Multi-Channel Preemptive Priority Model for Spectrum Mobility in Cognitive Radio Networks

Cognitive Radio techniques have been proposed for improving utilization of the spectrum by exploiting the unoccupied bands of the licensed spectrum. This paper proposes a preemptive multi-channel access model for prioritized cognitive radio networks using an iterative method of queuing theory to solve the spectrum scarcity problem. The proposed model formulates accurate closed form of an expected waiting time in the queue, an expected number of users in the queue, an expected waiting time in the system, and an expected number of users in the system. The results compared to the basic model (without preemptive priority) show that, the waiting time in queue and the waiting time in the system compared to the basic model will be improved by 92.99% and 33.15% respectively for class one secondary user. The results also show that, the waiting time in queue and the waiting time in the system will be improved by 43.25% and 15.42% respectively for class two secondary users. The proposed model investigates the desirable schedules of primary and secondary users

Parallelising reception and transmission in queues of secondary users

In a cognitive radio network, the secondary users place the packets to be transmitted on a queue to control the order of arrival and to adapt to the network state. Previous conceptionsassigned to each secondary user a single queue that contains both received and forwarded packets. Our present article divides the main queue into two sub queues: one to receive the arrived packets and the other to transmit the available packets. This approach reduces the transmission delay due on the one hand; to the shifting of data placed on the single queue, and on the other hand; to the sequential processing of reception and transmission, in theprevious designs. All without increasing the memory capacity of the queue, in the new approach

A multi-class channel access scheme for cognitive edge computing-based Internet of Things networks

Edge computing-based framework is capable of improving users’ quality of experience in cognitive Internet of Things (IoT) networks. To explore the advantages of this edge computing-based framework, possible offloading and processing delay resulting from computation bottlenecks, and the offloading latency caused due to inter-cell interference must be properly considered. This paper thus considered a multi-class channel access mechanism for cognitive edge computing-based IoT networks where IoT users were categorized based on their quality of experience requirements. Essential IoT devices are permitted to offload to the edge server at any time following the hybrid channel access model, while delay-tolerant IoT devices are only permitted to offload to the server when the channel is idle following the overlay channel access model. Analyses were obtained for transmission rate and offloading delay to demonstrate the performance of the proposed mechanism, while important metrics such as total offloading latency and total offloading cost were investigated. The total offloading costs were formulated through the mixed strategy Nash equilibrium method. The proposed mechanism achieves lower offloading latencies and costs for both type 1 and type 2 CUs when compared with existing methods. The obtained results showed that multi-class channel access mechanisms can reduce packet offloading delay in cognitive edge computing-based IoT networks.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=25hj2023Electrical, Electronic and Computer Engineerin

Performance Analysis and Modelling of Spectrum Handoff Schemes in Cognitive Radio Networks. Modelling and Analysis of Spectrum Handoff Decision Schemes in Cognitive Radio Networks using the Queuing Theory and Simulation for Licensed and Unlicensed Spectrum Bands.

Recently, wireless access has become an essential part of modern society. Consequently, the demand for new wireless applications and services, as well as the number of wireless users, are gradually increasing. Given that this amount of expansion is eventually controlled by the available radio frequency spectrum, government regulatory agencies have recently adopted a strict approach to the licensing of limited amounts of spectrum to different entities (e.g., public safety, military, service providers, unlicensed devices, and TV). All of them possess exclusive transmissions to their assigned frequency channels. A new study on spectrum efficiency revealed big geographic and temporal variations in spectrum utilisation, ranging from 15-85% in the bands below 3GHz. These variations were less at frequencies above this figure. Recently, the Cognitive Radio (CR) has risen as an encouraging piece of technology to improve spectrum efficiency and to solve the problem of spectrum scarcity. This is because CR allows the secondary (unlicensed) users to occupy unused licensed spectrum bands temporarily, given that the interference of the primary (licensed) users is prohibited or minimised. In this thesis, various spectrum handoff management schemes have been proposed in order to improve the performance evaluation for CR networks. The proposed spectrum handoff schemes use the Opportunistic Spectrum Access (OSA) concept to utilise available spectrum bands. The handoff Secondary Users (SUs) have a higher priority to occupy available spectrum channels in the licensed and unlicensed spectrum bands without interfering with the legacy spectrum owner, i.e. primary users (PUs). However, existing spectrum handoff management schemes in CR networks do not provide high transmission opportunities for handoff secondary users to utilise the available radio spectrum resources. The first part of this thesis addresses the issue of spectrum handoff management in a licensed spectrum band environment. In this case, both reactive and proactive spectrum handoff schemes are proposed. Queuing theory or/and simulation experiments have been used to evaluate the performance of the proposed schemes and compare them with other existing schemes. Handoff delay has mainly been used to investigate the impact of successive handoff operations on the performance of the proposed CR networks. Implemented models have shown an improvement in the adopted performance measures. According to the achieved results, the improvement of the proposed, prioritised handoff schemes in some cases is approximately 75% when compared with existing schemes. On the other hand, the second part of this research proposed a prioritised spectrum handoff scheme in a heterogeneous spectrum environment, which is composed of a pool of licensed and unlicensed spectrum channels. In general, the availability of substantial numbers of the licensed spectrum channels is the key benefit of using this type of radio spectrum channel. Whereas, accessing with equal rights for all types of users is the main advantage of using unlicensed spectrum channels. In this respect, no transmission interruptions occur once a user obtains a channel. In addition, the proposed schemes use only the unlicensed spectrum channels as their backup channels. This enables the user to resume interrupted transmission in the case of the spectrum handoff operation (mainly; due to the appearance of the primary users), and thus facilitates a SUs communication. The proposed principle is investigated using a retrial queuing theory as well as extensive simulation experiments, and is compared with another non-prioritised scheme which do not give any preference to handoff SUs over new SUs. The results indicate that the proposed model has improved on current average handoff delay. This thesis contributes to knowledge by further enhancing the efficient utilisation of available radio spectrum resources and therefore subsequently provides an improvement in the spectrum capacity for wireless cognitive radio networks

A multi-user tasks offloading scheme for integrated edge-fog-cloud computing environments

This paper presents a multi-user, multi-class and multi-layer edge computing-based framework for effective task offloading and computation processes. Important system requirements that were not captured in the existing multi-layer solutions such as offloading, computations and deadline requirements were captured in the system modeling, while both wireless communications and task computation constraints were considered. We considered three layers system, where each device offloads its generated tasks in each time slot to any selected layer for computation. On its arrival at such a selected layer, the task is only accepted if the queue size is below the pre-defined threshold, otherwise, such a task is offloaded to the next layer. Tasks were classified into class 1 and class 2 tasks following tasks’ quality of service requirements. We adopted stochastic geometry, parallel computing and queueing theory techniques to model the performance of the considered integrated edge-fog-cloud computing environment and obtained analysis for various performance metrics of interest. The obtained analyses demonstrate the importance of multi-layer and multi-class edge computing systems towards improving the experience of both delay-sensitive and mission-critical applications in any task offloading environment.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=25hj2023Electrical, Electronic and Computer Engineerin