Narrow Results

We apply the mathematics of cognitive radio to a single receiver to obtain a new coherent energy metric. This allows us to derive the time correlation law separating Gaussian colored noise from coherent signal energy.

Cognitive radio technology is envisaged to alleviate both spectrum inefficiency and spectrum scarcity problems by exploiting the existing licensed spectrum opportunistically. However, cognitive radio ad hoc networks (CRAHNs) impose unique challenges due to the high dynamic scheduling in the available spectrum, diverse quality of service (QOS) requirements, as well as hidden terminals and shadow fading issues in a harsh radio environment. To solve these problems, this paper proposes a dynamic and variable time-division multiple-access scheduling mechanism (DV-TDMA) incorporated with dual collaborative spectrum sensing scheme for CRAHNs. This study involves the cross-layered cooperation between the Physical (PHY) layer and Medium Access Control (MAC) layer under the consideration of average sensing time, sensing accuracy and the average throughput of cognitive radio users (CRs). Moreover, multiple-objective optimization algorithm is proposed to maximize the average throughput of CRs while still meeting QOS requirements on sensing time and detection error. Finally, performance evaluation is conducted through simulations, and the simulation results reveal that this optimization algorithm can significantly improve throughput and sensing accuracy and reduce average sensing time.

Cognitive radio (CR) is the trending domain in addressing the inadequate bands for communication, and spectrum sensing is the hectic challenge need to be addressed extensively. In the conventional CRs, the communication is restricted to the secondary users (SUs) in the allocated bands causing the underutilization of the available band. Thus, with the aim to afford higher throughput and spectrum efficiency, this paper introduces the hybrid mixture model for spectrum sensing in the multiple-input–multiple-output (MIMO) systems and the effectiveness is evaluated based on the evaluation parameters, such as detection probability and probability of false alarm. The signal received through the orthogonal frequency-division multiplexing (OFDM) antenna is employed for analyzing the spectral availability for which the energy and Eigen statistics of the signal is generated, which forms the input to the Hybrid mixture model. The developed Hybrid mixture model is the integration of the Gaussian Mixture Model (GMM) and Whale Elephant-Herd Optimization (WEHO). The GMM is subjected to the optimal tuning using the WEHO, which is the modification of the standard Whale Optimization Algorithm (WOA) with the Elephant-Herd Optimization (EHO). The analysis reveals that the proposed spectrum sensing model acquired the maximal detection probability and minimal false alarm probability of 99.9% and 46.4%, respectively. The proposed hybrid mixture model derives the spectrum availability and ensures the effective communication in CR without any interference.

In cognitive radio ad hoc networks, the opportunistic access of vacant wireless channel opens a new frontier for efficient spectrum utilization as in many situations, a wide range of spectrum is not even partially utilized by the license owners (primary users, PUs). While the idea seems to be lucrative for spectrum hungry users without licenses, a natural competition between potential stake holders arises, which needs to be regulated in order to efficiently utilize available resources and avoid chaos. With the introduction of unlicensed users in licensed bands, the operations and interests of PUs need to be protected, hence the spectrum owners are given an advantage and control over the multiple access policy (a leader-follower scenario). In this work, we address the problems in spectrum access and channel selection equilibrium in a leader (PU)-follower (secondary user, SU) setup. In contrast to previous game formulations that lack efficient power and pricing schemes, we present a cooperative Stackelberg potential game for cognitive players. A dynamic cost function is articulated to induce awareness in players to mitigate the effects of selfish choices in spectrum access while at the same time steer the distributed network towards achieving Nash equilibrium. The proposed scheme is mutually beneficial for all players and focuses on improving the network performance and power efficiency. We design the network potential function such that the nodes have performance based incentives to cooperate and achieve a Nash equilibrium solution for efficient channel acquisition and capacity. Simulation results show fast convergence in channel selection strategies and increase in capacity for the entire network.

IEEE 802.22 Standard utilizes cognitive radio (CR) techniques to allow sharing unused spectrum band. CR is vulnerable to various attacks such as jamming attacks. This paper has focused on coordinated jamming attacks. A simple strategy for secondary users is to change their bands and switch to other appropriate bands when the jamming attack has occurred. Also, the malicious users should switch to other bands in order to jam the secondary users. To address this problem, a game theoretical method is proposed to analyze coordinated jamming attacks in CR. Then, using Nash equilibrium on the proposed game, the most appropriate bands have been found to switch as well as the optimal switching probabilities for both secondary and malicious users. Meanwhile, effects of different parameters like the number of malicious users are investigated in changing the optimal switching probabilities by analysis of the model.

In cognitive radio (CR), the sensed aggregate bandwidth could be as large as several GHz. This is especially challenging if the bandwidths and central frequencies of the sensed signals are unknown and need to be estimated. This work discusses a new improved method for MB spectrum sensing (iMB-SS) based on edge detection and using Wavelet Spectrum Filtering. The proposed iMB-SS method uses a Welch power spectrum density (PSD) estimate and a multi-scale Wavelet approach to reveal the spectrum transition (edges), which is deployed to characterize the spectrum occupancy in CR scenarios where the operation frequencies of the primary users (PUs) are unknown. The focus of this work lies in improving the performance of the MB spectrum sensor, particularly by refining the spectral edge location and reducing misleading detection. A comprehensive analytical description and numerical analysis have been carried out by focusing on orthogonal-frequency-division-multiplexing (OFDM) signal applications in CR networks. Numerical results corroborate the effectiveness of the proposed iMB-SS approach. The simulated results for the multiple-PU’s OFDM-based transmission CR system demonstrate that the proposed iMB-SS method can achieve high performance even under low signal-to-noise ratio (SNR) regime, turning it out as an attractive choice for SS in the MB CR systems.

A multichannel transceiver design with low hardware complexity, flexibility and efficient spectrum use intended for Impulse Radio (IR) Ultra-Wideband (UWB) apertures, is proposed in this paper. The transceiver supports 14 channels and is implemented in TSMC CMOS process. The transmitter adopts a symmetrical triangular narrow pulse generator, a multiband LC-VCO, a mixer and a Variable-Gain Amplifier. A digital control of transmitter output power and carrier frequency is achieved by a current-steering Digital-to-Analog Converter (CS-DAC). The receiver follows the noncoherent Energy Detection (ED) scheme including Low-Noise Amplifier (LNA) which takes the benefits of the body biasing technique to further reduce power consumption, squarer and comparator. The LNA achieves high gain of up to 30dB allowing to enhance the receiver sensitivity which is $-$83.7dBm at 25Mbps. The transmitter provides output pulses with a width of 3ns, a pulse repetition rate of 20ns and a maximum Power Spectral Density (PSD) of $-$42dBm/MHz. The power consumptions of transmitter and receiver are 35.6pJ/bit and 0.207nJ/bit at 25Mbps, respectively. Maximizing utilization of the scarce wireless spectrum, enhancing spectral flexibility and efficiency and reducing power consumption are the main contributions brought by this work, enabling this transceiver to be able to deal with the current debate on producing single multifunctional Cognitive Radio (CR) UWB systems.

A binary artificial rabbit optimization algorithm is proposed to maximize the cognitive radio (CR) network performance and fairness among users by addressing the issue of low spectrum resource utilization during the CR spectrum allocation process. Building upon the graph coloring spectrum allocation model, Sigmoid is introduced to transform the algorithm solution space. Incorporating the Lévy flight strategy for adaptive step size adjustment enhances the algorithm’s flexibility and convergence accuracy. Moreover, a selective opposition strategy based on Spearman’s coefficient is integrated into the algorithm to improve population diversity and convergence accuracy through reverse learning. Applying the binary artificial rabbit optimization algorithm to the CR spectrum allocation problem in the same CR network environment, we compare network efficiency and inter-user fairness through simulation experiments with the artificial rabbits optimization algorithm seagull optimization algorithm and particle swarm optimization algorithms. The experimental results show that the binary artificial rabbit optimization algorithm has higher convergence performance and global exploration ability, improves the overall network efficiency and user fairness of CR networks, and alleviates the problem of low spectrum resource utilization.

The past decade has witnessed a boom of wireless communications which necessitate an increasing improvement of data rate, error-rate performance, bandwidth efficiency, and information security. In this work, we propose a quadrature (IQ) differential chaos-shift keying (DCSK) modulation scheme for the application in cognitive radio (CR), named CR-IQ-DCSK, which offers the above improvement. Chaotic signal is generated in frequency domain and then converted into time domain via an inverse Fourier transform. The real and imaginary components of the frequency-based chaotic signal are simultaneously used in in-phase and quadrature branches of an IQ modulator, where each branch conveys two bits by means of a DCSK-based modulation. Schemes and operating principle of the modulator and demodulator are proposed and described. Analytical BER performance for the proposed schemes over a typical multipath Rayleigh fading channel is derived and verified by numerical simulations. Results show that the proposed scheme outperforms DCSK, CDSK and performs better with the increment of the number of channel paths.

Cognitive Radio Networks (CRNs) is a promising technology which deals with shared spectrum access and usage in order to improve the utilization of limited radio spectrum resources for future wireless communications and mobile computing. Security becomes a very challenging issue in CRNs as different types of attacks are very common to cognitive radio technology compared to general wireless networks. The proper working of cognitive radio and the functionality of CRNs relies on the compliant behaviour of the secondary user. In order to address this issue, we propose two approaches in this paper. Firstly, we propose a trust aware model to authenticate the secondary users of CRNs which offers a reliable technique to provide a security-conscious decision by using trust evaluation for CRNs. Secondly, we propose an analytical model for analyzing the availability of spectrum in CRNs using a stochastic approach. We have modeled and analyzed the availability of free spectrum for the usage of secondary users by adopting different activities in a spectrum management scheme to improve the spectrum availability in CRNs.

Cognitive Radio based Wireless Sensor Network is a novel concept that integrates the dynamic spectrum access capability of cognitive radio into wireless sensor networks for the futuristic sensor networks and wireless communication technology. Spectrum sensing plays a quintessential role in a cognitive radio network but is a major constraint for a battery powered sensor with stringent energy limitations. The spectrum sensing algorithms are expected to yield acceptable detection probability at low SNR under noise uncertainty with minimum power consumption in a WSN. In this paper, a new spectrum sensing method has been proposed to overcome sensing failure under low SNR environment. The proposed technique is based on adaptive double threshold theory which improves the detection performance by 39.63 and 27.22% at SNR = −10dB as compared to the conventional energy detection and available double threshold-based method respectively. Furthermore, the proposed method of spectrum sensing is evaluated for its deployment into a CR-WSN using the evaluation metrics: Time and Sample Complexity. The comparative evaluation of the spectrum sensing method in a WSN through simulations shows that the proposed technique offers substantial reduction in sample and time complexity of the wireless sensor nodes.

Cooperative spectrum sensing (CSS) in a cognitive radio uses a fusion center, which receives local sensing decisions from multiple secondary users to predict whether primary user is present or absent. Therefore, an ensemble classifier with heterogenous fusion center (EC-HFC) is proposed in this work, where the ensemble classifier comprise three classification algorithms such as logistic regression (LR), support vector machine (SVM), and gaussian naive bayes (GNB). In addition, voting classifier with its variants also employed for finding the best suitable classifier. Further, the performance metrics such as accuracy, F1-score, area under the curve (AUC), probability of detection and probability of false alarm are computed for evaluating the performance of proposed ensemble classifier-based fusion center for cooperative spectrum sensing in cognitive radio. Finally, the obtained receiver operating characteristics (ROC) and extensive simulation results shows that proposed fusion center resulted in superior performance as compared to individual secondary users.

Spectrum seems to be the lifeblood of wireless communication, which is of high demand as the traffic doubles every year. This increasing demand drives towards 5G NR, which is expected to support 100 folds increase in mobile devices, gigabit user data rate, ultra-low latency, high traffic and ultra-reliability. Since most of the available spectrum has been saturated, new methods that make use of spectrum in efficient manner must be considered. Through flexible 5G NR framework, 5G is aimed to utilize shared/unlicensed spectrum. Cognitive radio is the key enabler for operating through shared/unlicensed spectrum. In this situation, network interference caused by secondary users (SU) in accessing the unlicensed band for cognitive radio can be overcome by implementation of efficient resource management techniques. The interference issue among SU can be minimized to a large extent by efficiently allocating the available spectrum. Fuzzy Analytic hierarchy process (FAHP) seems to be an appropriate solution for spectrum allocation among SU without creating interference among themselves. The mathematical model proves that FAHP allocated the spectrum to the best SU.

The wavelet packet transform as a mathematical tool has found recent application in spectrum sensing. The result of this application has produced very promising results. Primarily, wavelets were designed for edge detection in images. Recently, cognitive radio literature have reported on wavelet application to detect sub-band frequency edges in wide band spectrum. In this paper, we present the combination of the Hilbert transform and the wavelet packet transform with the aim of enhancing the detection of the sub-band frequency edges of a wavelet-packet-decomposed signal. The simulation results show the effectiveness of this approach. The new scheme detected sub-band frequency edges of the wavelet-packet-decomposed signal much better than the wavelet packet transform without combination with the Hilbert transform.

In this paper, comparison of common gate-common source single ended and differential ended Low Noise Amplifier (LNA) for Cognitive Radio (CR) receiver is presented. LNA for CR receiver architectures operating over a frequency range of 50–900MHz is designed for narrow band applications. In this work, Noise figure (NF), Third-Order Intercept Point (IIP3), voltage gain and scattering parameters (SP) have been analyzed. The voltage gain is 10.16866dB for single ended and 29.21371dB for differential ended, NF is 1.012dB for single ended and 3.8dB for differential ended. Power gain (S21) obtained is 15.67481dB for single ended and 14.89dB for differential ended. Compression point is $-5.62487$dB for single ended and $-5.32922$dB for differential ended, Third-Order Intercept Point (IIP3) is 1.97705 for single ended and 5.01608dB for differential ended amplifier, respectively. The design has been implemented using CMOS technology with cadence virtuoso design environment/automation tools.

Modulation recognition is an important issue in cognitive radio research area, however, high recognition precision is usually achieved by relative large number of training data and more various features of digital signal, which call for much more resource. In this paper, a novel high order cumulant vectors is proposed as features for digital signal modulation recognition, which are constructed as features to train support vector machine classifiers for modulation signal recognition. The experimental results shows the proposed approach can get comparative high precision for PSK signal recognition in additive white Gaussian noise channel while using relative small number of training samples, which reduce cost remarkably.

Signal detection can be applied to the spectrum sensing, which is a key technology of cognitive radio (CR). The conventional signal detection algorithms calculate the sum of energy in interested frequency band to recognize whether a modulated signal is present or not. However, the noise power level is volatile in different cases, which deteriorates the detection performance. In this paper, we propose a new modulated signal detection algorithm by dividing the analyzed spectrum band into several blocks and then calculating the sum of their energy variances. Numerical results indicate that about 5dB lower signal-to-noise ratio (SNR) is needed to detect modulated signal in provided algorithm compared with traditional method.

An optimal multiband spectrum sensing method based on the particle swarm optimization (PSO), which can jointly detect the primary signals in multiple different narrowband channels, is proposed in this paper. The algorithm enhances the achievable throughput of cognitive radio networks while protecting the interference of the primary users under a given level. Unlike the traditional algorithms to deal with this problem, the proposed method uses PSO algorithm to search the global optimal solution in whole feasible domain for any system configuration of cognitive radio networks. By operating directly on the objective of the optimization we show that the multiband spectrum sensing problem can be solved consistently without any limitations imposed on the secondary sub-band utilization and the per-band interference. Results show that the proposed PSO-based method is efficient and stable.

This paper presents a new wideband spectrum sensing method based on the rank criterion. This proposed method first divides the sample covariance matrix of received signal into the “ideal” matrix, having a rank of q, and the “perturbed” matrix. The rank criterion function is then used to search for the optimal q value, which is used to determine the numbers and the locations of the occupied channels. Owing to the method repeatedly using matrix eigenvalue decomposition in the formula derivation, computation time is increased, making the method unconducive to the real-time processing of the algorithm. Thus, the algorithm was improved upon by using the signal sampling power to approximate the eigenvalues of the sample covariance matrix. This greatly improved the proposed method's operation speed. Simulation results verified the effectiveness of the proposed method.

In this paper, we consider the sensing order and sensing stopping problem arising from the opportunistic spectrum in cognitive networks. Due to the sensing cost, a secondary user (SU) needs to find an available channel with as small a cost as possible. On the other hand, the SU hopes to obtain sufficient information about these spectrum holes such that it can attain as much utility as possible. The paradoxical target calls for a delicate mechanism to minimize the trade-off between exploitation and exploration. Specifically, we consider two different scenarios: homogeneous channels and heterogeneous channels, and propose an easily implemented heuristic policy to achieve the twin goals of obtaining an available channel and obtaining sufficient information about those spectrum holes. Extensive numerical experiments also demonstrate the effectiveness of the proposed heuristic algorithm.