Low Complexity Energy-Efficient Collaborative Spectrum Sensing for Cognitive Radio Networks

Clustering approach is considered a management technology that arranged the distributed cognitive radio users into logical groups to improve the sensing performance of the network. A lot of works in this area showed that cluster-based spectrum sensing (CBSS) technique efficiently tackled the trade-off between performance and overhead issue. By employing the tree structure of the cluster, a multilevel hierarchical cluster-based spectrum sensing (MH-CBSS) algorithm was proposed to compromise between the gained performance and incurred overhead. However, the MH-CBSS iterative algorithm incurs high computational requirements. In this thesis, an energy-efficient low computational hierarchical cluster-based algorithm is proposed which reduces the incurred computational burden. This is achieved by predetermining the number of cognitive radios (CRs) in the cluster, which provides an advantage of reducing the number of iterations of the MH-CBSS algorithm. Furthermore, for a comprehensive study, the modified algorithm is investigated over both Rayleigh and Nakagami fading channels. Simulation results show that the detection performance of the modified algorithm outperforms the MH-CBSS algorithm over Rayleigh and Nakagami fading channels. In addition, a conventional energy detection algorithm is a fixed threshold based algorithm. Therefore, the threshold should be selected properly since it significantly affects the sensing performance of energy detector. For this reason, an energy-efficient hierarchical cluster-based cooperative spectrum sensing algorithm with an adaptive threshold is proposed which enables the CR dynamically adapts its threshold to achieve the minimum total cluster error. Besides, the optimal threshold level for minimizing the overall cluster detection error rate is numerically determined. The detection performance of the proposed algorithm is presented and evaluated through simulation results

Comprehensive survey on quality of service provisioning approaches in cognitive radio networks : part one

Much interest in Cognitive Radio Networks (CRNs) has been raised recently by enabling unlicensed (secondary) users to utilize the unused portions of the licensed spectrum. CRN utilization of residual spectrum bands of Primary (licensed) Networks (PNs) must avoid harmful interference to the users of PNs and other overlapping CRNs. The coexisting of CRNs depends on four components: Spectrum Sensing, Spectrum Decision, Spectrum Sharing, and Spectrum Mobility. Various approaches have been proposed to improve Quality of Service (QoS) provisioning in CRNs within fluctuating spectrum availability. However, CRN implementation poses many technical challenges due to a sporadic usage of licensed spectrum bands, which will be increased after deploying CRNs. Unlike traditional surveys of CRNs, this paper addresses QoS provisioning approaches of CRN components and provides an up-to-date comprehensive survey of the recent improvement in these approaches. Major features of the open research challenges of each approach are investigated. Due to the extensive nature of the topic, this paper is the first part of the survey which investigates QoS approaches on spectrum sensing and decision components respectively. The remaining approaches of spectrum sharing and mobility components will be investigated in the next part

Intelligent Approaches for Energy-Efficient Resource Allocation in the Cognitive Radio Network

The cognitive radio (CR) is evolved as the promising technology to alleviate the spectrum scarcity issues by allowing the secondary users (SUs) to use the licensed band in an opportunistic manner. Various challenges need to be addressed before the successful deployment of CR technology. This thesis work presents intelligent resource allocation techniques for improving energy efficiency (EE) of low battery powered CR nodes where resources refer to certain important parameters that directly or indirectly affect EE. As far as the primary user (PU) is concerned, the SUs are allowed to transmit on the licensed band until their transmission power would not cause any interference to the primary network. Also, the SUs must use the licensed band efficiently during the PU’s absence. Therefore, the two key factors such as protection to the primary network and throughput above the threshold are important from the PU’s and SUs’ perspective, respectively. In deployment of CR, malicious users may be more active to prevent the CR users from accessing the spectrum or cause unnecessary interference to the both primary and secondary transmission. Considering these aspects, this thesis focuses on developing novel approaches for energy-efficient resource allocation under the constraints of interference to the PR, minimum achievable data rate and maximum transmission power by optimizing the resource parameters such as sensing time and the secondary transmission power with suitably selecting SUs. Two different domains considered in this thesis are the soft decision fusion (SDF)-based cooperative spectrum sensing CR network (CRN) models without and with the primary user emulation attack (PUEA). An efficient iterative algorithm called iterative Dinkelbach method (IDM) is proposed to maximize EE with suitable SUs in the absence of the attacker. In the proposed approaches, different constraints are evaluated considering the negative impact of the PUE attacker on the secondary transmission while maximizing EE with the PUE attacker. The optimization problem associated with the non-convex constraints is solved by our proposed iterative resource allocation algorithms (novel iterative resource allocation (NIRA) and novel adaptive resource allocation (NARA)) with suitable selection of SUs for jointly optimizing the sensing time and power allocation. In the CR enhanced vehicular ad hoc network (CR-VANET), the time varying channel responses with the vehicular movement are considered without and with the attacker. In the absence of the PUE attacker, an interference-aware power allocation scheme based on normalized least mean square (NLMS) algorithm is proposed to maximize EE considering the dynamic constraints. In the presence of the attacker, the optimization problem associated with the non-convex and time-varying constraints is solved by an efficient approach based on genetic algorithm (GA). Further, an investigation is attempted to apply the CR technology in industrial, scientific and medical (ISM) band through spectrum occupancy prediction, sub-band selection and optimal power allocation to the CR users using the real time indoor measurement data. Efficacies of the proposed approaches are verified through extensive simulation studies in the MATLAB environment and by comparing with the existing literature. Further, the impacts of different network parameters on the system performance are analyzed in detail. The proposed approaches will be highly helpful in designing energy-efficient CRN model with low complexity for future CR deployment

Cognitive Radio Systems

Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems

Multi-Channel Sequential Sensing In Cognitive Radio Networks

Abstract Multi-Channel Sequential Sensing In Cognitive Radio Networks Finding white spaces and using them are major goals of cognitive radio networks. In this research work, we investigate multi-channel spectrum sensing for secondary users (SUs), and make improvements by forming sequential sensing as long as the secondary user does not get a channel to transmit on, and also as long as the user still has time left for transmission since waiting for the next cycle might not be the best scenario for the use of spectrum radio. We first formulate an optimization problem that maximizes the throughput of the system. Then, we introduce a power consumption model for our system since SUs are battery powered devices and the effectiveness of the system is jointly coupled with the energy consumption. Finally, we introduce an energy utility function, and we optimize it by considering both the throughput of the system and the amount of power consumed to achieve the optimal throughput. Numerical and simulation results are introduced at the end of this research, and they show better performance by the use of our suggested model compared to the work i the literature. The results also showed how to find the optimal number of channels to be sensed considering an efficient use of the SU’s battery

Power Control and Cooperative Sensing in Cognitive Radio

The traditional ways of spectrum management is inefficient as large portions of useable spectrum is left idle most periods of the day hence the call for more dynamic spectrum management techniques. Cognitive Radio (CR) is considered a viable means to vastly improve the efficiency of spectrum since it allows unlicensed users access to licenced spectrum as long as the quality of service is not downgraded. This research investigates the major problems associated with designing CRs. An in-depth analysis shows that the two major problems that hinders the successful design of CR systems are that of spectrum sensing (How the device detects the Primary User (PU)) and Power Control (which focuses on the level of transmit power of CR devices so as not to induce interference to PUs). To solve the problem of power control in this research, we consider a single cell scenario where N CR terminals are operating in a network with a Cognitive base station (CBS) together with one PU along with its Primary Base station (PBS). In the scenario, CR devices will generally seek to improve quality of service by increasing it’s transmit power. This increase introduces interference to the PU. To mitigate this, the CR devices are modelled as players of a non-cooperative game where offending devices are penalised till a Nash equilibrium level is achieved. At this point, the players can no longer influence the state of the game no matter the strategy they chose to play. The work is extended to cover CR internet of things devices by exploiting the adequate path loss exponent for the operational environment. The power control algorithm is compared with two other known power control algorithms and it outperforms them in average power, average SNR and rate of convergence. Spectrum sensing in CRs has been shown in literature to improve when done cooperatively rather than individually. To this end, this research focuses on cooperative sensing which allows the radios to make decision on their channel state based on the combine results of individual radios. The channel is modelled as a frame- by frame structure of equal length using the slotted aloha access contention technique. Each frame has a fixed length and is made up of sensing, prediction and transmission periods. It is seen observed that longer sensing periods results in better sensing results but considerable lower throughput. The scenario researched involves a CR network with K CRs and M sub-channels. It is assumed that the conditions of all sub-channels are equal, and each CR randomly chooses any one to sense and the throughput is measured. The interference caused to the PU are measured by collisions in the system. This are of two types: (1) Collisions with PUs due to missed detections and (2) collisions with other CRs due to access contention. Whenever there is a collision, the packet is withheld by the system and transmission is stopped. The throughput is a measure of successful packet transmissions. The derived algorithm improved the throughput by detecting the optimal sensing period. Using the K-of-M fusion decision rule, the sensing algorithm guarantees that optimal throughput can be achieved when 50% of the cognitive radio correctly detects the state of the spectrum. Cognitive radio throughput will be of very grave importance. Especially in spectrums like TVWSs and radar systems. A throughput model with power control is presented. The aim is to improve the throughput in interweave scenarios

Interference mitigation in cognitive femtocell networks

“A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of Philosophy”.Femtocells have been introduced as a solution to poor indoor coverage in cellular communication which has hugely attracted network operators and stakeholders. However, femtocells are designed to co-exist alongside macrocells providing improved spatial frequency reuse and higher spectrum efficiency to name a few. Therefore, when deployed in the two-tier architecture with macrocells, it is necessary to mitigate the inherent co-tier and cross-tier interference. The integration of cognitive radio (CR) in femtocells introduces the ability of femtocells to dynamically adapt to varying network conditions through learning and reasoning. This research work focuses on the exploitation of cognitive radio in femtocells to mitigate the mutual interference caused in the two-tier architecture. The research work presents original contributions in mitigating interference in femtocells by introducing practical approaches which comprises a power control scheme where femtocells adaptively controls its transmit power levels to reduce the interference it causes in a network. This is especially useful since femtocells are user deployed as this seeks to mitigate interference based on their blind placement in an indoor environment. Hybrid interference mitigation schemes which combine power control and resource/scheduling are also implemented. In a joint threshold power based admittance and contention free resource allocation scheme, the mutual interference between a Femtocell Access Point (FAP) and close-by User Equipments (UE) is mitigated based on admittance. Also, a hybrid scheme where FAPs opportunistically use Resource Blocks (RB) of Macrocell User Equipments (MUE) based on its traffic load use is also employed. Simulation analysis present improvements when these schemes are applied with emphasis in Long Term Evolution (LTE) networks especially in terms of Signal to Interference plus Noise Ratio (SINR)

Energy efficient cooperative spectrum sensing techniques in cognitive radio networks.

Master of Science in Electronic Engineering. University of KwaZulu-Natal, Durban 2017.The demand for spectrum is increasing particularly due to the accelerating growth in wireless data traffic generated by smart phones, tablets and other internet access devices. Most of prime spectrum is already licensed. The licensed spectrum is underutilized or used inefficiently, i.e. spectrum sits idle at any given time and location. Opportunistic Spectrum Access (OSA) is proposed as a solution to provide access to the temporarily unused spectrum commonly known as white spaces to improve spectrum utilization, increase spectrum efficiency and reduce spectrum scarcity. The aim of this research is to investigate potential impact of cooperative spectrum sensing techniques technologies on spectrum management. To fulfill this we focused on two spectrum sensing techniques namely; Firstly energy efficient statistical cooperative spectrum sensing in cognitive radio networks, this work exploits the higher order statistical (HOS) tests to detect the status of PU signal by a group of SUs. Secondly, an optimal energy based cooperative spectrum sensing in cognitive radio networks was investigated. In this work the performance of optimal hard fusion rules are employed in SU’s selection criteria and fusion of the decisions under Gaussian channel and Rayleigh channels. To optimize on the energy a two stage fusion and selection strategy is adopted to minimize the number of collaborating SUs