GEOLOCATION AWARE RESOURCE ALLOCATION IN CELLULAR BASED COGNITIVE RADIO NETWORKS WITH GREEN COMMUNICATION PERSPECTIVE

This paper puts forward a Geolocation aware spectrum and power allocation scheme for cellular-based cognitive radio network using the principle of sensing free spectrum access. The problem formulation to test the feasibility of deploying the secondary system within Terrestrial Trunked Radio (TETRA) based cellular primary system is carried out to maximize the served Secondary Users (SU) while keeping the interference to Primary Users (PU) under a predefined threshold. A novel model called Primary Mobility Contour (PMC) for the avoidance of harmful interference to PU is proposed, which will consider the velocity of PU, the time taken by the secondary base station for transmission and Geolocation information. Using this model sensing free spectrum and power allocation algorithm is developed for cellular-based cognitive radio network to maximize the served SU to enhance system throughput and achieve an enhanced energy efficiency of the system to attain green communication. Simulation results confirm that the proposed scheme maximizes the served SUs per cell, throughput and energy efficiency

Heterogeneous Dynamic Spectrum Access in Cognitive Radio enabled Vehicular Networks Using Network Softwarization

Dynamic spectrum access (DSA) in cognitive radio networks (CRNs) is regarded as an emerging technology to solve the spectrum scarcity problem created by static spectrum allocation. In DSA, unlicensed users access idle channels opportunistically, without creating any harmful interference to licensed users. This method will also help to incorporate billions of wireless devices for different applications such as Internet-of-Things, cyber-physical systems, smart grids, etc. Vehicular networks for intelligent transportation cyber-physical systems is emerging concept to improve transportation security and reliability. IEEE 802.11p standard comprising of 7 channels is dedicated for vehicular communications. These channels could be highly congested and may not be able to provide reliable communications in urban areas. Thus, vehicular networks are expected to utilize heterogeneous wireless channels for reliable communications. In this thesis, real-time opportunistic spectrum access in cloud based cognitive radio network (ROAR) architecture is used for energy efficiency and dynamic spectrum access in vehicular networks where geolocation of vehicles is used to find idle channels. Furthermore, a three step mechanism to detect geolocation falsification attacks is presented. Performance is evaluated using simulation results

Performance Analysis of Secondary Users in Heterogeneous Cognitive Radio Network

Continuous increase in wireless subscriptions and static allocation of wireless frequency bands to the primary users (PUs) are fueling the radio frequency (RF) shortage problem. Cognitive radio network (CRN) is regarded as a solution to this problem as it utilizes the scarce RF in an opportunisticmanner to increase the spectrumefficiency. InCRN, secondary users (SUs) are allowed to access idle frequency bands opportunistically without causing harmful interference to the PUs. In CRN, the SUs determine the presence of PUs through spectrum sensing and access idle bands by means of dynamic spectrum access. Spectrum sensing techniques available in the literature do not consider mobility. One of the main objectives of this thesis is to include mobility of SUs in spectrum sensing. Furthermore, due to the physical characteristics of CRN where licensed RF bands can be dynamically accessed by various unknown wireless devices, security is a growing concern. This thesis also addresses the physical layer security issues in CRN. Performance of spectrum sensing is evaluated based on probability of misdetection and false alarm, and expected overlapping time, and performance of SUs in the presence of attackers is evaluated based on secrecy rates

Design, Analysis, Implementation and Evaluation of Real-time Opportunistic Spectrum Access in Cloud-based Cognitive Radio Networks

Opportunistic spectrum access in cognitive radio network is proposed for remediation of spectrum under-utilization caused by exclusive licensing for service providers that are intermittently utilizing spectrum at any given geolocation and time. The unlicensed secondary users (SUs) rely on opportunistic spectrum access to maximize spectrum utilization by sensing/identifying the idle bands without causing harmful interference to licensed primary users (PUs). In this thesis, Real-time Opportunistic Spectrum Access in Cloud-based Cognitive Radio Networks (ROAR) architecture is presented where cloud computing is used for processing and storage of idle channels. Software-defined radios (SDRs) are used as SUs and PUs that identify, report, analyze and utilize the available idle channels. The SUs in ROAR architecture query the spectrum geolocation database for idle channels and use them opportunistically. The testbed for ROAR architecture is designed, analyzed, implemented and evaluated for efficient and plausible opportunistic communication between SUs

Geolocation-aware Resource Management in Cloud Computing Based Cognitive Radio Networks

With the rapid development of cognitive radios, spectrum efficiency in cognitive radio networks (CRN) has increased by secondary users (SU) accessing the licensed spectrum dynamically and opportunistically without creating harmful interference to primary users. However, the performance and security of CRN is considerably constrained by its limited power, memory and computational capacity. Fortunately, the advent of cloud computing has the potential to mitigate these constraints due its vast storage and computational capacity. In this paper, we propose geolocation-aware radio resource management algorithm for CRN where distributed storage and computing resource in cloud computing platform and geolocation of secondary users are leveraged to store spectrum occupancy information of heterogeneous wireless networks and facilitates the access of spectrum opportunities for secondary users (SU). The proposed algorithm leverages the geolocation of secondary users and idle licensed bands to facilitate efficient allocation of radio resources to SU. Furthermore, the secondary users who provide high benefit are admitted while satisfying the quality of service (QoS) requirement of secondary users in terms of data rate and service time. We also propose a scalable mapping method using storm, a real-time distributed processing model in cloud computing platform to dynamically partition the geographical area according to the SU density. Simulation results are presented to demonstrate the performance of the proposed geolocation-aware radio resource management algorithm