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

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

Airborne Integrated Access and Backhaul Systems : Learning-Aided Modeling and Optimization

The deployment of millimeter-wave (mmWave) 5G New Radio (NR) networks is hampered by the properties of the mmWave band, such as severe signal attenuation and dynamic link blockage, which together limit the cell range. To provide a cost-efficient and flexible solution for network densification, 3GPP has recently proposed integrated access and backhaul (IAB) technology. As an alternative approach to terrestrial deployments, the utilization of unmanned aerial vehicles (UAVs) as IAB-nodes may provide additional flexibility for topology configuration. The aims of this study are to (i) propose efficient optimization methods for airborne and conventional IAB systems and (ii) numerically quantify and compare their optimized performance. First, by assuming fixed locations of IAB-nodes, we formulate and solve the joint path selection and resource allocation problem as a network flow problem. Then, to better benefit from the utilization of UAVs, we relax this constraint for the airborne IAB system. To efficiently optimize the performance for this case, we propose to leverage deep reinforcement learning (DRL) method for specifying airborne IAB-node locations. Our numerical results show that the capacity gains of airborne IAB systems are notable even in non-optimized conditions but can be improved by up to 30 % under joint path selection and resource allocation and, even further, when considering aerial IAB-node locations as an additional optimization criterion.acceptedVersionPeer reviewe

Max-Min Fair Resource Allocation in Millimetre-Wave Backhauls

5G mobile networks are expected to provide pervasive high speed wireless connectivity, to support increasingly resource intensive user applications. Network hyper-densification therefore becomes necessary, though connecting to the Internet tens of thousands of base stations is non-trivial, especially in urban scenarios where optical fibre is difficult and costly to deploy. The millimetre wave (mm-wave) spectrum is a promising candidate for inexpensive multi-Gbps wireless backhauling, but exploiting this band for effective multi-hop data communications is challenging. In particular, resource allocation and scheduling of very narrow transmission/ reception beams requires to overcome terminal deafness and link blockage problems, while managing fairness issues that arise when flows encounter dissimilar competition and traverse different numbers of links with heterogeneous quality. In this paper, we propose WiHaul, an airtime allocation and scheduling mechanism that overcomes these challenges specific to multi-hop mm-wave networks, guarantees max-min fairness among traffic flows, and ensures the overall available backhaul resources are fully utilised. We evaluate the proposed WiHaul scheme over a broad range of practical network conditions, and demonstrate up to 5 times individual throughput gains and a fivefold improvement in terms of measurable fairness, over recent mm-wave scheduling solutions

Self-Adaptive Power Control Mechanism in D2D Enabled Hybrid Cellular Network with mmWave Small Cells: An Optimization Approach

Millimeter wave (mmWave) and Device-to-Device (D2D) communications have been considered as the key enablers of the next generation networks. We consider a D2D-enabled hybrid cellular network compromising of $\mu W$ macro-cells coexisting with mmWave small cells. We investigate the dynamic resource sharing in downlink transmission to maximize the energy efficiency (EE) of the priority, or cellular users (CUs), that are opportunistically served by either macrocells or mmWave small cells, while satisfying a minimum quality-of-service (QoS) level for the D2D pairs. In order to solve this problem, we first formulate a self-adaptive power control mechanism for the D2D pairs subject to the interference threshold constraint set for the CUs, while maintaining its minimum QoS level. Subsequently, the original EE optimization problem, which aimed at maximizing the EE for both CUs and D2D pairs, has been broken up into two subproblems that manage the radio resource allocation for D2D pairs and maximize EE exclusively for CUs, in that order. We then propose an iterative algorithm to provide a near-optimal EE solution for CUs

Design and Performance Analysis of Next Generation Heterogeneous Cellular Networks for the Internet of Things

The Internet of Things (IoT) is a system of inter-connected computing devices, objects and mechanical and digital machines, and the communications between these devices/objects and other Internet-enabled systems. Scalable, reliable, and energy-efficient IoT connectivity will bring huge benefits to the society, especially in transportation, connected self-driving vehicles, healthcare, education, smart cities, and smart industries. The objective of this dissertation is to model and analyze the performance of large-scale heterogeneous two-tier IoT cellular networks, and offer design insights to maximize their performance. Using stochastic geometry, we develop realistic yet tractable models to study the performance of such networks. In particular, we propose solutions to the following research problems: -We propose a novel analytical model to estimate the mean uplink device data rate utility function under both spectrum allocation schemes, full spectrum reuse (FSR) and orthogonal spectrum partition (OSP), for uplink two-hop IoT networks. We develop constraint gradient ascent optimization algorithms to obtain the optimal aggregator association bias (for the FSR scheme) and the optimal joint spectrum partition ratio and optimal aggregator association bias (for the OSP scheme). -We study the performance of two-tier IoT cellular networks in which one tier operates in the traditional sub-6GHz spectrum and the other, in the millimeter wave (mm-wave) spectrum. In particular, we characterize the meta distributions of the downlink signal-to-interference ratio (sub-6GHz spectrum), the signal-to-noise ratio (mm-wave spectrum) and the data rate of a typical device in such a hybrid spectrum network. Finally, we characterize the meta distributions of the SIR/SNR and data rate of a typical device by substituting the cumulative moment of the CSP of a user device into the Gil-Pelaez inversion theorem. -We propose to split the control plane (C-plane) and user plane (U-plane) as a potential solution to harvest densification gain in heterogeneous two-tier networks while minimizing the handover rate and network control overhead. We develop a tractable mobility-aware model for a two-tier downlink cellular network with high density small cells and a C-plane/U-plane split architecture. The developed model is then used to quantify effect of mobility on the foreseen densification gain with and without C-plane/U-plane splitting