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.

This dissertation considers the design, evaluation and optimization of algorithms/ techniques/ system parameters for distributed wireless networks specifically ad-hoc and cognitive wireless networks. In the first part of the dissertation, we consider ad-hoc networks using opportunistic carrier sense multiple access (CSMA) protocols. The key challenge in optimizing the performance of such systems is to find a good compromise among three interdependent quantities: the density and channel quality of the scheduled transmitters, and the resulting interference seen at receivers. We propose two new channel-aware slotted CSMA protocols and study the tradeoffs they achieve amongst these quantities. In particular, we show that when properly optimized these protocols offer substantial improvements relative to regular CSMA -- particularly when the density of nodes is moderate to high. Moreover, we show that a simple quantile based opportunistic CSMA protocol can achieve robust performance gains without requiring careful parameter optimization. In the second part of the dissertation, we study a cognitive wireless network where licensed (primary) users and unlicensed 'cognitive' (secondary) users coexist on shared spectrum. In this context, many system design parameters affect the joint performance, e.g., outage and capacity, seen by the two user types. We explore the performance dependencies between primary and secondary users from a spatial reuse perspective, in particular, in terms of the outage probability, node density and joint network capacity. From the design perspective the key system parameters determining the joint transmission capacity, and tradeoffs, are the detection radius (detection signal to interference and noise power ratio (SINR) threshold) and decoding SINR threshold. We show how the joint network capacity region can be optimized by varying these parameters. In the third part of the dissertation, we consider a cognitive network in a heterogeneous environment, including indoor and outdoor transmissions. We characterize the joint network capacity region under three different spectrum (white space) detection techniques which have different degrees of radio frequency (RF) - environment awareness. We show that cognitive devices relying only on the classical signal energy detection method perform poorly due to limitations on detecting primary transmitters in environments with indoor shadowing. This can be circumvented through direct use (e.g., database access) of location information on primary transmitters, or better yet, on that of primary receivers. We also show that if cognitive devices have positioning information, then the secondary network's capacity increases monotonically with increased indoor shadowing in the environment. This dissertation extends the recent efforts in using stochastic geometric models to capture large scale performance characteristics of wireless systems. It demonstrates the usefulness of these models towards understanding the impact of physical /medium access (MAC) layer parameters and how they might be optimized. / text