Reinforcement Learning in Self Organizing Cellular Networks

Self-organization is a key feature as cellular networks densify and become more heterogeneous, through the additional small cells such as pico and femtocells. Self- organizing networks (SONs) can perform self-configuration, self-optimization, and self-healing. These operations can cover basic tasks such as the configuration of a newly installed base station, resource management, and fault management in the network. In other words, SONs attempt to minimize human intervention where they use measurements from the network to minimize the cost of installation, configuration, and maintenance of the network. In fact, SONs aim to bring two main factors in play: intelligence and autonomous adaptability. One of the main requirements for achieving such goals is to learn from sensory data and signal measurements in networks. Therefore, machine learning techniques can play a major role in processing underutilized sensory data to enhance the performance of SONs. In the first part of this dissertation, we focus on reinforcement learning as a viable approach for learning from signal measurements. We develop a general framework in heterogeneous cellular networks agnostic to the learning approach. We design multiple reward functions and study different effects of the reward function, Markov state model, learning rate, and cooperation methods on the performance of reinforcement learning in cellular networks. Further, we look into the optimality of reinforcement learning solutions and provide insights into how to achieve optimal solutions. In the second part of the dissertation, we propose a novel architecture based on spatial indexing for system-evaluation of heterogeneous 5G cellular networks. We develop an open-source platform based on the proposed architecture that can be used to study large scale directional cellular networks. The proposed platform is used for generating training data sets of accurate signal-to-interference-plus-noise-ratio (SINR) values in millimeter-wave communications for machine learning purposes. Then, with taking advantage of the developed platform, we look into dense millimeter-wave networks as one of the key technologies in 5G cellular networks. We focus on topology management of millimeter-wave backhaul networks and study and provide multiple insights on the evaluation and selection of proper performance metrics in dense millimeter-wave networks. Finally, we finish this part by proposing a self-organizing solution to achieve k-connectivity via reinforcement learning in the topology management of wireless networks

1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

Beam scanning by liquid-crystal biasing in a modified SIW structure

A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

Optical Wireless Data Center Networks

Bandwidth and computation-intensive Big Data applications in disciplines like social media, bio- and nano-informatics, Internet-of-Things (IoT), and real-time analytics, are pushing existing access and core (backbone) networks as well as Data Center Networks (DCNs) to their limits. Next generation DCNs must support continuously increasing network traffic while satisfying minimum performance requirements of latency, reliability, flexibility and scalability. Therefore, a larger number of cables (i.e., copper-cables and fiber optics) may be required in conventional wired DCNs. In addition to limiting the possible topologies, large number of cables may result into design and development problems related to wire ducting and maintenance, heat dissipation, and power consumption. To address the cabling complexity in wired DCNs, we propose OWCells, a class of optical wireless cellular data center network architectures in which fixed line of sight (LOS) optical wireless communication (OWC) links are used to connect the racks arranged in regular polygonal topologies. We present the OWCell DCN architecture, develop its theoretical underpinnings, and investigate routing protocols and OWC transceiver design. To realize a fully wireless DCN, servers in racks must also be connected using OWC links. There is, however, a difficulty of connecting multiple adjacent network components, such as servers in a rack, using point-to-point LOS links. To overcome this problem, we propose and validate the feasibility of an FSO-Bus to connect multiple adjacent network components using NLOS point-to-point OWC links. Finally, to complete the design of the OWC transceiver, we develop a new class of strictly and rearrangeably non-blocking multicast optical switches in which multicast is performed efficiently at the physical optical (lower) layer rather than upper layers (e.g., application layer). Advisors: Jitender S. Deogun and Dennis R. Alexande

Study, Measurements and Characterisation of a 5G system using a Mobile Network Operator Testbed

The goals for 5G are aggressive. It promises to deliver enhanced end-user experience by offering new applications and services through gigabit speeds, and significantly improved performance and reliability. The enhanced mobile broadband (eMBB) 5G use case, for instance, targets peak data rates as high as 20 Gbps in the downlink (DL) and 10 Gbps in the uplink (UL). While there are different ways to improve data rates, spectrum is at the core of enabling higher mobile broadband data rates. 5G New Radio (NR) specifies new frequency bands below 6 GHz and also extends into mmWave frequencies where more contiguous bandwidth is available for sending lots of data. However, at mmWave frequencies, signals are more susceptible to impairments. Hence, extra consideration is needed to determine test approaches that provide the precision required to accurately evaluate 5G components and devices. Therefore, the aim of the thesis is to provide a deep dive into 5G technology, explore its testing and validation, and thereafter present the OTE (Hellenic Telecommunications Organisation) 5G testbed, including measurement results obtained and its characterisation based on key performance indicators (KPIs)

The Design of Novel Pattern Reconfigurable Antennas for Mobile Networks

This research evaluates a beam reconfigurable basestation transceiver for cellular applications from both a systems and antenna design perspective. The novelty in this research is the investigation of an automatic azimuth beamwidth switching antenna, which can effectively respond to homogeneous traffic distribution in a cellular mobile network. The proposed technique which this antenna uses is azimuth beam switching which incorporates PIN diodes to provide a reconfigurable reflecting ground plane for a three sector antenna. Numerical systems analysis has been carried out on a hexagonal homogeneous cellular network to evaluate how this reconfigurable antenna can balance mean and cell edge capacity through azimuth beamwidth reconfiguration. The optimum azimuth beamwidth is identified as 60°, which achieves the best cell capacity, and by reconfiguring the azimuth beamwidth from 60° to 110°, the maximized capacity at the edges of the cell can be improved. The influence of mechanical tilt, inter site distance, path loss model and vicinity of the cell edge for this antenna are described. This research shows that a mean cell edge improvement from 15Mbit/s to 18Mbit/s is achievable when beamwidth reconfiguration is used, and that this improvement is consistent for cell sizes from 500m to 1500m. Results from a test of an as-manufactured reconfigurable antenna are presented here, and show similar results compared to simulations. To overcome network coverage deterioration at large antenna downtilt angles in a homogeneous cellular mobile network, different beam shaping techniques in the elevation plane, including antenna sidelobe suppressing and null filling, are discussed here. By filling up the first upper-side null for a 12-element antenna array, both the average cell edge and cell capacity can be improved. The application of this beam shaping pattern for a 12-element array is described here, for the purpose of optimising a specific cell within a mobile network which is shown below average coverage and/or capacity. By choosing a proper antenna downtilt angle for this specific cell, whilst keeping the optimum tilt angle for other cells in the network, the cell’s coverage/capacity can be increased without impacting too much on the performance of other surrounding cells. Lastly, the effects of number of antenna elements for a 60° azimuth beamwidth antenna array on the network coverage/capacity are discussed here. This research shows that, as a result of an increasing number of antenna elements in an elevation direction, network capacity can be increased along with the optimum tilt angle. This suggests that a high gain antenna array in a cellular mobile network can be potential for large site deployment and fewer installations

Facilitating Internet of Things on the Edge

The evolution of electronics and wireless technologies has entered a new era, the Internet of Things (IoT). Presently, IoT technologies influence the global market, bringing benefits in many areas, including healthcare, manufacturing, transportation, and entertainment. Modern IoT devices serve as a thin client with data processing performed in a remote computing node, such as a cloud server or a mobile edge compute unit. These computing units own significant resources that allow prompt data processing. The user experience for such an approach relies drastically on the availability and quality of the internet connection. In this case, if the internet connection is unavailable, the resulting operations of IoT applications can be completely disrupted. It is worth noting that emerging IoT applications are even more throughput demanding and latency-sensitive which makes communication networks a practical bottleneck for the service provisioning. This thesis aims to eliminate the limitations of wireless access, via the improvement of connectivity and throughput between the devices on the edge, as well as their network identification, which is fundamentally important for IoT service management. The introduction begins with a discussion on the emerging IoT applications and their demands. Subsequent chapters introduce scenarios of interest, describe the proposed solutions and provide selected performance evaluation results. Specifically, we start with research on the use of degraded memory chips for network identification of IoT devices as an alternative to conventional methods, such as IMEI; these methods are not vulnerable to tampering and cloning. Further, we introduce our contributions for improving connectivity and throughput among IoT devices on the edge in a case where the mobile network infrastructure is limited or totally unavailable. Finally, we conclude the introduction with a summary of the results achieved

Wireless Backhaul Architectures for 5G Networks

This thesis investigates innovative wireless backhaul deployment strategies for dense small cells. In particular, the work focuses on improving the resource utilisation, reliability and energy efficiency of future wireless backhaul networks by increasing and exploiting the flexibility of the network. The wireless backhaul configurations and topology management schemes proposed in this thesis consider a dense urban area scenario with static users as well as an ultra-dense outdoor small cell scenario with vehicular traffic (pedestrians, bus users and car users). Moreover, a diverse range of traffic types such as file transfer, ultra-high definition (UHD) on-demand and real-time video streaming are used. In the first part of this thesis, novel dynamic two-tier Software Defined Networking (SDN) architecture is employed in backhaul network to facilitate complex network management tasks including multi-tenancy resource sharing and energy-aware topology management. The results show the proposed architecture can deliver efficient resource utilisation, and QoS guarantee. The second part of the thesis presents wireless backhaul architectures that serve ultra-dense outdoor small cells installed on street-level fixtures. The characteristics of vehicular communications including diverse mobility patterns and unevenly distributed traffic are investigated. The system-level performance of two key technologies for 5G backhaul are compared: massive MIMO backhaul using sub-6GHz band and millimetre (mm)-wave backhaul in the 71 – 76 GHz band. Finally, innovative wireless backhaul architectures delivered from street fibre cabinets for ultra-dense outdoor small cells with vehicular traffic is proposed, which can effectively minimise the need for additional sites, power and fibre infrastructure. Multi-hop backhaul configurations are presented in order to bring in an extra level of flexibility, and thus, improve the coverage of a street cabinet mm-wave backhaul network as well as distribute traffic loads