Spectrum Handoff Research Articles

A Secure Spectrum Handoff Mechanism in Cognitive Radio Networks

The need for more efficient spectrum utilization is becoming more pronounced in our increasingly digitalized society. However, this also introduces a variety of new security threats. In this paper, 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.

Stochastic approach for channel selection in cognitive radio networks using optimization techniques

Secondary Users (SU) are guided by Cognitive Radio Device in identifying a channel licensed to Primary Users (PU) when it is free. Whenever PU arrives, SU shall vacate and search for the alternate band by a conditional handoff. Existing handoff methods reduce the network’s efficiency. The proposed stochastic approach under hybrid Spectrum Hand-off is (SHO) employing the Invasive Weed Optimization (IWO) algorithm thus increasing the SHO efficiency in CR Networks. The proposed Centralized Cognitive Device monitors load balancing, minimizes handoff delay as well as the SU’s service time, conserves energy, speeds up the data transmission, supports the Internet of Things, multimedia applications including audio, live video streaming and still images over resource constrained WSNs. This algorithm is compared with the existing Genetic algorithm and Particle Swarm Optimization. The channel selection accuracy of the proposed IWO method is found to be 97.8% and it outperforms conventional methods. Besides, the pre-emptive resume priority M/G/1 queuing model is also utilized. This paper presents a complete system model along with a detailed study of its parameters proving the technique’s effectiveness.

Dynamic Flow-Adaptive Spectrum Leasing with Channel Aggregation in Cognitive Radio Networks.

Cognitive radio networks (CRNs), which allow secondary users (SUs) to dynamically access a network without affecting the primary users (PUs), have been widely regarded as an effective approach to mitigate the shortage of spectrum resources and the inefficiency of spectrum utilization. However, the SUs suffer from frequent spectrum handoffs and transmission limitations. In this paper, considering the quality of service (QoS) requirements of PUs and SUs, we propose a novel dynamic flow-adaptive spectrum leasing with channel aggregation. Specifically, we design an adaptive leasing algorithm, which adaptively adjusts the portion of leased channels based on the number of ongoing and buffered PU flows. Furthermore, in the leased spectrum band, the SU flows with access priority employ dynamic spectrum access of channel aggregation, which enables one flow to occupy multiple channels for transmission in a dynamically changing environment. For performance evaluation, the continuous time Markov chain (CTMC) is developed to model our proposed strategy and conduct theoretical analyses. Numerical results demonstrate that the proposed strategy effectively improves the spectrum utilization and network capacity, while significantly reducing the forced termination probability and blocking probability of SU flows.

Multichannel spectrum access based on reinforcement learning in cognitive internet of things

With the development of Internet of Things(IoT), the demands for communication spectrum have increased rapidly, resulting in the shortage of limited spectrum resources. Cognitive IoT (CIoT) based on cognitive radio (CR) can improve the spectrum utilization by accessing the idle spectrum licensed to a primary user (PU). In this paper, a multichannel spectrum access scheme based on reinforcement learning (RL) is proposed to improve the spectrum access of CIoT, wherein the CIoT can use multiple channels for transmissions to reduce the communication interruptions. The channels are ranked in the decreasing order of their predicted idle probabilities, which can make the CIoT find enough idle channels quickly via decreasing the number of sensing operations and spectrum handoffs. The simulation results show that our proposed scheme is superior to the single-channel spectrum access scheme in terms of throughput, communication interruption, average collision probability and average spectrum switching frequency.

Assessment of Spectrum Handoff Performance in Cognitive Radio Cellular Networks

Spectrum Handoff is an essential process to assure unified, effective, and robust service for cognitive users (CUs) in cognitive radio cellular networks (CRCNs). This letter models the key handoff performance measuring metrics: link maintenance probability, link failure probability, and service completion probability of a non-stationary CU and analyzes the impact of mobility parameters to investigate the characteristics and behavior of a CRCN. In addition, we present the impact of general service time distributions on service completion probability. Results show that the service time distributions have a significant impact on spectrum handoff performance metrics for cases of higher mobility.

Performance analysis of secondary users under heterogeneous licensed spectrum environment in cognitive radio ad hoc networks

Cognitive radio (CR) is a hopeful technology to sort out spectrum scarcity and underutilization problem in ad hoc networks. With the help of cognitive radio technology, unlicensed users can efficiently utilize the unused part of heterogeneous licensed spectrum. In this article, we present a three-dimensional (3D) Markov chain analysis for spectrum management scheme under heterogeneous licensed bands of two different licensed spectrum pools in cognitive radio ad hoc networks. We present the concept of interpool and intrapool spectrum handoff in the proposed model and derive blocking probability, dropping probability, non-completion probability, and throughput to estimate the performance of the secondary users under heterogeneous licensed spectrum environment. The impact of secondary users dynamic along with the primary users’ activity model on the performance measuring metrics in terms of blocking probability, dropping probability, non-completion probability, and throughput for three different cases is also investigated. The proposed model offers significant improvement in the performance of secondary users under heterogeneous licensed spectrum environment in a CR ad hoc network.

Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks

Through the expeditious expansion of the wireless network, the unlicensed bandwidth-based devices are growing substantially as compared to the present vacant bandwidth. Cognitive radio networks present a proficient solution to the spectrum shortage diminution hitch by allowing the usage of the vacant part of the spectrum that is not currently in use of the Primary User licensed bandwidth to the secondary user or cognitive radio user. Spectrum management procedure in cognitive radio network comprises of spectrum sharing, sensing and handoff. Spectrum handoff plays a vital role in spectrum management and primarily focuses on single handoff strategies. This paper presents a primary user traffic pattern-based opportunistic spectrum handoff (PUTPOSH) approach to use in the cognitive radio networks. PUTPOSH permits a secondary user to sense the arrival of a primary user and use an opportunistic handoff scheme. The opportunistic handoff scheme firstly detects the arrival of the primary users by energy detection sensing and secondly, it allows a cognitive radio user to decide whether to do handoff or not contingent upon the overall service time to reduce the unused handoffs. The handoffs can either be reactive or proactive based on the arrival rate of the primary user. The simulation results show that the presented PUTPOSH approach (a) minimizes the number of handoffs and the overall service time, and (b) maintains the channel utilization and throughput of the system at a maximal point.

Spectrum Handoff Based on DQN Predictive Decision for Hybrid Cognitive Radio Networks.

Spectrum handoff is one of the key techniques in a cognitive radio system. In order to improve the agility and the reliability of spectrum handoffs as well as the system throughput in hybrid cognitive radio networks (HCRNs) combing interweave mode with underlay mode, a predictive (or proactive) spectrum handoff scheme based on a deep Q-network (DQN) for HCRNs is proposed in this paper. In the proposed spectrum handoff approach, spectrum handoff success rate is introduced into an optimal spectrum resource allocation model to ensure the reliability of spectrum handoff, and the closed-form expression for the spectrum handoff success rate is obtained based on the Poisson distribution. Furthermore, we exploit the transfer learning strategy to further improve the DQN learning process and finally achieve a priority sequence of target available channels for spectrum handoffs, which can maximize the overall HCRNs throughput while satisfying constraints on secondary users’ interference with primary user, limits on the spectrum handoff success rate, and the secondary users’ performance requirements. Simulation results show that the proposed spectrum handoff scheme outperforms the state-of-the-art spectrum handoff algorithms based on predictive decision in terms of the convergence rate, the handoff success rate and the system throughput.

A novel three-phase target channel allocation scheme for multi-user Cognitive Radio Networks

Generation and allocation of Target Channel Sequence (TCS) in relation to spectrum handoff introduces several challenges in a multi-user Cognitive Radio Network. The primary challenge is to ensure fair and unique TCS allocation to multiple Secondary Users (SUs) in order to prevent channel access conflict during concurrent handoff operations. Another challenge is to perform dynamic TCS generation to guard against channel obsolescence effects under time-varying user activities. Inclusion of real-time SUs further mandates the use of time-bound TCS generation in order to prevent call drops due to time-outs during handoff. To address these issues of fairness, uniqueness, dynamic and time-bounded TCS generation, this paper proposes a novel machine-learning enabled three-phase TCS generation and allocation scheme for multiple real-time SUs involved in Voice over IP (VoIP) communication. While proactive and periodic phases compute unique TCS in O (n log n) time, the reactive phase performs on-demand TCS generation. Generation of TCS is formulated as a fractional knapsack problem and greedy technique is applied therein to optimally select channels based on their existing conditions. A novel HMM-BiLSTM (Hidden Markov Model-Bidirectional Long Short Term Memory) framework is further devised to safeguard VoIP interests through periodic channel eligibility prediction. Additionally, an analytical framework is developed which records negligible probability of channel access conflict among multiple SUs. Simulation results confirm fair and dynamic channel allocation with reduced handoff delays and call-drops and intelligent learning based decision-making operations with high accuracy in comparison to existing approaches. Finally, test-bed implementation validates practical applicability of the proposed methodology.

Modified Artificial Bee Colony with firefly algorithm based spectrum handoff in cognitive radio network

The metaheuristic algorithm such as Artificial Bee Colony (ABC) and Firefly Algorithm (FA) is a nature and biological inspired for solving the optimization problem. The cognitive radio network is an intellectual network which adapts its performance and operations to provide a perfect result towards its environment. The spectrum sensing and spectrum handoff plays a major role in cognitive radio network. Spectrum sensing is more important for identifying the underutilized spectrum during the spectrum handoff. While progressing the channel, there occurs a premature convergence and local minima problem. The Secondary User (SU) is examining for the white space in the spectrum to produce a smooth transmission, but the channel should not be utilized by the Primary User (PU). When the channel is essential for primary user, the secondary user has to vacate and recognize a new free channel for fast transmission which is referred as spectrum handoff. In this work, Modified Artificial Bee Colony with Firefly Algorithm (MABCFA) is proposed for cognitive radio network to overcome the above challenges. So this proposed work mainly focuses on sensing the best optimal channel for the secondary user and to moderate the delay of spectrum handoff in wireless network. Some of the benchmark functions are used to test the performance of this proposed algorithm. Experimental results show that the performance of MABCFA is 23.81% more robust and effective than the other metaheuristic algorithm.

Analysis of Spectrum Sensing and Spectrum Access in Cognitive Radio Networks with Heterogeneous Traffic and p-Retry Buffering

It is well known that cognitive radio (CR) techniques have great potential to deal with the problem of radio spectrum scarcity. Spectrum sensing technique plays a critical role in enabling unlicensed secondary users (SUs) to utilize spectrum holes in cognitive radio networks (CRNs). However, a licensed primary user (PU) can be appropriately protected by simultaneously performing spectrum sensing and data transmission i.e., by using full-duplex (FD) mode. In this paper, we have proposed and analyzed a strategy which includes spectrum sensing and spectrum access mechanisms both. We consider heterogeneous traffic of real-time and non real-time SUs based on their different delay tolerance characteristics. We address the issue of false alarm rate (FAR) associated with FD sensing. Spectrum handoff and call buffering strategies with <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> -Retry policy are employed jointly so that SUs that would otherwise be blocked or forcibly dropped could be buffered and possibly served later. To evaluate the performance of the proposed strategy, five-dimensional continuous time Markov chain (CTMC) model is developed and the queueing-theoretic approach is utilized. Numerical results demonstrate the influence of spectrum sensing errors on the performance of such CRNs. Results also reveal that the provision of buffers under retrial policy increases the overall network resource utilization while decreasing blocking and dropping probabilities.

Analysis of spectrum handoff schemes for cognitive radio networks considering secondary user mobility

Cognitive radio has been proposed as an intelligent wireless communication system towards the usage of dynamic spectrum access in next generation networks, where the primary users (PU) and secondary users (SU) can share the spectrum, while causing minimum interference to each other. Spectrum mobility is the main element for the effective execution of cognitive radio networks. Spectrum mobility can be achieved by means of spectrum handoff (SHO). Initially, SUs continuously sense the channels to identify an idle channel. Error in sensing channel is possible. A detection theory is put forth to analyse the spectrum-sensing errors with the receiver operating characteristic (ROC) considering false alarm probability, missed detection and detection probability. Also, we meticulously investigate and analyse the probability of spectrum handoff (PSHO) with lognormal-3 and Hyper-Erlang distribution models considering SU call duration and residual time of spectrum holes availability designed for tele-traffic analysis.

Analysis of spectrum handoff schemes for cognitive radio networks considering secondary user mobility

Analysis of spectrum handoff schemes for cognitive radio networks considering secondary user mobility

Analysis of spectrum handoff delay using finite queuing model in cognitive radio networks

Analysis of spectrum handoff delay using finite queuing model in cognitive radio networks

Hybrid Spectrum Handoff Scheme of Imperfect Sensing in Cognitive Radio Networks

Background: In the actual cognitive radio network, there exist the imperfect sensing such as false alarm and missed detection which can lead to the inaccurate spectrum handoff and cause more uncertain problems. Meanwhile, the predetermined target channel has high risk of being unavailable in the proactive-decision spectrum handoff scheme. Objective: To solve the problem that the state of the predetermined target channel is updated in the proactive-decision spectrum handoff scheme, we present a novel hybrid spectrum handoff scheme based on Preemptive Resume Priority (PRP) M/G/m queuing network model in this paper. Methods: The proposed scheme combines the advantages of proactive-decision spectrum handoff and reactive-decision spectrum handoff, and performs the spectrum sensing during the waiting time of the cognitive user on the target channel. On this basis, we consider the influence of multiple cognitive users, multiple authorization channels, multiple spectrum handoffs and other factors on the spectrum handoff delay of cognitive user. Then, the detailed analysis and derivations of cognitive user’s extended data delivery time are conducted. Furthermore, the effect of imperfect spectrum sensing on the spectrum handoff performance of cognitive users is discussed. Results: Compared with traditional spectrum handoff schemes, numerical results show that the extended data delivery time of the proposed scheme is smaller, and the performance of spectrum handoff is further improved. In addition, the performance of the proposed scheme is less affected by the probability of false alarm and missed detection with frequent channel states variations. Simulation results are given to validate the analytical results. Conclusion: The hybrid spectrum handoff scheme provide better performance on the spectrum sharing between primary users and cognitive users in cognitive radio network under imperfect sensing condition.

Dual Processor Based Centralized Device for Spectrum Handoff in Cognitive Radio Networks

Wireless communication has been evidently proven to be the best source of communication technology. This has led to a rapid growth in this technology. This growth in the wireless communication technology has been done on the cost of the valuable resource spectrum which is depleting slowly. The time has shifted from Fixed to Dynamic allocation of spectrum. Cognitive Radio Networks (CRN) has been the emerging Dynamic scheme lately. CRNs allow the unlicensed user also termed as secondary user (SU) to utilize the licensed channel in the absence of the licensed users which are termed as primary users (PU). When a PU visits a channel that is currently being utilized by a SU, the channel needs to be vacated to switch to some other vacant channel in the spectrum. This switch of the channel is termed as spectrum handoff. It helps the system as a whole for better usage of the spectrum on the cost of the performance of the SU transmission increasing its data delivery time. This paper uses a centralized cognitive device in order to decrease the handoff latency of the SU. The centralized device used in this paper is dual processor based which helps in increasing the accuracy of the target channel selection process. The central cognitive device chooses the right channel for transmitting the data using the current channel status flag register in the memory of the device along with a probabilistic approach as presented in this paper. This novel technique improves the parameters such as accuracy, handoff latency, processor speed and processor memory.

Reinforcement learning‐based spectrum handoff scheme with measured PDR in cognitive radio networks

Spectrum handoff plays an important role in cognitive radio networks (CRNs). Secondary users (SUs) use spectrum handoff to hold on the idle channel or to free the channel for primary users (PUs). Spectrum handoff scheme greatly affects the transmission quality and the success rate of SUs connection. In this Letter, a reinforcement learning-based spectrum handoff scheme with the measured packet drop rate (PDR) for multimedia transmissions over CRNs is proposed. In a system model with multiple PUs and SUs, a new state space description method is designed and an observed state includes not only the status whether PUs arrive on each channel but also several other important factors. Also, the measured PDR, instead of the calculated one, is presented to update the mean opinion score, the Q-table and the handoff policy. Compared with the existing schemes with the calculated PDR from the Quality-of-Experience model, the authors' proposed scheme can converge more rapidly in the dynamic radio environment, and reduce the PDR of SUs more significantly.

Spectrum Handoff based on Imperfect Channel State Prediction Probabilities with Collision Reduction in Cognitive Radio Ad Hoc Networks.

The spectrum handoff is highly critical as well as challenging in a cognitive radio ad hoc network (CRAHN) due to lack of coordination among secondary users (SUs), which leads to collisions among the SUs and consequently affects the performance of the network in terms of spectrum utilization and throughput. The target channel selection mechanism as part of handoff process can play an enormously significant role in minimizing the collisions among the SUs and improving the performance of a cognitive radio network (CRN). In this paper, an enhanced target channel selection scheme based on imperfect channel state prediction is proposed for the spectrum handoff among the SUs in a CRAHN. The proposed scheme includes an improved frame structure that increases coordination among the SUs in the ad hoc environment and helps in organizing the SUs according to the shortest job first principle during channel access. Unlike the existing prediction-based spectrum handoff techniques, the proposed scheme takes into account the accuracy of channel state prediction; the SUs affected due to false prediction are compensated by allowing them to contend for channel access within the same transmission cycle and thus enabling them to achieve higher throughput. The proposed scheme has been compared with the contemporary spectrum handoff schemes and the results have demonstrated substantial improvement in throughput and extended data delivery time by virtue of the reduced number of collisions.

Spectrum Handoff by Baum-Welch algorithm for services in Cognitive Radio Networks

Cognitive Radio Networks (CRN) is the upcoming future prospect in 5G networks. Lack of available spectrum is a serious problem in the networking industry nowadays since, for each individual organization only a limited spectrum bandwidth is offered by National Telecommunications and Information Administration (NTIA). The problem arises due to the increase in the number of users who are supposed to use a limited amount of available bandwidth. Using spectrum handoff allows a cognitive user to access the available licensed spectrum in the absence of the primary user in that particular channel. Efficient spectrum sensing has to be done to check the availability of unused spectrum holes. Machine learning models such as Markov model and Hidden Markov model are used to predict the probabilities. In this paper we have presented a model for efficient sensing using Baum-Welch algorithm, a neural network algorithm which can train inner layer channel traits for given sequence of switching services to yield accurate results without huge datasets. Following emission probabilities are obtained for the channels that are trained from transition probabilities of channel services such as video, voice and data. From the obtained probability values each channel can be offered with best suited services.

Joint Optimal Design of Sensing Time and Transmission Power for Maximizing Energy Efficiency in Cognitive Radio System

The average throughput of a cognitive radio (CR) system can be increased by performing spectrum handoff, however, it may cause high energy consumption. Therefore, it is necessary to build an energy efficient CR system while performing spectrum handoff. In this paper, a joint optimization problem of sensing time and transmission power is formulated to maximize energy efficiency (EE) of a CR system subject to the constraint on sufficient protection to the primary users while considering spectrum handoff. Swarm intelligence techniques, namely particle swarm optimization (PSO), human behavior-based PSO (HPSO), and whale optimization algorithm (WOA) are used in this study to evaluate joint optimal sensing time and transmission power. The performance comparison of the three techniques is presented in terms of fitness function (EE) and average computational time. The simulation results show that the three techniques achieve a unique joint optimal sensing time and transmission power at which EE attains the highest peak; WOA outperforms PSO and HPSO in terms of average computational time. The impact of joint optimization of sensing time and transmission power, the impact of variation of target detection probability as well as the variation of number of channels on EE are discussed. A quantitative comparison is also performed between the proposed work and the existing work.