Primary User Signal Research Articles

From Sensing to Predictions and Database Technique: A Review of TV White Space Information Acquisition in Cognitive Radio Networks

Strategies to acquire white space information is the single most significant functionality in cognitive radio networks (CRNs) and as such, it has gone some evolution to enhance information accuracy. The evolution trends are spectrum sensing, prediction algorithm and recently, geo-location database technique. Previously, spectrum sensing was the main technique for detecting the presence/absence of a primary user (PU) signal in a given radio frequency (RF) spectrum. However, this expectation could not materialized as a result of numerous technical challenges ranging from hardware imperfections to RF signal impairments. To convey the evolutionary trends in the development of white space information, we present a survey of the contemporary advancements in PU detection with emphasis on the practical deployment of CRNs i.e. Television white space (TVWS) networks. It is found that geo-location database is the most reliable technique to acquire TVWS information although, it is financially driven. Finally, using financially driven database model, this study compared the data-rate and spectral efficiency of FCC and Ofcom TV channelization. It was discovered that Ofcom TV channelization outperforms FCC TV channelization as a result of having higher spectrum bandwidth. We proposed the adoption of an all-inclusive TVWS information acquisition model as the future research direction for TVWS information acquisition techniques.

Spectrum sensing based on cumulative power spectral density

This paper presents new spectrum sensing algorithms based on the cumulative power spectral density (CPSD). The proposed detectors examine the CPSD of the received signal to make a decision on the absence/presence of the primary user (PU) signal. Those detectors require the whiteness of the noise in the band of interest. The false alarm and detection probabilities are derived analytically and simulated under Gaussian and Rayleigh fading channels. Our proposed detectors present better performance than the energy (ED) or the cyclostationary detectors (CSD). Moreover, in the presence of noise uncertainty (NU), they are shown to provide more robustness than ED, with less performance loss. In order to neglect the NU, we modified our algorithms to be independent from the noise variance.

An improved-two stage detection technique for IEEE 802.22 WRAN

In wireless regional area networks (WRAN's), spectrum sensing (SS) is one of the vital functions who detects primary users (PU) licensed signal for cognitive radios. In this paper, an improved-two stage detection (improved-TSD) technique for spectrum sensing has been proposed. In improved-TSD technique, two detectors (energy detector with single adaptive threshold (ED_SAT) and energy detector with two adaptive thresholds (ED_TAT)) work simultaneously, and then based on the decision of both detectors final decision has been takes place by decision device (DD) using OR-rules. Detectors have adaptive thresholds, ED_SAT has single adaptive threshold and ED_TAT has two adaptive thresholds. Thresholds depend on noise variance (σω2), and noise variance value changes according to noise signal. Due to this, the values of thresholds also change. Therefore, the thresholds are adaptive thresholds. Graphical results confirm that proposed improved-TSD technique improves detection performance and outperforms the multiple-ED and ED with adaptive double thresholds detector-2013 (MED and ED-ADT-2013), energy detection technique for adaptive spectrum sensing-2015 (EDT-ASS-2015), ED and ED-ADT detector-2015, and conventional-ED by 1.4%, 10.4%, 0.5% and 31.3% at −10dB signal-to-noise ratio (SNR) respectively. Further, in terms of sensing time the presented scheme yields smaller sensing time frame than EDT-ASS-2015 SS scheme, ED and ED-ADT detector-2015, and multiple-ED and ED-ADT detector-2013 i.e. 0.5ms, 1.7ms and 2.4ms at −20dB SNR. It is further found that cooperative SS (CSS) with improved-TSD beats hierarchical with quantization-2012 by 11.5% in terms of detection performance at −11dB SNR, and meanwhile, it detects PU signal at −19.5dB SNR level.

Wide Band Full Duplex Spectrum Sensing with Self-Interference Cancellation–An Efficient SDR Implementation

Full duplex Cognitive Radio (CR) achieves more spectral efficiency if it operates in listen and talk mode. It also facilitates to find multiple free available spectrum in a wide band and fast frequency handoff in the case the secondary user uses the frequency occupied by the Primary User (PU). Full duplex CR works using two RF chain with employing the sensing by a dedicated RF chain and transmitting the data with other RF chain. The main challenge involved in full duplex sensing is the self-interference problem, particularly for energy detector based spectrum sensing. Energy detector based spectrum sensing is widely used because of less computational complexity and PU signal knowledge is not required which is an essential feature in wideband spectrum sensing. This work proposes an SDR implementation of full duplex CR using energy detector with self-interference cancellation using Wiener filter. The self-interference signal is predicted by Wiener filter and it is removed in the received signal before apply to the energy detector. The proposed scheme is simulated and validated through an experimental study on SDR platform. The results show the improvement in probability of detection of PU signal. Parallel and pipelined architectures are employed to implement the entire system using the Kintext 7 FPGA on NI USRP RIO 2943R SDR, which result in reduction of sensing time.

Cooperative Spectrum Sensing With Quantization Combining Over Imperfect Feedback Channels

A practical fusion rule with quantization combining is proposed for cooperative spectrum sensing over imperfect feedback channels that are inherent in the wireless environment. Local sensor nodes generate decision symbols regarding the existence of a primary user signal by multilevel detection and send them over feedback channels to the fusion center, in which demodulated symbols are combined to draw the cooperative decision. The proposed quantization combining scheme over imperfect feedback channels is analyzed, based on which detection probability and false alarm probability are derived. Operating parameters of the fusion rule with quantization combining are optimally determined under Neyman-Pearson criterion by using a differential evolution algorithm. The proposed fusion rule is compared with other schemes in terms of the detection performance, the amount of system information required for operation, the sensitivity to the channel estimation error, and the computational complexity required for parameter optimization. It is shown that the proposed fusion rule offers the reliable sensing capability even over imperfect feedback channels with requiring small amount of system information and complexity.

Soft Cooperative Spectrum Sensing using Quantization Method in the Presence of Smart PUE Attack

To overcome the frequency spectrum scarcity, cognitive radio (CR) network is proposed where secondary users opportunistically use vacant frequency band. CR intrinsic properties impose threats to these wireless networks. Smart primary user emulation attack (PUEA) is one of these threats where a malicious user performs spectrum sensing and after being aware of radio environment sends fake signals identical to the primary user signals with the desired signal occurrences in both vacant and occupied licensed frequency bands. In this paper, while considering smart PUEA, we propose a soft cooperative spectrum sensing method. In this method, the sensing information values of N secondary users are transmitted to the fusion center (FC) and combined with some coefficients with the aim of minimizing the error probability of primary user detection, under the given constant false alarm probability. We also propose a method for quantization of this sensing information that should be sent to FC. Furthermore, we have worked on the investigation of channel estimation error impact on detection performance. Simulation results show that our method significantly improves the spectrum sensing probability of the error, in comparison with hard combination scheme, AND fusion and OR fusion rules, either considering quantization or in the presence of channel estimation error.

Multiuser MIMO Precoders With Proactive Primary Interference Cancelation and Link Quality Enhancement for Cognitive Radio Relay Systems

A novel multiuser multiple-input multiple-output (MU-MIMO) cognitive radio (CR) system with an improved received signal-to-interference-noise ratio (SINR) and enhanced primary user (PU) interference suppression is proposed for systems with the independent parallel streams of CR multiusers communication. In the overlay CR system with a licensed PU and unlicensed CR users (CUs), the performance of PU tends to be degraded by the interference of CU. Likewise, the transmission efficiency of CU tends to be reduced by the interference of PU. In conventional MU-MIMO systems, precoding techniques are mainly designed to constrain the CU transmitted power, which can prevent interference to PU. However, the transmitted signal of PU unavoidably induces some interference to CUs. In performance studies of the conventional CR systems, the signals of PU interference are regarded as white noise. In the paper, a cooperative MU-MIMO CR relay technique is proposed to improve the quality of the CR transceiver via the relay station canceling the PU interference in two time slots. Specifically, the relay station applies MU-MIMO precoding scheme to carry multiple negative PU signals to CUs for canceling PU interference and to acquire an improved received SINR. Furthermore, the proposed MU-MIMO CR relay system is simulated to confirm the performance being better than a conventional CR system. Besides, the sum rate linear growth of the proposed system is due to its robustness against strong interference from the PU, against interference from multiple CUs, and against stream interference.

MDI-SS: matched filter detection with inverse covariance matrix-based spectrum sensing in cognitive radio

Spectrum sensing has been a major issue while dealing with cognitive radio (CR) networks. Predominantly the situation arises where noise is falsely interpreted as primary user signal called as probability of false alarm (Pfa). The user presence is estimated based on the parameters Pfa, probability of detection (PD) and receiver operating characteristics (ROC). Matched filter detection (MFD) and Neyman Pearson (NP) observer approaches are existing methods used to identify the Pfa. MFD measured ROC with different algorithms and suggests NP observer to improve the PD and minimise the Pfa. This paper proposes a novel method of matched filter detection with inverse covariance matrix-based spectrum sensing (MDI-SS). The ROC is measured, compared between MDI-SS and MFD. Next by comparing NP observer and MDI-SS, the affected samples of Pfa are identified, tabulated for different SNR levels. Finally, comparative analysis has been proposed between MDI-SS with NP observer for PD and Pfa.

Optimal Resource Allocation in Simultaneous Cooperative Spectrum Sensing and Energy Harvesting for Multichannel Cognitive Radio

In this paper, a simultaneous cooperative spectrum sensing and energy harvesting model is proposed to improve the transmission performance of the multichannel cognitive radio. The frame structure is divided into sensing slot and transmission slot. In the sensing slot, the secondary user (SU) splits the subchannels into two subchannel sets, one for sensing the primary user (PU) by multichannel cooperative spectrum sensing and the other one for collecting the radio frequency energy of the PU signal and noise by multichannel energy harvesting. In the transmission slot, the harvested energy is supplied to compensate the sensing energy loss in order to guarantee the throughput. We have formulated the resource allocation of the proposed model as a class of optimization problems, which maximize aggregate throughput, harvested energy, and energy efficiency of the SU over all the subchannels through jointly optimizing subchannel set, sensing time, and transmission power, respectively. To achieve the sub-optimal solutions to the optimization problems, we have proposed the subchannel allocation algorithm and the joint optimization algorithm of sensing time and transmission power based on the Greedy algorithm and the alternating direction optimization. The stopping criteria of SU is described, when the PU is not present but the harvested energy is not enough. The simulation results are presented to demonstrate the validity and predominance of our proposed algorithms.

Blind Central-Symmetry-Based Feature Detection for Spatial Spectrum Sensing

State-of-the-art sensing methods mostly detect the spectrum holes by exploring the feature in frequency, time, and geography dimensions. In this paper, we analyze the sensing problem from angle/space domain by using the angle of arrival (AoA) estimation technology. We show a property that the spatial spectrum of noise has the feature of central symmetry, which the arriving signal does not have in general. Hence, the existence of the central symmetry feature depends on the presence or absence of the primary user signal. Motivated by this, we introduce a novel spatial spectrum sensing framework and propose a blind central-symmetry-based feature detection (CSFD) method correspondingly. Different from conventional spectrum sensing, the designed sensing framework reduces the complexity of spatial spectrum sensing and is available for spectrum access. Taking advantage of the inherent central symmetry feature of noise spatial spectrum, the proposed CSFD can achieve higher probability of detection even at low signal-to-noise ratios (SNRs) and offer AoA information. Theoretical performance analysis of the proposed CSFD method is also provided. Simulation results are presented to verify the efficiency of the proposed algorithm.

Dynamic Cooperative Sensing–Access Policy for Energy-Harvesting Cognitive Radio Systems

We consider energy-harvesting cognitive radio (CR) systems with finite batteries. The energy-constrained secondary users (SUs) can be coordinated to enhance both the primary user (PU) detection and the opportunistic utilization of the PU spectrum. We employ centralized cooperative spectrum sensing, and the optimal sensors are chosen from among the SUs with varied received PU signal power levels and energy-arrival rates. The finite-horizon partially observable Markov decision process (POMDP) is applied to obtain the optimal cooperation among the SUs for sensing and access to maximize throughput with the available energy, while satisfying the PU detection constraint. The sensing-access policy decides whether to sense, the set of sensing SUs, the sensing detection threshold, and the SU that accesses the spectrum opportunities. The optimal policy achieves an optimum tradeoff between SU transmissions, preventing energy shortage and obtaining sensing information for the future gains.

Perform sensing and transmission in parallel in cognitive radio systems: Spectrum and energy efficiency

Radio spectrum is a precious and limited resource for wireless communication networks. The proposed “Always Transmit (AT)” strategy in cognitive radio is a precious solution to overcome the inefficient spectrum allocation policy, since secondary user (SU) exploits spectrum sensing phase (first part of a time slot) for data transmission. This leads to a new challenge: “how SU can perform sensing simultaneously with data transmission in the same primary band?” In order to overcome this new problem, in this paper we propose two spectrum sensing detectors: we adapt the classical blind detector “energy detection” to the proposed scheme and we propose “own-waveform-based sensing” as an alternative innovative solution to detect collision/no collision between SU and primary user (PU) signals if PU is active/absent. In addition, since the transmit power and the interference power can be limited either by an instantaneous or an average constraint, we show, how SU can manage its power allocation policy under the proposed “AT” strategy in order to improve the short/long term of quality of service (QoS) of secondary system while the requirement of the short/long term of QoS for primary system is guaranteed. The closed form of outage probabilities are derived over Nakagami-m fading channels. Numerical results show that the proposed scheme “AT” outperforms other traditional/classical cognitive radio access strategies, guarantees 20.86% as a maximum degradation limit, and reduces the outage probability of the secondary system (a) by a factor of 492.83% versus interweave mode, (b) by a factor of 21.41% versus underlay mode and (c) by a factor of 398.89% versus mixed mode.

Outage Probability and Power Allocation for Collaborative Relaying of Multiple-Access Primary Users by Secondary User With Partial/Full Channel Knowledge

This paper investigates collaborative spectrum sharing (CSS) for which a cognitive secondary transmitter acts as a decode-and-forward (DF) relay for two primary users (PUs) occupying an interfering multiple-access channel. PUs are assumed to send their signals simultaneously; hence, interference between the primary signals exists due to the multiple-access nature. The secondary user (SU) selects and forwards one of the PU's signals to a desired destination, and at the same time, the SU also transmits its own signal only if it is not harmful to PUs. The selection of a primary signal to relay is investigated, depending on SU's knowledge of two primary source–destination channels, for which closed-form outage probabilities for PUs and SU are provided accordingly. With the help of the closed-form PU's outage probability, an optimal selection strategy between the primary signals and an optimal power allocation between the primary and secondary signals are also obtained in closed form, which minimizes SU's outage probability while keeping the PU's outage performance at an SU-free level. Numerical investigation is used to verify and illustrate the analysis given in this paper. Numerical results also show that the SU's performance can vary significantly according to the amount of channel knowledge that the SU can have.

Convolutive Superposition for Multicarrier Cognitive Radio Systems

Recently, we proposed a spectrum-sharing paradigm for single-carrier cognitive radio (CR) networks, where a secondary user (SU) can maintain or even improve the performance of a primary user (PU) transmission, while also obtaining a low-data rate channel for its own communication. According to such a scheme, when the SU is able to receive the signal transmitted from the PU, it can superimpose its transmission on the PU signal by simple multiplicative precoding, without requiring any cooperation between the primary and secondary systems. The scope of this paper is to extend such a paradigm to a multicarrier CR network, where the PU employs an orthogonal frequency-division multiplexing (OFDM) modulation scheme. To improve its achievable data rate, the SU is allowed to transmit multiple symbols in parallel over the OFDM subcarriers of the primary system. In particular, the SU convolves its block-precoded symbols with the received PU signal in the time domain, which gives rise to the term convolutive superposition . An information-theoretic analysis of the proposed scheme is developed, which considers different amounts of channel state information at the secondary transmitter, as well as different precoding strategies for the SU. Extensive simulations illustrate the merits of our analysis and designs, in comparison with other CR schemes, by using as a performance indicator the ergodic channel capacity.

Detection performance analysis of spectrum sensing in cognitive radio networks with mobile secondary users

The majority of existing papers about spectrum sensing have the assumption that secondary users (SUs) are stationary. However, mobility is an essential feature of mobile communications networks. In this paper, the detection performance of spectrum sensing by mobile SUs was analyzed. Three performance metrics, i.e., detection probability, miss detection probability and false alarm probability, were thoroughly investigated. In our analysis, a critical variable was the real-time received primary user signal power by a mobile SU. Its probability distribution and mathematical expectation were analytically derived. Moreover, the three performance metrics in single-node spectrum sensing and multi-node collaborative spectrum sensing systems were also derived. Extensive simulations were performed. The results are consistent with the theoretical analysis. And it is concluded that SU mobility has a significant impact on the detection probability and the miss detection probability, but not on the false alarm probability.

An Improved Energy Detection Scheme based on Channel Estimation

Energy detection is most commonly employed technique for spectrum sensing in cognitive radio networks due to its low computational and implementation complexities. However, the performance of energy detector deteriorate considerably under severe fading environment especially in low SNR region. The performance metrics used for energy detector are probability of detection, probability of false alarm and number of samples requirements. The detection threshold plays a significant role in PU signal detection and thus must be chosen appropriately to meet tradeoff between probability of detection and probability of false alarm under severe fading. In this paper, we have proposed adaptive threshold detection scheme for severe fading environment to work under low SNR. The simulated results are presented to validate the proposed scheme and it has been shown that proposed scheme is more robust against noise uncertainty under severe fading environment and performs better than conventional energy detection scheme to yield same detection performance.

Cognitive radio signal classification based on subspace decomposition and RBF neural networks

Spectrum sensing is one of the major challenges for commercial development of cognitive radio systems, since the detection of the presence of a primary user is a complex task that requires high reliability. This work proposes a signal classifier capable of detecting and identifying a primary user signal on a given channel of the radio spectrum. The proposed approach combines eigen-decomposition techniques and neural networks not only to decide about the presence of a primary user, but also to identify the primary user signal type, a feature that is not encountered in the current approaches proposed in literature. Besides the advantage of identifying the primary user type, the proposed method also considerably reduces the computational cost of the detection process. The proposed classification method has been applied to the development of five primary user signal Classification Modules, which includes wireless microphone, orthogonal frequency-division multiplexing and Digital Video Broadcasting-Terrestrial signals. The results show that the proposed classifier correctly detects and identifies the primary users, even under low signal to noise ratio and multipath scenarios.

Clustering scheme for cooperative spectrum sensing in cognitive radio networks

For cognitive radio (CR), cooperation in spectrum sensing (SS) using energy detection can increase the detection of primary user signal. However, it also brings extra cooperative sensing overhead due to mutual exchange of large information among CR users. To reduce cooperative sensing overhead and improve sensing performance, the authors propose a novel clustering scheme consists of three stages: pruning, selecting, and clustering. In pruning stage, the CR users without sensing results will not join the following two stages; in the subsequent selecting stage, the CR user who has the most reliable sensing data is selected as the cluster header. For CR network, channel availability is an essence cause of cluster change. Hence, the authors proposed cluster constructing based on SS results correlation in the clustering stage. The simulation results show that the proposed clustering scheme not only has low overhead but also suit to perform in the low signal noise ratio environment.

Information Combining Schemes for Cooperative Spectrum Sensing: A Survey and Comparative Performance Analysis

Spectrum scarcity has evolved as a challenging problem in the field of wireless communication. Cognitive radio (CR) has emerged as a solution to this problem which uses available spectrum efficiently in an opportunistic way. Spectrum sensing unit as part of a CR deals with the reliable detection of primary user's signal. However individual CRs do not able to detect the primary user due to factors such as noise uncertainty, multipath fading, shadowing etc. This paper presents a survey on state of the art information combining schemes for cooperative spectrum sensing. Different algorithms are employed for optimizing the probability of detection at a specified probability of false alarm. The performance of the state of the art techniques are compared with that achieved using evolutionary algorithm (EA). The simulation results show that the EAs perform better than the statistical techniques and reduce the computational complexity and time by a high margin. Different statistical tests are carried out to assess the stability of the simulation results offered by the heuristic EAs. In addition, the sensitivity analysis of different parameters is performed to demonstrate their impact on the overall performance of the system.

SVM based Two Level Authentication for Primary user Emulation Attack Detection

Background/Objectives: Cognitive radio network is the evolutionary network to solve the spectrum scarcity problem. Primary User Emulation Attack is a major security issue that cause to fail the dynamic spectrum access of the cognitive radio network. Methods/Statistical Analysis: This paper proposes two level verification of the primary user signal to enhance the security of the spectrum sensing under PUEA attack. Support Vector Machine is used in the two level verification for detector classify the received data in to a location boundary and higher order statics at second level. Findings: At first level using the location information of the primary user, the primary user signal is verified for its validity. The SVM is first trained to learn the Primary user Location and with the help of known location of the Primary user it can be verified. Then the trained SVM is used to find the location boundary of the user that transmitted the signal. If the signal boundary is within the primary used location boundary it is concluded that the signal is from the primary user; else it is from the PUEA attacker .After verifying in the first level on the second level higher order statics values are calculated then using that as the feature vector the SVM classifier is used to classify the received signal as two class: one from original primary user and other from the PUEA attacker. This two level verification of the primary user signal will be more accurate method of PUEA attack detection comparing to the single level schemes. Applications/Improvements:This two level verification scheme improves the accuracy from 82% at Linear SVM kernel to 100% and also from 77% at RBF SVM kernel to 100%.