System design issues in dense urban millimeter wave cellular networks

Upcoming deployments of cellular networks will see an increasing use of millimeter wave (mmWave) frequencies, roughly between 20-100 GHz. The goal of this dissertation is to investigate some key design issues in dense urban mmWave cellular networks by developing mathematical models that are representative of these networks. In the first contribution, stochastic geometry (SG) is used to study the per user rate performance of multi-user MIMO (MU-MIMO) in downlink mmWave cellular network incorporating the impact of a spatially sparse blockage dependent multipath channel and hybrid precoding. Performance of MU-MIMO is then compared with single-user beamforming and spatial multiplexing in different network scenarios considering coverage, rate and power consumption tradeoffs to suggest when to use which MIMO scheme. The second contribution reconsiders a popular received signal power model used in system capacity analysis of MIMO wireless networks employing single user beamforming. A modification is suggested to the model by introducing a correction factor. An approximate analysis is done to justify incorporating such a factor and simulations are performed to validate it's importance. Although this contribution does not study a new system design issue for mmWave cellular, it highlights a shortcoming with using the popular received signal power model to study design issues in mmWave cellular networks. The third and fourth contributions investigate resource allocation in self-backhauled mmWave cellular networks. In order to enable affordable initial deployments of mmWave cellular, self-backhauling is envisioned as a cost-saving solution. The third contribution investigates how to divide resources between uplink and downlink for access and backhaul in self-backhauled networks with single hop wireless backhauling. The performance of dynamic time division duplexing (TDD) and integrated access-backhaul (IAB) is compared with static TDD and orthogonal access backhaul (OAB) strategies using a SG based model. The last contribution of this dissertation addresses the following key question for self-backhauled networks. What is the maximum extended coverage area that a single fiber site can support using multi-hop relaying, while still achieving a minimum target per user data rate? The problem of maximizing minimum per user rates is studied considering a series of deployments with a single fiber site and varying number of relays. Several design guidelines for multi-hop mmWave cellular networks are provided based on the analytical and empirical results.Electrical and Computer Engineerin

Control-data separation architecture for cellular radio access networks: a survey and outlook

Conventional cellular systems are designed to ensure ubiquitous coverage with an always present wireless channel irrespective of the spatial and temporal demand of service. This approach raises several problems due to the tight coupling between network and data access points, as well as the paradigm shift towards data-oriented services, heterogeneous deployments and network densification. A logical separation between control and data planes is seen as a promising solution that could overcome these issues, by providing data services under the umbrella of a coverage layer. This article presents a holistic survey of existing literature on the control-data separation architecture (CDSA) for cellular radio access networks. As a starting point, we discuss the fundamentals, concepts, and general structure of the CDSA. Then, we point out limitations of the conventional architecture in futuristic deployment scenarios. In addition, we present and critically discuss the work that has been done to investigate potential benefits of the CDSA, as well as its technical challenges and enabling technologies. Finally, an overview of standardisation proposals related to this research vision is provided

6G White Paper on Machine Learning in Wireless Communication Networks

The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless connectivity for humans and machines. Recent advances in ML research has led enable a wide range of novel technologies such as self-driving vehicles and voice assistants. Such innovation is possible as a result of the availability of advanced ML models, large datasets, and high computational power. On the other hand, the ever-increasing demand for connectivity will require a lot of innovation in 6G wireless networks, and ML tools will play a major role in solving problems in the wireless domain. In this paper, we provide an overview of the vision of how ML will impact the wireless communication systems. We first give an overview of the ML methods that have the highest potential to be used in wireless networks. Then, we discuss the problems that can be solved by using ML in various layers of the network such as the physical layer, medium access layer, and application layer. Zero-touch optimization of wireless networks using ML is another interesting aspect that is discussed in this paper. Finally, at the end of each section, important research questions that the section aims to answer are presented