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Spectrum sensing and occupancy prediction for cognitive machine-to-machine wireless networksThe 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.
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An enhanced cross-layer routing protocol for wireless mesh networks based on received signal strengthThe research work presents an enhanced cross-layer routing solution for Wireless Mesh Networks (WMN) based on Received Signal Strength. WMN is an emerging technology with varied applications due to inherent advantages ranging from self-organisation to auto-con guration. Routing in WMN is fundamen- tally achieved by hop counts which have been proven to be de cient in terms of network performance. The realistic need to enhance the link quality metric to improve network performance has been a growing concern in recent times. The cross-Layer routing approach is one of the identi ed methods of improving routing process in Wireless technology. This work presents an RSSI-aware routing metric implemented on Optimized Link-State Routing (OLSR) for WMN. The embedded Received Signal Strength Information (RSSI) from the mesh nodes on the network is extracted, processed, transformed and incorporated into the routing process. This is to estimate efficiently the link quality for network path selections to improved network performance. The measured RSSI data is filtered by an Exponentially Weighted Moving Average (EWMA) filter. This novel routing metric method is called RSSI-aware ETT (rETT). The performance of rETT is then optimised and the results compared with the fundamental hop count metric and the link quality metric by Expected Transmission Counts (ETX). The results reveal some characteristics of RSSI samples and link conditions through the analysis of the statistical data. The divergence or variability of the samples is a function of interference and multi-path e effect on the link. The implementation results show that the routing metric with rETT is more intelligent at choosing better network paths for the packets than hop count and ETX estimations. rETT improvement on network throughput is more than double (120%) compared to hop counts and 21% improvement compared to ETX. Also, an improvement of 33% was achieved in network delay compared to hop counts and 28% better than ETX. This work brings another perspective into link-quality metric solutions for WMN by using RSSI to drive the metric of the wireless routing protocol. It was carried out on test-beds and the results obtained are more realistic and practical. The proposed metric has shown improvement in performance over the classical hop counts metric and ETX link quality metric.
