Double‐threshold cooperative detection for cognitive radio based on weighing

Overcoming sensing failure problem in double threshold based cooperative spectrum sensing

Adopting cognitive radios (CRs) having multiple antennas in blind non-cooperative and cooperative spectrum sensing (CSS) under fading channels has gained attention due to higher detection performances provided by the spatial diversity gain of multi-sensors in different geographical locations and lower complexity, respectively. However, most studies do not consider sensing scenarios of more practical significance: for example, sometimes adopting only uncalibrated antenna arrays and sometimes only correlated signals at antenna arrays of CRs, but almost always, none of these impairments. Therefore, this paper studies these combined impairments on the performances of two blind techniques in centralized CSS with decision fusion (DF) and data fusion for different numbers of CRs and antennas per CR, both under frequency selective fading channels. One is the circular folding cooperative power spectral density split cancellation (CFCPSC), and the other is the generalized likelihood ratio test (GLRT). Extensive numerical results show, for instance, that with sample fusion (SF) and calibrated antennas, GLRT outperforms CFCPSC independently of correlation, numbers of antennas, or CRs. However, uncalibrated antennas severely penalize GLRT while surprisingly benefiting CFCPSC. Correlation is detrimental to GLRT and CFCPSC with SF but may help CFCPSC in DF and GLRT in DF or eigenvalue fusion. Generally, CFCPSC outperforms GLRT.

Energy detection is one of the popular spectrum sensing technique for cognitive radio. Better performance can be obtained by cooperative detection, but only when cognitive radios did not have different geographic locations and channel environment. To avoid this drawback, the paper presents an improved energy-based weighted cooperative spectrum sensing method which allows to achieve higher detection probability, to reduce the number of cognitive nodes involved in the detection procedure and to use efficiently the channel resources. Keywords—cognitive radio, cooperative spectrum sensing, energy detection, optimization

We propose two novel cooperative detection schemes based on the AF (Amplify and Forward) and DF (Decode and Forward) protocols to achieve spatial diversity gains for cognitive radio networks, which are referred to as the AF-CDS, (AF-based Cooperative Detection Scheme) and DF-CDS (DF-based Cooperative Detection Scheme), respectively. Closed-form expressions of detection probabilities for the noncooperation scheme, AND-CDS (AND-based Cooperative Detection Scheme), AF-CDS and DF-CDS, are derived over Rayleigh fading channels. Also, we analyze the overall agility for the proposed cooperative detection schemes and show that our schemes can further reduce the detection time. In addition, we compare the DF-CDS with the AF-CDS in terms of detection probability and agility gain, depicting the advantage of DF-CDS at low SNR region and high false alarm probability region.

A modified soft combination scheme for cooperative sequential detection to achieve reduced average number of samples in cooperative sensing that is proposed here, in a fast-fading scenario. This is done to sense primary user in fast-fading environment where the samples are independent and nonidentically distributed samples (i.n.i.d.), with decreased number of samples. Log-likelihood ratio is also studied in fast-fading environment; variance of received signal and variance of noise signal are considered as random variables. First, we studied the generalized log-likelihood ratio (GLLR) considering i.n.i.d. samples caused due to fast-fading. Then, a modified rule for soft combination of test statistics for performing the cooperative sequential detection, to achieve even less number of samples, is proposed, assuming received signal variance and noise signal variances’ probability density functions are unknown. Lastly, it is shown by simulation that the proposed cooperative sequential detection with modified soft combination rule performed better in this fast-fading environment by reducing average number of samples (ANS).

The ever-increasing demand for higher data rates in wireless communications in the face of inadequate or underutilized spectral resources has motivated the introduction of cognitive radio. In cognitive radio network, unlicensed users continuously scan a vast range of frequencies to detect `white spaces' or `spectrum holes' that are temporarily and spatially not being used by licensed user, process of which is commonly known as spectrum sensing. In order to implement the cooperative spectrum sensing among Cognitive Radio (CR) users, data fusion schemes are superior to that of decision fusion ones in terms of detection performance but suffer from the drawback of huge traffic overhead when restriction of bandwidth of communication channels and energy consumption of the radio network comes into consideration. In this paper, a combination of data and decision fusion approach is implemented to mutually adventure the advantages of both and a selective weight setting algorithm is proposed by utilizing normal and modified deflection coefficients maximization under Neyman-Pearson criterion in order to obtain a final decision. Also, cluster-based Cooperative Spectrum Sensing (CSS) has been proposed in cognitive radio network to improve the energy efficiency. The simulations show promising results as the hybridization process visibly reduces the network traffic overhead while attaining a highly reasonable detection performance.

Cooperative detection without fusion center has many applications such as spectrum sensing in cognitive radio or intrusion detection in ad hoc network. In this paper, we propose a new asynchronous fully-distributed cooperative algorithm which does not require any knowledge on the underlying nodes network. Discussion about the threshold choice and the respective duration of the sensing step and the gossip step is conducted.

In this paper, the performance of cooperative spectrum sensing (CSS) with censoring of cognitive radios (CRs) has been evaluated using a simple majority logic fusion in presence of Rayleigh fading affecting both the sensing (S) and reporting (R) channels. Each CR senses the primary user (PU) using a double threshold based energy detection over the sensing channel. Censoring of CRs is done on the basis of the quality of Rayleigh faded R-channel. Typically two types of censoring such as rank-based and threshold-based censoring are considered to select the best CR based on the quality of the R-channels between CRs and a fusion center (FC). The performance of CSS, in terms of average missed detection, is evaluated in terms of relevant network parameters such as; (i) number of selected CRs, (ii) censoring thresholds, (iii) fail sensing probabilities, (iv) S and R-channel signal to noise ratios (SNRs) under both the perfect and imperfect channel estimation. The performance in terms of agility gain, normalized average number of transmitted bits for different number of CRs is also studied. Further the performance of double threshold based energy detection is compared with that of single threshold based energy detection. Our studies demonstrate that censoring with double threshold outperforms the censoring with single threshold.

We present a survey of state-of-the-art algorithms for spectrum sensing in cognitive radio. The algorithms discussed range from energy detection to sophisticated feature detectors. The feature detectors that we present all have in common that they exploit some known structure of the transmitted signal. In particular we treat detectors that exploit cyclostationarity properties of the signal, and detectors that exploit a known eigenvalue structure of the signal covariance matrix. We also consider cooperative detection. Specifically we present data fusion rules for soft and hard combining, and discuss the energy efficiency of several different sensing, sleeping and censoring schemes in detail.

This paper suggests a cooperative spectrum sensing using dynamic double thresholds energy detection and adaptive grid search to obtain the highest probability detection. The proposed double thresholds are adaptive to noise fluctuation. A new fusion method combined of weighting and voting is used in cooperative sensing. Further, in order to obtain the best sensing performance, we firstly use adaptive grid search to find the optimal double thresholds. Simulation results show that the proposed method has excellent robustness to noise fluctuation and good sensing performance even under low signal to noise ratio (SNR).

For cognitive radio (CR) networks with user hierarchy, the sensing strategy with "listen-before-talk" (LBT) policy plays a key role in spectrum utilization and primary user (PU) protection. However, existing sensing strategies do not handle satisfactorily the randomness of both user locations and channel conditions in the network environment, resulting in inefficient spectrum utilization. To cope with such randomness, this paper develops three dynamic sensing strategies that can adaptively schedule the sensing slots/cycles according to the online link conditions without assuming knowledge of the PU traffic model. The proposed strategies can improve the efficiency of spectrum utilization while being robust with respect to the uncertainty in the PU traffic pattern. To maximize spectrum utilization, the proposed strategies are formulated through closed-form expressions. Efficient methods are introduced to compute the optimal values of the parameters used in the strategies, such as sensing time and sensing threshold. Simulations verify that the proposed sensing strategies offer an evident improvement on the spectrum utilization of the CR network. © 2006 IEEE.

In this paper, we obtain approximations for the optimal Log-Likelihood Ratio (LLR) decision rule in cooperative detection when local energy detectors are assumed. Considering conditional independence, we also show under which bandwidth and sampling frequency regimes these approximations hold best. Furthermore, we present simulations where the performance of the approximated LLR decision rule is compared with other sub-optimal decision rules given in the literature such as the optimal linear weighting. The simulations show that the density functions of the approximations exhibit negligible error in comparison with the exact ones, when conditions on bandwidth and sampling frequencies are met. The approximations presented in this paper allow to perform efficiently the joint LLR decision rule for a set of nodes without requiring Monte-Carlo simulations.

For smart grid applications, power line communications (PLC) have become a promising candidate to support some emerging services. Although it may employ frequencies above 500 kHz to guarantee reliable performance and fast response, potential interferences between such PLC access system and medium wave (MW)/short wave (SW) broadcast radios may be caused. This paper targets to evaluate the interference level specifically, based on which some detection and mitigation solutions are also proposed as cognitive PLC approaches. It starts with spectrum analyzer based measurements on the loop antenna and at two typical low voltage (LV) power line access networks in China to investigate the ingress caused by MW/SW broadcast radios, and the Electro Magnetic Interference (EMI) issue of PLC is also assessed by a field test. Then by performing a statistical analysis on the narrow-band radio interference level above the power line noise floor, an Adaptive Detection (AD) method for identifying radio stations is presented. Moreover, cooperative detection in cognitive radio (CR) is introduced as well by merging the detecting results achieved at different positions of the network to enhance the performance. Finally, the design aspects for such cognitive PLC access system are briefly discussed.

One of the key challenges in cognitive radio (CR) networks is to perform spectrum sensing in environments characterized by shadowing and fading effects as well as non-Gaussian noise distributions. Existing literature on spectrum sensing focuses mainly on the Gaussian noise model assumption, which does not properly characterize all the various noise types found in practical CR systems. This paper addresses the problem of spectrum sensing in the presence of non-Gaussian noise and interference for cognitive radio systems. A novel detector based on the Rao test is proposed for the detection of a primary user in the non-Gaussian noise environments described by the generalized Gaussian distribution (GGD). The test statistic of the proposed Rao detector is derived and its detection performance is analyzed and compared to that of the traditional energy detection. The Rao-based detection is then extended to a multi-user cooperative framework based on an improved decision fusion rule. It is shown through computer simulations that for a given probability of false alarm, the Rao detector can significantly enhance the spectrum sensing performance over conventional energy detection in non-Gaussian noise. Furthermore, the proposed cooperative detection scheme has a significantly higher global probability of detection than the non-cooperative scheme.

In wireless communication, there is an enormous increase in the number of users which results in spectrum shortage. This can be defeated through dynamic spectrum access scheme and cognitive radio is the best solution to achieve this. A cognitive radio (CR) is programmable, configurable and can dynamically use the spectrum in the wireless environment. One of the important tasks of CR is to perform spectrum sensing. Sensing done on spectrum facilitates the cognitive radio user to find the unoccupied area of the spectrum. This CR is prone to many attacks in the wireless environment like primary user emulation attack (PUEA), denial of service attack, replay attack, etc. Among them, the most important attack is PUEA. When a user mimics the primary user and acquires priority over other users to access the spectrum then it is termed as PUEA. In this work, PUEA detection and its defense techniques have been modeled to avoid the degradation of spectrum. To detect the malicious users feature detection-based sensing with double threshold has been proposed. If the detected signal falls above the upper threshold or below the lower threshold then it is considered as primary user signal, whereas if the signal falls in between the thresholds then that signal is identified as malicious user signal. The detection of PUEA cannot be accurate due to factors like false alarm and miss detection. Hence, a game model has been designed for the legitimate nodes to reach strategic defense decisions in the presence of multiple attackers. Based on the actions of the players, the defenders’ payoff is determined to obtain optimal defense decisions.