Backhaul-Aware Dimensioning and Planning of Millimeter-Wave Small Cell Networks

The massive deployment of Small Cells (SCs) is increasingly being adopted by mobile operators to face the exponentially growing traffic demand. Using the millimeter-wave (mmWave) band in the access and backhaul networks will be key to provide the capacity that meets such demand. However, dimensioning and planning have become complex tasks, because the capacity requirements for mmWave links can significantly vary with the SC location. In this work, we address the problem of SC planning considering the backhaul constraints, assuming that a line-of-sight (LOS) between the nodes is required to reliably support the traffic demand. Such a LOS condition reduces the set of potential site locations. Simulation results show that, under certain conditions, the proposed algorithm is effective in finding solutions and strongly efficient in computational cost when compared to exhaustive search approaches.H2020 research and innovation project 5G-CLARITY 871428Spanish Ministry of Science, Innovation and Universities PID2019-108713RB-C5

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

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

Joint Path Selection and Resource Allocation in Multi-Hop mmWave-based IAB Systems

Recently proposed by 3GPP, Integrated Access and Backhaul (IAB) technology promises to deliver a cost-efficient and flexible solution for network densification in 5G/6G systems. Since IAB architecture is based on multi-hop topology and advanced functionalities, such as multi-connectivity transmission and multi-routing, the potential utilization of IAB systems raises an issue of efficient system design. In this paper, we develop an optimization framework capable of jointly selecting transmission paths and allocating radio resources in compliance with half-duplexing and interference constraints. The presented numerical results illustrate that directional mm Wave beams employed at the wireless backhaul are essential for capacity boosting, thus allowing to fully exploit the radio resources in self-backhauled systems. We also establish that the multi-hop IAB topology provides advantages in terms of end-to-end user throughput as compared to single-hop systems.Peer reviewe

Unmanned Aerial Vehicles (UAVs) for Integrated Access and Backhaul (IAB) Communications in Wireless Cellular Networks

An integrated access and backhaul (IAB) network architecture can enable flexible and fast deployment of next-generation cellular networks. However, mutual interference between access and backhaul links, small inter-site distance and spatial dynamics of user distribution pose major challenges in the practical deployment of IAB networks. To tackle these problems, we leverage the flying capabilities of unmanned aerial vehicles (UAVs) as hovering IAB-nodes and propose an interference management algorithm to maximize the overall sum rate of the IAB network. In particular, we jointly optimize the user and base station associations, the downlink power allocations for access and backhaul transmissions, and the spatial configurations of UAVs. We consider two spatial configuration modes of UAVs: distributed UAVs and drone antenna array (DAA), and show how they are intertwined with the spatial distribution of ground users. Our numerical results show that the proposed algorithm achieves an average of 2.9× and 6.7× gains in the received downlink signal-to-interference-plus-noise ratio (SINR) and overall network sum rate, respectively. Finally, the numerical results reveal that UAVs cannot only be used for coverage improvement but also for capacity boosting in IAB cellular networks