Global Probability Of False Alarm Research Articles

An analysis of 5G-MIMO communication system based SS for centralized cooperative and non-cooperative users

An analysis of 5G-MIMO communication system based SS for centralized cooperative and non-cooperative users

An analysis of low complexity of 5G-MIMO communication system based CR using hybrid filter detection

The exponential growth in wireless communication has resulted in a high demand for novel wireless services within the frequency bands that can deal with the challenge pertaining to the current spectrum shortage. Thus, a 5G-MIMO based CR communication system has been developed to enhance the spectrum by employing a spectrum sensing (SS) algorithm. This recommended SS algorithm, based on the hybrid filter detection (HFD) method, employs the Cosine law for filtering the traffic signal, and then enables segmentation by applying the Welch algorithm. Then, the Hann algorithm is employed to window the traffic signal, and facilitates damping the MIMO impact for different waveforms. This also includes universal filtered multi-carrier (UFMC), filtered-orthogonal frequency division multiplexing (F-OFDM), filter bank multi-carrier (FBMC), non-orthogonal multiple access (NOMA), and Generalized Frequency Division Multiplexing (GFDM) waveforms. Also, evaluation of the operating parameters such as SNR, the antenna count, the signal span, and power as well as computational complexity was done. Based on the simulation results, a notable achievement was seen for the parameters that were below 0 dB of SNR, <0.09 % global system error probability, >96 % global detection probability, <0.06 % global false alarm probability, and possessing simple complexity. The recommended system’s parameters were assessed, which demonstrated its superiority versus other previously developed systems. This algorithm was found to identify all types of 5G signals and not just one type of 5G.

A CR-5G network based on multi-user for various waveforms detection

The 5G network is promised to provide all the requirements surfacing from the speedily rising number of mobile phone users rather than huge amount of data transmission. Many 5G waveforms can be used by 5G network such as Filtered-Orthogonal Frequency Division Multiplexing (F-OFDM), Universal Filtered Multi-Carrier (UFMC), or Filter Bank Multi-Carrier (FBMC) waveform. To effectively capitalize on the spectrum resources, Internet of Thing (IoT), and multimedia transmission, the Cooperative Spectrum Sensing (CSS) system is being extensively utilized by Cognitive Radio (CR). It is able to meet the requisite communication services, however, the conventional CSS systems have been designed to detect only single kind of 5G waveform and poor detection performance for low signal-to-noise ratio (SNR). In this paper, a hybrid filter based CSS system is proposed for detection of various 5G waveforms by multi-user. The proposed filter includes three cascaded stages: cosine filtering, Welch segmentation, and Hamming windowing. The proposed detection system is applied on different data such as various of waveforms and SNRs. The simulation results exhibit a significant detection performance for the parameters of less than zero dB of SNR, greater than 95% global detection probability, less than 1% global system error probability, and less than 1% global false alarm probability. In addition, the detection performance reveals that the proposed system is outperformed related works as shown in the comparison in terms of the previous parameters.

Improved EGC method for increasing detection in cognitive radio networks

Abstract The detrimental effects of shadowing and fading, affect the reliability of single user spectrum sensing in Cognitive Radio Networks. Hence to overcome this issue, cooperative spectrum sensing is performed. Fusion rules play an important role in improving the consistency of cooperative spectrum sensing. In this work, in addition to analysing the different soft fusion rules, an improved Equal Gain Combining method (EGC) is proposed to obtain better detection performance than conventional methods. Here, the number of results reported to Fusion Center also get reduced without loss in detection performance, thereby paving the way for energy conservation. Also the proposed improved EGC method is further modified in to two different schemes such as improved EGC method with conditions 1 and 2 (IEGCC1 and IEGCC2) to address the trade-off between throughput and energy consumption. Simulations have been carried out in MATLAB to compare and justify the performance of the proposed method and the modified schemes along with the conventional ones. It is observed that there is about 44% increase in detection in the proposed improved EGC method than the conventional EGC method, while simulating 20 users with SNR range of -30 to -11 and 48% increase while simulating 10 users with SNR range of -30 to -21 for a global false alarm probability of value 0.1. Also IEGCC2 method is found to have increased detection performance and highest energy efficiency of all the compared methods with about 23.31% increase in detection than the improved EGC method at a false alarm probability of 0.1.

Energy harvesting, cooperative spectrum sensing, and data transmission improvement in energy‐efficient multichannel cognitive sensor networks

SummaryIn this paper, we investigate the energy harvesting capability in a multichannel wireless cognitive sensor networks for energy‐efficient cooperative spectrum sensing and data transmission. Spectrum sensors can cooperatively scan the spectrum for available channels, whereas data sensors transmit data to the fusion center (FC) over those channels. We select the sensing, data transmission, and harvesting sensors by setting the sensing time, data transmission time, and also harvesting time to maximize the network data transmission rate and improve the total energy consumption in the multichannel network under global probability of false alarm and global probability of detection constraints. We formulate our optimization problem and employ the convex optimization method to obtain the optimal times and nodes for spectrum sensing, data transmission, and harvesting energy in each subchannel for multiband cognitive sensor networks. Simulation results show that in our proposed algorithm, the network data transmission rate is improved while more energy is saved compared with the baseline methods with equal sensing time in all subchannels.

Separating Function Estimation Test for Binary Distributed Radar Detection With Unknown Parameters

This paper addresses the problem of distributed detection in the case where, under the signal-present hypothesis, the signal-to-noise ratio (SNR) is unknown and/or observations are correlated. We assume that each local detector makes a (binary) decision while meeting a local false alarm constraint; it then transmits its decision to a fusion center. The unknown SNR at each local detector induces an unknown probability of detection and, hence, the optimal detector at the fusion center does not exist. We begin with the case most often considered in the literature: independent observations. In this case, we derive the asymptotically optimal separating function estimation test (AOSFET) and the generalized likelihood ratio test (GLRT). Moreover, we propose a method to set the local false alarm rates to achieve the maximum probability of detection at the fusion center (while meeting a constraint on the global probability of false alarm). The second part of this paper considers the case of correlated observations. We show that the AOSFET for this problem does not exist. As alternatives, we propose three suboptimal SFETs: based on an approximation to the AOSFET, the Kullback–Leibler divergence, and the Euclidean distance of the estimated probability mass function (pmf) of the observations under each hypotheses. Finally, we propose two methods to improve the performance of the estimation of the pmfs using a library of training labeled data based on the maximum likelihood estimation and expected maximization methods.

Energy consumption minimization and throughput improvement in cognitive radio networks by joint optimization of detection threshold, sensing time and user selection

Cooperative spectrum sensing schemes proposed to solve the hidden terminal problem and mitigate multipath fading and shadowing effects, which enhance the sensing performance and throughput in cognitive radio (CR) neworks. However, increasing the number of cooperative SUs leads to more communication overhead, which will increase the energy consumption of the CR network. In this paper, a new scheme is proposed to solve the joint optimization problem of the sensing time, the detection threshold and the selection of the sensing and data transmitting secondary users (SUs) for improvement of the throughput and minimization of the energy consumption of the CR network under the constrains on the global probability of detection and global probability of false alarm. For these purposes, we find the optimal values of detection threshold and sensing time such that the detection constraints are satisfied. The convex optimization methods are used to determine the sensing and data transmitting SUs. The simulation results show that there exists the optimal detection threshold and sensing time for selected sensing and transmitting SUs that can improve the average throughput and minimize the energy consumption of the CR network in comparison to other schemes.

Circular Folding Cooperative Power Spectral Density Split Cancellation Algorithm for Spectrum Sensing

The cooperative power spectral density split cancellation (CPSC) algorithm for spectrum sensing has low computational complexity and is robust under dynamical noise. We propose a novel circular folding CPSC (CF-CPSC) algorithm that expressively outperforms the original CPSC. Making use of the incomplete regularized Beta function, the cumulative distribution functions of the main random variables that form the decision statistic of the CF-CPSC model are derived. It is also derived an expression for the global probability of false alarm of the CF-CPSC, which is particularized to an approximate simple closed form that yields very accurate results. A closed-form expression of the decision threshold is provided as well. The analytical results are verified by Monte Carlo simulations. Other simulation results are provided to demonstrate the suitability of the CF-CPSC for scenarios of practical interest.

Two‐stage constant false alarm rate detection for distributed multiple‐input–multiple‐output radar

In this study, the authors consider a two-stage constant false alarm rate (TS-CFAR) detection problem in a distributed multiple-input-multiple-output radar. This study is motivated by the fact that the clutter plus noise power and the signal to clutter plus noise ratio may be both unknown in practice. Two TS-CFAR detectors are proposed, that is, TS generalised likelihood ratio test detector and TS adaptive matched filter detector. Each of the proposed detectors has two thresholds: the first one called local threshold controls the communication rate and the second one called global threshold adjusts the global probability of false alarm. It is proved that both of the proposed detectors have CFAR property. Numerical results illustrate that the two proposed detectors have good detection performance at a low communication rate.

To AND or To OR: On Energy-Efficient Distributed Spectrum Sensing With Combined Censoring and Sleeping

Distributed spectrum sensing improves the detection reliability of a cognitive radio network but generally comes at the price of a large power consumption. Since cognitive radios are generally low-power sensors with limited batteries, a combined censoring and sleeping scheme is considered as an energy-efficient algorithm for distributed spectrum sensing. Each sensor switches off its sensing module with a specific sleeping rate. When the sensor is on, a censoring policy is employed to send the sensing result to the fusion center. The result is only transmitted, if it is deemed to be informative. Hence, the energy consumption of each sensor, including the sensing and transmission energies, is reduced. The underlying sensing parameters are derived by minimizing the maximum average energy consumption per sensor subject to a lower-bound on the global probability of detection and an upper-bound on the global probability of false alarm. We analyze the problem for the OR and the AND rule and provide a performance analysis for a case study based on the IEEE 802.15.4/ZigBee standard. It is shown that the combined censoring and sleeping scheme achieves a significant energy saving compared to the case where no censoring or sleeping is taken into account.

Throughput Improvement in Energy-Efficient Cooperative Spectrum Sensing Based on Sensor Selection

Cooperative spectrum sensing in cognitive sensor networks has been shown to be very effective in improving the sensing performance. Sensors in these networks are assumed to be power re-source constrained. We propose an energy efficient cooperative spectrum sensing, in which all sensors are not participated in spectrum sensing and some nodes send their data to a fusion center only in order to improve the throughput of the network. We determine the nodes which sense the spectrum and the nodes which only contribute in data transmitting. The constraints on our problem are the global probability of detection, global probability of false alarm and the minimum required throughput. Based on the standard optimization techniques, the optimal conditions are obtained and a closed-form equation is expressed to determine the priority of nodes for spectrum sensing and data transmitting. Simulation results show that our algorithm leads to less energy consumption and improves the throughput in different conditions.

An iterative method for decision fusion in multiple sensor systems

Distributed decision fusion has been intensively studied in the past. This interest has been sparked by the requirement of a distributed surveillance system to be more reliable and immune to electronic attack than a single sensor system. In this paper, we consider a binary decentralized distributed decision fusion in which a system of multiple independent sensors monitors a common volume and provides relevant binary decisions about the state of the environment to a data fusion center. The fusion center combines the binary decisions of the individual distributed sensors into a final global decision. We propose a simple iterative method for optimizing multiple sensor decision fusion systems, in terms of both the sensors and the fusion center. The proposed iterative method determines, for a given global false alarm probability, the corresponding optimum setting of the individual sensors thresholds and the fusion center combining strategy that yields maximum global detection probability. The proposed method enables efficient search for the optimum solution by starting from a variety of initial trial values. The performance of the proposed method is provided in case of Rayleigh distributed observations and is proved to be cost effective and efficient.

Asymptotic Optimality in Bayesian Changepoint Detection Problems under Global False Alarm Probability Constraint

In the 1960s Shiryaev developed the Bayesian theory of changepoint detection in independent and identically distributed (i.i.d.) sequences. In Shiryaev's classical setting the goal is to minimize an average delay to detection under the constraint imposed on the average probability of false alarm. Recently, Tartakovsky and Veeravalli (Theory Probab. Appl., 49 (2005), pp. 458-497) developed a general Bayesian asymptotic changepoint detection theory (in the classical setting) that is not limited to a restrictive i.i.d. assumption. It was proved that Shiryaev's detection procedure is asymptotically optimal under traditional average false alarm probability constraint, assuming that this probability is small. In the present paper, we consider a less conventional approach where the constraint is imposed on the global, supremum false alarm probability. An asymptotically optimal Bayesian change detection procedure is proposed and thoroughly evaluated for both i.i.d. and non- i.i.d. models when the global false alarm probability approaches zero.

Asymptotic Optimality in Bayesian Changepoint Detection Problems under Global False Alarm Probability Constraint

Asymptotic Optimality in Bayesian Changepoint Detection Problems under Global False Alarm Probability Constraint

Optimization of a parallel distributed detection system using distance measures

In this paper, we use both J -divergence and Bhattacharyya coefficient as distance measures to optimize a parallel distributed detection system consisting of N local detectors and a global decision maker. The optimization is obtained by allowing the thresholds of the local detectors to be functions of the global probability of false alarm. Numerical results for the case of four and six local detectors are obtained. These results indicate the superiority of our approach over those reported in the literature in which the local thresholds were specified without taking the level of global probability of false alarm into account.

Optimization of a serial distributed detection system with 2 bits communication constraint

In this paper, we consider a serial distributed detection system of two sensors in which the first sensor is allowed to communicate 2 bits to the second sensor (global detector). We use the J divergence to optimize the system and the optimization is obtained by varying the binary thresholds of the first sensor to obtain the maximum value of the global probability of detection for a specified global probability of false alarm. The numerical results obtained for the J divergence does not satisfy the expected performance improvement. Motivated by this, we propose a new more direct optimization procedure. The numerical results indicate the performance superiority of the proposed procedure over that of the J divergence method. Moreover, the performance of J divergence and the proposed method is better than that of the distributed system in which 1 bit hard decision is communicated between sensors.

Distributed CFAR detection in homogeneous and nonhomogeneous backgrounds

The performance of distributed constant false alarm rate (CFAR) detection with data fusion both in homogeneous and nonhomogeneous Gaussian backgrounds is analyzed. The ordered statistics (OS) CFAR detectors are employed as local detectors. With a Swerling type I target model, in the homogeneous background, the global probability of detection for a given fixed global probability of false alarm is maximized by optimizing both the threshold multipliers and the order numbers of the local OS-CFAR detectors. In the nonhomogeneous background with multiple targets or clutter edges, the performance of the detection system is analyzed and its performance is compared with the performance of the distributed cell-averaging (CA) CFAR detection system.