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In recent development and improvement of wireless communication system, the cognitive radio (CR) is a potential approach to utilize spectrum efficiently. Spectrum sensing technique arguably is the most significant component of cognitive radio. Cooperative spectrum sensing (CSS) is utilized to improve the detection performance of the system. Several fusion strategies of decision making are presented for sensing the primary user but they do not perform well under low signal to noise ratio (SNR) conditions. This paper proposes artificial neural network (ANN) based CSS under Rayleigh multipath fading channel in IEEE 802.22 wireless regional area network (WRAN). We implemented an ANN in the fusion centre. First, the energy of the received signal is calculated using discrete wavelet packet transform (DWPT). Then, calculated energy, SNR and false alarm probability are used jointly to make a data set of 2048 samples and are used to train Levenberg-Marquardt back propagation training algorithm-based feed-forward neural network (FFNN). Using this trained neural network, CSS in WRAN is simulated under Rayleigh multipath fading channel and results demonstrate the superiority of the proposed approach over traditional CSS with DWPT and Fast fourier transform (FFT) based energy detection schemes in low SNR environment.

This letter presents an analytical study of the performance of the maximum-likelihood gamma constant false alarm rate (MLG-CFAR) detector operating over a homogenous gamma-distributed (GM) clutter. To accomplish this task, a novel closed-form expression for the probability of false alarm and a highly accurate approximation for the probability of detection are derived. Specifically, this proposed approximation is also given in a closed form. These expressions, which can be of great interest to the radar community, are given in terms of the Gauss hypergeometric function and the Tricomi function. Besides, the proposed expressions can be easily implemented and computed using most popular scientific software packages such as MATLAB, Maple, and Mathematica. Our propositions are then tested and validated using numerical computing and Monte Carlo simulations. The obtained results demonstrate a high accuracy and significant computational advantages of the proposed expressions compared to the alternative ones. Moreover, the detection performance of the MLG-CFAR detector is also conducted assuming a homogenous clutter with a known shape parameter. [ABSTRACT FROM AUTHOR]

Sea clutter has been a long-standing issue in old and modern radars. Under this context, the K-distribution has emerged as a promising clutter model to accurately mimic sea signal variations in a large variety of radar systems. To guarantee an adequate radar performance in the presence of sea clutter, the family of constant false-alarm rate (CFAR) detectors has been commonly used. In particular, due to its adequate balance between performance and implementation, the cell-averaging CFAR (CA-CFAR) detector has been considered an attractive detection mechanism to enhance radar performance over various clutter environments. In this work, we assess radar performance considering a CA-CFAR detector operating over K-distributed sea clutter with fully correlated texture. More precisely, we derive novel closed-form expressions for the probability of detection ($P_{\text {D}}$) and the probability of false alarm ($P_{\text {FA}}$) that can be readily evaluated using any mathematical software. Monte-Carlo (MC) simulations corroborate our analytical findings. [ABSTRACT FROM AUTHOR]

Spectrum sharing alleviates the severe shortage of spectrum in sub-6 GHz frequency bands through the harmonious coexistence of two or more wireless technologies on the same frequency resources. In this work, we study underlay radar-massive MIMO cellular coexistence in LoS/near-LoS channels, where both systems have 3D beamforming capabilities. Using mathematical tools from stochastic geometry, we derive an upper bound on the average interference power at the radar due to the 3D massive MIMO cellular downlink under the worst-case ‘cell-edge beamforming’ conditions. To overcome the technical challenges imposed by asymmetric and arbitrarily large cells, we devise a novel construction in which each Poisson Voronoi (PV) cell is bounded by its circumcircle to bound the effect of the random cell shapes on average interference. Since this model is intractable for further analysis due to the correlation between adjacent PV cells’ shapes and sizes, we propose a tractable nominal interference model, where we model each PV cell as a circular disk with an area equal to the average area of the typical cell. We quantify the gap in the average interference power between these two models and show that the upper bound is tight for realistic deployment parameters. We also compare them with a more practical but intractable MU-MIMO scheduling model to show that our worst-case interference models show the same trends and do not deviate significantly from realistic scheduler models. Under the nominal interference model, we characterize the interference distribution using the dominant interferer approximation by deriving the equi-interference contour expression when the typical receiver uses 3D beamforming. Finally, we use tractable expressions for the interference distribution to characterize radar’s spatial probability of false alarm/detection in a quasi-static target tracking scenario. Our results reveal useful trends in the average interference as a function of the deployment parameters (BS density, exclusion zone radius, antenna height, transmit power of each BS, etc.). We also provide useful system design insights using radar receiver operating characteristic (ROC) curves by applying our analytical results to design the minimum exclusion zone radius in current and future radar-cellular spectrum sharing scenarios. [ABSTRACT FROM AUTHOR]

Acquisition and tracking systems form an important component of free-space optical communications due to directional nature of the optical signal. Acquisition subsystems are needed in order to search and locate the receiver terminal in an uncertainty/search region with very narrow laser beams. In this paper, we have proposed and analyzed two adaptive search schemes for acquisition systems that perform better—for the low probability of detection—than the spiral scanning approach. The first of these schemes, the adaptive spiral search, provides a better acquisition time performance by dividing the search region into a number of smaller subregions, and prioritizing search in regions of higher probability mass. The second technique—the shotgun approach—searches the region in a random manner by sampling the search region according to a Gaussian distribution. The adaptive spiral scheme outperforms the shotgun approach in terms of acquisition time, especially if the number of search subregions is large enough. However, a higher pointing accuracy is required by the adaptive spiral search in order to search the region precisely. On the other hand, the shotgun scanning approach does not require such stringent pointing accuracy. [ABSTRACT FROM AUTHOR]

Pointing and acquisition are an important aspect of free-space optical communications because of the narrow beamwidth associated with the optical signal. In this paper, we have analyzed the pointing and acquisition problem in free-space optical communications for photon-counting detector arrays and Gaussian beams. In this regard, we have considered the maximum likelihood detection for detecting the location of the array, and analyzed the one-shot probabilities of missed detection and false alarm using the scaled Poisson approximation. Moreover, the upper/lower bounds on the probabilities of missed detection and false alarm for one complete scan are also derived, and these probabilities are compared with Monte Carlo approximations for a few cases. Additionally, the upper bounds on the acquisition time and the mean acquisition time are also derived. The upper bound on mean acquisition time is minimized numerically with respect to the beam radius for a constant signal-to-noise ratio scenario. Finally, the complementary distribution function of an upper bound on acquisition time is also calculated in a closed form. Our study concludes that an array of smaller detectors gives a better acquisition performance (in terms of acquisition time) as compared to one large detector of similar dimensions as the array. [ABSTRACT FROM AUTHOR]

This paper studies energy efficiency (EE) and average throughput maximization for cognitive radio systems in the presence of unslotted primary users. It is assumed that primary user activity follows an ON–OFF alternating renewal process. Secondary users first sense the channel possibly with errors in the form of miss detections and false alarms, and then, start the data transmission only if no primary user activity is detected. The secondary user transmission is subject to constraints on collision duration ratio, which is defined as the ratio of average collision duration to transmission duration. In this setting, the optimal power control policy which maximizes the EE of the secondary users or maximizes the average throughput while satisfying a minimum required EE under average/peak transmit power and average interference power constraints is derived. Subsequently, low-complexity algorithms for jointly determining the optimal power level and frame duration are proposed. The impact of probabilities of detection and false alarm, transmit and interference power constraints on the EE, average throughput of the secondary users, optimal transmission power, and the collisions with primary user transmissions are evaluated. In addition, some important properties of the collision duration ratio are investigated. The tradeoff between the EE and average throughput under imperfect sensing decisions and different primary user traffic are further analyzed. [ABSTRACT FROM PUBLISHER]

This paper studies energy-efficient power allocation schemes for secondary users in sensing-based spectrum sharing cognitive radio systems. It is assumed that secondary users first perform channel sensing possibly with errors and then initiate data transmission with different power levels based on sensing decisions. In this setting, the optimization problem is to maximize energy efficiency (EE) subject to peak/average transmission power constraints and peak/average interference constraints. By exploiting the quasi-concave property of the EE maximization problem, the original problem is transformed into an equivalent parameterized concave problem, and an iterative power allocation algorithm based on Dinkelbach’s method is proposed. The optimal power levels are identified in the presence of different levels of channel side information (CSI) regarding the transmission and interference links at the secondary transmitter, namely, perfect CSI of both transmission and interference links, perfect CSI of the transmission link, imperfect CSI of the interference link, imperfect CSI of both links, or only statistical CSI of both links. Through numerical results, the impact of sensing performance, different types of CSI availability, and transmit and interference power constraints on the EE of the secondary users is analyzed. [ABSTRACT FROM PUBLISHER]

The generalized likelihood ratio test (GLRT) is a very widely used technique for detecting signals of interest amongst noise, when some of the parameters describing the signal (and possibly the noise) are unknown. The threshold of such a test is set from a desired probability of false alarm Pfa and hence this threshold depends on the statistical assumptions made about noise. In practice however, the noise statistics are seldom known and it becomes crucial to characterize Pfa under a mismatched distribution. In this letter, we address this problem in the case of a simple binary composite hypothesis testing problem (matched filter) when the threshold is designed under a Gaussian assumption while the noise actually follows an elliptically contoured distribution. We also consider the inverse situation. Generic expressions for the assumed and actual Pfa are derived and illustrated on the particular case of Student distributions for which simple, closed-form expressions are obtained. The latter show that the GLRT based on Gaussian assumption is not robust while that based on Student assumption is. [ABSTRACT FROM AUTHOR]

This paper studies the symbol error rate performance of cognitive radio transmissions in the presence of imperfect sensing decisions. Two different transmission schemes, namely sensing-based spectrum sharing (SSS) and opportunistic spectrum access (OSA), are considered. In both schemes, secondary users first perform channel sensing, albeit with possible errors. In SSS, depending on the sensing decisions, they adapt the transmission power level and coexist with primary users in the channel. On the other hand, in OSA, secondary users are allowed to transmit only when the primary user activity is not detected. Initially, for both transmission schemes, general formulations for the optimal decision rule and error probabilities are provided for arbitrary modulation schemes under the assumptions that the receiver is equipped with the sensing decision and perfect knowledge of the channel fading, and the primary user's received faded signals at the secondary receiver has a Gaussian mixture distribution. Subsequently, the general approach is specialized to rectangular quadrature amplitude modulation (QAM). More specifically, the optimal decision rule is characterized for rectangular QAM, and closed-form expressions for the average symbol error probability attained with the optimal detector are derived under both transmit power and interference constraints. The effects of imperfect channel sensing decisions, interference from the primary user and its Gaussian mixture model, and the transmit power and interference constraints on the error rate performance of cognitive transmissions are analyzed. [ABSTRACT FROM PUBLISHER]

In this paper, throughput achieved in cognitive radio channels with finite blocklength codes under buffer limitations is studied. Cognitive users first determine the activity of the primary users' through channel sensing and then initiate data transmission at a power level that depends on the channel sensing decisions. It is assumed that finite blocklength codes are employed in the data transmission phase. Hence, errors can occur in reception and retransmissions can be required. Primary users' activities are modeled as a two-state Markov chain and an eight-state Markov chain is constructed in order to model the cognitive radio channel. Channel state information (CSI) is assumed to be perfectly known by either the secondary receiver only or both the secondary transmitter and receiver. In the absence of CSI at the transmitter, fixed-rate transmission is performed whereas under perfect CSI knowledge, for a given target error probability, the transmitter varies the rate according to the channel conditions. Under these assumptions, throughput in the presence of buffer constraints is determined by characterizing the maximum constant arrival rates that can be supported by the cognitive radio channel while satisfying certain limits on buffer violation probabilities. Tradeoffs between throughput, buffer constraints, coding blocklength, and sensing duration for both fixed-rate and variable-rate transmissions are analyzed numerically. The relations between average error probability, sensing threshold and sensing duration are studied in the case of variable-rate transmissions. [ABSTRACT FROM AUTHOR]

The development of automated monitoring systems for the detection of singularities, such as leakages in dikes, is indispensable to avoid mass disaster. An efficient solution for dike survey is the use of distributed temperature sensors (DTSs) based on optical fiber, offering a multitude of advantages such as low cost, extreme robustness, long-range measurement, etc. However, the temperature data acquired with DTSs, being not directly interpretable, require intervention of signal processing techniques. This paper addresses this signal processing aspect, exploiting the key idea that the temperature variations over the course of a day for singular zones are quite different from those for nonsingular zones. A daily reference temperature variation, which is representative of the nonsingular zones, is estimated using singular value decomposition (SVD). The residue subspace of SVD contains information linked to the deviations from this reference, thus allowing the degree of singularity to be quantified by a dissimilarity measure such as the L2-norm. To detect only the singularities in dikes, such as leakages or drains, a constant false alarm rate (CFAR) detector is proposed by modeling each daily dissimilarity measure with a mixture of Gamma and uniform distributions. The proposed automatic singularity detection system was validated under different scenarios on real data over periods from 2005 to 2007. The first scenario depicted the detection of percolation-type artificial leakages with their detection strength depending on their flow rates. Another scenario allowed detecting the presence of a real water leakage at the site, which was previously unobserved during manual inspections. The repeatability of the system was also verified by periodic analysis. [ABSTRACT FROM AUTHOR]

Detection is the first task in establishing a communication link, or tracking a target with a radar system. This is a decision-making problem, and it is performed through a hypothesis-testing procedure. Hypothesis testing is nothing but constant-false- alarm-rate (CFAR) detection. These issues are reviewed, and simple MATLAB™ codes are presented. [ABSTRACT FROM AUTHOR]

The constant false alarm rate (CFAR) detector based on the fast Fourier transform (FFT) filter bank is computationally efficient and widely used for the detection and frequency estimation of narrowband signals embedded in noise. Depending on the expected bandwidth of the signals of interest, techniques involving the processing of multiple input data blocks, with or without overlap, may be used to optimize the detection performance. These include the predetection summation (averaging) of power spectral estimates or the use of a two-stage processing strategy where the final detection decision is based on the majority vote of detection decisions obtained for the individual input data blocks. This paper shows that the actual performance gain obtained by doubling the number of input data blocks is dependent on the number of data blocks and is always less than 3 dB. [ABSTRACT FROM AUTHOR]

This paper examines the fluctuations in low-grazing-angle 100-kHz backscatter from a rocky limestone sea bed near Copenhagen, Denmark, at horizontal ranges up to 420 m. The sea-bed reverberation was characterized by strong short spatial-scale variations in scattering strength and statistical parameters. The measured areal backscatter strengths were in the range from -50 to -24 dB at grazing angles less than 3°, showing a strong local variability and grazing-angle dependence definitely not in accordance with Lambert's law. The observed echo-amplitude distributions varied between log-normal and Rayleigh models, with more Rayleigh-like probability density functions having higher scintillation indices and skewness (approaching values of 1.0 and 0.63, respectively). The scintillation index and skewness parameters were found to increase mildly with both horizontal range and water-current magnitude. A simple model using the coherent superposition of multiple scatterers was proposed to explain the observed scattering statistics. This model is based on the assumption that the sea bed is effectively immobile, with water-borne scintillation and micro-multipaths providing fluctuations in scatterer phase. This simple model shows that echo-amplitude fluctuations can deviate from the Rayleigh model through two mechanisms: 1) decreasing the levels of water-borne phase fluctuation and 2) increasing the nonuniformity of the sea-bed scatterer amplitudes. [ABSTRACT FROM AUTHOR]

Performance analysis of the normalized adaptive matched filter (NAMF) operating in homogeneous Gaussian clutter has received considerable attention in the recent years (see [1]-[6]). In this letter, for the first time, exact and explicit expressions are derived for the associated probability of false alarm and the probability of detection. The practical advantages of using the derived expressions are established. [ABSTRACT FROM AUTHOR]