Eigenvalues-Based Universal Spectrum Sensing Algorithm in Cognitive Radio Networks

In recent years, channel-hopping-based medium access control protocols have been proposed to improve the capacity in a decentralized multichannel cognitive radio (CR) network without using extra control channels. Each CR user has to stochastically follow a default channel-hopping sequence in order to locate a channel and conduct its frame transmission. In this paper, theoretical analysis is conducted on the probability of channel availability and the average frame delay for primary users (PUs) by considering the impact caused by imperfect sensing of CR users and imperfect synchronization between the primary and CR networks. According to the proposed analytical model with realistic considerations, an optimal channel-hopping sequence (OCS) approach is designed for the CR users based on a dynamic programming technique. It is designed by exploiting the optimal load balance between channel availability and channel utilization within the delay constraints of PUs. By adopting the OCS approach, maximum aggregate throughput of CR users can be achieved while considering PU's quality-of-service (QoS) requirements. Moreover, in addition to the paired CR networks, the logical partition problem that occurs in generalized CR networks will also be addressed. This problem can severely degrade the aggregate throughput due to the decreased probability of connectivity between CR users, especially in a CR network with heavy traffic. Therefore, both wake-up successive contention (WSC) and wake-up counter-reset successive contention (WCSC) algorithms are proposed to increase the number of negotiations by both exploring the blind spot of imperfect sensing and amending the contention mechanisms between CR users. Compared to conventional channel-hopping sequences, numerical results illustrate that the proposed approaches can effectively maximize aggregate throughput for CR users under the QoS requirements of PUs.

In cognitive radio networks, security issues arise when intelligent malicious users can attack and decrease the permitted channel for unlicensed users (secondary users). In this paper, we propose a novel scheme, based on friend or foe (FoF) detection technique with physical layer network coding, to enable discrimination between secondary users and intelligent malicious users. Theoretical expressions are derived for probabilities of detection of secondary user, miss detection, and false alarm. In addition, the effectiveness of the proposed approach is evaluated by theoretical analysis and Monte Carlo simulation. Furthermore, an algorithm is proposed for distinguishing between secondary user and intelligent malicious user. Finally, based on the outcome of simulation of probabilities and normalised cross-correlation, it is determined that the proposed scheme outperforms in terms of orthogonal frequency-division multiplexing (OFDM) signal compared with quadrature phase-shift keying (QPSK) signal over cognitive radio network.

Satellite cognitive network is currently facing a lot of complex spectrum environment with a lot of interference, and the required user signal strength will change with a variety of external factors, which directly affects the above series of results obtained through the decision mechanism and it can not be well applied to satellite cognitive network. So a new blind detection algorithm based on maximum and minimum eigenvalues of sampling covariance matrix is proposed. In this algorithm, the ratio of the difference and sum of the maximum and minimum eigenvalues of the sampling covariance matrix is used as the perceptual decision quantity. Then, by introducing the latest results of the distribution of the maximum and minimum eigenvalues of the sampling covariance matrix in large dimensional random matrix, an effective decision threshold calculation method is designed. Compared with the classical eigenvalue detection algorithm, the new algorithm has the advantage of accurate calculation of perceptual decision threshold, and can effectively improve the detection performance and the reliability of decision results.

There has been recent interest within the networking research area to understand the transmission delay in Cognitive Radio (CR) Networks with overlapping primary network and secondary network. In this paper, we investigate the scaling behavior of transmission delay in large scale ad hoc CR networks. We take different scenarios of CR networks into consideration and thus obtain a wind range of results. We first neglect propagation delay and study the ratio of transmission delay to distance, denoted by γ (λsAp and figure out its exact value in supercritical secondary network. In case of subcritical secondary network, we introduce a multi-cluster hop transmission process to get the lower bound of γ (λsAp. Then we take propagation delay into consideration to obtain further results. Finally, we use simulation results to verify our theoretical analysis. The results present the scaling behavior of transmission delay in CR networks and provide the design guidelines for large scale wireless networks.

The dimension boundary between finite random matrices and infinite random matrices is originally defined in this letter. The proposed boundary provides a theoretical approach to classify random matrices based on their dimensions. Two methods are proposed to determine the dimension boundary. One is based on the eigenvalue distribution and the other is based on the eigenvalue interval. In particular, a boundary-based threshold generation scheme in cognitive radio networks is studied. The theoretical analysis and numerical results verify the proposed dimension boundary and the corresponding boundary decision methods.

The nonlinear power amplifier (NPA) in a cognitive radio (CR) network can degrade sensing performance. While spectrum sensing primarily focuses on the receiver, modeling the transmitter's NPA is crucial to understand its impact on detection. This paper investigates spectrum sensing performance in CR networks assisted by intelligent reconfigurable surfaces (IRS), considering the impact of NPA distortions. A novel multiple‐input single‐output (MISO) IRS‐aided CR system, termed MICR, is proposed. The sensing performance of the proposed MICR system is analyzed in terms of the probabilities of false alarm and detection, considering the effect of IRS elements. The results are presented using receiver operating characteristic (ROC) curves. Furthermore, the impact of various system parameters such as the number of samples, number of IRS elements, IRS position, number of transmitting antennas, and phase shifts is analyzed. Additionally, the effects of line‐of‐sight (LoS) and non–line‐of‐sight (NLoS) propagation are evaluated. The ROC performance using different signal detectors is analyzed. The impact of interference, IRS overhead, and sensing energy on the proposed MICR system is also investigated. Extensive simulations demonstrate that IRS significantly improves the sensing performance of the CR network, both with and without NPA.

This paper proposes a Bayesian detector for spectrum sensing in a multi-antenna cognitive radio (CR) network in which no channel state information (CSI) is available. The Bayesian approach for detection necessitates a prior distribution of the CSI in terms of the spatial covariance matrix, and unfortunately it is often improper and cannot be applied directly. We shall introduce the use of the Fractional Bayes Factor (FBF) approach to handle improper prior, which in turn yields a well-defined Bayes factor as the test statistic for detection. A number of priors of the CSI are examined and a closed-form expression for the test statistics is derived. The developed Bayesian detector is compared with those by using the conjugate priors for both hypotheses and the generalized likelihood ratio test (GLRT), and it yields considerable improvement in detection performance.

Due to the development and evolution in Cognitive Radio Networks (CRNs), the performance of Transmission Control Protocol (TCP) over CRN has become a crucial factor. TCP ensures the reliable end-to-end delivery of data, which is very important for the quality of service (QoS) requirements. TCP performance is severely affected by channel switching and channel availability in CRNs. The frequent occurrence of such events is due to CRNs nature which unnecessarily reduces the transmission rate which results in degrading the overall performance. In this paper, the authors investigate the limitations of using TCP NewReno (i.e., standard TCP) over CRNs, and further propose a cross-layer-design protocol based on an interaction between transport layer and lower layers to improve TCP performance over CRNs. The design is called Cross-Layer Design for TCP (TCP-CLD). It primarily aims at improving the TCP performance over CRNs via controlling and adapting a few selected TCP parameters. Specifically, the authors propose freezing the data transmission and re-transmission timeout (RTO) of TCP during the time of channel switching and channel unavailability. In addition, the authors update the size of congestion window (cwnd) of TCP based on the bandwidth and buffer size of relay nodes. Moreover, in the case of channel switching, the authors update the Round Trip Time (RTT) of TCP based on the new route delay. Simulation experiments have been conducted using ns-2 network simulator tool whereas results show that the proposed design achieves a significant performance improvement compared with the standard TCP NewReno protocol and the other known protocol, namely, TCP-CRAHN.

Spectrum sensing, through efficient channel estimation methods utilizing cognitive radio networks, has become an increasingly researched area in recent times. This has become more pronounced in recent times, especially with increasing scarcity in availability of radio frequency spectrum. Wireless state of the art communication standards demand high bandwidth to provide seamless connectivity and high degree of mobility, which requires more of radio frequency band for functioning. Hence, intelligent methods of spectrum allocation have been an increasing challenge in recent times. This paper proposes a hybrid learning based-Ada Boost classifier model for efficient spectrum allocation through channel state estimation through a learning and double classification approach. The proposed algorithm has been experimented in a high bandwidth characterized 5G communication simulation settings and observed for its performance measures namely collision rate analysis, throughput, probability of detection, false alarm detection and bit error rate. The proposed technique has been compared against benchmark techniques such as conventional fast Fourier transform based energy detector, fuzzy cognitive engine and adaptive neuro fuzzy inference model without Ada Boost and found to exhibit superior performance in all performance measures. The proposed technique exhibits a spectral efficiency of nearly 90% and considered to be a suitable spectrum sensing scheme for high bandwidth and narrowband utilities.

It is the trend of the development of modern competitive sports to put scientific and technological analysis methods and means into the study of volleyball, and it is also one of the powerful guarantee ways to promote the competitive level of all countries. The random matrix model algorithm has unique advantages to construct the team’s collective technical and tactical ability structure model. The quantitative relationship of the model describes the relationship between the technical and tactical ability structure and the result of victory and defeat and makes the advantages and disadvantages of the team clear, which is conducive to the subsequent targeted training and improvement. The technical and tactical abilities of the teams in different seasons were input to verify the prediction accuracy of the model for the teams in different seasons. In the face of the rapidly changing game situation, the coach team timely transmits the adjusted technical and tactical strategies to the players on the field and deals with the changes accurately and effectively. After the game, the opponent’s strengths and weaknesses should be clarified, and the team’s daily training details should be summarized to provide reference for the cultivation of collective technical and tactical consciousness. The random sample covariance matrix of the random monitoring matrix is constructed and the maximum and minimum eigenvalues of the sample covariance matrix are solved. The ratio of characteristic values is used to construct the detection index of characteristic values, and the detection threshold algorithm of characteristic values is determined to judge the competitive ability of volleyball players. In the case of false alarm rate and matrix size, based on Tracy-Widom distribution characteristics, the maximum eigenvalue and minimum eigenvalue approximations of sample covariance matrix are used to improve the eigenvalue index detection threshold algorithm, and the influence of false alarm rate, matrix size, and other parameters on the improved eigenvalue index detection threshold is further studied. Then, Iris data set was used to verify the effectiveness of the algorithm in terms of accuracy, recall rate, and comprehensive effective value, and the validation results proved that the accuracy of the algorithm reached more than 90%.

In this paper, we study the problem of jointing power and rate optimization in multi-channel Heterogeneous Cognitive Radio Networks (H-CRNs), where the Leader-Follower relationship between Primary Users (PUs) in macrocells and Secondary Users (SUs) in femtocells is modeled as a Stackelberg game. We formulate the problem of power allocation in primary networks as a Non-Cooperative Game, while formulating the jointing power and rate optimization in cognitive networks as a Market Competition Equilibrium problem. Furthermore, we propose a Primary networks and Cognitive networks Joint Resource Allocation (PCJRA) algorithm to maximize the utilities of SUs subject to the maximum rate requirements for PUs. Theoretical analysis and simulation results demonstrate both the feasibility of the multi-channel H-CRNs model and the effectiveness of the proposed PCJRA algorithm.

The statistical analysis of failure detection decisions in terms of the instantaneous probabilities of false alarm and correct detection for a specified failure magnitude at each check-time have previously been performed for several different failure detection techniques that utilize a Kalman filter. By performing a discrete-time specialization of a result of Gallager and Helstrom on a tightened upper bound for continuous-time level-crossing probabilities, upper bounds on the probabilities of false alarm and correct detection over a time interval have been obtained for the specific technique of CR2 tailnre detection (to allow an accounting for the effect of time correlations of the filter estimates). When these upper bounds are optimized to be as tight as possible to the desired probabilities, the resulting optimization problem for discrete-time is a collection of quadratic programming (QP) problems, which may easily be solved exactly without recourse to approximate solutions as were resorted to in the continuous-time formulation. This technique for evaluating tightened upper bounds on the false alarm and correct detection probabilities may be of general interest, since it can be applied to any failure detection technique or signal detection technique that can relate an exceeding of the deterministic decision threshold by the test statistic directly to a deterministic level being exceeded by a scalar Gaussian random process.

The inefficient use of spectrum is the key subject to overcome the upcoming spectrum crunch issue. This paper presents a study of performance of cooperative cognitive network via hard combining of decision fusion schemes. Simulation results presented different cooperative hard decision fusion schemes for cognitive network. The hard-decision fusion schemes provided different discriminations for detection levels. They also produced small values of Miss-Detection Probability at different values of Probability of False Alarm and adaptive threshold levels. The sensing performance was investigated under the influence of channel condition for proper operating conditions. An increase in the detection performance was achieved for cognitive users (secondary users) of the authorized unused dynamic spectrum holes (primary users) while operating in a very low signal-to-noise ratio with the proper condition of minimum total error rate.

In this paper, we optimize probability of detection and probability of false alarm in cognitive radio network to minimize probability of error of a particular SU in a centralized cognitive radio network using Genetic algorithm (GA). Our objective is to minimize probability of error and find out optimum values of probability of occupancy detection or probability of detection and probability of false alarm. We use Genetic Algorithm to solve this optimization problem. The result is compared with Differential Evolution algorithm and it is evident from the comparison that DE finds better solution and takes much lesser number of evaluations to find optimum solution.

We propose a Cognitive Radio nEtworking ArchiTecture (CREATE) with joint cross-layer design of routing and dynamic spectrum allocation that balances the throughput-delay trade-offs under realistic physical channel effects. Dynamic routing is implemented by integrating a spectrum-aware backpressure-like algorithm with shortest-path routing to improve both the average delay and the achievable throughput under hard deadline constraints. CREATE supports spectrum adaptability by establishing asynchronous negotiation and coordination channels instead of a dedicated common control channel. For end-to-end experimental evaluation of CREATE, we propose NEST, a Network Emulator and Simulator Testbed, to deploy and experiment with cognitive radio (CR) networking protocols and applications on real programmable radios. NEST combines a low-cost and scalable simulation environment with realistic hardware performance to provide high fidelity and repeatable CR network experiments. Our experimental results characterize the effects of network topology and physical channel on the spectrum agility, situational awareness and resiliency across the protocol stack in multi-hop CR networks.