The research presented covers the theory and design of feedback-driven matching networks for radio frequency power amplifiers. The study examines amplifier classifications, types of tunable components, feedback typologies, and control systems to achieve the desired operation. The work centers on designing and implementing a tunable matching network for an amplifier's input and output. The tunable systems provide the amplifier with a wide range of operational frequencies at reasonable power levels comparable to today's modern communication systems and produce millisecond-based tuning times. Simulated results are verified against a fabricated system prototype and tweaked to provide further insight into the design's operation.

Near-field magnetic induction (NFMI) is a short range wireless technology that uses loop antennas coupled by a magnetic field. NFMI antennas are electrically small and thus extremely inefficient and narrow band, making system design for multi-user and high-bitrate applications challenging. The goals of this thesis are to develop a test platform suitable for NFMI antenna testing, to model, design and test NFMI antennas that have high bandwidth-efficiency, and to explore the possibility of using MIMO (multiple-input multiple-output) to increase the capacity of the NFMI channel. This thesis provides system implementations, test results, and channel modeling to aid in the design of future NFMI systems. Implementation of a multi-channel discrete-time wireless system are provided for PC-based software and FPGA-based firmware as a platform for antenna testing. Optimized antenna designs in terms of efficiency and bandwidth are presented, achieving the theoretical bandwidth-efficiency bound for small antennas. Preliminary modeling and simulation results for the NFMI-MIMO channel are included, which show that the information-theoretic capacity of the NFMI-MIMO channel is approximately double the standard single-antenna NFMI capacity at 10 bits/s/Hz.

The wireless spectrum is limited and the demand for its use is increasing due to technological advancements in wireless communication, resulting in persistent interference issues. Despite progress in addressing interference, it remains a challenge for effective spectrum usage, particularly in the use of license-free and managed shared bands and other opportunistic spectrum access solutions. Therefore, efficient and interference-resistant spectrum usage schemes are critical. In the past, most interference solutions have relied on avoidance techniques and expert system-based mitigation approaches. Recently, researchers have utilized artificial intelligence/machine learning techniques at the physical (PHY) layer, particularly deep learning, which suppress or compensate for the interfering signal rather than simply avoiding it. In addition, deep learning has been utilized by researchers in recent years to address various difficult problems in wireless communications such as, transmitter classification, interference classification and modulation recognition, amongst others. To this end, this dissertation presents a thorough analysis of deep learning techniques for interference classification and suppression, and it thoroughly examines complexity (sample and model) issues that arise from using deep learning. First, we address the knowledge gap in the literature with respect to the state-of-the-art in deep learning-based interference suppression. To account for the limitations of deep learning-based interference suppression techniques, we discuss several challenges, including lack of interpretability, the stochastic nature of the wireless channel, issues with open set recognition (OSR) and challenges with implementation. We also provide a technical discussion of the prominent deep learning algorithms proposed in the literature and also offer guidelines for their successful implementation. Next, we investigate convolutional neural network (CNN) architectures for interference and transmitter classification tasks. In particular, we utilize a CNN architecture to classify interference, investigate model complexity of CNN architectures for classifying homogeneous and heterogeneous devices and then examine their impact on test accuracy. Next, we explore the issues with sample size and sample quality with regards to the training data in deep learning. In doing this, we also propose a rule-of-thumb for transmitter classification using CNN based on the findings from our sample complexity study. Finally, in cases where interference cannot be avoided, it is important to suppress such interference. To achieve this, we build upon autoencoder work from other fields to design a convolutional neural network (CNN)-based autoencoder model to suppress interference thereby ensuring coexistence of different wireless technologies in both licensed and unlicensed bands. / Doctor of Philosophy / Wireless communication has advanced a lot in recent years, but it is still hard to use the limited amount of available spectrum without interference from other devices. In the past, researchers tried to avoid interference using expert systems. Now, researchers are using artificial intelligence and machine learning, particularly deep learning, to mitigate interference in a different way. Deep learning has also been used to solve other tough problems in wireless communication, such as classifying the type of device transmitting a signal, classifying the signal itself or avoiding it. This dissertation presents a comprehensive review of deep learning techniques for reducing interference in wireless communication. It also leverages a deep learning model called convolutional neural network (CNN) to classify interference and investigates how the complexity of the CNN effects its performance. It also looks at the relationship between model performance and dataset size (i.e., sample complexity) in wireless communication. Finally, it discusses a CNN-based autoencoder technique to suppress interference in digital amplitude-phase modulation system. All of these techniques are important for making sure different wireless technologies can work together in both licensed and unlicensed bands.

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Some pages are blank, but are kept to satisfy the page count of the thesis. / <p> Indoor optical wireless links can potentially achieve high bitrates because there is a wide and unregulated bandwidth in the optical spectrum. Moreover, optical wireless links can be implemented using simple and inexpensive devices. However, indoor optical wireless links have their own drawbacks such as limited power due to safety issues and incapability of passing thorough opaque objects, which limit their mobility, range and bandwidth and have prevented them from being used widely in commercial systems. Therefore, there has been much effort to find new configurations for indoor optical wireless links which are able to overcome these limitations. In this thesis, a novel configuration for indoor optical wireless communication, termed the dynamic spot diffusing (DSD) channel, is proposed. In the DSD system, the transmitter sends optical signals to a small moving area on the ceiling termed a spot. The receiver receives reflections of optical signal from the spot when spot is in field of view of the receiver. This configuration is shown to achieve high bitrates and provide a good deal of mobility for users inside the room. In this work, a theoretical model for the DSD channel is proposed and the DSD channel capacity is discussed and computed. Furthermore, the DSD system design is explained and design issues are discussed in order to approach capacity. Finally, using computer simulations, achievable rates inside a room are computed and shown to be close to calculated channel capacity.</p> / Thesis / Master of Applied Science (MASc)

No / Software radio and reconfigurable systems represent reconfigurable functionalities of the radio interface. Considering turbo decoding function in battery-powered devices like 3GPP mobile terminals, it would be desirable to choose the optimum decoding algorithm: SOVA in terms of latency, and log-MAP in terms of performance. In this paper it is shown that the two algorithms share common operations, making feasible a reconfigurable SOVA/log-MAP turbo decoder with increased efficiency. Moreover, an improvement in the performance of the reconfigurable architecture is also possible at minimum cost, by scaling the extrinsic information with a common factor. The implementation of the improved reconfigurable decoder within the 3GPP standard is also discussed, considering different scenarios. In each scenario various frame lengths are evaluated, while the four possible service classes are applied. In the case of AWGN channels, the optimum algorithm is proposed according to the desired quality of service of each class, which is determined from latency and performance constraints. Our analysis shows the potential utility of the reconfigurable decoder, since there is an optimum algorithm for most scenarios.

Yes / IET Editorial: In the future communications will be pervasive in nature, allowing users access at the “touch of button” to attain any service, at any time, on any device. The future device design process requires both a reconfigurable RF front end and back end with high tuning speed, energy efficiency, excellent linearity and intelligence to maximise the “greenness” of the network. But energy efficiency and excellent linearity are the main topics that are driving the designs of future transceivers, including their efforts to minimise network contributions to climate changes such as the effect of CO2 emissions: the minimisation of these is a requirement for information and communication technology (ICT) as much as for other technologies. Recently, information and communication technologies were shown to account for 3% of global power consumption and 2% of global CO2 emissions, and hence far from insignificant. The approach towards energy conservation and CO2 reduction in future communications will require a gret deal of effort which should be targeted both at the design of energy efficient, low-complexity physical, MAC and network layers, while maintaining the required Quality of Service (QoS). There is also a need, in infrastructures, networks and user terminals, to take a more holistic approach to improving or achieving green communications, from radio operation, through functionality, up to implementation. The increasing demand for data and voice services is not the only cause for concern since energy management and conservation are now at the forefront of the political agenda. The vision of Europe 2020 is to become a smart, sustainable and inclusive economy, and as part of these priorities the EU have set forth the 20:20:20 targets, whereby greenhouse gas emissions and energy consumption should be reduced by 20% while energy from renewables should be increased by 20%.

Yes / Exploring innovative cellular architectures to achieve enhanced system capacity and good coverage has become a critical issue towards realizing the next generation of wireless communications. In this context, this paper proposes a novel concept of Universal Intelligent Small Cell (UniSCell) for enabling the densification of the next generation of cellular networks. The main motivating factors behind the proposed small cell concept are the need of public infrastructure reengineering and the recent advances in several enabling technologies such as spectrum awareness, adaptive beamforming, source localization, new multiplexing schemes, etc. In this paper, first, we highlight the main concepts of the proposed small cell platform. Subsequently, we present two deployment scenarios taking into account of both technical and business aspects. Then, we describe the key future technologies for enabling the proposed UniScell Concept and present an use case example with the help of numerical results. Finally, we conclude this paper by providing some interesting future recommendations.

This research investigated the efficient utilization of wireless bandwidth in Code Division Multiple Access (CDMA)systems that support multiple data rates with Orthogonal Variable Spreading Factor (OVSF)codes. The specific problem being addressed was that currently proposed public-domain algorithms for assigning OVSF codes make inefficient use of wireless bandwidth for bursty data traffic sources with different Quality of Service (QoS) requirements. The purpose of this research was to develop an algorithm for the assignment of OVSF spreading codes in a Third-Generation (3G)Wideband CDMA (WCDMA)system. The goal of this algorithm was to efficiently utilize limited, wireless resources for bursty data traffic sources with different QoS requirements. The key contribution of this research was the implementation and testing of two code sharing techniques which are not implemented in existing OVSF code assignment algorithms. These techniques were termed statistical multiplexing and dynamic code sharing. The statistical multiplexing technique used a shared channel to support multiple bursty traffic sources. The dynamic code sharing technique supported multiple data users by temporarily granting access to dedicated channels. These techniques differed in terms of both complexity and performance guarantees. / Ph. D.

Turbo codes are a class of forward error correction (FEC) codes that offer energy efficiencies close to the limits predicted by information theory. The features of turbo codes include parallel code concatenation, recursive convolutional encoding, nonuniform interleaving, and an associated iterative decoding algorithm. Although the iterative decoding algorithm has been primarily used for the decoding of turbo codes, it represents a solution to a more general class of estimation problems that can be described as follows: a data set directly or indirectly drives the state transitions of two or more Markov processes; the output of one or more of the Markov processes is observed through noise; based on the observations, the original data set is estimated. This dissertation specifically describes the process of encoding and decoding turbo codes. In addition, a more general discussion of iterative decoding is presented. Then, several new applications of iterative decoding are proposed and investigated through computer simulation. The new applications solve two categories of problems: the detection of turbo codes over time-varying channels, and the distributed detection of coded multiple-access signals. Because turbo codes operate at low signal-to-noise ratios, the process of phase estimation and tracking becomes difficult to perform. Additionally, the turbo decoding algorithm requires precise estimates of the channel gain and noise variance. The first significant contribution of this dissertation is a study of several methods of channel estimation suitable specifically for turbo coded systems. The second significant contribution of this dissertation is a proposed method for jointly detecting coded multiple-access signals using observations from several locations, such as spatially separated base stations. The proposed system architecture draws from the concepts of macrodiversity combining, multiuser detection, and iterative decoding. Simulation results show that when the system is applied to the time division multiple-access cellular uplink, a significant improvement in system capacity results. / Ph. D.