False Alarm Probability Constraints Research Articles

Soft fusion‐based cooperative spectrum sensing using particle swarm optimization for cognitive radio networks in cyber‐physical systems

SummaryAs a multi‐dimensional complex system, Cyber physical systems (CPS) integrates computing, network, and physical environment. How to effectively fuse the local detection results is the key to improve the sensing performance in CPS. In order to improve the spectrum sensing performance of cognitive radio, a soft fusion‐based cooperative spectrum sensing using particle swarm optimization (PSO) for cognitive radio networks is proposed. To find the optimal weighting coefficient of soft fusion, the traditional PSO algorithm is investigated and improved. To further reduce the local convergence of PSO and accelerate the convergence speed of the algorithm, the immune algorithm is introduced and chaotic sequence mechanism is applied to ensure the diversity of the particles. Moreover, the inertia weight is adjusted adaptively according to the convergence of particles so as to maintain the tradeoff between the search ability and the convergence of particles. Simulation results show that the proposed algorithm can achieve fast convergence in CPS. Compared with other typical methods, it can obtain better sensing performance under different signal‐to‐noise ratio environments and the constraint of false alarm probability.

Cluster-Based Energy Efficient Collaborative Spectrum Sensing for Cognitive Sensor Network

The high energy efficiency for cognitive sensor network (CSN) has been widely studied for supporting various applications in military and civil fields. By identifying time fraction, local detection threshold, and the number of cognitive sensors, we discuss some design considerations for a cluster-based collaborative spectrum sensing scheme. We formulate the optimization problem to maximize energy efficiency of CSN under a collision constraint and false alarm probability constraint. In order to overcome the problem of finding the optimal detection threshold when k-out-of-M fusion rule is used by the sink node (SN), we design a fictitious cognitive sensor (FCS), which has the same sensing performance as SN. The process for finding the optimal local detection threshold can be converted into the process for searching an optimal received SNR of FCS. Theoretical analysis shows that there exists a unique time fraction to maximize the energy efficiency. Finally, our theoretical analysis is verified through numeric simulations.

A Mathematical Approach for Hidden Node Problem in Cognitive Radio Networks

Cognitive radio (CR) technology has emerged as a realistic solution to the spectrum scarcity problem in present day wireless networks. A major challenge in CR radio networks is the hidden node problem, which is the inability of the CR nodes to detect the primary user. This paper proposes energy detector-based distributed sequential cooperative spectrum sensing over Nakagami-m fading, as a tool to solve the hidden node problem. The derivation of energy detection performance over Nakagami-m fading channel is presented. Since the observation represents a random variable, likelihood ratio test (LRT) is known to be optimal in this type of detection problem. The LRT is implemented using the Neyman-Pearson Criterion (maximizing the probability of detection but at a constraint of false alarm probability). The performance of the proposed method has been evaluated both by numerical analysis and simulations. The effect of cooperation among a group of CR nodes and system parameters such as SNR, detection threshold and number of samples per CR nodes is investigated. Results show improved detection performance by implementing the proposed model.

Spectrum Sensing Algorithm Based on Double Threshold and Two-Stage Detection Under a Low SNR

In cognitive radio networks, spectrum sensing algorithms almost perform poor in low signal-to-noise ratio (SNR) sensing environment, it does need to improve detection probability by increasing the detection time. In order to solve the mentioned problems, a new spectrum sensing algorithm based on the double threshold and two-stage detection strategy under the condition of low SNR is put forward in this paper. First of all, when a false-alarm probability is given, the sampling length is found out under the different SNR level. Then the first stage detection is carried out by designing the two detection threshold. Finally the second stage detection is implemented for the center portion of the double threshold and the final detection result is gotten. Simulations and analysis show that, when the SNR is low, the proposed algorithm not only has a short average sensing time but also has a good performance in a small false-alarm probability constraint condition and low algorithm complexity.

Quickest Detection of Parameter Changes in Stochastic Regression: Nonparametric CUSUM

We consider the problem of detecting abrupt parameter changes in a stochastic regression with unknown noise distribution. The process changes at some unknown point of time. Under general conditions on the regression function and unknown distributions of observations before and after the disruption, the paper develops a nonparametric cumulative sum procedure (CUSUM). Unlike likelihood-based CUSUM algorithms, constructed mostly on log-likelihood ratio statistics, we use a special system of basic statistics in Page's procedure. By applying a sequential sampling scheme, which measures time in terms of accumulated Kullback-Leibler (K-L) divergence, we come to a system of statistics with the martingale properties close to those of the log-likelihood ratios. The proposed approach suggests also an alternative performance criterion in the analysis of the procedure by replacing the expected detection delay by the corresponding K-L divergence. We show that, under the false alarm probability constraint, the nonparametric CUSUM rule is optimal in the sense that it ensures the logarithmic asymptotic for the detection delay.

Optimal Cooperator Set Selection in Social Cognitive Radio Networks

While there is a huge body of research on cooperative spectrum sensing in cognitive radio networks (CRNs), incentives for cooperative behavior or the conditions under which cooperation is more likely have not been explored. We model cooperation among cognitive radios (CRs) as a function of social ties among CRs. In this social CRN , where CRs do not necessarily fulfill every cooperative sensing request, we focus on the cooperator set selection problem , i.e., in which CRs ask for cooperation so that the resulting throughput and sensing accuracy are maximized, subject to detection and false alarm probability constraints. For a single-channel scenario, we devise a multiobjective optimization model and obtain the solution using an evolutionary multiobjective algorithm. Our evaluations show that our solution is near optimal, in terms of throughput under legitimate operation, i.e., no malicious users, whereas it outperforms the expected-throughput-optimal scheme in the case of attackers. A numerical analysis demonstrates the robustness of our proposal with a slight loss in performance when the network is subject to common sensing attacks. In addition, our analysis underlines one significant drawback of existing works: assuming all CRs to be cooperative leads to a substantial overestimation of throughput capacity. Finally, to tackle the increasing complexity under a multichannel setting, we propose a heuristic for multichannel cooperative sensing for the considered social CRN.

Joint Spectrum Sensing and Data Transmission Optimization for Energy Efficiency in Cognitive Radio Sensor Networks: A Dynamic Cooperative Method

To address the inherent energy constraint in cognitive radio sensor networks, a novel joint optimization method of spectrum sensing and data transmission for energy efficiency is investigated in this paper. To begin with, a cooperative spectrum sensing scheme based on dynamic censoring is employed to shorten sensing time and save unnecessary spectrum sensing energy. Then to jointly optimize the energy efficiency, the distortion constrained probabilistic transmission scheme is utilized. Afterwards the sensing threshold solving issue can be formulated as a nonlinear minmax optimization problem with the detection probability and false alarm probability constraints. Solving by the Matlab software with the free OPTI toolbox, simulation results demonstrate that significant energy can be saved via the the proposed joint optimization method in various mobile cloud scenarios.

Sensing-Energy Tradeoff in Cognitive Radio Networks With Relays

Earlier works studied the tradeoff between sensing performance and throughput of secondary users (SUs) for cooperative spectrum sensing. However, The tradeoff between sensing performance and energy consumption has not been well investigated in cognitive radio (CR) networks with relays. In this paper, the issues on cooperative spectrum sensing in a CR network with amplify-and-forward relay are studied. In particular, an optimization problem is formulated to analyze the tradeoff between sensing performance and energy consumption. The objective is to minimize energy consumption for spectrum sensing under given detection probability and false alarm probability constraints. We do so via finding an optimal pair of the parameters, i.e., the number of samples (proportional to sensing time) and the amplification gain. First, the case of fixed amplification gain is considered, in which the minimum number of samples is identified. Then, the case of varying amplification gain is further discussed, where an appropriate amplification gain is found such that the energy consumption is minimized while the sensing performance is kept above a threshold and the required throughput of the SU can be satisfied. The analytical and simulation results show that there exists an optimal pair of the number of samples and the amplification gain to strike the best tradeoff between sensing performance and energy consumption.

Jointly optimizing sensing time and resource allocation in multichannel cognitive radio networks

SUMMARYSpectrum sensing is a key technique for determining the spectrum available in cognitive radio (CR) networks. In this paper, we study how to jointly optimize sensing time and resource allocation to maximize the sum transmission rate of all CR users of a multichannel CR network. We take into consideration the transmission power and interference constraints to protect primary users from harmful interference, as well as constraints of detection probability and false alarm probability. Under these constraints, we propose an asymptotically optimal resource allocation algorithm. The optimal sensing time can be obtained using the traditional one‐dimensional exhaustive search. However, owing to the high complexity of searching for the sensing time, we propose a simplified method to get the optimal sensing time under the assumption that false alarm probability is small. Simulation results show that the simplified method can obtain the optimal sensing time efficiently under strict constraint of false alarm probability. Copyright © 2012 John Wiley & Sons, Ltd.

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