Spectrum sensing and occupancy prediction for cognitive machine-to-machine wireless networks

A thesis submitted to the University of Bedfordshire, in partial fulfil ment of the requirements for the degree of Doctor of Philosophy (PhD)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

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