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Recent research has shown that several spectrum bands are mostly underutilised. To resolve the issue of underutilisation of spectrum bands across the networks, the concept of Cognitive Radio (CR) technology was envisaged. The CR technology allows Secondary Users (SUs) to acquire opportunistic access to large parts of the underutilised spectrum bands on wireless networks. In CR networks, SUs may scan and identify the vacant channels in the wireless spectrum bands and then dynamically tune their receivers to identify vacant channels and transmitters, and commence communication among themselves without causing interference to Primary/Licensed Users (PUs). Despite the developments in the eld of CR technology, recent research shows that still there are many challenges unaddressed in the eld. Thus, there is a need to reduce additional handshaking over control and data channels, to minimise large sized control frames and to introduce reliable channel selection process and maintenance of SUs' communication when PUs return to a licensed channel. A fundamental challenge a ecting this technology is the identi cation of reliable Data Channels (DCHs) for SUs communication among available channels and the continuation of communication when the PU returns. This doctoral research investigates in detail how to resolve issues related to the protocol design for Cognitive Radio Networks (CRNs) on Medium Access Layers (MAC) for Ad-Hoc networks. As a result, a novel Reliable and Energy e cient Cognitive Radio multi-channel MAC protocol (RECR-MAC) for Ad-Hoc networks is proposed to overcome the shortcomings mentioned. After discussing the background, operation and architecture of CR technology, this research proposes numerous platforms and testbeds for the deployment of personal and commercial applications of the CRNs. Side by side, optimised control frames and a reduced number of handshakes over the CCH are suggested to extend the transmitting time for data communication. In addition, the reliable channel selection process is introduced instead of random selection of DCHs for successful data communication among the SUs. In RECR-MAC, the objective of every SU is to select reliable DCHs, thereby ensuring high connectivity and exchanging the successful data frames across the cognitive network. Moreover, the selection criteria of the DCHs are based on multiple factors, such as an initial selection based on the maximum free time recorded by the SUs over the DCH channel ranking, which is proportional to the number of positive/negative acknowledgements, and the past history of DCHs. If more than two DCHs have an equal value during the second, third and following iterations, then the DCHs are selected based upon the maximum free time. The priorities of the DCHs are then assigned based on Reliable Data Channels, that is, RDCH 1, RDCH 2, RDCH 3, and RDCH 4 respectively (where RDCH 1 and RDCH 2 have the highest priority, DRCH 3 and RDCH 4 have the next priority, and so on). The impacts of channel selection process and Backup Data Channel (BDC) over the proposed RECR-MAC protocol are analysed in combination with comparative benchmark CR-MAC protocols based on the timing diagrams proposed. Finally, the RECR-MAC protocol is validated by using a MATLAB simulator with PU impact over the DCHs, both with and without BDC, and by comparing results, such as communication time, transmitting energy and throughput, with benchmark CR-MAC protocols.

The rapid growth of the Internet of Things (IoT) introduces an additional challenge to the existing spectrum under-utilisation problem as large scale deployments of thousands devices are expected to require wireless connectivity. Dynamic Spectrum Access (DSA) has been proposed as a means of improving the spectrum utilisation of wireless systems. Based on the Cognitive Radio (CR) paradigm, DSA enables unlicensed spectrum users to sense their spectral environment and adapt their operational parameters to opportunistically access any temporally unoccupied bands without causing interference to the primary spectrum users. In the same context, CR inspired Machine-to-Machine (M2M) communications have recently been proposed as a potential solution to the spectrum utilisation problem, which has been driven by the ever increasing number of interconnected devices. M2M communications introduce new challenges for CR in terms of operational environments and design requirements. With spectrum sensing being the key function for CR, this thesis investigates the performance of spectrum sensing and proposes novel sensing approaches and models to address the sensing problem for cognitive M2M deployments. In this thesis, the behaviour of Energy Detection (ED) spectrum sensing for cognitive M2M nodes is modelled using the two-wave with dffi use power fading model. This channel model can describe a variety of realistic fading conditions including worse than Rayleigh scenarios that are expected to occur within the operational environments of cognitive M2M communication systems. The results suggest that ED based spectrum sensing fails to meet the sensing requirements over worse than Rayleigh conditions and consequently requires the signal-to-noise ratio (SNR) to be increased by up to 137%. However, by employing appropriate diversity and node cooperation techniques, the sensing performance can be improved by up to 11.5dB in terms of the required SNR. These results are particularly useful in analysing the eff ects of severe fading in cognitive M2M systems and thus they can be used to design effi cient CR transceivers and to quantify the trade-o s between detection performance and energy e fficiency. A novel predictive spectrum sensing scheme that exploits historical data of past sensing events to predict channel occupancy is proposed and analysed. This approach allows CR terminals to sense only the channels that are predicted to be unoccupied rather than the whole band of interest. Based on this approach, a spectrum occupancy predictor is developed and experimentally validated. The proposed scheme achieves a prediction accuracy of up to 93% which in turn can lead to up to 84% reduction of the spectrum sensing cost. Furthermore, a novel probabilistic model for describing the channel availability in both the vertical and horizontal polarisations is developed. The proposed model is validated based on a measurement campaign for operational scenarios where CR terminals may change their polarisation during their operation. A Gaussian approximation is used to model the empirical channel availability data with more than 95% confi dence bounds. The proposed model can be used as a means of improving spectrum sensing performance by using statistical knowledge on the primary users occupancy pattern.

Current propogation models are no longer sufficient for wireless network planning. They are neither accurate (empirical) nor fast enough (deterministic) to be applicable in the applications of Automated Cell Planning. This thesis focuses on the development of a new method, namely Intelligent Ray Launching Algorithm (IRLA), which is based on a fast, accurate and robust algorithm that is especially suitable for wireless network planning. The infrastructure of IRLA is thoroughly analysed in this thesis and the results are presented. Foster's design methodology has been used to parallelise the new model. Various scenarios for outdoor, indoor, indoor-to-outdoor and outdoor-to-indoor settings have been employed to test the effectiveness and efficiency of IRLA. The field strengths (path loss) and multipath information were calculated, which were used to demonstrate the application of IRLA. The accuracy of IRLA is guaranteed via the use of a meta-based heuristics calibration procedure. In order to achieve a simulation within a realistic time scale, acceleration techniques such as avoid double marking, multi-threading and the use of Parallel Object-Oriented Programming C++ have been employed. Since multipath for a large number of receiver locations can be easily obtained via IRLA, the study of delay spread has been presented. The success of the integration with a wireless network planning platform exemplifies that IRLA is suitable for wireless network planning and optimisation, which is beneficial to relevant academics and industries. Testing demonstrated that depending on various scenarios, IRLA obtains industrially-recognised accuracy ranging from 5 to 8 dB Root-Mean-Square-Error. This model is highly-efficient because its required runtime for most simulations is from a few seconds to a few minutes.

Thesis (M.Sc. (Computer Science)) - Rhodes University, 2008.

Thesis (M.S.)--Florida State University, 2006. / Advisor: Kartik Gopalan, Florida State University, College of Arts and Sciences, Dept. of Computer Science. Title and description from dissertation home page (viewed Sept. 22, 2006). Document formatted into pages; contains ix, 46 pages. Includes bibliographical references.