Spectrum and energy efficient multi-antenna spectrum sensing for green UAV communication

Unmanned Aerial Vehicle (UAV) communication is a promising technology that provides swift and flexible on-demand wireless connectivity for devices without infrastructure support. With recent developments in UAVs, spectrum and energy efficient green UAV communication has become crucial. To deal with this issue, Spectrum Sharing Policy (SSP) is introduced to support green UAV communication. Spectrum sensing in SSP must be carefully formulated to control interference to the primary users and ground communications. In this paper, we propose spectrum sensing for opportunistic spectrum access in green UAV communication to improve the spectrum utilization efficiency. Different from most existing works, we focus on the problem of spectrum sensing of randomly arriving primary signals in the presence of non-Gaussian noise/interference. We propose a novel and improved p-norm-based spectrum sensing scheme to improve the spectrum utilization efficiency in green UAV communication. Firstly, we construct the p-norm decision statistic based on the assumption that the random arrivals of signals follow a Poisson process. Then, we analyze and derive the approximate analytical expressions of the false-alarm and detection probabilities by utilizing the central limit theorem. Simulation results illustrate the validity and superiority of the proposed scheme when the primary signals are corrupted by additive non-Gaussian noise and arrive randomly during spectrum sensing in the green UAV communication

Advanced PHY/MAC Design for Infrastructure-less Wireless Networks

Wireless networks play a key role in providing information exchange among distributed mobile devices. Nowadays, Infrastructure-Less Wireless Networks (ILWNs), which include ad hoc and sensor networks, are gaining increasing popularity as they do not need a fixed infrastructure. Simultaneously, multiple research initiatives have led to different findings at the physical (PHY) layer of the wireless communication systems, which can effectively be adopted in ILWNs. However, the distributed nature of ILWNs demand for different network control policies that should have into account the most recent findings to increase the network performance. This thesis investigates the adoption of Multi-Packet Reception (MPR) techniques at the PHY layer of distributed wireless networks, which is itself a challenging task due to the lack of a central coordinator and the spatial distribution of the nodes. The work starts with the derivation of an MPR system performance model that allows to determine optimal points of operation for different radio conditions. The model developed and validated in this thesis is then used to study the performance of ILWNs in high density of transmitters and when the spectrum can be sensed a priori (i.e. before each transmission). Based on the theoretical analysis developed in the thesis, we show that depending on the propagation conditions the spectrum sensing can reduce the network throughput to a level where its use should be avoided. At the final stage, we propose a crosslayered architecture that improves the capacity of an ILWN. Different Medium Access Control (MAC) schemes for ILWNs adopting MPR communications are proposed and their performance is theoretically characterized. We propose a cross-layer optimization methodology that considers the features of the MPR communication scheme together with the MAC performance. The proposed cross-layer optimization methodology improves the throughput of ILWNs, which is validated through theoretical analysis and multiple simulation results

Improved energy detectors for cognitive radios with randomly arriving or departing primary users

New and improved energy detectors for cognitive radios are derived by considering the effect of the primary user traffic on spectrum sensing. The new energy detectors are designed based on the assumption that the primary user randomly arrives or departs during the sensing period. Numerical results show that the new energy detector outperforms the conventional energy detector in all the cases examined. The performance gain depends on the operating signal-to-noise ratio as well as the sample size used

Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event

Modelling and optimisation of resource usage in an IoT enabled smart campus

University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilized efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organizations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory. The building blocks of this thesis consist of three pillars of execution, namely, IoT deployment, predictive modelling, and optimization. Together, these components create an end-to-end framework that provides informed decisions to estate manager in regards to the optimal allocation of campus resources. The main contributions of this thesis are three application domains, which lies on top of the execution pillars, defining campus resources as classrooms, car parks, and transit buses. Specifically, our contributions are: i) We evaluate several IoT occupancy sensing technologies and instrument 9 lecture halls of varying capacities with the most appropriate sensing solution. The collected data provides us with insights into attendance patterns, such as cancelled lectures and class tests, of over 250 courses. We then develop predictive models using machine learning algorithms and quantile regression technique to predict future attendance patterns. Finally, we propose an intelligent optimisation model that allows allocations of classes to rooms based on the dynamics of predicted attendance as opposed to static enrolment number. We show that the data-driven assignment of classroom resources can achieve a potential saving in room cost of over 10\% over the course of a semester, while incurring a very low risk of disrupting student experience due to classroom overflow; ii) We instrument a car park with IoT sensors for real-time monitoring of parking demand and comprehensively analyse the usage data spanning over 15 months. We then develop machine learning models to forecast future parking demand at multiple forecast horizons ranging from 1 day to 10 weeks, our models achieve a mean absolute error (MAE) of 4.58 cars per hour. Finally, we propose a novel optimal allocation framework that allows campus manager to re-dimension the car park to accommodate new paradigms of car use while minimizing the risk of rejecting users and maintaining a certain level of revenue from the parking infrastructure; iii) We develop sensing technology for measuring an outdoor orderly queue using ultrasonic sensor and LoRaWAN, and deploy the solution at an on campus bus stop. Our solution yields a reasonable accuracy with MAE of 10.7 people for detecting a queue length of up to 100 people. We then develop an optimisation model to reschedule bus dispatch times based on the actual dynamics of passenger demand. The result suggests that a potential wait time reduction of 42.93% can be achieved with demand-driven bus scheduling. Taken together, our contributions demonstrates that there are significant resource efficiency gains to be realised in a smart-campus that employs IoT sensing coupled with predictive modelling and dynamic optimisation algorithms

Modelling and optimisation of resource usage in an IoT enabled smart campus

University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilized efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organizations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory. The building blocks of this thesis consist of three pillars of execution, namely, IoT deployment, predictive modelling, and optimization. Together, these components create an end-to-end framework that provides informed decisions to estate manager in regards to the optimal allocation of campus resources. The main contributions of this thesis are three application domains, which lies on top of the execution pillars, defining campus resources as classrooms, car parks, and transit buses. Specifically, our contributions are: i) We evaluate several IoT occupancy sensing technologies and instrument 9 lecture halls of varying capacities with the most appropriate sensing solution. The collected data provides us with insights into attendance patterns, such as cancelled lectures and class tests, of over 250 courses. We then develop predictive models using machine learning algorithms and quantile regression technique to predict future attendance patterns. Finally, we propose an intelligent optimisation model that allows allocations of classes to rooms based on the dynamics of predicted attendance as opposed to static enrolment number. We show that the data-driven assignment of classroom resources can achieve a potential saving in room cost of over 10\% over the course of a semester, while incurring a very low risk of disrupting student experience due to classroom overflow; ii) We instrument a car park with IoT sensors for real-time monitoring of parking demand and comprehensively analyse the usage data spanning over 15 months. We then develop machine learning models to forecast future parking demand at multiple forecast horizons ranging from 1 day to 10 weeks, our models achieve a mean absolute error (MAE) of 4.58 cars per hour. Finally, we propose a novel optimal allocation framework that allows campus manager to re-dimension the car park to accommodate new paradigms of car use while minimizing the risk of rejecting users and maintaining a certain level of revenue from the parking infrastructure; iii) We develop sensing technology for measuring an outdoor orderly queue using ultrasonic sensor and LoRaWAN, and deploy the solution at an on campus bus stop. Our solution yields a reasonable accuracy with MAE of 10.7 people for detecting a queue length of up to 100 people. We then develop an optimisation model to reschedule bus dispatch times based on the actual dynamics of passenger demand. The result suggests that a potential wait time reduction of 42.93% can be achieved with demand-driven bus scheduling. Taken together, our contributions demonstrates that there are significant resource efficiency gains to be realised in a smart-campus that employs IoT sensing coupled with predictive modelling and dynamic optimisation algorithms

Optimisation of wireless communication system by exploitation of channel diversity

Communication systems are susceptible to degradation in performance because of interference received through their side lobes. The interference may be deliberate electronic counter measure (ECM), Accidental RF Interference (RFI) or natural noise. The growth of interference communication systems have given rise to different algorithms, Adaptive array techniques offer a possible solution to this problem of interference received through side lobes because of their automatic null steering in both spatial and frequency domains. Key requirement for space-time architecture is to use robust adaptive algorithms to ensure reliable operation of the smart antenna. Space division multiple access (SDMA) involves the use of adaptive nulling to allow two or more users (mobiles) in the same cell to share same frequency and time slot. One beam is formed for each user with nulls in the direction of other users. Different approaches have been used to identify the interferer from desired user. Thus a basic model for determining the angle of arrival of incoming signals, an appropriate antenna beam forming and adaptive algorithms are used for array processing. There is an insatiable demand for capacity in wireless data networks and cellular radio communication systems. However the RF environment that these systems operate in is harsh and severely limits the capacity of traditional digital wireless networks. With normal wireless systems this limits the data rate in cellular radio environments to approximately 200 kbps whereas much higher data rates in excess of 25Mbps are required. A common wireless channel problem is that of frequency selective multi-path fading. To combat this problem, new types of wireless interface are being developed which utilise space, time and frequency diversity to provide increasing resilience to the channel imperfections. At any instant in time, the channel conditions may be such that one or more of these diversity methods may offer a superior performance to the other diversity methods. The overall aim of the research is to develop new systems that use a novel combination of smart antenna MIMO techniques and an advanced communication system based on advanced system configuration that could be exploited by IEEE 802.20 user specification approach for broadband wireless networking. The new system combines the Multi-input Multi-output communication system with frequency diversity in the form of an OFDM modulator. The benefits of each approach are examined under similar channel conditions and results presented.EThOS - Electronic Theses Online ServiceGBUnited Kingdo