Cooperative Energy Detection Research Articles

A Cooperative Cognitive Radio Spectrum Sensing Based on Correlation Sum Method with Linear Equalization

For moving forward toward the next generations of information technology and wireless communication, it is becoming necessary to find new resources of spectrum to fulfill the requirements of next generations from higher data rates and more capacity. Increasing efficiency of the spectrum usage is an urgent need as an intrinsic result of the rapidly increasing number of wireless users and the conversion of voice-oriented applications to multimedia applications. Spectrum sensing techniques in cognitive radio technology work upon an optimal usage of the available spectrum determined by the Federal Communication Commission (FCC). In this paper, the performance of a cooperative cognitive radio spectrum sensing detection based on the correlation sum method by utilizing the multiuser multiple input multiple output (MU_MIMO) technique over fading and Additive White Gaussian Noise (AWGN) channel is analyzed. Equalization is used at the receiver to compensate the effect of fading channels and improve the reliability of spectrum sensing. The performance is compared with the performance of Energy detection technique. The simulation results show that the detection performance of cooperative correlation sum method is more efficient than that obtained for the cooperative Energy detection technique.

Bhattacharyya distance criterion based multibit quantizer design for cooperative spectrum sensing in cognitive radio networks

Cooperative spectrum sensing (CSS) is crucial for dynamic spectrum access in cognitive radio networks. This paper considers a CSS scheme by using a multilevel quantizer in each sensing node (SN) to quantize the local energy detector’s observation. A log-likelihood ratio test detector by using quantized data received from each SN is proposed to determine the presence or absence of the primary user signal. The Bhattacharyya distance (BD) of the cooperative sensing system is derived. Then, a quantizer design scheme by maximizing the BD that as the criterion function in an optimization problem with respect to the quantization thresholds is proposed. As nonlinear and high-dimensional of the objective function, a particle swarm optimization algorithm is employed to solve operating parameters for the quantizer. Furthermore, we derive the upper bound performance on cooperative energy detection (CED) in CSS. To validate the effectiveness of the proposed approach, The quantizer is compared with other schemes in terms of detection performance. Simulation results show that 2- or 3-bit quantization of the proposed approach achieves comparable performance to upper bound of CED without quantization.

A Two-Step Cooperative Energy Detection Algorithm Robust to Noise Uncertainty

In order to achieve accurate interference detection in complex electromagnetic environments, a two-step cooperative stochastic resonance energy detection (TCSRED) algorithm is proposed to address the problem, where the traditional energy detection (ED) performance is susceptible to noise uncertainty. By combining two thresholds and two-step cooperation, the generalized stochastic resonance is applied to the energy detection, which effectively reduces the complexity and detection time. In particular, when a certain decision result is obtained in the first step of detection, the decision is finished and the second step of detection is unnecessary. Otherwise, the second step of detection is performed to obtain the final decision result. Simulation results show that the proposed algorithm is robust to the noise uncertainty. Even in the case of a low signal-to-noise ratio (SNR), it also performs better than existing methods without significant increment of the complexity.

Cooperative Energy Detection With Heterogeneous Sensors Under Noise Uncertainty: SNR Wall and Use of Evidence Theory

The analyzed system model in this paper is a distributed parallel detection network in which each secondary user (SU) evaluates the energy-based test statistic from the received observations and sends it to a fusion center (FC), which makes the final decision. Uncertainty in the noise variance at each SU is modeled as an unknown constant in a certain interval around the nominal noise variance. It is assumed that the SUs are heterogeneous in that the nominal noise variances and the uncertainty intervals can be different for different SUs. Moreover, the received signal power at each SU may be different. For the considered system model, the paper presents important results for two inter-related themes on cooperative energy detection (CED) in the presence of noise uncertainty (NU). First, the expressions for generalized signal-to-noise ratio (SNR) walls are derived for the classical CED fusion rule, i.e., sum of energies from all SUs. Second, a Dempster–Shafer theory based CED is proposed in the presence of NU with heterogeneous sensors. In the proposed scheme, the test statistic from each SU is the energy-based basic mass assignment values, which are first discounted depending on the uncertainty level associated with the SU and then fused at the FC using the Dempster rule of combination to arrive at the global decision. It is shown that the proposed scheme outperforms the traditional sum fusion rule in terms of detection performance as well as the location of SNR wall.

On the performance of the energy detector subject to impulsive noise in κ — μ, α — μ, and η — μ fading channels

This paper presents a unified theoretical analysis of the energy detection of Gaussian and M-PSK signals in κ - μ, α - μ, and η - μ fading channels at the output of an energy detector subject to impulsive noise (Bernoulli-Gaussian model). As a result, novel, simple, and accurately approximated expressions for the probability of detection are derived. More precisely, the generalized Gauss-Laguerre quadrature is applied to approximate the probability of detection as a simple finite sum. Monte Carlo simulations corroborate the accuracy and precision of the derived approximations. The results are further extended to cooperative energy detection with hard decision combining information.

Fast Cooperative Energy Detection under Accuracy Constraints in Cognitive Radio Networks

Cooperative energy detection (CED) is a key technique to identify the spectrum holes in cognitive radio networks. Previous study on this technique mainly aims at improving the detection accuracy, while paying little attention to the performance of detection time. This paper concentrates on the issue of fast CED, which is achieved by minimizing its detection time subject to the constraints on detection accuracy. Firstly, the prevalent counting rule based CED algorithm is optimized. Taking the special cases of counting rule (AND rule and OR rule), for example, we show that detection time can be minimized by selecting an optimal number of secondary users. Moreover, we prove that OR rule is superior to AND rule in detection time, and thus OR rule based CED is faster than AND rule based CED. Then, a sequential test (ST) based CED algorithm is proposed to exploit the benefit of ST and detect primary user even faster. After analyzing its detection time, we illustrate that ST based CED is able to spend the minimal detection time in satisfying the accuracy constraints by choosing an optimal sample number. Simulation results are provided to verify the effectiveness of both fast algorithms discussed in this paper.

Optimisation of cooperative spectrum sensing for cognitive radio networks in the presence of smart primary user emulation attack

AbstractCooperative spectrum sensing, which is an effective method to improve the detection performance in cognitive radio (CR), imposes some threats to the network. One of these common threats is primary user emulation attack, where some malicious users try to mimic the primary signal and deceive secondary users to prevent them from accessing the vacant frequency. Here, we propose a cooperative spectrum sensing scheme for CR network in the presence of smart primary user emulation attack with spectrum sensing capability. More precisely, we optimise main parameters involved in cooperative energy detection spectrum sensing, such as the voting rule, the number of samples used for making the decision and the detection threshold so as to minimise the probability of error when energy detection is corrupted by malicious user's attack. Simulation results indicate performance improvement brought by our proposed method compared with conventional defense techniques in the presence of attacker. Copyright © 2014 John Wiley & Sons, Ltd.

Sequential Cooperative Spectrum Sensing Technique in Time Varying Channel

Cognitive radio opportunistically accesses the spectrum while the licensed user is idle. A spectrum sensing procedure to monitor primary users' existence is therefore vital to cognitive radios. In this paper, we investigate the energy detection based sequential cooperative spectrum sensing technique in time varying channels. By utilizing past local observations from previous sensing slots, cognitive radio nodes can aggregate the current and previously received energy values to improve the detection performance. We propose the weighted sequential energy detector (SED) in which a fixed number of past observations are taken for decision making. In addition, we propose two adaptive schemes, namely the Two-Stage SED and the Differential SED, where a cognitive radio user uses previously received energy values until it detects a change in primary user's activity. The probability of false alarm and detection for the Weighted SED and Two-Stage SED schemes are derived. The results show that the proposed schemes achieve better detection performance over the conventional cooperative energy detection technique. In particular, the Two-Stage SED and Differential SED schemes can satisfy the IEEE 802.22 standard's requirement of 10% false alarm and 90% detection in scenarios that conventional techniques cannot accommodate.

Small Sample Size Performance of the Energy Detector

We examine the small sample size performance of the energy detector for spectrum sensing in AWGN. By making use of the cube-of-Gaussian approximation of chi-squared random variables, we derive a novel, simple, and accurate analytical expression for the minimum number of samples required to achieve a desired probability of detection and false alarm. This way, the number of samples can be calculated by the energy detector with low complexity. We also propose a useful approximation for the performance of cooperative energy detection.

Decentralized Neyman-Pearson Test with Belief Propagation for Peer-to-Peer Collaborative Spectrum Sensing

In this paper we propose a decentralized approach for cooperative signal detection, based on peer-to-peer collaboration among sensor nodes. The proposed method combines belief propagation, implemented in a distributed fashion through the exchange of local messages to and from neighboring nodes, with a Neyman-Pearson framework, that allows control over the false-alarm rate of each node. At the same time, nodes gradually learn their degree of correlation with neighbors, and clusters of nodes under homogeneous conditions are formed automatically. The performance of the resulting va Neyman-Pearson belief propagation" (NP-BP) algorithm is shown to be nearly equivalent to that of cooperative energy detection applied separately at each cluster. Thanks to its decentralized structure, NP-BP provides improved robustness, flexibility, and scalability compared to traditional, centralized schemes. In addition, its ability to adaptively form clusters makes the algorithm suitable for heterogeneous or time-varying radio environments.

Cooperative Spectral Covariance Sensing under Correlated Shadowing

This paper investigates the theoretical limits of white space sensing in a cognitive radio (CR) network limited by channel correlation. In a log-normal shadowing channel, the received signal power is correlated based on the distance between the sensors and this makes sensing the presence of a signal difficult, even with several cooperative sensors. In the proposed system, each sensor uses the spectral covariance sensing (SCS) algorithm to detect the primary signal and then sends its decision statistic to the base station (BS). The BS, using the Neyman Pearson log-likelihood ratio test, makes the final decision. We analyze the probability of a false alarm (PFA) and compare it with that of the cooperative energy detector. We show that an asymptotic lower bound on the PFA is an order of magnitude lower than that of the energy detector. We also demonstrate improvements in the cooperation gain in terms of the effective number of independent sensors, and the required number of sensors for a given detection metric. The results of this paper show that cooperative SCS detection has far better white space sensing properties than cooperative energy detection in correlated channels.

Adaptive Sequential Cooperative Energy Detection Scheme for Primary User Detection in Cognitive Radio

This paper proposes an adaptive sequential cooperative energy detection scheme for primary user detection in cognitive radio to minimize the detection time while guaranteeing the desired detection accuracy. Simulation results are provided to show the effectiveness of the proposed scheme.

Soft versus Hard Cooperative Energy Detection under Low SNR

Amidst conflicting views about whether soft cooperative energy detection scheme (SCEDS) outperforms hard cooperative energy detection scheme (HCEDS) greatly in cognitive radio, we establish the bridge that mathematically connects SCEDS and HCEDS by closed approximations. Through this bridge, it is demonstrate that, if the number of detectors of HCEDS is 1.6 times as that of SCEDS, they have nearly the same performance which is confirmed by numerical simulations, enabling a quantitative evaluation of the relation between them and a resolution of the conflicting views.