Spectrum Handoff Research Articles

Analysis of delays in medium access control devices in cognitive radio systems with correlation of time intervals in request flows

This article is about the development of a method for analyzing the average waiting time of a request in a queue with correlated inter-arrival and service times. The ON-OFF process is often used to model the primary user channel occupancy in different questions related to cognitive radio, such as resource allocation, medium access control protocols, spectrum handoff and others. It is assumed that the time intervals of the ON- and OFF- periods are not correlated. More realistic models of primary user activity require the presence of correlation not only between consequent periods, but also the presence of correlation between channel occupancy and other quantities interpreted as service time in models based on queuing systems. As a result, here is proposed a variant of constructing the probability density function of a random variable which is the difference between the service time of the i-th request and the time interval between the moments of arriving of the i-th and (i-1)-th requests with correlated inter-arrival and service times. Solution based on the use of a two-dimensional characteristic function, so in this case it is possible to more purposefully specify the components of the statistical connection of previously described sequences. These components are the cumulants of the described distribution. For a queuing system with hyperexponential distribution of inter-arrival and service intervals, an analytical expression for the average waiting time of a request in a queue is found.

Experimental Evaluation of Spectrum Handoff Management with Machine Learning Algorithms Using Software Defined Radio

Experimental Evaluation of Spectrum Handoff Management with Machine Learning Algorithms Using Software Defined Radio

Spectrum and Energy Efficiency in CRNs Using Dynamic Channel Reservation and Komodo Mlipir Optimization

Cognitive Radio Networks (CRNs) block new arriving services and remove existing services to increase the spectrum efficiency. Particularly, Secondary Users (SUs) experience a decline in service deprivation in the arrival of Primary Users (PUs). To overcome this, a Spectrum and energy Efficiency in Cognitive Radio Networks using Dynamic Channel Reservation and Komodo Mlipir Optimization (SE-KMOA-CRN) is proposed. The proposed method splits reserved and non-reserved bands from the accessible spectrum. Numerous channels from the non-reserved band are dynamically assigned to the reserved band to support services that experience disruptions when functioning on non-reserved band. From the non-reserved band, a count of channels dynamically assigned to the reserved band to support services that experience disruptions when functioning on the non-reserved band. It enables priority-based channel assignment and termination while providing dynamic access to the obtainable spectrum. Here, Allocation of channels to Reserved Band CRN using Dynamic Channel Reservation Algorithm (DCRA). Komodo Mlipir Optimization Algorithm (KMOA) is employed for Service selection and Channel restoration. The proposed SE-KMOA-CRN method is modelled utilizing Continuous Time Markov Chain (CTMC) with mathematical equations for some quality of service (QoS) factors is obtained. The SE-KMOA-CRN approach attains higher Throughput, lower End-to-End Delay, higher Energy Efficiency, greater spectral efficiency and higher Packet Delivery Ratio compared with existing methods, like Multiple objective optimization for spectrum along energy efficacy trade-off in IRS-assisted CRNs using NOMA (SE-MOO-CRN), Energy efficacy in CRN utilizing cooperative spectrum sensing under hybrid spectrum handoff (SE-TOA-CRN) and Energy-effectual cross-layer spectrum sharing on CR green IoT (SE-CRG-CRN) respectively.

Intelligent Spectrum Handoff Decision in Cognitive Radio Networks: A Fuzzy System Approach with Adaptive Membership Functions

Intelligent Spectrum Handoff Decision in Cognitive Radio Networks: A Fuzzy System Approach with Adaptive Membership Functions

A QoS-aware framework for spectrum characterization and switching decision in cognitive radio networks

The increasing demand for seamless wireless services coupled with underutilization of limited spectrum due to fixed channel allocation policy have posed numerous challenges to wireless communications. There is need to design appropriate channel allocation strategies for continuous communication by mobile users for higher spectral efficiency. Cognitive radio network (CRN) offers a solution to the spectrum scarcity problem inherent in 4G and other networks through dynamic spectrum access, by allowing unlicensed secondary users (SUs) with cognitive devices to opportunistically access the spectrum holes when the licensed primary users (PUs) are not occupying them. Often times, measurements taken by SUs during the sensing process are uncertain due to multipath fading, shadowing and varying channel conditions. This results in imprecise spectrum detection and selection by SUs during switching decisions causing incessant spectrum handoff and undesirable ping-pong effect. In this paper, a support vector machine (SVM) classifier is used to categorize spectrum into two classes of busy and idle. Then, based on heterogeneous quality of service (QoS) requirements of the SUs, dynamic activities of PUs, and the fluctuating channel state information, a QoS-aware Adaptive Neuro-Fuzzy Inference System (ANFIS) framework is developed for spectrum switching decision using underlay spectrum access model. SVM predicted spectrum holes with 98.8% accuracy. ANFIS model yielded a 91.62% accuracy in the task of allocating spectrum holes to SUs for coexistent with PUs. Results further indicate that the intelligent framework can ensure fairness among SUs, reduce interference, improve throughput, and spectral efficiency. It can be deployed in disaster relief and emergency, public safety, and battlefield environments.

Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks.

The rapid advancement of wireless communication combined with insufficient spectrum exploitation opens the door for the expansion of novel wireless services. Cognitive radio network (CRN) technology makes it possible to periodically access the open spectrum bands, which in turn improves the effectiveness of CRNs. Spectrum sensing (SS), which allows unauthorized users to locate open spectrum bands, plays a fundamental part in CRNs. A precise approximation of the power spectrum is essential to accomplish this. On the assumption that each SU's parameter vector contains some globally and partially shared parameters, spectrum sensing is viewed as a parameter estimation issue. Distributed and cooperative spectrum sensing (CSS) is a key component of this concept. This work introduces a new component-specific cooperative spectrum sensing model (CSCSSM) in CRNs considering the amplitude and phase components of the input signal including Component Specific Adaptive Estimation (CSAE) for mean squared deviation (MSD) formulation. The proposed concept ensures minimum information loss compared to the traditional methods that consider error calculation among the direct signal vectors. The experimental results and performance analysis prove the robustness and efficiency of the proposed work over the traditional methods.

Fuzzy Tree Schematic for Spectrum Handoff Reduction in Cognitive Radio Networks

Spectrum is a limited resource, so it must be used effectively. The fixed spectrum allocation method currently in operation may not sustain the growth in the network access users, which continues to increase over time. Many studies have shown that the spectrum deficiency problem can be solved with cognitive radio network (CRN) technology. CRN allows flexible spectrum utilization. Spectrum handoff is one of the fundamental prerequisites for dynamic spectrum access in cognitive radio networks. The ping-pong effect is one of the problems caused by the frequent occurrence of unnecessary handoff spectrum. So, in CRN, the right handoff decision is needed to avoid the ping-pong effect. The proposed fuzzy system carries the concept of a fuzzy tree that uses two fuzzy inference systems (FIS), which work in two stages, namely FIS-1 and FIS-2. In the early stages, the FIS-1 controls the secondary users (SU) power and prevents interference with the primary users (PU). FIS-2 is used to execute precise spectrum handoff decisions. The test results show that the amount of handoff spectrum has decreased by up to 43 % compared to other methods, which are only 60 %. This results in a decrease of 16.67% from the previous method. Thus, the ping-pong effect can be minimized with the right handoff decisions.

Load-Balancing Target Channel Selection for Spectrum Handoff in Cognitive Radio Networks

In this study, a new probabilistic approach is proposed to balance the traffic loads of secondary users (SUs) among channels. Furthermore, an adaptive load-balancing model is formulated to meet the time-varying radio environment. We employ the overall system time of SUs to evaluate the latency performance of the proposed approach. Besides, we derive the optimal channel selection probability to minimize the overall system time. Simulation results clearly verify the validity of our approach by comparing it with other alternative channel selection strategies.

Snake Optimization Technique for Spectrum Handoff in Cluster based Cognitive Radio Network

In Cognitive Radio (CR) networks, the use of Secondary Users (SU) in the spectrum has an undesirable effect on spectrum handoff, which causes a handoff delay. The handoff procedure can result in service outages and considerable transmission quality delays, making it a regular source of concern for the SU. An effective spectrum handoff strategy that utilizes the Spectrum Binary Snake Optimization (SBSO) algorithm and the M/G/1 queuing model has been proposed in this study. The use of Cluster Based Cooperative Spectrum Sensing improves SU performance and reduces channel congestion. In order to report the active and inactive channels in the spectrum, the cluster head is connected to the SU base station, and a decision report is subsequently generated by the fusion center. With the use of a bitwise and mutation operator format, SBSO reduces the overall service time required for handoff in the approach that is being proposed. The proposed methodology also provides a framework for observing how primary user activity and spectrum handoff delays behave in the presence of potential interruptions in a CR network. The simulation model of the proposed work optimizes the packet delivery ratio with the three benchmark functions, and provides optimal handoff, and is compared to SBSO and other models that offer a better trade off over delay achievement.

An Improved Proactive Handoff Scheme (IPHS) for Target Channel Selection in Cognitive Radio Network

In recent years, one of the challenges of spectrum allocation and utilization was fixed spectrum allocation which lead to spectrum scarcity and underutilization. In an attempt to address this challenge, Cognitive Radio Network (CRN) which uses Dynamic Spectrum Access (DSA) was proposed. It allows licensed users’ or Primary Users’ (PUs) spectrum to be shared with unlicensed users or Secondary Users (SUs). DSA could be achieved by developing an effective channel selection scheme for SUs spectrum handoff. Selecting an appropriate channel for the SUs to continue their interrupted transmission is a challenging task. Several researchers have used different techniques such as Novel Proactive Handoff Scheme (NPHS) to enhance accurate channel selection for spectrum handoff by considering only channel occupancy. These techniques still suffer some set back like high number of spectrum handoff and delay in channel selection. This paper presents an Improved Proactive Spectrum Handoff Scheme (IPHS) for accurate target channel selection in CRN. The improvement is achieved by considering channel signal quality in addition to channel occupancy as a criteria for the selection of a backup channel for spectrum handoff. Simulation results showed that the IPHS reduced the number of spectrum handoff by 15% and 26% as compared to NPHS and IEEE 802.11 scheme respectively. The average delay was also reduced by 13% and 35% as compared to NPHS and IEEE 802.11 scheme respectively.

AN IMPROVED CHANNEL SELECTION ALGORITHM (ICSA) FOR SPECTRUM HAND-OFF IN COGNITIVE RADIO AD HOC NETWORK

The exponential growth in wireless communication technology has led to spectrum scarcity. Because of this, the world has moved from Fixed Spectrum Allocation (FSA) Strategy to Dynamic Spectrum Allocation (DSA) Strategy. Cognitive Radio (CR) is a rapidly growing technique that makes use of DSA, where the licensed users, otherwise known as Primary Users (PUs), share their channel with the unlicensed users, known as Secondary Users (SUs). The SUs can use the PUs channel when they are not being used for transmission, but they have to vacate to another vacant channel when the PUs arrive in their channel. Switching to another vacant channel on the arrival of the PUs to their channels is known as spectrum hand-off. Finding another suitable vacant channel for the SUs to continue their interrupted transmission is challenging. Several researchers have used different techniques to address the challenge of target channel selection for spectrum hand-off by considering channel occupancy alone. Still, they suffer challenges like the high number of spectrum hand-offs, high delay, and low throughput. Therefore, this research focuses on developing an Improved Channel Selection Algorithm (ICSA) that considers channel occupancy serially with signal quality requirements for selecting a particular backup channel for spectrum hand-off. The simulation was carried out using Network Simulator (NS), and the results were plotted using Matlab. The results showed that the ICSA had better performance when compared with the Novel Proactive Hand-off Scheme (NPHS) regarding the number of hand-offs, average delay, and average throughput.

Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm.

A cognitive radio network (CRN) is an intelligent network that can detect unoccupied spectrum space without interfering with the primary user (PU). Spectrum scarcity arises due to the stable channel allocation, which the CRN handles. Spectrum handoff management is a critical problem that must be addressed in the CRN to ensure indefinite connection and profitable use of unallocated spectrum space for secondary users (SUs). Spectrum handoff (SHO) has some disadvantages, i.e., communication delay and power consumption. To overcome these drawbacks, a reduction in handoff should be a priority. This study proposes the use of dynamic spectrum access (DSA) to check for available channels for SU during handoff using a metaheuristic algorithm depending on machine learning. The simulation results show that the proposed "support vector machine-based red deer algorithm" (SVM-RDA) is resilient and has low complexity. The suggested algorithm's experimental setup offers several handoffs, unsuccessful handoffs, handoff delay, throughput, signal-to-noise ratio (SNR), SU bandwidth, and total spectrum bandwidth. This study provides an improved system performance during SHO. The inferred technique anticipates handoff delay and minimizes the handoff numbers. The results show that the recommended method is better at making predictions with fewer handoffs compared to the other three.

Hybrid blockchain-based spectrum sharing algorithm for dynamic channel selection in cognitive radio

Currently, the available CR spectrum is not being used to their full potential. Certainly, the CR system is capable of meeting this challenge. Whenever a licensed user has to start another transmission on that channel, spectrum handoff allows an unlicensed user to leave its current channel. The SU then switches to a different channel to finish the incomplete transmission. This technique uses the handshaking protocol to transmit and receive an acknowledgment before data transmission to obtain the user's wait time.This paper describes the Convolution Neural Network automatically extracts the features without any human supervision. For classification, a two-layer hidden neural-network is utilized once features are extracted. The CNN performs convolution operation to extract the complex features of the data that is significant for doing regression. First, the preprocessed data is delivered to the CNN's input layer. The CNN conducts a convolution operation on the data to extract the complex features that are important for regression. . The developed model consists of a sequence layer, Long Short Term Memory (LSTM) layer, fully connected layer, and a regression layer. LSTM memory cells were used in the LSTM layer to provide extended memory support. LSTM units save the essential past state information to increase performance by accounting for dependencies, and they erase the unnecessary information to save memory an [1]d energy. The fully linked layer collects the processed output from all of the LSTM hidden units. The regression layer receives a single output as a result of this. The regression layer computes an output for which the loss value is computed and propagated backwards during the training phase. This is done for all of the training iterations until the loss is zero. The anticipated values of waiting time are next tested using a fully trained regression model. The observed waiting time as a function of the number of rounds played. The loss and RMSE (Root mean square error) keep decreasing for increasing number of iterations of training.

A novel spectrum handoff switching decision scheme for improved performance of secondary users in cognitive radio network

SummarySpectrum handoff (SH) in the cognitive radio network (CRN) is considered as a key challenging area to enhance the performance of secondary users (SUs) in CRN. If the primary user is detected, the SU may pause and stay on the same channel or may perform SH to another idle channel. An accurate and precise handoff decision improves the overall throughput and quality of experience of end‐users. In this paper, we introduce a new SH algorithm and continuous short‐sensing strategy to improve the overall throughput of SUs. In addition, we have derived the minimum length of the target channel sequence based on network‐specific parameters like desired call dropping probability. Further, an optimum channel search time is obtained to minimize the handoff delay. The simulation result shows that the proposed scheme improves the overall throughput of CRN, and the mean opinion score of different video applications increases by 10%, 4.6%, and 1% for rapid motion, gentle walk, and slight motion types of video applications. In the case of VoIP applications, the maximum simultaneous call is improved by 2 times in the case of G.711, 1.72 times in the case of G.729, and 1.66 times in the case of iLBC.

Intelligent process of spectrum handoff for dynamic spectrum access in cognitive radio network using swarm intelligence

The CRN is an intelligent network in identifying the empty space from the spectrum without causing any interference to the primary user (PU). Due to the stable allocation of the channel, a lack of spectrum shortage problem occurs which gets resolved by the concept of the cognitive radio network (CRN). In this state, the secondary user (SU) is enforced to perform handoff during the arrival of the PU which contains higher priority. This paper suggests dynamic spectrum access to check the free channel for the SU during handoff and improved swarm-based cognitive radio is proposed to resolve this problem. Simulation results of the proposed algorithm are compared with the genetic algorithm and spectrum PSO. The experimental setup of the proposed algorithm provides the total bandwidth of the spectrum, SU bandwidth, signal to noise ratio, throughput and high data rates. This paper gives a better system performance over the spectrum handoff.

Support Vector Machine based spectrum handoff scheme for seamless handover in Cognitive Radio Networks

SummaryThe advancements in wireless communication go in leaps and bounds ushering in due attention to spectrum sharing. Spectrum scarcity is one of the major limitations causing hardships in the existing wireless networks. Cognitive Radio Networks (CRNs) emerge as a solution to tide over such humps. It prompts the secondary user (SU) to look out for unused spectrum and utilize them. The CRN helps the SU by permitting it to switch over to unused portions of the spectrum. When a primary user (PU) claims back the spectrum, SU is obliged to perform a spectrum handoff. The SU decides the type of policy to be chosen for the handoff. Such a decision‐making step during the handoff of the spectrum is imperative only if a changing policy is required. In this research work, Artificial Neural Networks (ANNs), Logistic Regression and Support Vector Machine (SVM) are proposed and implemented for a seamless handoff in CRN. From the experimental verifications, it is observed that the training accuracy is 97.9% and 97.6% for ANN and SVM, respectively. But during the actual phase, SVM to a certain extent performed better. This is due to the convergence nature of SVM on global minima.

Measurement of the Performance of a Cognitive Radio Spectrum Handoff Model

Wireless access has become an essential part of modern society. Consequently, new wireless applications and services, as well as the number of wireless users, are sharply increasing. Cognitive radio has improved the spectral efficiency of licensed radio frequency bands by accessing unused part of the band opportunistically without interfering with a licensed primary user. Spectrum mobility improves fast and smooth transition that lead to minimum performance degradation. An important requirement of mobility management protocols is a good performance in relation to spectrum handoff. To reduce the adverse effects of spectrum handoff interrupted secondary users may be provided with a higher priority over new secondary users for utilizing the available spectrum band. In this paper a queuing model of a spectrum handoff scheme for cognitive radio wireless networks is proposed. The handoff performance of the secondary users operating in a heterogeneous spectrum situation composed of licensed channels and unlicensed channels is tested., Three types of users: primary users, new secondary users, and handoff (Interrupted) secondary users are considered. The discrete event Simulator Matrix Laboratory (MATLAB) package tool is used to carry numerical calculations illustrating the effect of a priority queuing system on the performance of handoff of Secondary Users (SUs).

Spectral Efficiency Enhancement using Hybrid Pre-Coder Based Spectrum Handover Mechanism

The use of Millimeter-wave (mm-Wave) has recently immensely enhanced in various communication applications due to massive technological developments in wireless communications. Furthermore, mm-Wave consists of a high bandwidth spectrum that can handle large demands of data transmission and internet services. However, high interference is observed in previous research at the time of spectrum handover from secondary (unlicensed) users to primary (licensed) users. Thus, interference reduction by achieving high spectral efficiency and an easy spectrum handoff process with minimum delay is an important research area. Therefore, a Hybrid Pre-coder Design based Spectrum Handoff (HPDSH) Algorithm is proposed in this article to increase spectrum efficiency in Cognitive Radio Networks(CRNs) and to access the large bandwidth spectrum of mm-Wave systems to meet the high data rate demands of current cellular networks. Moreover, a HPDSH Algorithm is presented to enhance spectral efficiency and this algorithm is utilized to take handover decisions and select backup channels. Here, different scenarios and parameters are considered to evaluate the performance efficiency of the proposed HPDSH Algorithm in terms of spectral efficiency and Signal to Noise (SNR) ratio. The proposed HPDSH Algorithm is compared against precoding methods like the OMP algorithm and SIC based methods.

Enhanced Spectrum Handoff Security Mechanism in Cognitive Radio Network

Abstract: In this work we introduce a novel cognitive user emulation attack (CUEA) in a cognitive radio network (CRN), which can be exploited by intruders during spectrum handoff. Then, we propose a secure handoff mechanism that can 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. Malicious users can be effectively identified by the coordinating cognitive user by looking up the trust values. The performance of the proposed mechanism is validated using MATLAB simulations. The simulation results show that the utility of the proposed mechanism in terms of its probability in correctly identifying false authentication, detection rate, throughput rate, and transmission delay.

Intelligent Process of Spectrum Handoff in Cognitive Radio Network

Spectrum handoff is a crucial function of Cognitive Radio (CR) which is the change of operating frequency. The main problem in spectrum handoff is the time taken in the searching, selection, and switching to a new available channel which can cause a significant amount of delay during spectrum handoff. This research aims to minimize the delay that occurs during spectrum handoff. A Proactive Fuzzy-Based Backup Channel Selection Scheme (PFBBCSS) was proposed where the Secondary User (SU) gathers backup channels in advance before the return of the Primary User (PU), then fuzzy logic would be used for the selection of the best channel out of the available backup channels. The proposed scheme was simulated and evaluated using the MATLAB Simulation tool and the result was compared with a Pure Proactive Spectrum Handoff Scheme. Results showed, in terms of throughput and efficient time utilization under a varying number of licensed channels, that the proposed scheme performed better, making it a good mechanism to be used for handoff decisions by the Secondary User (SU).