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This paper presents a novel spectrum sensing and resource allocation method for Cognitive Radio Networks (CRNs) using the Hazelnut Tree Search (HTS) algorithm in a frequency-hopping framework. CRNs enable secondary users (SUs) to utilize unused licensed spectrum without causing interference to primary users (PUs). The proposed Spectrum Sensing and Resource Allocation using Hazelnut Tree Search in CRN (SS-RA-HTS-CRN) method detects PU presence, prompting SUs to switch channels to avoid interference, guided by a designated guide node (GN). The HTS algorithm selects optimal sensing nodes and improves spectrum sensing, enhancing resource allocation reliability. Simulations in NS-3.26 show that SS-RA-HTS-CRN achieves delay reductions of 28.93%, 31.9%, and 22.45%, delivery ratio improvements of 28.21%, 17.41%, and 11.565%, and packet drop rate reductions of 35.4%, 44.3%, and 23.12% compared with Energy-Aware Resource Allocation with Backtracking Search Optimization (SS-RA-BSO-CRN), Spectrum Sensing with Hybrid Particle Swarm and Gravitational Search (SS-hybrid PSOGSA-CRN), and Spectrum Sensing using Modified Spider Monkey Optimization (SS-MSMO-CRN). The proposed SS-RA-HTS-CRN method significantly enhances efficiency, reliability, and adaptability in CRNs, making it a promising solution for dynamic spectrum management. [ABSTRACT FROM AUTHOR]

The cognitive radio network (CRN) is important for efficiently allocating spectrum. In general, uncertainty in channels and interference decreases the system's efficiency. An energy-efficient spectrum-sharing optimization framework in the cognitive radio network based on the GarraRufa Fish Optimization algorithm and Binary Jaya algorithm (EESS-CRN–GRFOA-BJA) is proposed in thispaper to address the load utilization problem. Firstly, spectrum sharing contains the sensing model that increases its efficiency and the quality of service. The spectrum-scheduling approach is employed for sensing the obtainable spectrum and also scheduling the load to free up the spectrum. Then, the GarraRufa fish optimization algorithm (GRFOA) is used for implementing spectrum sensing and detecting the obtainable spectrum. Here, GRFOA method, along with signal-to-interference-plus-noise ratio (SINR), interference, and capacity constraint efficiently senses the spectrum. Then the Binary Jaya algorithm (BJA) is implemented for scheduling loads to free up spectrum and is used to perform the bandwidth scheduling. The metrics, such as probability detection vs signal to noise ratio (SNR), throughput, energy efficiency, and delay are analyzed. The efficacy of the proposed methods is evaluated and attains 12.35%, 19.54%, and 22.38% higher SNR, 20.45%, 17.12%, and 26.31% higher throughput, and 15.36%, 19.15%, and 21.49% higher energy efficiency than the existing models, such as multi-objective optimization for spectrum and energy efficiency tradeoff in IRS-assisted CRNs withNOMA (EESS-CRN-MOO), Gannet optimization algorithm-enabled framework for spectrum sensing (SS) in OFDM-based cognitive radio network (EESS-CRN-GOA), and Support Vector Machine-Red Deer Optimization algorithm for maximizing spectrum sensing energy efficiency in CRN (EESS-CRN-SVM-RDOA) respectively. [ABSTRACT FROM AUTHOR]

A minimal interrupted communication link set-up is the primary objective of the medium access control (MAC) layer. The MAC layer is responsible for accessing the communication channel. At MAC, the control information is shared through the control channel to select a collision-free data channel. Therefore, the sharing of control information prior to data channel selection plays a pivotal role in achieving desired QoS in cognitive radio (CR) technology. Random data channel selection is used in conventional CR scenarios. The random selection lacks in performance due to the unpredictability of primary user (PU) appearances in the data channels. This unpredictability in PU appearances will increase the number of handoffs in the scenario and sometimes may cause communication link failure. These adverse effects can be avoided if a common data channel selection scheme is implemented. The common data channel selection scheme selects a channel that has fewer chances of PU appearance. In the reported research paper, the work has been carried out in two parts. In the first part, a primary user free channel list (PCL) is formed at each node. Each node shares its PCL with all neighbour nodes. The sharing of PCL helps in implementing a common data channel selection scheme among nodes. The selected common channel is used for data transmission. The conventional OSI layered structure is used to observe the performances. In the second part, a cross-layer framework is proposed to improve the performance. The cross-layering has been done between the network and MAC layer. The proposed cross-layer framework helps in sharing PCL in an efficient manner. The need for an extra beacon to transfer PCL is avoided in the proposed cross-layer design. The performance of control overhead and average delay becomes better. The simulation results show that the proposed cross-layer design approach performs satisfactorily in terms of packet delivery ratio, average throughput, average delay, average energy consumption and control overhead. The observed results are also tested in the worst scenarios. The proposed scheme has applications in small throughput scenarios like cognitive radio aided sensor networks. [ABSTRACT FROM AUTHOR]

In wireless networks, there are two prominent challenges. The first challenge is ensuring that users have opportunities to access channels and request new services. The second challenge is maintaining connections for data flows. These challenges are compounded by the occurrence of channel failures, which often occur due to characteristics of radio transmission such as signal attenuation, signal blockage or device and power outages. Channel failures can significantly impact the effectiveness of both the primary and secondary networks. Therefore, it becomes crucial to prioritize retainability which denotes the need to maintain uninterrupted user connections even during network disruptions. This paper proposes an analytical model that evaluates performance of cognitive radio networks in the context of random channel failure rates. Additionally, the dynamic channel reservation (DCR) scheme is introduced. It can be integrated into dynamic spectrum access (DSA) strategies. This integration aims to give priority to existing services over requests from users to provide cognitive networks with more opportunities to allocate idle channels or maintain their current services. Moreover, the cost functions for both the primary user (PU) and the secondary user (SU) are calculated. This calculation considers the failure rate specifically in either reserved channels (RCN) or non-reserved channels (N-RCN) to meet different performance requirements. The results show a decrease in the SUs cost function, which guarantees that the quality of service (QoS) requirements for the PU are fulfilled. Importantly, this reduction in SU cost leads to an enhancement in SU channel availability or throughput when compared to previous models.

Efficient bandwidth utilisation is significant in new communication systems where secondary users can be used besides primary users considering interference issues and idle state of primary users. Using secondary users as relays to transmit their own signals in addition to the primary signals can be applied for more reliability of the system where opportunistic relay selection can significantly enhance the performance of the system. The best-condition secondary user is selected as the optimum relay for retransmission of primary/secondary signal. Outage probability is analysed in this paper based on decode and forward technique in secondary users while the closed-form statement for outage probability is provided and verified by numerical evaluations. [ABSTRACT FROM AUTHOR]

Cognitive Radio Network provides opportunistic spectrum access to unlicensed users in the absence of licensed users using dynamic spectrum access technology. The key challenge here is to impartially share the existing spectrum to all the secondary users in the network. A Round Robin Priority (RRP) algorithm along with reservation spectrum is proposed in this paper to impartially schedule available spectrum band and to provide continuous uninterrupted service to all secondary users. The QoS of secondary users are evaluated and compared with traditional spectrum allocation methods. The QoS parameters considered are blocking probability, the forced termination probability, spectrum utilization, average waiting time and the average delay of secondary users. The proposed Round Robin Priority Algorithm with Reservation scheme results reveals 22.82% reduction in Blocking Probability, 15.28% reduction in Forced Termination Probability, 12.54% reduction in average waiting time and 24.58% increase in Spectrum utilization when compared with traditional Constant Channel Aggregation Scheme. On comparing RRP with reservation with Variable Channel Aggregation schemes shows 19.32% reduction in Blocking Probability, 12.74% reduction in Forced Termination Probability and 10.57% increase in Spectrum Utilization. [ABSTRACT FROM AUTHOR]

Summary: A routing protocol for cognitive radio ad hoc networks (CRAHNs), namely, primary user supported routing (PSR) is demonstrated in this research. Unlike existing routing protocols for CRAHN, where routing of cognitive users (CUs) is accomplished within CUs, in this proposed protocol, some of the primary users (PUs) support CUs to communicate, by forwarding CU packets. This service provided by PU is of voluntary nature. However, such assistance shall be provided by the PUs, only when they are idle, thereby, preserving the principles of cognitive radio networks (CRNs), which indicates that the operation of PU should not be disturbed by the CU activities. The proposed work is compared with cognitive ad hoc on‐demand distance vector (CAODV) protocol. The performance parameters considered are routing overhead, rate of loss of packets, and e2e packet delay. PSR outperforms CAODV in all these performance parameters. There has been on average 26.25% improvement in routing overhead, 34.12% decrease in loss, and 27.01% improvement in e2e delivery in the proposed PSR. [ABSTRACT FROM AUTHOR]

In this work, a cognitive relay network (CRN) with interference constraint from the primary user (PU) with a mobile end user is studied. The proposed system model employs a half-duplex transmission between a single PU and a single secondary user (SU). In addition, an amplify and forward (AF) relaying technique is employed between the SU source and SU destination. In this context, the mobile end user (SU destination) is assumed to move at high vehicular speeds, whereas other nodes (SU Source, SU relays and PU) are assumed to be stationary. The proposed scheme dynamically determines the best relay for transmission based on the highest signal-to-noise (SNR) ratio by deploying selection combiner at the SU destination, thereby achieving diversity. All channels connected with the stationary nodes are modelled using Rayleigh distribution, whereas all other links connected with the mobile end user are modelled using Nakagami-m fading distribution ( m < 1 ). The outage probabilities (OPs) are obtained considering several scenarios and Monte Carlo simulation is used to verify the numerical results. The obtained results show that a variety of factors, including the number of SU relays, the severity of the fading channels, the position of the PU, the fading model, and the mobile end user speed, might influence the CRN's performance. [ABSTRACT FROM AUTHOR]

The spectrum scarcity problem in today's wireless communication network is addressed through the use of a cognitive radio network (CRN). Detection in the spectrum is made easier by cooperative spectrum sensing (CSS), which is a tool developed by the military. The fusion centre receives the sensing information from each secondary user and uses it to make a global conclusion about the presence of the principal user. Literature has offered several different methods for decision making that lack scalability and robustness. CSS censoring is inspected in the attendance of faded settings in the current study. Rayleigh fading, which affects reporting channels (R), is examined in detail. Multiple antennae and an energy detector (ED) are used by each secondary user (SU). A selection combiner (SC) combines the ED outputs with signals from the primary user (PU), which are established by several antennas on SU, before the joint signal is utilised to make a local result. SUs are expurgated at the fusion centre (FC) using a hybrid Support Vector Machine (SVM) that significantly improves detection performance and reduces the number of false positives. With a minimum false alarm probability of 0.1, error rate of 0.04, spectrum utilization of 99%, throughput of 2.9kbps and accuracy of 99%, proposed model attains better performance than standard SVM and Artificial Neural Network (ANN) models. [ABSTRACT FROM AUTHOR]

Cognitive radio wireless sensor networks (CRWSN) is a promising technology for developing bandwidth constrained applications. Future Internet of Things (IoT) applications may extensively use CRWSN. CRWSN consists of cognitive radio enabled sensor nodes which are energy constrained, in general. Hierarchical cluster based approach for overall network management is suitable for network stability and scalability. Thus node clustering is an important problem in CRWSN setup. Objective of this work is to develop a suitable node clustering algorithm for CRWSN, in which nodes are expected to be mobile. A node clustering protocol for CRWSN has been proposed in this paper. The proposed clustering protocol is based on evolutionary game theory (EGT). Initial clusters are formed through a simple partitioning approach. Eventually, initial clusters are merged to form the final clusters. After forming the clusters by the resourceful sink node, the cluster head nodes for respective clusters are determined. The sink node runs the EGT based algorithm to identify the most capable node as the cluster head. The strength of this approach is that while identifying the cluster head node, various parameters such as residual energy level, geographic location, mobility, and the probability of primary user (PU) arrival are considered. The clusters and therefore, the cluster head nodes are distributed uniformly in the geographical area. The proposed clustering protocol has been compared with LEACH, RARE and the spectrum aware clustering algorithm. The simulation results show that the proposed clustering protocol outperforms all these similar node clustering protocols. On average, the proposed protocol outperforms the benchmarks protocols by 25% in terms of number of high energy nodes selected as cluster head, by 37% in terms of uniform geographical distribution of cluster head nodes, by 23% in terms of total energy consumed during the simulation time, and by 27% in terms of network lifetime. The future scope of the work has been outlined.

Spectrum Sensing (SS) has been profoundly used in the Cognitive Radio (CR) framework due to its ability to identify spectrum holes and avoid intrusion for Primary Users (PUs). Several SS approaches based on filter banks (FBs) have been proposed in the literature. These techniques have the benefit of reusing the sensing filter bank for transmission as well, thereby providing an additional way to communicate at no extra cost. But the implementation complexity issues involved with the filter banks for CR were not taken into account. In this paper, we propose a Wideband Spectrum Sensing (WSS) based on a low complexity reconfigurable Modified FRM filter bank (ModFRM-FB) for detecting multiple active channels simultaneously. The proposed method uniformly divides the wideband signals into multiple-narrowband signals and each sub-band is sensed to evaluate the occupancy status of the sub-band by energy detection. Unlike traditional sensing algorithms, an adaptive threshold paradigm for energy detection is used to modify the threshold according to the noise power. This improves the detection performance even at low Signal-to-Noise Ratio (SNR) levels. The hardware complexity of our proposal is found to be comparatively lower than previous techniques in the literature. [ABSTRACT FROM AUTHOR]

Over the last few years, we have seen dramatic increase in the demand for radio spectrum in the world due to rapid growth in wireless services and applications. In order to meet the growing demand for radio spectrum, new schemes should be developed for utilizing free spectrum effectively. Cognitive radio (CR) is promising technology that enables solving spectrum scarcity problem effectively. Thus, in this paper, we propose two schemes to motivate primary user (PU) to share spectrum with secondary users (SUs). The key concern of the proposed schemes is maximizing PU’s profit by hiring free spectrum to wealthy SUs. In the eviction scheme, SUs with less profit are expelled from service to serve wealthy SUs. The folding scheme reconfigures spectrum request to serve it. It reduces the size of request if the available size is not sufficient for the request. The simulation results demonstrate that the ability of new schemes to maximize PU’s profit. Apparently, the results confirm the ability of the proposed schemes to utilize the free spectrum efficiently.

In the Internet of Things (IoT), spectral efficiency is a major challenge since many sensors and devices are placed widely throughout the network. Spectrum scarcity issues have been a concern for the Internet of Things since its inception. The use of Cognitive Radio (CR) technology is thought to be a potential remedy. Two of the problems that CR presents in the IoT are quick response times and efficient spectrum detection even at low signal-to-noise ratios. In order to address the spectrum scarcity issue, spectrum prediction is a key task of CR. As a result of spectrum prediction, sensor and decision-making delays are reduced, resulting in fewer collisions between primary and secondary users. To overcome this problem, an innovative GSCI-FELM technique has been proposed. The ultimate aim of the proposed method is to identify the spectrum holes and allocate the best suitable channel to the SU-IoT device. The Global Channel State Information (GSCI) estimation is first used to identify the spectrum holes locally at the SU-IoT devices, which solves the PU identification issue by converting it into an Idle Channel State (IDC) or Busy Channel State (BCS). Eight different features are extracted and given to Fuzzy ELM for classification. The secondary users are given access to the available node once it has been identified. According to the findings, the proposed technique is effective in predicting the spectrum availability of the various nodes in the IoT spectrum network. The accuracy of the proposed FELM is 5.76%, 2.96%, 7.4%, and 1.85% better than LSTM-RNN, STFT-CNN, SVM and SHAE respectively.

Summary: In recent times, evolution of communication technology and standard has grown in leaps and bounds from a conventional 1G communication technology towards the recent 5G and 6G technologies in a very short span of time. However, due to increasing scarcity of spectrum for these devices, cognitive radio networks (CRNs) have emerged to be promising solutions to allocate the required spectrum to the users in an intelligent manner. The method of compressive sensing‐based cyclo‐stationary feaure detection method is implemented based on a powerful CNN classifier to detect the presence or absence of PU activity. Detection probability and MSE have been improved, and the optimal detection has been reformulated for minimizing the possibility of error. Performance metrics have been compared against benchmark methods and superior performance reported. The sensing conduction and the accuracy of the proposed design are increased as 98.5%. [ABSTRACT FROM AUTHOR]

In this work, a cognitive relay network (CRN) with interference constraint from the primary user (PU) with a mobile end user is studied. The proposed system model employs a half-duplex transmission between a single PU and a single secondary user (SU). In addition, an amplify and forward (AF) relaying technique is employed between the SU source and SU destination. In this context, the mobile end user (SU destination) is assumed to move at high vehicular speeds, whereas other nodes (SU Source, SU relays and PU) are assumed to be stationary. The proposed scheme dynamically determines the best relay for transmission based on the highest signal-to-noise (SNR) ratio by deploying selection combiner at the SU destination, thereby achieving diversity. All channels connected with the stationary nodes are modelled using Rayleigh distribution, whereas all other links connected with the mobile end user are modelled using Nakagami-m fading distribution (m<1). The outage probabilities (OPs) are obtained considering several scenarios and Monte Carlo simulation is used to verify the numerical results. The obtained results show that a variety of factors, including the number of SU relays, the severity of the fading channels, the position of the PU, the fading model, and the mobile end user speed, might influence the CRN’s performance.

Cognitive radio wireless sensor networks (CRWSN) is a promising technology for developing bandwidth constrained applications. Future Internet of Things (IoT) applications may extensively use CRWSN. CRWSN consists of cognitive radio enabled sensor nodes which are energy constrained, in general. Hierarchical cluster based approach for overall network management is suitable for network stability and scalability. Thus node clustering is an important problem in CRWSN setup. Objective of this work is to develop a suitable node clustering algorithm for CRWSN, in which nodes are expected to be mobile. A node clustering protocol for CRWSN has been proposed in this paper. The proposed clustering protocol is based on evolutionary game theory (EGT). Initial clusters are formed through a simple partitioning approach. Eventually, initial clusters are merged to form the final clusters. After forming the clusters by the resourceful sink node, the cluster head nodes for respective clusters are determined. The sink node runs the EGT based algorithm to identify the most capable node as the cluster head. The strength of this approach is that while identifying the cluster head node, various parameters such as residual energy level, geographic location, mobility, and the probability of primary user (PU) arrival are considered. The clusters and therefore, the cluster head nodes are distributed uniformly in the geographical area. The proposed clustering protocol has been compared with LEACH, RARE and the spectrum aware clustering algorithm. The simulation results show that the proposed clustering protocol outperforms all these similar node clustering protocols. On average, the proposed protocol outperforms the benchmarks protocols by 25% in terms of number of high energy nodes selected as cluster head, by 37% in terms of uniform geographical distribution of cluster head nodes, by 23% in terms of total energy consumed during the simulation time, and by 27% in terms of network lifetime. The future scope of the work has been outlined. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) is a field which is gaining a lot of interest of researchers in around the whole world. It is also termed as nextgen technology as it aims to solve the problem of unutilized electromagnetic spectrum assigned to licensed users. With increasing wireless devices there is a demand of more spectrum resources and cognitive radio can help in making available the unused or underused spectrum for real time communication. When number of cognitive users communicate together at the same time there exists the problem of interference due to transmit power of all the devices to the licensed users. So there is a need of controlling power of transmission for cognitive users so that they may not interfere with Primary licensed users and maintain QoS with other cognitive users. The power control of a large number of devices together can be done by the application of computational intelligence techniques. In this paper three nature inspired computational intelligence techniques like GA, PSO and ABC for optimization of transmit power in cognitive radio networks are discussed.

Abstract Energy efficiency and privacy preserving have become essential for the wireless sensor networks. In this paper, a joint energy and time resource allocation problem for the cognitive users (CUs) in a non-selfish symbiotic cognitive relaying scheme (NSCRS) is considered. We aim to maximize the total energy efficiency (EE) of the primary user and CUs with the consideration of information privacy under the total energy constraints of CUs. With full channel state information (CSI), an optimal energy and time resource allocation algorithm is proposed based on the exhaustive searching. Besides, in order to reduce the overhead of CSI feedback, a suboptimal algorithm, in which only the partial instantaneous CSI is required, is additionally proposed. Simulation results demonstrate the EE of primary and CUs in the NSCRS with consideration of information privacy can be greatly improved by the proposed algorithms.

Underutilized radio frequencies are the chief apprehension in advance radio communication. The radio recourses are sparse and costly and their efficient allocation has become a challenge. Cognitive radio networks are the ray of hope. Cognitive radio networks use dynamic spectrum access technique to opportunistically retrieve and share the licensed spectrum. The licensed users are called primary users and the users that opportunistically access the licensed spectrum all called secondary users. The proposed system is a feedback system that work on demand and supply concept, in which secondary receivers senses the vacant spectrum and shares the information with the secondary transmitters. The secondary transmitters adjust their transmission parameters of transmit power and data rate in such a way that date rate is maximized. Two methods of spectrum access using frequency division multiple access (FDMA) and Time division multiple access (TDMA) are discussed. Interference temperature limit and maximum achievable capacity are the constraints that regulate the entire technique. The aim of the technique is to control the transmitter power according to the data requirements of each secondary user and optimizing the resources like bandwidth, transmit power using machine learning and feed forward back propagation deep neural networks making full use of the network capacity without hampering the operation of primary network. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication technology, the spectrum resources are increasingly strained which needs optimal solutions. Cognitive radio (CR) is one of the key technologies to solve this problem. Spectrum sensing not only includes the precise detection of the communication signal of the primary user (PU), but also the precise identification of its modulation type, which can then determine the a priori information such as the PU' service category, so as to use this information to make the cognitive user (CU) aware to discover and use the idle spectrum more effectively, and improve the spectrum utilization. Spectrum sensing is the primary feature and core part of CR. Classical sensing algorithms includes energy detection, cyclostationary feature detection, matched filter detection, and so on. The energy detection algorithm has a simple structure and does not require prior knowledge of the PU transmitter signal, but it is easily affected by noise and the threshold is not easy to determine. The combination of multiple-input multiple-output (MIMO) with CR improves the spectral efficiency and multipath fading utilization. To best utilize the PU spectrum while minimizing the overall transmit power, an iterative technique based on semidefinite programming (SDP) and minimum mean squared error (MMSE) is proposed. Also, this article proposed a new method for max-min fairness beamforming. When compared to existing algorithms, the simulation results show that the proposed algorithms perform better in terms of total transmitted power and signal-tointerference plus noise ratio (SINR). Furthermore, the proposed algorithm effectively improved the system performance in terms of number of iterations, interference temperature threshold and balance SINR level which makes it superior over the conventional schemes. [ABSTRACT FROM AUTHOR]

Over the last few years, we have seen dramatic increase in the demand for radio spectrum in the world due to rapid growth in wireless services and applications. In order to meet the growing demand for radio spectrum, new schemes should be developed for utilizing free spectrum effectively. Cognitive radio (CR) is promising technology that enables solving spectrum scarcity problem effectively. Thus, in this paper, we propose two schemes to motivate primary user (PU) to share spectrum with secondary users (SUs). The key concern of the proposed schemes is maximizing PU's profit by hiring free spectrum to wealthy SUs. In the eviction scheme, SUs with less profit are expelled from service to serve wealthy SUs. The folding scheme reconfigures spectrum request to serve it. It reduces the size of request if the available size is not sufficient for the request. The simulation results demonstrate that the ability of new schemes to maximize PU's profit. Apparently, the results confirm the ability of the proposed schemes to utilize the free spectrum efficiently. [ABSTRACT FROM AUTHOR]

In a cognitive radio network (CRN), the primary users (PUs) do not operate their spectra, full time. Thus, they can sell them to the secondary users (SUs), for a second use, during the free time slots. In this article, we assume that the market is perfect, monopolized by a single PU, and all players are rational. After formulating the PU’s profit, we established a necessary and sufficient condition that guarantees the introduction of the PU into the market. In addition, the expressions of the SUs’ profits, showed us that in non-cooperative form, some ones got zero profit, even after maximizing their profits. Therefore, we have considered to study the effect of cooperation on the profits of this category of SUs. By following this step, we established a cooperation strategy, to avoid zero profits for all SUs. In order to analyze the impact of this cooperation on the PU, we have expressed the profits of the PU in the cooperative and non-cooperative forms; as result, we found that the cooperation between SUs brought better than the non-cooperative form.

The common control channel (CCC) design plays a pivotal role in achieving the desired quality of service in cognitive radio (CR) technology. Various schemes of CCC designs help CR network to obtain better performance. The reported work proposes a novel hybrid MAC protocol with a target channel selection scheme in proactive handoff. To implement hybridization, the protocol floats between unlicensed out-band and licensed in-band control channel design approaches. The compromised scheme of hybridization is used to achieve advantages of both in-band and out-band designs. The advantages like tight node synchronization, better throughput performance, less congestion in dedicated control channel are achieved while maintaining the decorum of CR technology. To perform hybridization, a primary user free channel list (PCL) is constructed at each CR node and shared with other nodes prior to data transfer. A rendezvous data channel selection has been done between transmitter and receiver nodes for data transfer. Further, the regular sharing of PCL works as a framework for best target channel selection in proactive handoff. The PCL statistics are used to calculate the primary user (PU) reappearance probability of channels at CR node. Prioritizing channels based on parameters like PU reappearance probability, bandwidth, and packet error rate helps CR nodes switch proactively to the best target channel. To observe the performance, a CR ad hoc network setup is created in NS-2. The performance of the proposed protocol is checked under variable traffic conditions and compared with different standard protocols and schemes like IEEE802.11 (out-band control channel design and reactive handoff) and proactive handoff. The proposed protocol outperforms the stated schemes in terms of average throughput and average delay at the cost of increased control overhead. As compared to the reactive handoff scheme, the required number of handoffs in the proposed protocol is reduced by an average of 19.03% for increasing packet generation rate and 32.09% for the increasing number of CR nodes in the scenario. [ABSTRACT FROM AUTHOR]

Cognitive Radio Network is an emerging popular wireless network, designed for efficient spectrum utilization. It enables unlicensed users to access the unused portion of the licensed spectrum opportunistically. The major challenge is to share the unused portion of the spectrum efficiently to the unlicensed users. Channel aggregation is an interesting approach by which channels can be grouped and allocated to unlicensed users. A combined Round Robin Priority (RRP) scheduling algorithm is proposed by which the starvation of low priority SUs are minimized and QoS parameters are better compared to the traditional static and dynamic channel aggregation method. Fuzzy Inference System is used to evaluate the priority of the Secondary User. The QoS parameters utilized for comparison are spectrum utilization, capacity of the secondary network, delay, blocking probability and forced termination probability. [ABSTRACT FROM AUTHOR]

Cognitive Radio (CR) Network is a wireless communication technology, in which a detection device smartly detects occupied and unoccupied channels. During traffic, unused primary user spectrum space is allotted to a secondary user, without causing any intervention with the primary user. Though this has reduced network traffic to a great extent, many issues related to security has became an alarming problem, in which the primary user's space is misused by some malicious secondary users without the knowledge of primary user. To address this issue, in this paper we have proposed a Boundary detection method that uses the estimated location of each SU, which is obtained using the Recurrent Neural Network algorithm, to determine the boundary of PU coverage. Then Malicious User Detection by Ordering (MUDO) methodology is proposed, in which all secondary users are weighed using Basic Probability Analysis (BPA), and based on the orders the SUs are paired with corresponding PUs. The SUs with the least orders are discarded as they might be malicious users. The proposed methodology possesses higher detection speed and precise detection thereby enhancing the performance of CR. [ABSTRACT FROM AUTHOR]

A real time scenario of dynamic primary user (PU) is considered in additive Laplacian noise. Two transitions or status changes of PU in the fixed sensing time are considered. The last status change point (LSCP) is estimated with maximum likelihood estimation by using dynamic programming. We consider Cumulative Sum (CuSum) based weighted samples for detection. We consider three detection schemes such as sample mean detector, energy detection and improved absolute value cumulation detection. We derive closed form expressions of detection probability (P D) and false alarm probability (P F) for all the three schemes. We present our results with receiver operating characteristic (ROC) for the considered schemes. We also present simulation results, which are closely matching with their analytical counterparts. We compare the ROC of the considered system with the ROC of conventional techniques. In the conventional techniques, all the samples in the sensing time are used for detection without LSCP estimation and weight. It is found that the considered system outperforms the conventional schemes. [ABSTRACT FROM AUTHOR]