Spectrum Handoff Scheme Research Articles

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.

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).

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).

Genetic algorithm-based hybrid spectrum handoff strategy in cognitive radio-based internet of things

In Cognitive radio-based Internet of Things (CR-IoT) systems, the return of the primary user (PU) causes the secondary user (SU) that is communicating to face the spectrum handoff problem. In the process of spectrum handoff, the user terminal cant get the idle channels in time because of the unknown channel usage state.To solve this problem, a hybrid spectrum handoff algorithm based on genetic algorithm is proposed. The algorithm considers the regularity of PU activities in space and time, defines the idle probability of channels from the perspective of week attributes and time periods, obtains the optimal time period length using genetic algorithm,generates a channel idle probability table, and provides the target channel sequence for SUs in combination with the proposed channel ordering scheme. Simulation results show that when the total number of SUs is within 10 \(\sim \) 20, the proposed algorithm has a spectrum handoff outage probability of less than 7%, an average delivery time of less than 13s, a total packet error rate of less than 5.5%, a channel utilization of consistently above 70%, and an average detection times of less than 7 times.

Design and implementation of a novel two-phase spectrum handoff scheme for QoS aware mobile users in cognitive radio networks

Quality of Service (QoS) aware spectrum handoff operation is critical for Secondary Users (SUs) executing real-time applications like Voice over IP (VoIP) in Cognitive Radio Networks (CRNs). The problem is aggravated by the inclusion of occasionally mobile SUs where pre-selected target channels are often rendered insignificant during handoff under dynamic conditions. Conventional proactive and reactive schemes may underperform due to channel obsolescence issues and unacceptable handoff latency respectively while a combined approach is not yet explored under the constraints of practical applicability. In view of these challenges and enormous significance of VoIP based CRN in 5G networks, this paper proposes a practically feasible two-phase spectrum handoff methodology with I_Phase and S_Phase operating under alternate conditions. I_Phase deploys a three-level dropping decision scheme followed by a two-tier spectrum handoff policy and is motivated by an integrated approach spanning every CR operation. S_Phase incorporates user mobility and application-oriented aspects where channel drop is governed by a joint two-order Hidden Markov Model — custom Damerau–Levenshtein distance metric based policy and spectrum handoff is monitored by a novel non-cooperative TOPSIS method. These phases are assisted by two proposed prerequisite techniques namely ACT (Adaptive Call Transmission) involving CR timing parameters and VER (VoIP Early Resumption) dealing with early call resumption. Comparative analysis of QoS metrics including per-instance handoff latency, transmission duration per target channel, channel drop and call drop probabilities, etc. in analytical and simulation platforms record significant performance improvement. Novelty of this work further relies on design and execution of SU prototype with the proposed scheme in test-bed that successfully provides QoS guarantees during spectrum handoff.

Optimization techniques for spectrum handoff in cognitive radio networks using cluster based cooperative spectrum sensing

Spectrum handoff has an undesirable effect in utilizing the space for Secondary user (SU) in the spectrum, which causes a handoff delay in cognitive radio network. The SU frequently faces the problem of handoff process which is likely to interrupt the service and substantial delay over the quality of service during the transmission. It struggles towards identifying the channel during the handoff by occupying a major role in today’s era. Based on this research, an effectual spectrum handoff scheme is anticipated using Spectrum Binary Particle Swarm Optimization (SpecBPSO) algorithm and M/G/1 queuing model. Towards improving the efficiency of SU and reducing the congestion over channel, Cluster Based Cooperative Spectrum Sensing (CBCSS) is used. The cluster head is selected dynamically based on the sensing signal of the SU. The cluster head is associated with the SU base station to report the active and inactive channel in the spectrum and later decision report is generated by the fusion center. In this proposed method, SpecBPSO uses the Boolean variable to reduce the total service time for handoff to find the optimal global value using bitwise and mutation operator format. This study work also presents an outline to observe the outcome of primary user’s activity and the delay performance of spectrum handoff with the possible interruptions in a CR network. The simulation setup of the proposed work is compared with spectrum particle swarm optimization (SpecPSO), binary particle swarm optimization (BPSO) and ant colony optimization that provide a better tradeoff over the delay achievement, maximize the SNR with the three benchmark functions and optimal handoff.

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.

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.

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.

Analysis of Efficient Spectrum Handoff in a Multi-Class Hybrid Spectrum Access Cognitive Radio Network Using Markov Modelling.

Cognitive radio networks (CRNs) rely on sensing of the licensed spectrum of a primary network to dynamically ascertain underutilized portion of the spectrum, thus affording additional communication opportunities. In a CRN, a single homogeneous spectrum access, such as interweave only deprives the secondary users (SUs) of channel access during handoff, particularly at high primary network traffic. Therefore, providing quality-of-service (QoS) to multi-class SUs with diverse delay requirements during handoff becomes a challenging task. In this paper, we have evolved a Markov-based analytical model to ascertain the gain in non-switching spectrum handoff scheme for multi-class SUs employing hybrid interweave-underlay spectrum access strategy. To satisfy the QoS requirements of the delay-sensitive traffic, we have analyzed the impact of hybrid spectrum access scheme for prioritized multi-class SUs traffic. The results show substantial improvement in spectrum utilization, average system throughput and extended data delivery time compared to conventional CRN using interweave only spectrum access. This demonstrates the suitability of the proposed scheme towards meeting QoS requirements of the delay-sensitive SU traffic while improving the overall performance for delay-tolerant SU traffic as well.

A novel proactive handoff scheme with CR receiver based target channel selection for cognitive radio network

Spectrum handoff in cognitive radio networks (CRN) enables the cognitive radio (CR) users to guarantee the desired QoS of the primary users. The handoff schemes grant an efficient exploitation of the available spectrum holes in the network. In the presented work, a novel proactive spectrum handoff scheme is made for ad hoc network scenario. Here, a network coordination schemes for unlicensed users is incorporated first. In our proposed framework, unlicensed users coordinate with each other using a Common Control Channel (CCC). Primary user free channel lists (PCL’s) are shared among CR users before actual data transmission to achieve better CR node coordination as well as to share information of nearby scenario. The PCL history stored at each CR transmitter (CRTx) node is used to make channel switching policies and to let unlicensed users vacate a channel before a licensed user utilizes it to avoid unwanted interference. Moreover, the node channel matrix created for the scenario helps to improve the performance further. The improvement is achieved through taking PCL history of CRRx into count. So, a novel proactive approach is developed here which uses the PCL history of both CRTx and CRRx. Simulation results show that proposed novel proactive spectrum handoff outperforms the reactive spectrum handoff approach in terms of higher throughput and fewer collisions to licensed users. Furthermore, a distributed channel selection can achieve higher packet delivery rate compared with existing channel selection schemes.

A delay-aware spectrum handoff scheme for prioritized time-critical industrial applications with channel selection strategy

Cognitive radio has emerged as an enabling technology in the realization of a spectrum-efficient and delay-sensitive industrial wireless communication where nodes are capable of responding in real-time. However, particularly for time-critical industrial applications, because of the link-varying channel capacity, the random arrival of a primary user (PU), and the significant delay caused by spectrum handoff (SH), it is challenging to realize a seamless real-time response which results in a quality of service (QoS) degradation. Therefore, the objectives of this paper is to increase spectrum utilization efficiency by allocating channel based on the priority of a user QoS requirements, to reduce SH delay, to minimize latency by preventing avoidable SHs, and to provide real-time response. To achieve an effective spectrum utilization, we proposed an integrated preemptive/non-preemptive priority scheme to allocate channels according to the priority of user QoS requirements. On the other hand, to avoid significant SH delays and substantial latency resulting from random PU arrival, a unified spectrum sensing technique was developed by integrating proactive sensing and the likelihood estimation technique to differentiate between a hidden and a co-existence PU, and to estimate the mean value of the busy and the idle periods of a channel respectively. Similarly, to prevent poor quality channel selection, a channel selection technique that jointly combines a reward system that uses metrics, e.g. interference range, and availability of a common channel to ranks a set of potential target channels, and a cost function that optimizes the probability of selecting the channel with the best characteristics as candidate channels for opportunistic transmission and for handoffs was developed. The simulation results show a significant performance gain of the delay-PritSHS in terms of number of SHs, Latency, as well as throughput for time-critical industrial applications in comparison to other schemes.

Analysis of handoff delay for proactive spectrum handoff scheme with PRP M/G/1/K queuing system in cognitive radio networks

Spectrum handoff has a negative impact on the performance of cognitive users (CUs) in terms of handoff delay in cognitive radio (CR) networks. In this study, a pre-emptive resume priority (PRP) M/G/1/K queuing network model is proposed with a finite number of allowable interruptions for proactive decision spectrum handoff scheme in order to minimise the cumulative handoff delay (CHD) and total service time (TST) for CUs. The CHD and TST for different proactive decision handoff schemes: non-switching spectrum handoff, switching spectrum handoff, and random spectrum handoff are modeled under the proposed PRP M/G/1/K queuing network model. Comprehensive results of CHD and TST are obtained to compare the performances of the proactive decision handoff schemes under the proposed PRP M/G/1/K queuing network model. This study also presents an analytical framework to examine the effect of primary users activity and buffer size on spectrum handoff delay performance with a finite number of allowable interruptions in a CR network. Thereafter, the optimal buffer size (K) is estimated for the proposed PRP M/G/1/K queuing network model, which gives performance similar to the infinite buffer size PRP M/G/1 queuing model with negligible (<1%) error.

Spectrum handoff strategy for cognitive radio-based MAC for real-time industrial wireless sensor and actuator networks

A spectrum handoff or channel switching mechanism for Industrial Wireless Sensor and Actuator Networks (IWSAN) which is capable of detecting interference and hopping to another channel (in the same or other band) in a deterministic time is proposed. This is possible because the spectrum handoff strategy detects interference in the MAC layer, and can ensure the high requirements in terms of time and reliability demanded by real-time IWSAN applications. An existing cognitive radio-based MAC is used as the basis. It has been evaluated with and without the proposed spectrum handoff mechanism to assess its performance. Using Network Calculus, a theoretical analysis has been provided to determine bounds on the delay, and simulations in OPNET have been carried out in order to validate the theoretical results. Several environments have been considered in the simulation (lognormal shadowing, Rice and Rayleigh fading and interference) so as to characterize the performance of the algorithm under the most common conditions of industrial channels. The results prove the benefits of using the proposed spectrum handoff, which provides robustness and guarantees delay bounds in industrial environments with interference.

Design, analysis and implementation of a time-bounded spectrum handoff algorithm for real-time industrial wireless sensor and actuator networks

Industrial control applications require real-time communication systems with time-critical and safety-critical features, as well as the ability to operate in harsh propagation environments. This means that data communications need to be bounded both in time and reliability domains, while dealing with severe interference coming from other wireless communication systems, including jammers, and with signal degradation phenomena such as delay spread, deep fading or Doppler spread. The present work proposes a spectrum handoff (i.e. channel switching) mechanism for Industrial Wireless Sensor and Actuator Networks (IWSAN) which is capable of detecting interferences and hopping to another channel within a given time bound. This is possible because the spectrum handoff strategy is capable of detecting interference in the MAC layer with the high requirements demanded by real-time IWSAN applications in terms of time and reliability. A theoretical analysis has first been provided using Network Calculus, which determines the delay bounds, and simulations in OPNET have been done afterwards in order to validate the theoretical results, taking into account several industrial environments (e.g. lognormal shadowing, Rice and Rayleigh fading and interference). Furthermore, the proposed communication system, based on the IEEE 802.11a/g physical layer, has been implemented in a FPGA, and simulations in OPNET network simulator and measurements on real hardware have been carried out in order to characterize its performance. The obtained results prove the capability of the proposed spectrum handoff mechanism to guarantee bounded delays and reliable operation in harsh propagation environments with interference, what makes it a suitable candidate for replacing wired communication systems in industrial environments.

Channel/Beam Handoff Control in Multi-Beam Antenna Based Cognitive Radio Networks

A novel spectrum handoff scheme, called feature stacking (FEAST) is proposed to achieve the optimal “channel + beam” handoff (CBH) control in cognitive radio networks (CRNs) with multi-beam smart antennas. FEAST uses the online supervised learning based on the support vector machine to maximize the long-term quality of experience of user data. The spectrum handoff uses the mixed preemptive/non-preemptive M/G/1 queueing model with a discretion rule in each beam to overcome the interruptions from the primary users and to resolve the channel contentions among different classes of secondary users (SUs). A real-time CBH scheme is designed to allow the packets in an interrupted beam of a SU to be detoured through its neighboring beams, depending their available capacity and queue sizes. The proposed scheme adapts to the dynamic channel conditions and performs spectrum decision in time- and space-varying CRN conditions. The simulation results demonstrate the effectiveness of our CBH-based packet detouring scheme, and show that the proposed FEAST-based spectrum decision can adapt to the complex channel conditions and improves the quality of real-time data transmissions compared to the conventional spectrum handoff schemes.

A hardware testbed for learning-based spectrum handoff in cognitive radio networks

A real-time cognitive radio network (CRN) testbed is implemented by using the universal software radio peripheral (USRP) and GNU Radio to demonstrate the use of reinforcement learning and transfer learning schemes for spectrum handoff decisions. By considering the channel status (idle or occupied) and channel condition (in terms of packet error rate), the sender node performs the learning-based spectrum handoff. In reinforcement learning, the number of network observations required to achieve the optimal decisions is often prohibitively high, due to the complex CRN environment. When a node experiences new channel conditions, the learning process is restarted from scratch even when the similar channel condition has been experienced before. To alleviate this issue, a transfer learning based spectrum handoff scheme is implemented, which enables a node to learn from its neighboring node(s) to improve its performance. In transfer learning, the node searches for an expert node in the network. If an expert node is found, the node requests the Q-table from the expert node for making its spectrum handoff decisions. If an expert node cannot be found, the node learns the spectrum handoff strategy on its own by using the reinforcement learning. Our experimental results demonstrate that the machine learning based spectrum handoff performs better in the long term and effectively utilizes the available spectrum. In addition, the transfer learning requires less number of packet transmissions to achieve an optimal solution, compared to the reinforcement learning.11DISTRIBUTION STATEMENT A: Approved for Public Release; distribution unlimited 88ABW-2017–6274; 13 Dec 2017.

A Spectrum Handoff Scheme for Optimal Network Selection in NEMO based Cognitive Radio Vehicular Networks: A Cost Function MADM Method based on Grey Theory based Approach

Multiple Attribute Decision Making (MADM) is one of the intelligent network selection methodologies for Intelligent Transportation System (ITS). The fundamental objective of MADM method is to select the optimal network among the finite set of alternative. In this paper, we have proposed a cost function MADM method based on the grey theory based approach which outperforms the existing MADM method for ITS. The method is validated to perform a spectrum handoff for optimal network selection in Network Mobility (NEMO) based Cognitive Radio (CR) vehicular networks. In NEMO based CR vehicular networks, the Mobile Reuter (MR) requires spectrum handoff when a mobile network changes its point of attachment in CR vehicular networks. It becomes difficult for MR to select the optimal network from the available access networks. The application of MADM methods provides wider and optimum choice among the available networks with quality of service. Numerical results revel that the proposed MADM method is effective for spectrum handoff decision for optimal network selection with reduced complexity in NEMO based CR vehicular networks.