Caching at the network edge has emerged as a viable solution for alleviating the severe capacity crunch in content-centric next generation 5G wireless networks by leveraging localized content storage and delivery. Caching generally works in two phases namely (i) storage phase where parts of popular content is pre-fetched and stored in caches at the network edge during time of low network load and (ii) delivery phase where content is distributed to users at times of high network load by leveraging the locally stored content. Cache-aided networks therefore have the potential to leverage storage at the network edge to increase bandwidth efficiency. In this dissertation we ask the following question - What are the theoretical and practical guarantees offered by cache aided networks for reliable content distribution while minimizing transmission rates and increasing network efficiency? We furnish an answer to this question by identifying fundamental Shannon-type limits for cache aided systems. To this end, we first consider a cache-aided network where the cache storage phase is assisted by a central server and users can demand multiple files at each transmission interval. To service these demands, we consider two delivery models - (i) centralized content delivery where demands are serviced by the central server; and (ii) device-to-device-assisted distributed delivery where demands are satisfied by leveraging the collective content of user caches. For such cache aided networks, we develop a new technique for characterizing information theoretic lower bounds on the fundamental storage-rate trade-off. Furthermore, using the new lower bounds, we establish the optimal storage-rate trade-off to within a constant multiplicative gap and show that, for the case of multiple demands per user, treating each set of demands independently is order-optimal. To address the concerns of privacy in multicast content delivery over such cache-aided networks, we introduce the problem of caching with secure delivery. We propose schemes which achieve information theoretic security in cache-aided networks and show that the achievable rate is within a constant multiplicative factor of the information theoretic optimal secure rate. We then extend our theoretical analysis to the wireless domain by studying a cloud and cache-aided wireless network from a perspective of low-latency content distribution. To this end, we define a new performance metric namely normalized delivery time, or NDT, which captures the worst-case delivery latency. We propose achievable schemes with an aim to minimize the NDT and derive information theoretic lower bounds which show that the proposed schemes achieve optimality to within a constant multiplicative factor of 2 for all values of problem parameters. Finally, we consider the problem of caching and content distribution in a multi-small-cell heterogeneous network from a reinforcement learning perspective for the case when the popularity of content is unknown. We propose a novel topology-aware learning-aided collaborative caching algorithm and show that collaboration among multiple small cells for cache-aided content delivery outperforms local caching in most network topologies of practical interest. The results presented in this dissertation show definitively that cache-aided systems help in appreciable increase of network efficiency and are a viable solution for the ever evolving capacity demands in the wireless communications landscape. / Ph. D. / Caching at the network edge has emerged as a viable solution for alleviating the severe capacity crunch in content-centric next generation 5G wireless networks by leveraging localized content storage and delivery. Caching generally works in two phases namely (<i>i</i>) <i>storage phase</i> where parts of popular content is pre-fetched and stored in caches at the network edge during time of low network load and (<i>ii</i>) <i>delivery phase</i> where content is distributed to users at times of high network load by leveraging the locally stored content. Cache-aided networks therefore have the potential to leverage storage at the network edge to increase bandwidth efficiency. In this dissertation we study cache-aided systems from an information theoretic perspective and identify fundamental Shannontype limits for such systems. The results presented in this dissertation show definitively that cacheaided systems help in appreciable increase of network efficiency and are a viable solution for the ever evolving capacity demands in the wireless communications landscape.

To provide mobile hosts with seamless and broadband wireless Internet access, two fundamental problems that need to be tackled in wireless networking are transparently supporting host mobility and effectively utilizing wireless bandwidth. The increasing heterogeneity of wireless networks and the proliferation of wireless devices, however, severely expose the limitations of the paradigms adopted by existing solutions. In this work, we explore new research directions for addressing network heterogeneity and bandwidth scarcity in future wireless data networks. In addressing network heterogeneity, we motivate a transport layer solution for transparent mobility support across heterogeneous wireless networks. We establish parallelism and transpositionality as two fundamental principles to be incorporated in designing such a transport layer solution. In addressing bandwidth scarcity, we motivate a cooperative wireless network model for scalable bandwidth utilization with wireless user population. We establish base station assistance and multi-homed peer relay as two fundamental principles to be incorporated in designing such a cooperative wireless network model. We present instantiations based on the established principles respectively, and demonstrate their performance and functionality gains through theoretic analysis, packet simulation, and testbed emulation.

ITC/USA 2010 Conference Proceedings / The Forty-Sixth Annual International Telemetering Conference and Technical Exhibition / October 25-28, 2010 / Town and Country Resort & Convention Center, San Diego, California / The concept of a range area network dedicated to the reception of telemetry from airborne test articles is explored. The range area network consists of ground-based radios that receive telemetry packets from an airborne test article and relay those packets through the network to a data sink (e.g., the main telemetry display and processing center). The network may use either "dumb" nodes or "smart" nodes and this choice presents a trade-off involving node complexity, network bandwidth, and required RF power. Using a somewhat idealized, but nonetheless realistic example at the Edwards AFB complex and link budgets based on the emerging iNET standard, we show that a network consisting of just 6 nodes reduces the L-band airborne transmitter power to 6W and the ground-based transmitters to 3W. If the airborne transmitter is restricted to 1W at L-band, then coverage can be provided by a grid of 50 nodes.

Wireless scheduling is a fundamental problem in wireless networks that involves scheduling transmissions of multiple users in order to support data flows with as high rates as possible. This problem was first addressed by Tassuilas and Ephremides, resulting in the celebrated Back-Pressure network scheduling algorithm. This algorithm schedules network links to maximize throughput in an opportunistic fashion using instantaneous network state information (NSI), i.e., queue and channel state knowledge across the entire network. However, the Back-Pressure (BP) algorithm suffers from various drawbacks - (a) it requires knowledge of instantaneous NSI from the whole network, i.e. feedback about time-varying channel and queue states from all links of the network, (b) the algorithm requires solving a global optimization problem at each time to determine the schedule, making it highly centralized. Further, Back-pressure algorithm was originally designed for wireless networks where interference is modeled using protocol interference model. As recent break-throughs in full-duplex communications and interference cancelation techniques provide greatly increased capacity and scheduling flexibility, it is not clear how BP algorithm can be modified to improve the data rates and reduce the delay. In this thesis, we address the drawbacks of Back-Pressure algorithm to some extent. In particular, our first work provides a new scheduling algorithm (similar to BP) that allows users to make individual decisions (distributed) based on heterogeneously delayed network state information (NSI). Regarding the complexity issue, in our second work, we analyze the performance of the greedy version of BP algorithm, known as Greedy Maximal Scheduling (GMS) and understand the effect of channel variations on the performance of GMS. In particular, we characterize the efficiency ratio of GMS in wireless networks with fading. In our third and fourth work, we propose and analyze new scheduling algorithms that can benefit from new advancements in interference cancelation techniques. / text

Enabling efficient media streaming over wireless channels requires efficient utilization of the limited wireless spectrum while satisfying multimedia applications' quality of service (QoS) requirements. In this dissertation, we provide insights into network and application-centric approaches for media streaming over wireless channels. In network-centric approaches, the fundamental problem is how to model network variations at the different layers and optimize the total quality across these layers. We use Finite-state Markov chain (FSMC) models to investigate the packet loss and delay performance over a wireless link. We propose a new method for partitioning the received SNR space that results in a FSMC model with tractable queueing performance. We then use this model to derive closed-form expressions for the {\em Effective Bandwidth\/} subject to either packet loss or packet delay constraints. In application-centric approaches, we take into account the VBR nature of video frames and channel dynamics and integrate in the analysis the dynamics of the playback buffer occupancy. We introduce a mixture of sourec/channel rate adaptation schemes that target efficient utilization of the wireless spectrum and safeguard the continuity of media streaming over wireless channels. First, we propose two source-rate control schemes for streaming video over wireless channels that provide gracefully degraded quality and soft guarantees on frame delay. The schemes are designed to maximize the source bit rate at the encoder while preventing/reducing events of starvation at the decoder. Second, we present a novel cycle-based rate adaptation scheme. The scheme is designed to maximize the source bit rate at the encoder while guaranteeing an upper bound on the probability of starvation at the playback buffer. This approach can be applied to both {\em one-way} and {\em interactive} video. Finally, we propose a playback-adaptive source/channel rate control (SCRC) for video streaming over wireless channels. We exploit the so-called playback adaptation margin and the playback buffer occupancy to control the source and channel rates. The SCRC scheme is designed to limit potential playback discontinuities that may occur due to variations in the wireless link.

This thesis concerns the design of body-centric wireless communications for short-range indoor Telecare/Telemedicine applications. Such communications are starting to be used to convey key, relatively low data-rate body-sensor data wirelessly between on-body sensor node(s) located on potentially mobile clients/patients and fixed off-body Access Point(s). From the outset, key practical considerations/constraints were assumed; in particular that, wherever possible, existing components (including antennas) and established protocols would be employed. This approach should enable existing manufacturers of mobile wireless components to rapidly adapt to the potential Telecare/Telemedicine market segment with minimum R&D capital outlay. In addition, maximum user convenience of the on-body nodes has been taken into account to ensure that they are readily accepted and hence actually used. As anticipated, using existing mobile antennas (designed for nominally free space use) in close proximity to the human body poses several limitations. These are quantified here for a particular candidate commercial device. In the process, however, a novel unanticipated effect of the nearby body was also discovered; namely that the body completely depolarises the (otherwise reasonably polarised) antenna patterns. A potential physical explanation for this effect is identified and evaluated by means of analysis based on a modified Geometric Optics approach. The result of this analysis agrees with those simulated and measured here to remarkable accuracy. The thesis then presents several multi-antenna schemes to overcome these severe limitations and identifies that best suited to the indoor Telecare/Telecommunication application here. Simulations at the Physical Layer are reported with this optimum Wireless Links for Telecare and Telemedicine using Compact Body-Worn Antennas 8 single-input multiple-output (SIMO) antenna scheme for a typical indoor scenario. These quantify the overall system performance when such measures are adopted, demonstrating that it is adequate in this role. Finally, promising techniques are suggested for Future Work which could afford further significant system improvements for future upgrades of the solution presented here. In particular, the use of metamaterial techniques are indicated which could substantially reduce on-body transmit powers currently required. This would give highly desirable increases in battery lifetimes for the mobile battery powered on-body nodes.

Location estimation when deployed on wireless networks supports a range of services including user tracking and monitoring, health care support and push and pull marketing. The main subject of this thesis is improving indoor and outdoor location estimation accuracy using received signal strength (RSS) from neighbouring base stations (BSs) or access points (APs), without using the global positioning system (GPS) or triangulation methods. For the outdoor environment, state-of-the-art deterministic and probabilistic algorithms are adapted to exploit principal components (PCs) and clustering. The accuracy is compared with K-nearest neighbour (KNN) algorithms using different partitioning models. The proposed scheme clusters the RSS tuples based on deviations from an estimated RSS attenuation model and then transforms the raw RSS in each cluster into new uncorrelated dimensions, using PCs. As well as simple global dimensionality reduction using PCs, the data reduction and rotation within each cluster improves estimation accuracy because a) each cluster can model the different local RSS distributions and b) it efficiently preserves the RSS correlations that are observed (some of which are substantial) in local regions and which independence approximations ignore. Different simulated and real environments are used for the comparisons. Experimental results show that positioning accuracy is significantly improved and fewer training samples are needed compared with traditional methods. Furthermore, a technique to adjust RSS data so that radio maps collected in different environmental conditions can be used together to enhance accuracy is also demonstrated. Additionally, in the radio coverage domain, a non-parametric probability approach is used for the radio reliability estimation and a semi-supervised learning model is proposed for the monitoring model training and evolution according to real-time mobile users’ RSS feedback. For the indoor environment, an approach for a large multi-story indoor location estimaiii tion using clustering and rank order matching is described. The accuracies using WiFi RSS alone, cellular GSM RSS alone and integrated WiFi and GSM RSS are presented. The methods were tested on real indoor environments. A hierarchical clustering method is used to partition the RSS space, where a cluster is defined as a set of mobile users who share exactly the same strongest RSS ranking set of transmitters. The experimental results show that while integrating of WiFi RSS with GSM RSS creates a marginal improvement, the GSM data can be used to ameliorate the loss of accuracy when APs fail.

OFDMA will be the multiple access scheme for next generation networks, including LTE and LTE-A. These networks will provide higher data rates than now, up to several hundred Mbps. These new networks, using a higher carrier frequency and offering flexible bandwidth for different application types, require advanced techniques for radio resource management. One approach that has been suggested to improve the radio resource management is to use smart and semi-smart antennas, so that the coverage of a certain cell can be divided into several adjustable sectors by using different antenna patterns. However, for a multi-cell environment, there is a need to prevent there being a gap between adjacent cells as the antenna patterns change. In this work, Genetic Algorithms are used to optimize the antenna patterns to get better coverage together with better cell throughput, at the same time making sure there are no gaps. This thesis not only considers the overall problem, but also investigates the suitability of the Genetic Algorithm itself, with it being optimized to improve the performance of the radio resource management in LTE networks. The influence of selection rate and mutation rate on GA is investigated and tested by simulation. These Genetic Algorithms are used in a model of multi-cell LTE networks to optimise subchannel allocation combined with dynamic sectorisation. Different types of scenarios are considered and the Genetic Algorithm is used to solve the problem of combining subchannel allocation with dynamic sectorisation to give the best overall performance of the LTE network.

As the demand for wireless communication services grows quickly, spectrum scarcity has been on the rise sharply. In this context, cognitive radio (CR) is being viewed as a new intelligent technology to solve the deficiency of fixed spectrum assignment policy in wireless communications. Spectrum sensing is one of the most fundamental technologies to realise dynamic spectrum access in cognitive radio networks. It requires high accuracy as well as low complexity. In this thesis, a novel adaptive threshold setting algorithm is proposed to optimise the trade-off between detection and false alarm probability in spectrum sensing while satisfying sensing targets set by the IEEE 802.22 standard. The adaptive threshold setting algorithm is further applied to minimise the error decision probability with varying primary users' spectrum utilisations. A closed-form expression for the error decision probability, satisfied SNR value, number of samples and primary users' spectrum utilisation ratio are derived in both fixed and the proposed adaptive threshold setting algorithms. By implementing both Welch and wavelet based energy detectors, the adaptive threshold setting algorithm demonstrates a more reliable and robust sensing result for both primary users (PUs) and secondary users (SUs) in comparison with the conventional fixed one. Furthermore, the wavelet de-noising method is applied to improve the sensing performance when there is insu cient number of samples. Finally, a novel database assisted spectrum sensing algorithm is proposed for a secondary access of the TV White Space (TVWS) spectrum. The proposed database assisted sensing algorithm is based on the developed database assisted approach for detecting incumbents like Digital Terrestrial Television (DTT) and Programme Making and Special Events (PMSE), but assisted by spectrum sensing to further improve the protection to primary users. Monte-Carlo simulations show a higher SUs' spectrum efficiency can be obtained for the proposed database assisted sensing algorithm than the existing stand-alone database assisted or sensing models.

Wireless sensors and their applications have become increasingly attractive for industry, building automation and energy control, paving the way for new applications of sensor networks which go well beyond traditional sensor applications. In recent years, there has been a rapid growth in the number of wireless devices operating in close proximity to the human body. Wearable sensor nodes are growing popular not only in our normal living lifestyle, but also within healthcare and military applications, where different radio units operating in/on/off body communicate pervasively. Expectations go beyond the research visions, towards deployment in real-world applications that would empower business processes and future business cases. Although theoretical and simulation models give initial results of the antenna behaviour and the radio channel performance of wireless body area network (WBAN) devices, empirical data from different set of measurements still form an essential part of the radio propagation models. Usually, measurements are performed in laboratory facilities which are equipped with bulky and expensive RF instrumentation within calibrated and controllable environments; thus, the acquired data has the highest possible reliability. However, there are still measurement uncertainties due to cables and connections and significant variations when designs are deployed and measured in real scenarios, such as hospitals wards, commercial buildings or even the battle field. Consequently, more flexible and less expensive measurement tools are required. In this sense, wireless sensor nodes offer not only easiness to deploy or flexibility, but also adaptability to different environments. In this thesis, custom-built wireless sensor nodes are used to characterise different on-body radio channels operating in the IEEE 802.15.4 communication standard at the 2.45 GHz ISM band. Measurement results are also compared with those from the conventional technique using a Vector Network Analyser. The wireless sensor nodes not only diminished the effect of semi-rigid or flexible coaxial cables (scattering or radiation) used with the Vector Network Analyser (VNA), but also provided a more realistic response of the radio link channel. The performance of the wireless sensors is presented over each of the 16 different channels present at the 2.45 GHz band. Additionally, custom-built wireless sensors are used to characterise and model the performance of different on-body radio links in dynamic environments, such as jogging, rowing, and cycling. The use of wireless sensors proves to be less obstructive and more flexible than traditional measurements using coaxial cables, VNA or signal generators. The statistical analysis of different WBAN channels highlighted important radio propagation features which can be used as sport classifiers models and motion detection. Moreover, specific on-body radio propagation channels are further explored, with the aim to recognize physiological features such as motion pattern, breathing activity and heartbeat. The time domain sample data is transformed to the frequency domain using a non-parametric FFT defined by the Welch’s periodogram. The Appendix-Section D explores other digital signal processing techniques which include spectrograms (STFT) and wavelet transforms (WT). Although a simple analysis is presented, strong DSP techniques proved to be good for signal de-noising and multi-resolution analysis. Finally, preliminary results are presented for indoor tracking using the RSS recorded by multiple wireless sensor nodes deployed in an indoor scenario. In contrast to outdoor environments, indoor scenarios are subject to a high level of multipath signals which are dependent on the indoor clutter. The presented algorithm is based on path loss analysis combined with spatial knowledge of each wireless sensor.