Architecture of a cognitive non-line-of-sight backhaul for 5G outdoor urban small cells

Densely deployed small cell networks will address the growing demand for broadband mobile connectivity, by increasing access network capacity and coverage. However, most potential small cell base station (SCBS) locations do not have existing telecommunication infrastructure. Providing backhaul connectivity to core networks is therefore a challenge. Millimeter wave (mmW) technologies operated at 30-90GHz are currently being considered to provide low-cost, flexible, high-capacity and reliable backhaul solutions using existing roof-mounted backhaul aggregation sites. Using intelligent mmW radio devices and massive multiple-input multiple-output (MIMO), for enabling point-to-multipoint (PtMP) operation, is considered in this research. The core aim of this research is to develop an architecture of an intelligent non-line-sight (NLOS) small cell backhaul (SCB) system based on mmW and massive MIMO technologies, and supporting intelligent algorithms to facilitate reliable NLOS street-to-rooftop NLOS SCB connectivity. In the proposed architecture, diffraction points are used as signal anchor points between backhaul radio devices. In the new architecture the integration of these technologies is considered. This involves the design of efficient artificial intelligence algorithms to enable backhaul radio devices to autonomously select suitable NLOS propagation paths, find an optimal number of links that meet the backhaul performance requirements and determine an optimal number of diffractions points capable of covering predetermined SCB locations. Throughout the thesis, a number of algorithms are developed and simulated using the MATLAB application. This thesis mainly investigates three key issues: First, a novel intelligent NLOS SCB architecture, termed the cognitive NLOS SCB (CNSCB) system is proposed to enable street-to-rooftop NLOS connectivity using predetermined diffraction points located on roof edges. Second, an algorithm to enable the autonomous creation of multiple-paths, evaluate the performance of each link and determine an optimal number of possible paths per backhaul link is developed. Third, an algorithm to determine the optimal number of diffraction points that can cover an identified SCBS location is also developed. Also, another investigated issue related to the operation of the proposed architecture is its energy efficiency, and its performance is compared to that of a point-to-point (PtP) architecture. The proposed solutions were examined using analytical models, simulations and experimental work to determine the strength of the street-to-rooftop backhaul links and their ability to meet current and future SCB requirements. The results obtained showed that reliable multiple NLOS links can be achieved using device intelligence to guide radio signals along the propagation path. Furthermore, the PtMP architecture is found to be more energy efficient than the PtP architecture. The proposed architecture and algorithms offer a novel backhaul solution for outdoor urban small cells. Finally, this research shows that traditional techniques of addressing the demand for connectivity, which consisted of improving or evolving existing solutions, may nolonger be applicable in emerging communication technologies. There is therefore need to consider new ways of solving the emerging challenges

Cloud Empowered Cognitive Inter-cell Interference Coordination for Small Cellular Networks

In this article, we present a Cloud empowered Cognitive Inter-Cell Interference Coordination (C2-ICIC) scheme for small cellular networks. The scheme leverages a recently proposed cloud radio access network (C-RAN) architecture for enabling intra-tier coordination and relaxes the need for inter-tier coordination by adopting the phantom cell architecture. Employing tools from stochastic geometry, we characterize the downlink success probability for a Mobile User (MU) scheduled under the proposed coordination scheme. It is shown that, compared to un-coordinated scheduling, significant performance gains can be realized in ultra dense small cell deployment scenarios under the proposed C2-ICIC scheme. This is attributed to the robust interference protection provisioned by the scheme. It is demonstrated that the gains are particularly large for the users experiencing a weak received signal strength. Indeed, for these users, the received signal-to-interference ratio (SIR) can only be improved by reducing the experienced aggregate co-channel interference. The closed-form expression derived for the downlink success probability is employed to quantify the link level throughput under the proposed scheme. Finally, we briefly explore the design space of the C2-ICIC scheme in terms of interference protection cap which determines both the downlink throughput of the MU scheduled in the coordination mode and the transmission opportunity for the co-channel small cells

Learning and Reasoning Strategies for User Association in Ultra-dense Small Cell Vehicular Networks

Recent vehicular ad hoc networks research has been focusing on providing intelligent transportation services by employing information and communication technologies on road transport. It has been understood that advanced demands such as reliable connectivity, high user throughput, and ultra-low latency required by these services cannot be met using traditional communication technologies. Consequently, this thesis reports on the application of artificial intelligence to user association as a technology enabler in ultra-dense small cell vehicular networks. In particular, the work focuses on mitigating mobility-related concerns and networking issues at different mobility levels by employing diverse heuristic as well as reinforcement learning (RL) methods. Firstly, driven by rapid fluctuations in the network topology and the radio environment, a conventional, three-step sequence user association policy is designed to highlight and explore the impact of vehicle speed and different performance indicators on network quality of service (QoS) and user experience. Secondly, inspired by control-theoretic models and dynamic programming, a real-time controlled feedback user association approach is proposed. The algorithm adapts to the changing vehicular environment by employing derived network performance information as a heuristic, resulting in improved network performance. Thirdly, a sequence of novel RL based user association algorithms are developed that employ variable learning rate, variable rewards function and adaptation of the control feedback framework to improve the initial and steady-state learning performance. Furthermore, to accelerate the learning process and enhance the adaptability and robustness of the developed RL algorithms, heuristically accelerated RL and case-based transfer learning methods are employed. A comprehensive, two-tier, event-based, system level simulator which is an integration of a dynamic vehicular network, a highway, and an ultra-dense small cell network is developed. The model has enabled the analysis of user mobility effects on the network performance across different mobility levels as well as served as a firm foundation for the evaluation of the empirical properties of the investigated approaches

Location-specific Spectrum Sharing in Heterogeneous Networks

The popularity of wireless mobile communication with enormous production of smart devices and applications increases the number of users in the wireless network. This increase of mobile users in the wireless network results insatiable demand for additional bandwidth. To improve network capacity of mobile operators efficient use of spectrum is critical. To improve the system capacity of operators and to provide flexible use of spectrum, we investigate a localized spectrum sharing between operators located at the same geographical area. We provide a coordination mechanism for operators to form a common spectrum pool and to use it dynamically. The coordination between the operators is modeled using a game theoretical approach in a non-cooperative basis. We study the spectrum sharing at localized and non-localized level, where at localized level operators agree on spectrum sharing at small scale. In localized spectrum sharing operators share their spectrum at smaller areas, when compared to non-localized spectrum sharing. Through numerical simulation, we analyze the performance of localized and non-localized spectrum sharing in comparison to the default orthogonal spectrum sharing mechanism. From the simulation results, we conclude that localized spectrum sharing outperforms non-localized spectrum sharing. Thus, spectrum sharing at smaller areas provides a better performance improvement than spectrum sharing at larger geographical areas

Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review

Three convergent processes are likely to shape the future of the internet beyond-5G: The convergence of optical and millimeter wave radio networks to boost mobile internet capacity, the convergence of machine learning solutions and communication technologies, and the convergence of virtualized and programmable network management mechanisms towards fully integrated autonomic network resource management. The integration of network virtualization technologies creates the incentive to customize and dynamically manage the resources of a network, making network functions, and storage capabilities at the edge key resources similar to the available bandwidth in network communication channels. Aiming to understand the relationship between resource management, virtualization, and the dense 5G access and fronthaul with an emphasis on converged radio and optical communications, this article presents a review of how resource management solutions have dealt with optimizing millimeter wave radio and optical resources from an autonomic network management perspective. A research agenda is also proposed by identifying current state-of-the-art solutions and the need to shift all the convergent issues towards building an advanced resource management mechanism for beyond-5G

User Association in 5G Networks: A Survey and an Outlook

26 pages; accepted to appear in IEEE Communications Surveys and Tutorial

A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing

Accelerating Reinforcement Learning for Dynamic Spectrum Access in Cognitive Wireless Networks

This thesis studies the applications of distributed reinforcement learning (RL) based machine intelligence to dynamic spectrum access (DSA) in future cognitive wireless networks. In particular, this work focuses on ways of accelerating distributed RL based DSA algorithms in order to improve their adaptability in terms of the initial and steady-state performance, and the quality of service (QoS) convergence behaviour. The performance of the DSA schemes proposed in this thesis is empirically evaluated using large-scale system-level simulations of a temporary event scenario which involves a cognitive small cell network installed in a densely populated stadium, and in some cases a base station on an aerial platform and a number of local primary LTE base stations, all sharing the same spectrum. Some of the algorithms are also theoretically evaluated using a Bayesian network based probabilistic convergence analysis method proposed by the author. The thesis presents novel distributed RL based DSA algorithms that employ a Win-or-Learn-Fast (WoLF) variable learning rate and an adaptation of the heuristically accelerated RL (HARL) framework in order to significantly improve the initial performance and the convergence speed of classical RL algorithms and, thus, increase their adaptability in challenging DSA environments. Furthermore, a distributed case-based RL approach to DSA is proposed. It combines RL and case-based reasoning to increase the robustness and adaptability of distributed RL based DSA schemes in dynamically changing wireless environments

5G Backhaul Challenges and Emerging Research Directions: A Survey

5G is the next cellular generation and is expected to quench the growing thirst for taxing data rates and to enable the Internet of Things. Focused research and standardization work have been addressing the corresponding challenges from the radio perspective while employing advanced features, such as network densi cation, massive multiple-input-multiple-output antennae, coordinated multi-point processing, intercell interference mitigation techniques, carrier aggregation, and new spectrum exploration. Nevertheless, a new bottleneck has emerged: the backhaul. The ultra-dense and heavy traf c cells should be connected to the core network through the backhaul, often with extreme requirements in terms of capacity, latency, availability, energy, and cost ef ciency. This pioneering survey explains the 5G backhaul paradigm, presents a critical analysis of legacy, cutting-edge solutions, and new trends in backhauling, and proposes a novel consolidated 5G backhaul framework. A new joint radio access and backhaul perspective is proposed for the evaluation of backhaul technologies which reinforces the belief that no single solution can solve the holistic 5G backhaul problem. This paper also reveals hidden advantages and shortcomings of backhaul solutions, which are not evident when backhaul technologies are inspected as an independent part of the 5G network. This survey is key in identifying essential catalysts that are believed to jointly pave the way to solving the beyond-2020 backhauling challenge. Lessons learned, unsolved challenges, and a new consolidated 5G backhaul vision are thus presented