Spectrum Handoff Research Articles

Spectrum Handoff Mechanism in the Framework of Mobility Management in Cognitive Radio Networks

Cognitive radio appears as an innovative technology in the field of access to wireless systems, aimed at significantly improving the use of the radio spectrum by allowing an opportunistic access manner. This article deals with some of the important characteristics of the spectrum mobility in cognitive radio networks (CRNs).The new management approach to the mobility and the connection are designed to reduce the latency and loss of information during spectrum handoff. A list of channel safeguards are maintained in this effect, but the maintenance and updates are a challenge. In this article, the authors describe the reasons and mechanisms for spectrum handoff. Algorithms have been developed to illustrate this handoff mechanism and make the comparison between the different methods of spectrum handoff. The simulation results obtained confirm that the proposed method and the algorithms developed presents a better performance.

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.

Analysis of Spectrum Handoff delay using Finite Queuing model in Cognitive Radio Networks

Spectrum handoff is an inevitable phenomenon to exploit dynamic spectrum access in cognitive radio networks (CRNs) for better spectrum utilisation. When a licensed or primary user reclaims its operating channel, the cognitive user has to initiate a suitable handoff procedure for successful completion of its ongoing transmission. This paper analyses the prioritised proactive spectrum handoff decision for a finite capacity cognitive radio network using preemptive resume priority (PRP) M/G/1/K queuing model. Cumulative handoff delay (CHD) and total service time (TST) are taken to investigate the performance of the handoff strategies. We establish an analytical model of cumulative handoff delay and total service time of proactive switching spectrum handoff for the proposed finite length queuing network. The impact of queue length on performance measuring metrics such as CHD and TST in terms of arrival rate of primary users and mobility factor of spectrum holes are presented extensively.

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.

Machine Learning-Based Algorithm for Channel Selection Utilizing Preemptive Resume Priority in Cognitive Radio Networks Validated by NS-2

This paper utilizes the cognitive radio (CR) spectrum to the fullest extent for extended applications requiring discretion. The CR technology provides various supports for cognitive radio networks (CRNs). The latter has CR nodes that sense free channels. Then, the CRN allocates the unused channels to secondary users (SUs) or unlicensed users. This allocation is termed the spectrum handoff. In this paper, by considering the identical channels in CR networks, a novel machine learning algorithm (the support vector machine—SVM) is employed. In addition, the queuing model of the preemptive resume priority M/M/1 is used. The proposed spectrum handoff algorithm selects the best possible CR network channel. The spectrum handoff algorithm uses the stated SVM algorithm scheme, which covers the transmitted and received power, the minimum service time, the data rate and the maximum vacancy time for the SU, to attain the maximum throughput. However, in multi-user greedy channel selection (GCS), only two parameters are considered. The proposed spectrum handoff algorithm based on the SVM scheme enhances the performance, and the SU throughput is improved to 68.7%. This approach is better than the GCS channel selection scheme. Additionally, this approach decreases the number of spectrum handoffs. As a result, the training accuracy of the SVM method is 97.6%, and it outperforms conventional methods.

Performance analysis of spectrum handoff under heterogeneous spectrum environment in ad hoc and centralized CR networks

The prime objective of cognitive radio (CR) is to address the problem of spectrum scarcity and inefficient usage of spectrum, by allowing the unlicensed or cognitive users (CUs) to access the underutilized licensed spectrum of the primary users (PUs). CUs experience considerable performance degradation due to the event of random interruptions by PUs in their respective bands of operation. In this article, we develop a spectrum management scheme and evaluate the performance of the CUs under heterogeneous spectrum environment (HetSE) in both ad-hoc (opportunistic) and centralized (negotiated) CR networks. We consider a finite threshold period (Dth) for spectrum handoff delay to improve the performance of the CUs for both opportunistic situation with backup channels (OSB) and negotiated situation with backup channels (NSB). The spectrum handoff performance measuring metrics: link maintenance probability, link failure probability, probability mass function of spectrum handoff, expected number of spectrum handoff and non-completion probability of the CUs, are derived under heterogeneous spectrum environment to investigate the characteristics of the network for both opportunistic and negotiated spectrum access strategies.

Efficient Spectrum Handoff Using Hybrid Priority Queuing Model in Cognitive Radio Networks

Cognitive radio networks can achieve high spectrum efficiency through dynamic spectrum allocation to the secondary users (SU) by way of using primary user (PU) spectrum. An efficient handoff scheme for cognitive radio networks using hybrid priority queuing model has been proposed using discretion rule. This method is divided into two parts: (1) hybrid priority queuing model with discretion rule is proposed by prioritizing the secondary users in the channel and then calculate the waiting time during the spectrum handoff and (2) to obtain better efficiency by reducing the waiting time, the learning rate of the secondary users from the other users should be increased. This can be done with multi teacher apprentice learning (MAL) along with sparse regression by considering multiple users into consideration at a time instead of a single user for better efficiency of the spectrum. The normalized load, arrival rate and the service time of the proposed model is compared to the existing models with respect to the average data delivery time of the users.

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.

An optimized general target channel sequence for prioritized cognitive radio networks

Cognitive radio networks serve different secondary user applications which have distinct delay requirements. In order to better support the delay-sensitive applications, we consider secondary user traffic to be classified into M prioritized classes based on delay requirements. The priority of a secondary user increases within the same class after each interruption to reduce the cumulative handoff delay. A mixed preemptive/non-preemptive resume priority M/G/1 queueing model is assumed to analyze multiple spectrum handoffs. Closed form expressions are derived for both the extended data delivery time and dropping probability for each secondary user class. The analysis assumes a general target channel sequence, where a secondary user can select either to stay at his current channel or change it after each interruption. This general target channel sequence is optimized using a genetic algorithm in order to minimize the average extended data delivery time of all secondary user classes. Numerical results show that the proposed interruption-based priority mechanism outperforms the existing priority mechanisms in terms of average handoff delay and extended data delivery time in case of changing the target channel while preserves the extended data delivery time in case of always stay. Moreover, the optimized general target channel sequence gives better performance compared to the target channel sequences mentioned in the IEEE 802.22 wireless regional area networks standard for different network cases.

An Effective Spectrum Handoff Based on Reinforcement Learning for Target Channel Selection in the Industrial Internet of Things.

The overcrowding of the wireless space has triggered a strict competition for scare network resources. Therefore, there is a need for a dynamic spectrum access (DSA) technique that will ensure fair allocation of the available network resources for diverse network elements competing for the network resources. Spectrum handoff (SH) is a DSA technique through which cognitive radio (CR) promises to provide effective channel utilization, fair resource allocation, as well as reliable and uninterrupted real-time connection. However, SH may consume extra network resources, increase latency, and degrade network performance if the spectrum sensing technique used is ineffective and the channel selection strategy (CSS) is poorly implemented. Therefore, it is necessary to develop an SH policy that holistically considers the implementation of effective CSS, and spectrum sensing technique, as well as minimizes communication delays. In this work, two reinforcement learning (RL) algorithms are integrated into the CSS to perform channel selection. The first algorithm is used to evaluate the channel future occupancy, whereas the second algorithm is used to determine the channel quality in order to sort and rank the channels in candidate channel list (CCL). A method of masking linearly dependent and useless state elements is implemented to improve the convergence of the learning. Our approach showed a significant reduction in terms of latency and a remarkable improvement in throughput performance in comparison to conventional approaches.

Intelligent Process of Spectrum Handoff/Mobility in Cognitive Radio Networks

Cognitive radio is an innovative technology in the field of wireless communication systems, aimed at significantly improving the use of the radio spectrum while allowing secondary users to access the spectral band opportunistically. Spectrum management mechanism ensures the transmission of data by controlling the efficiency of operation between the primary and secondary networks. The main task of spectrum management is to ensure that secondary users benefit from the spectrum without interfering with primary users. This paper deals with some of the important characteristics of spectrum mobility in the cognitive radio networks. The new management approaches of the mobility and the connection are designed to reduce the latency and loss of information during spectrum handoff, a list of channel safeguard is maintained in this effect, but the maintenance and update are a challenge. In this paper, we describe the reasons and mechanisms of spectrum handoff. Protocols have been developed to illustrate this handoff mechanism. We also make a comparison between the different methods of spectrum handoff. The simulation results obtained confirm that the protocols developed and the proposed method performed better than the pure reactive handoff method.

Cognitive AODV routing protocol with novel channel-route failure detection

Performance of routing protocol at network layer in Cognitive Radio Adhoc Networks (CRAHNs) is mainly based on the probability of channel availability for application data transmission. To attain, end-to-end channel-route control messages should be disseminated in an efficient mechanism with minimal channel-route re-connection delays. In CRAHNs, end-to-end channel-route failures can be mainly due to spectrum handoff (dynamic primary user intervention), selfish node activity, CR node handoff and bandwidth degradation. In order to enhance the application throughput, it is pivotal to determine the exact channel-route failure and provide alternate end-to-end channel-route path. To achieve, this paper proposes a “channel-route failure based Cognitive-AODV routing protocol” with the modifications in channel-route-error (channel-RERR) to detect the exact channel-route failure and provide the best alternate end-to-end channel-route path in between source and destination. Experimental results reveal that the performance of proposed Cognitive AODV routing protocol with selfish node activity, spectrum handoff and node handover is outperformed when compared with the existing Cognitive AODV routing protocols.

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.

A Comprehensive Review on Spectrum Management, Security and Energy-Efficient Cognitive Radio Networks

The explosive growth of mobile devices and the rapid increase in wireless services necessitate an improvement in communication techniques. This technology can boost spectrum utilization as well as meet the massive connectivity requirement to draw attention from academia and industry. Cognitive Radio Network (CRN) is an evident optimal solution in alleviating the challenges of spectrum unused potentially in wireless networks. The Secondary Users are allowed intelligently to access the sections of spectrum unused (spectrum holes) by the authorized Primary Users (PUs). The objective of this paper is to analyze channel throughput, delay, carrier aggregations, energy efficiency, and security challenges and provide research ideas to enhance the overall performance of the CRN. This review paper completely investigated the potential management of spectrum techniques, essentially in spectrum sensing, spectrum allocation, spectrum sharing, and spectrum handoff in CRN. In addition, we study the energy efficient routing techniques like network optimization frameworks in cooperative networks, protocols and different types of attack issues, approaches for handling the secure communication in CRNs. The state of art review, which deeply examines the Cognitive Radio Network providing an idea of important technology and challenges are addressed. The open research challenges related to spectrum management, energy efficient routing and security are discussed.

Probability-Based Centralized Device for Spectrum Handoff in Cognitive Radio Networks

Wireless communication technology is the future of communication, but rapid growth in wireless technology has led to a scarcity in the spectrum. Thus, the world has moved away from fixed spectrum allocation to dynamic spectrum allocation. Cognitive radio technology is a rapidly growing technique that allows spectrum to be shared between licensed or primary users (PUs) and unlicensed or secondary users (SUs). The SUs are allowed to use the licensed channels in the absence of the PUs. Upon the arrival of a PU on the channel, the SU has to leave the channel and resume its transmission on another channel. This process is known as spectrum mobility, and the shift in the channel is known as spectrum handoff. Typical transmission of data using this technology requires numerous spectrum handoffs, leading to fluctuations in the spectrum allotted to SUs. Reducing the number of handoffs and providing SUs with a better transmission environment require choosing an efficient handoff strategy. The current handoff strategies face various drawbacks that reduce the efficiency of the network. This paper presents a probability-based centralized device for increasing the efficiency of spectrum handoffs in cognitive radio networks. The handoff strategy presented in this paper improves the accuracy in sensing the right channel for handoff, reduces the energy consumed in the process, reduces the handoff time, and speeds up the transmission of data. This paper presents a complete model of the system, along with the detailed study of its parameters that proves the effectiveness of the technique.

An efficient spectrum handoff decision making scheme for cognitive radio networks

There has been a gigantic spike in the usage and development of wireless devices since the time wireless technology has come into existence. This has contributed to a very serious problem of spectrum unavailability or spectrum scarcity. The solution to this problem comes in the form of cognitive radio networks where the secondary user which is also known as the unlicensed users makes use of the spectrum in an opportunistic manner. The secondary user uses the spectrum in a manner such that the primary or the licensed user does not face interference above a threshold level of tolerance. Whenever a primary user comes back to reclaim its licensed channel, the secondary user using it needs to perform a spectrum handoff to another channel which is free of primary user. Our primary focus is on performing spectrum handoff decision making. The SU selects staying or changing policy based on the average extended delivery time. The spectrum handoff decision making is performed, i.e., the optimal channel for spectrum handoff is decided only if changing policy is adopted which is based on multiple attribute decision making (MADM). This spectrum handoff decision making scheme is later extended using artificial neural networks (ANN) and probabilistic Markov model.

Efficient Spectrum Management Techniques for Cognitive Radio Networks for Proximity Service

Cognitive radio networks improve spectrum efficiency by employing vigilant and accurate spectrum management techniques. This is done to enable an unlicensed user to use the underutilized spectrum. Spectrum sensing determines if a spectrum hole exists for the unlicensed user, this is accomplished using various techniques such as energy detection, minimum Eigen value detection, and matched filter technique. Conventional energy detection techniques do not achieve high values of detection; hence an improved adaptive energy detection technique has been proposed to improve the efficiency. The proposed methodology exhibits better numerical results than conventional techniques. Another important spectrum management procedure is spectrum handoff. The concept of multiple attributes for decision making was implemented, under which, the simple additive weights method and the technique for order preference by similarity to ideal solution method were compared based on a performance involving the triple play of services.

An efficient spectrum handoff decision making scheme for cognitive radio networks

An efficient spectrum handoff decision making scheme for cognitive radio networks

Predictive Multi-user Dynamic Spectrum Allocation Using Hidden Semi-Markov Model

For wideband Cognitive Radio (CR) devices, the availability of primary user (PU) activity prediction model can save sensing energy and time, track spectrum variations, reduce switching overhead, improve spectrum usage and lower interference to PU. Using Universal Software Radio Peripheral (USRP) generated data, this paper has verified the superiority of Hidden Semi-Markov model (HSMM) over the conventional HMM in learning and predicting temporal correlations in PU spectral activity under non-geometric distribution of state durations. Numerical simulations further show that in a coordinated multi-SU/PU scenario, compared to the traditional random Carrier Sense Multiple Access (CSMA), the proposed HSMM-dynamic spectrum allocation (DSA) protocol significantly reduces aggregate interference to PU. Depending on the number of SU, there exists a trade-off between “interference-free coexistence with PU” and “self-existence of a large number of SU”. This problem is addressed further by statistically modeling probability of free channels and using it to optimize the number of SU that can be allocated. The proposed scheme outperforms the conventional sensing based dynamic spectrum allocation (DSA) in terms of reduced PU interference, lower spectrum handoff requirements and higher spectrum utilization efficiency. The predictive optimized HSMM-DSA can be used as an additional layer of intelligence for any CR device for efficient spectrum sharing.