Primary User Signal Research Articles

Machine Learning for Spectrum Information and Routing in Multihop Green Cognitive Radio Networks

Research works on cognitive radio networks (CRNs) together with energy harvesting (EH) promise to address the spectrum scarcity and limited battery power problems on the wireless communication nodes. While radio frequency (RF) signal of primary user (PU) can be used in EH, its absence (non-transmission state) offers an unused spectrum for the availability of the secondary user (SU) data transmission. Spectrum sensing (SS) process that detects the presence or the absence of PU signal, occupies a considerable slot in time-frame and energy consumption in wireless nodes, hence, both SU throughput and battery power get reduced. To address the problems, this work explores a support vector machine (SVM) based PU activity (transmit/non-transmit mode) prediction without accomplishing any SS task in the system operation. Thereafter, a Deep Q-networks (DQN) based energy and spectrum efficient routing strategy is suggested to maximize the CRNs throughput. The proposed joint EH and routing scheme improve the sum throughput, spectrum and energy efficiencies by <inline-formula> <tex-math notation="LaTeX">$\sim 33.48\%$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\sim 34.24\%$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\sim 30.76\%$ </tex-math></inline-formula>, respectively over the conventional dedicated SS based approach.

Spectrum Sensing by Cepstral Covariance Detection

This work presents the cepstral covariance detector for spectrum sensing in cognitive radio. The power cepstral peaks of digitally modulated signals are exploited for spectrum sensing. To enhance the process of signal detection in low signal-to-noise ratio environments and with the knowledge of the data rate of the primary user signal, the cepstral covariance of the received signal is evaluated. The detection test statistic of the proposed detector is evaluated and an expression for the detection threshold is obtained under the null hypothesis. The detection performance of the proposed cepstrum-based detector is compared to covariance-based detectors and the obtained results validate its detection reliability at low signal-to-noise ratio levels with relatively low computational complexity.

Operational Characteristics of Square-Law Combining Energy Detector in MIMO-OFDM Cognitive Radio Systems

To ensure effective spectrum usage, cognitive radio networks (CRNs) are being investigated extensively. The important function of CRNs is spectrum sensing (SS). This is the process of sensing the unused frequency spectrum and deciding whether the signal transmission of a primary user (PU) exists. In this work, the results of a performance analysis related to detecting PU signals using the energy detection (ED) SS method realized by employing the square-law combining (SLC) technique are presented. The assessment of the effectiveness of the ED method is performed in relation to prominent communication technology based on Orthogonal Frequency Division Multiplexing (OFDM) and Multiple-Input Multiple-Output (MIMO) transmissions. Due to the impact of changes in the noise power of the PU signal, referred to as noise uncertainty (NU), the MIMO-OFDM transmission in a real communication environment cannot guarantee accurate sensing performed by employing ED based on the SLC method at the secondary user (SU). More precise SS can be achieved if dynamic detection threshold (DDT) changes are employed according to the channel conditions impacted by NU. Thus, this work investigated the effect of NU variations and DDT adjustments on the detection probability for various levels of false alarm probability. This interdependence is presented as receiver operating characteristic (ROC) curves. A mathematical model including different operational parameters was developed to define the fundamental parameters of the ED method based on the SLC approach. Using the developed mathematical model, an algorithm was created which simulates SLC ED processes using DDT in single-input single-output (SISO)- and MIMO-OFDM communication systems affected by NU. Based on the simulation results, comprehensive analyses of the impact of versatile DDT adaptations and NU variations on the shape of the ROC curves for the ED process performed with different numbers of transmitting and receiving antenna, modulation techniques, detection sample numbers, PU transmitting powers, and SNR values are presented.

Intelligent Reflecting Surface-Aided Spectrum Sensing for Cognitive Radio

Spectrum sensing is a key enabling technique for cognitive radio (CR), which provides essential information on the spectrum availability. However, due to severe wireless channel fading and path loss, the primary user (PU) signals received at the CR or secondary user (SU) can be practically too weak for reliable detection. To tackle this issue, we consider in this letter a new intelligent reflecting surface (IRS)-aided spectrum sensing scheme for CR, by exploiting the large aperture and passive beamforming gains of IRS to boost the PU signal strength received at the SU to facilitate its spectrum sensing. Specifically, by dynamically changing the IRS reflection over time according to a given codebook, its reflected signal power varies substantially at the SU, which is utilized for opportunistic signal detection. Furthermore, we propose a weighted energy detection method by combining the received signal power values over different IRS reflections, which significantly improves the detection performance. Simulation results validate the performance gain of the proposed IRS-aided spectrum sensing scheme, as compared to different benchmark schemes.

A Safe Cognitive Radio Spectrum Handoff Method Using Coordinating Cognitive User

A new cognitive user emulation attack (CUEA) in a cognitive radio network (CRN), which could be utilized by intruders during spectrum handoff is introduced. The need for more efficient spectrum utilization in our increasingly digitalized society is becoming more important. This has given raise to the variety of new security threats. A safe handoff mechanism that could successfully counter such an attack by introducing a coordinating cognitive user that computes the level of trust of each cognitive user based on its behavioral characteristics is proposed. Malicious users could be effectively identified by the coordinating cognitive user by looking up the trust values. The activity of the proposed mechanism is validated using MATLAB simulation. The simulation executed describes the utilization of the proposed mechanism by correctly identifying the probability of false detection, detection rate, incorrect detection shown as it decreases the data transmission time or increases the transmission rates of primary user's signals.

Spectrum Sensing using Energy Measurement in Wireless Telemetry Networks using Logarithmic Adaptive Learning

&lt;p&gt;To identify primary user signals in cognitive radios spectrum sensing method is used. Due to statistical variances in received signal, noise is present in primary user signals, this noise powers are varied due to random nature of noise signals and leads to noise uncertainty problem in the performance of energy detection. The task of energy measurement and further detecting the unused frequency spectrum is a key task in cognitive radio applications. For avoiding these problems, least logarithmic absolute difference (LLAD) algorithm is proposed in which noise powers are adjusted at sensing point of licensed users. With help of proposed method, estimated noise signals are eliminated. Sign regressor version of LLAD algorithm is considered due to it reduces computational complexity and convergence rate is improved. Further probability of detection (&lt;em&gt;P&lt;/em&gt;&lt;sub&gt;od&lt;/sub&gt;), probability of false alarm (&lt;em&gt;P&lt;/em&gt;&lt;sub&gt;ofa&lt;/sub&gt;) is estimated to know threshold value. From results, it is clear that good performance in terms of &lt;em&gt;P&lt;/em&gt;&lt;sub&gt;ofa&lt;/sub&gt; versus &lt;em&gt;P&lt;/em&gt;&lt;sub&gt;od&lt;/sub&gt; in range of low signal to noise ratio in multiple nodes. Therefore, the proposed energy measurement-based spectrum sensing method is useful in remote health care monitoring, medical telemetry applications by sharing the un-used spectrum.&lt;/p&gt;

Source and distribution characteristics of 239, 240, 241Pu, 237Np and 134, 137Cs in sediments in the Northwest and Central Equatorial Pacific after the Fukushima nuclear accident

To understand the possible influence of the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) accident on the deep sea, as well as the geochemical behavior and transport of radionuclides, 134Cs, 137Cs, 239, 240Pu, 241Pu, and 237Np were measured in the abyssal sediments of the Northwest Pacific (NWP) and Central Equatorial Pacific (CEP) Ocean. Data on the characteristics of these sediments obtained after the FDNPP accident are extremely rare, especially in the NWP subtropical gyre (NPSG) region. FDNPP-derived radio-Cs (134Cs, 137Cs) arrived at the open sea floor of the NWP before 2018 but was only found in the Kuroshio-Oyashio Extension (KOE) region. No FDNPP-derived Pu was detected in the abyssal sediments of the NWP or CEP. Pu in the NWP mainly originated from global fallout and the Pacific Proving Ground (PPG) close-in fallout, except for at station WP1 (39°N in the KOE region), where an abnormal but non-FDNPP-derived Pu signal was detected. Pu in the eastern CEP sediment was less affected by the PPG close-in fallout from the Marshall Islands and was mainly derived from global fallout, with some close-in fallout from the Johnston Atoll test. The KOE region was the area most affected by PPG close-in fallout Pu via Kuroshio transport, while the lowest inventories of 239+240Pu and 237Np were found in the NPSG region due to its oligotrophic environment. The 237Np originated from the same source as Pu, and the latitudinal pattern of 237Np was consistent with that of Pu. Station SS (in the marginal sea of the NWP) contained high 237Np/239Pu atom ratios in the deeper layers of sediment and had a 237Np depth profile opposite that of the 239+240Pu profile, compared to other stations; these differences are mainly attributed to differences in the behaviors of 237Np and 239Pu.

Secure collaborative cognitive radio based on chaotic modulation and compressive sensing

Cognitive radio (CR) is a wireless technology that is used to overcome the spectrum scarcity problem. CR includes several stages, spectrum sensing is the first stage in the CR cycle. Traditional spectrum sensing (SS) techniques have many challenges in the wideband spectrum. CR security is an important problem, since when an attacker from outside the network access the sensing information this produces an increase in sensing time and reduces the opportunities for exploiting vacant band. Compressive sensing (CS) is proposed to capture all the wideband spectrum at the same time to solve the challenges and improve the performance in the traditional techniques and then one of the traditional SS techniques are applied to the reconstructed signal for detection purpose. The sensing matrix is the core of CS must be designed in a way that produces a low reconstruction error with high compression. There are many types of sensing matrices, the chaotic matrix is the best type in terms of security, memory storage, and system performance. Few works in the literature use the chaotic matrix in CS based CR and these works have many challenges: they used sample distance in the chaotic map to generate a chaotic sequence which consumes high resources, they did not take into consideration the security in reporting channel, and they did not measure their works using real primary user (PU) signal of a practical application under fading channel and low SNR values. In this paper, we propose a chaotic CS based collaborative scenario to solve all challenges that have been presented. We proposed a chaotic matrix based on the Henon map and use the differential chaotic shift keying (DCSK) modulation to transmit the measurement vector through the reporting channel to increase the security and improve the performance under fading channel. The simulation results are tested based on a recorded real-TV signal as PU and Compressive Sampling Matching Pursuit (CoSaMP) recovery algorithm under AWGN and TDL-C fading channels in collaborative and non-collaborative scenarios. The performance of the proposed system has been measured using recovery error, mean square error (MSE), derived probability of detection (Pdrec), and sensitivity to initial values. To measure the improvement introduced by the proposed system, it is evaluated in comparison with selected chaotic and random matrices. The results show that the proposed system provides low recovery error, MSE, with high Pdrec, security, and compression under SNR equal to −30 dB in AWGN and TDL-C fading channels as compared to other matrices in the literature.

On Selection of Parameters for Cooperative Spectrum Sensing Schemes over κ–μ Fading Channels

The current paper studies comprehensive analysis of various cooperative spectrum sensing (CSS) schemes in generalized κ − μ fading environment. More precisely, the optimization of several network parameters as well as throughput performance analysis of various CSS schemes are studied. In each CSS scheme, every cognitive radio user (CRU) first senses and receives the primary user (PU) signal using sensing channels, performs an energy detection (ED) operation. Depending upon the type of the scheme, CRU makes and forwards its local binary decision or simply forwards its data to the fusion center (FC) with the help of reporting channels. Then FC implements any one of the CSS schemes for making a final decision about busy or free status on PU. The novel expressions for missed and false detection probabilities subject to CSS scheme under fading are developed. The performance of several decision and data CSS schemes through total error rate, considering different network and κ − μ fading parameters is investigated. The impact of erroneous channel conditions on each CSS scheme is investigated. The expressions for optimal system parameters of decision-based CSS schemes under fading are developed and optimal values for different network conditions are estimated. Further, the throughput performance of decision-based CSS schemes for different networks and κ − μ fading parameters values is studied.

Primary-User-Friendly Dynamic Spectrum Anti-Jamming Access: A GAN-Enhanced Deep Reinforcement Learning Approach

This letter studies the problem of deep reinforcement learning (DRL)-based dynamic spectrum anti-jamming access in overlay cognitive radio networks. To prevent secondary user (SU) from interfering with primary user (PU) and being jammed by jammer, we propose a PU-friendly dynamic spectrum anti-jamming access scheme. First, a generative adversarial network (GAN)-based virtual environment is proposed to simulate spectrum environment. Then, a DRL-based channel decision network (CDN) is trained to learn the optimal spectrum access policy in the virtual environment. Finally, SU accesses spectrum environment under the guidance of the trained CDN. Simulation results show that the proposed scheme is able to elude both PU signals and jamming completely and converges much faster than the scheme that trains the CDN in spectrum environment from scratch.

Analysis of the Impact of Detection Threshold Adjustments and Noise Uncertainty on Energy Detection Performance in MIMO-OFDM Cognitive Radio Systems.

Due to the capability of the effective usage of the radio frequency spectrum, a concept known as cognitive radio has undergone a broad exploitation in real implementations. Spectrum sensing as a core function of the cognitive radio enables secondary users to monitor the frequency band of primary users and its exploitation in periods of availability. In this work, the efficiency of spectrum sensing performed with the energy detection method realized through the square-law combining of the received signals at secondary users has been analyzed. Performance evaluation of the energy detection method was done for the wireless system in which signal transmission is based on Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing. Although such transmission brings different advantages to wireless communication systems, the impact of noise variations known as noise uncertainty and the inability of selecting an optimal signal level threshold for deciding upon the presence of the primary user signal can compromise the sensing precision of the energy detection method. Since the energy detection may be enhanced by dynamic detection threshold adjustments, this manuscript analyses the influence of detection threshold adjustments and noise uncertainty on the performance of the energy detection spectrum sensing method in single-cell cognitive radio systems. For the evaluation of an energy detection method based on the square-law combining technique, the mathematical expressions of the main performance parameters used for the assessment of spectrum sensing efficiency have been derived. The developed expressions were further assessed by executing the algorithm that enabled the simulation of the energy detection method based on the square-law combining technique in Multiple-Input Multiple-Output—Orthogonal Frequency Division Multiplexing cognitive radio systems. The obtained simulation results provide insights into how different levels of detection threshold adjustments and noise uncertainty affect the probability of detection of primary user signals. It is shown that higher signal-to-noise-ratios, the transmitting powers of primary user, the number of primary user transmitting and the secondary user receiving antennas, the number of sampling points and the false alarm probabilities improve detection probability. The presented analyses establish the basis for understanding the energy detection operation through the possibility of exploiting the different combinations of operating parameters which can contribute to the improvement of spectrum sensing efficiency of the energy detection method.

Eigenvalue fusion-based machine learning approach for cooperative spectrum sensing in cognitive radio

Spectrum sensing is the most critical and fundamental function of the cognitive radio (CR) for dynamic spectrum usage. Machine learning (ML) techniques that allow CRs to learn the environment adaptively are most essential in spectrum sensing. This paper proposes a novel machine learning-based cooperative spectrum sensing (CSS) technique using various advanced ML schemes to enhance the probability of detection. ML classifiers used in the fusion centre of the CSS schemes are trained with eigenvalue-based test statistics extracted from the covariance matrix of a received signal. The proposed system recognises the received signal samples as a PU signal or a noise signal and distinguishes the PU signal from noise effectively under low SNR conditions using a threshold that possesses the self-learning ability. The simulation results exhibit the performance analysis of various ML algorithms for CSS under different wireless scenarios and their suitability is compared with conventional approaches.

Federated Learning for 5G Radio Spectrum Sensing.

Spectrum sensing (SS) is an important tool in finding new opportunities for spectrum sharing. The users, called Secondary Users (SU), who do not have a license to transmit without hindrance, need to employ SS in order to detect and use the spectrum without interfering with the licensed users’ (primary users’ (PUs’)) transmission. Deep learning (DL) has proven to be a good choice as an intelligent SS algorithm that considers radio environmental factors in the decision-making process. It is impossible though for SU to collect the required data and train complex DL models. In this paper, we propose to employ a Federated Learning (FL) algorithm in order to distribute data collection and model training processes over many devices. The proposed method categorizes FL devices into groups by their mean Signal-to-Noise ratio (SNR) and creates a common DL model for each group in the iterative process. The results show that detection accuracy obtained via the FL algorithm is similar to detection accuracy obtained by employing several DL models, namely convolutional neural networks (CNNs), specialized in spectrum detection for a PU signal with a given mean SNR value. At the same time, the main goal of simplification of the SS process in the network is achieved.

Robust and efficient cooperative spectrum sensing against probabilistic hard Byzantine attack

AbstractCognitive radio (CR) is regarded as a powerful technology to solve the problem of spectrum shortage and underutilization. As a key function of CR technology, cooperative spectrum sensing (CSS) allows secondary users (SUs) to detect the primary user (PU)'s signal so that they identify and opportunistically access the available spectrum. However, the openness of CSS paradigm makes cognitive radio networks (CRNs) suffer from Byzantine attack, thereby undermining the premise of CR framework. To this aim, we formulate a probabilistic hard Byzantine attack model, in which malicious users (MUs) can conduct various attack strategies, and make an in‐depth investigation on the blind scenario. On the one hand, in order to ensure the robustness of CSS, a method to evaluate the reliability of the secondary user (SU)'s sensing result based on the channel status detection is proposed and an innovative weight coefficient is considered to selectively utilize the sensing information from MUs. On the other hand, we design a sequential fusion method based on reputation value (RV) and differential mechanism, with the aim of improving the efficiency of CSS. According to above methods and mechanism, the weighted differential sequential symbol (WDS2) algorithm is designed, which integrates the weight evaluation into sequential method to make the global decision for CSS. Finally, compared to the existing various data fusion algorithms, simulation results show that the proposed WDS2 not only defends against various Byzantine attacks to secure the robustness of CSS, but also requires less samples in support of an accurate global decision to improve the efficiency of CSS.

Primary user adversarial attacks on deep learning-based spectrum sensing and the defense method

The spectrum sensing model based on deep learning has achieved satisfying detection performence, but its robustness has not been verified. In this paper, we propose primary user adversarial attack (PUAA) to verify the robustness of the deep learning based spectrum sensing model. PUAA adds a carefully manufactured perturbation to the benign primary user signal, which greatly reduces the probability of detection of the spectrum sensing model. We design three PUAA methods in black box scenario. In order to defend against PUAA, we propose a defense method based on autoencoder named DeepFilter. We apply the long short-term memory network and the convolutional neural network together to DeepFilter, so that it can extract the temporal and local features of the input signal at the same time to achieve effective defense. Extensive experiments are conducted to evaluate the attack effect of the designed PUAA method and the defense effect of DeepFilter. Results show that the three PUAA methods designed can greatly reduce the probability of detection of the deep learning-based spectrum sensing model. In addition, the experimental results of the defense effect of DeepFilter show that DeepFilter can effectively defend against PUAA without affecting the detection performance of the model.

Online Primary User Emulation Attacks in Cognitive Radio Networks Using Thompson Sampling

Spectrum sensing is one of the main components of the cognitive radio (CR) system, based on which secondary users (SUs) can get access to spectrum holes available. On the other hand, a malicious adversary can also attack the primary user (PU) system and the legitimate CR system via spectrum sensing, which can lead to serious security issue for both systems. In this article, we study an online attacking strategy, referred to as PU emulation attacker (PEA), which transmits forged PU signals over available channels to deteriorate the spectrum sensing performance of the SUs. We propose an online learning based attacking scheme for both the single attacker and the multiple-attacker cases, and analyze the regret upper bound of the proposed algorithm. The proposed PEA strategy can work in both stationary and non-stationary CR networks where the statistical characteristics of channels and the access strategy of SUs change over time. Numerical results show that it is more efficient than others in two different performance metrics: successful accesses of SUs and effective attacks of PEAs. Our proposal raises an interesting open question on how to develop CR networks with security guarantee.

Autocorrelation Based White Space Detection in Energy Harvesting Cognitive Radio Network

Accurate detection of White Space (WS) is of paramount importance in a Cognitive Radio Network (CRN) to prevent authorized users from harmful interference. However, channel impairment such as multipath fading and shadowing affects accurate detection of WS resulting in interference. The Existing Feature Detection (EFD) technique used to address the problem is faced with computational complexity and synchronization resulting in long sensing time, bandwidth inefficiency, energy constrain and poor detection rate. Hence, this paper proposes autocorrelation based multiple antenna with energy harvesting for WS detection in a CRN using Radio Frequency (RF) energy harvesting and autocorrelation of the received signal with a modified Equal Gain Combiner (mEGC). Antenna Switching (AS) RF energy harvesting with mEGC are used to harvest energy and information from the received PU signal in a multiple antenna configuration. Autocorrelation is then obtained and compared with the set threshold of zero to determine the presence or absence of WS. The proposed technique is evaluated using Spectral Efficiency (SE), Probability of Detection (PD) and Sensing Time (ST) by comparing with EFD technique. The results obtained revealed that the proposed technique shows better performance than EFD.

A Gaussian mixture model method for eigenvalue-based spectrum sensing with uncalibrated multiple antennas

In this article, a novel spectrum sensing technique for cognitive radio (CR) is proposed where eigenvalues of the covariance matrix of the received signal are used as features for detection with multiple uncalibrated antennas. In the proposed scheme, training decision vectors composed of eigenvalue components are approximated as Gaussian mixture model (GMM) and underlying distribution parameters are extracted using expectation-maximization (EM) algorithm. Using the obtained parameters, posterior probability of subsequent decision vectors are computed and the channel is classified as either occupied or unoccupied. This is different form existing spectrum sensing techniques where elements of the covariance matrix or its eigenvalues are reduced to a single decision statistic resulting in loss of useful discriminatory information. Proposed technique overcomes this limitation by forming decision vectors from the eigenvalue features and performing GMM based classification in multidimensional space. Simulation results also reveal that proposed method outperforms state-of-the-art techniques for detection of primary user (PU) signal using uncalibrated antennas.

Performance Analyses of Energy Detection Based on Square-Law Combining in MIMO-OFDM Cognitive Radio Networks.

Cognitive radio (CR) technology has the potential to detect and share the unutilized spectrum by enabling dynamic spectrum access. To detect the primary users’ (PUs) activity, energy detection (ED) is widely exploited due to its applicability when it comes to sensing a large range of PU signals, low computation complexity, and implementation costs. As orthogonal frequency-division multiplexing (OFDM) transmission has been proven to have a high resistance to interference, the ED of OFDM signals has become an important local spectrum-sensing (SS) concept in cognitive radio networks (CRNs). In combination with multiple-input multiple-output (MIMO) transmissions, MIMO-OFDM-based transmissions have started to become a widely accepted air interface, which ensures a significant improvement in spectral efficiency. Taking into account the future massive implementation of MIMO-OFDM systems in the fifth and sixth generation of mobile networks, this work introduces a mathematical formulation of expressions that enable the analysis of ED performance based on the square-law combining (SLC) method in MIMO-OFDM systems. The analysis of the ED performance was done through simulations performed using the developed algorithms that enable the performance analysis of the ED process based on the SLC in the MIMO-OFDM systems having a different number of transmit (Tx) and receive (Rx) communication branches. The impact of the distinct factors including the PU Tx power, the false alarm probability, the number of Tx and Rx MIMO branches, the number of samples in the ED process, and the different modulation techniques on the ED performance in environments with different levels of signal-to-noise ratios are presented. A comprehensive analysis of the obtained results indicated how the appropriate selection of the analyzed factors can be used to enhance the ED performance of MIMO-OFDM-based CRNs.

An Authentication Based Approach for Prevention of Spectrum Sensing Data Falsification Attacks in Cognitive Radio Network

This is the era of Intelligent Cognitive Radio Network (CRN) technology that provides the available spectrum with efficient utilization. Cognitive Radio (CR) technology must promise to allow interference-free spectrum access by users. The paper discusses the several attacks and motives of attacks. The authentication mechanism role to prevent the attacks for hassle-free spectrum utilization is demonstrated. In this paper, resolving the cognitive network security issues by the authentication mechanism and the methods and need of authentication is discussed. This paper addresses the research challenges in the way of securing the CRN and countermeasures in CRN security strategies. Spectrum sensing data falsification (SSDF) attack is most disruptive in which the malicious users degrade the decision-making process by sending the false sensing reports to data fusion center’s thus preventing honest users from utilizing the spectrum. In this paper, to counter the SSDF attack, the trust-based security mechanism is demonstrated to authenticate the honest users and it is observed that the proposed framework in the MATLAB environment is efficient and able to detect malicious users. CR technology is the strategy applied to the spectrum to make it efficient for wireless communication. The strategy is an intelligent way to access the spectrum as it can learn its environment and make decisions by easy adaptation of operating parameters. The initial step is to sense the spectrum available further steps are spectrum decision making, spectrum management, and spectrum mobility. The network is vulnerable to various attacks on spectrum sensing and policy protocols which lead to disturbing functionality of CR technology. The defence mechanism based on public-key cryptography is proposed in which the Primary User (PU) is authenticated by appending signature provided to PU signal. Authentication with a tag to the primary users is another perspective proposed. CRN technology should provide integrity, confidentiality and authenticity to the users.