propagation due to various reflections is often encountered. In this paper, the directions-of-arrival (DOA) estimation of the cyclostationary coherent signals is investigated. A new approach is proposed for estimating the DOA of the coherent signals impinging on a uniform linear array (ULA) by utilizing the spatial smoothing (SS) technique. In order to improve the robustness of the DOA estimation by exploiting the cyclic statistical information sufficiently and handling the coherence effectively, we give a cyclic algorithm with multiple lag parameters and the optimal subarray size. The performance of the presented method is verified and compared with the conventional methods through numerical examples.

In this letter, we propose new methods for estimating frequency and phase of a complex sinusoid in complex white Gaussian noise. These new estimators use the cyclostationarity of the sinusoid which is a cyclostationary signal type. Only one component corresponding to a lag of zero of cyclic autocorrelations is used to reduce the computational load. The performances of our proposed estimators are compared to those of Kay estimator, Cramer-Rao bound (CRB) and maxim-likelihood estimator (MLE). Simulation results show that our proposed methods can estimate the frequency and phase correctly even in low signal-to-noise ratio (SNR).