NOVEL USER-CENTRIC ARCHITECTURES FOR FUTURE GENERATION CELLULAR NETWORKS: DESIGN, ANALYSIS AND PERFORMANCE OPTIMIZATION

Ambitious targets for aggregate throughput, energy efficiency (EE) and ubiquitous user experience are propelling the advent of ultra-dense networks. Inter-cell interference and high energy consumption in an ultra-dense network are the prime hindering factors in pursuit of these goals. To address this challenge, we investigate the idea of transforming network design from being base station-centric to user-centric. To this end, we develop mathematical framework and analyze multiple variants of the user-centric networks, with the help of advanced scientific tools such as stochastic geometry, game theory, optimization theory and deep neural networks. We first present a user-centric radio access network (RAN) design and then propose novel base station association mechanisms by forming virtual dedicated cells around users scheduled for downlink. The design question that arises is what should the ideal size of the dedicated regions around scheduled users be? To answer this question, we follow a stochastic geometry based approach to quantify the area spectral efficiency (ASE) and energy efficiency (EE) of a user-centric Cloud RAN architecture. Observing that the two efficiency metrics have conflicting optimal user-centric cell sizes, we propose a game theoretic self-organizing network (GT-SON) framework that can orchestrate the network between ASE and EE focused operational modes in real-time in response to changes in network conditions and the operator's revenue model, to achieve a Pareto optimal solution. The designed model is shown to outperform base-station centric design in terms of both ASE and EE in dense deployment scenarios. Taking this user-centric approach as a baseline, we improve the ASE and EE performance by introducing flexibility in the dimensions of the user-centric regions as a function of data requirement for each device. So instead of optimizing the network-wide ASE or EE, each user device competes for a user-centric region based on its data requirements. This competition is modeled via an evolutionary game and a Vickrey-Clarke-Groves auction. The data requirement based flexibility in the user-centric RAN architecture not only improves the ASE and EE, but also reduces the scheduling wait time per user. Offloading dense user hotspots to low range mmWave cells promises to meet the enhance mobile broadband requirement of 5G and beyond. To investigate how the three key enablers; i.e. user-centric virtual cell design, ultra-dense deployments and mmWave communication; are integrated in a multi-tier Stienen geometry based user-centric architecture. Taking into account the characteristics of mmWave propagation channel such as blockage and fading, we develop a statistical framework for deriving the coverage probability of an arbitrary user equipment scheduled within the proposed architecture. A key advantage observed through this architecture is significant reduction in the scheduling latency as compared to the baseline user-centric model. Furthermore, the interplay between certain system design parameters was found to orchestrate the ASE-EE tradeoff within the proposed network design. We extend this work by framing a stochastic optimization problem over the design parameters for a Pareto optimal ASE-EE tradeoff with random placements of mobile users, macro base stations and mmWave cells within the network. To solve this optimization problem, we follow a deep learning approach to estimate optimal design parameters in real-time complexity. Our results show that if the deep learning model is trained with sufficient data and tuned appropriately, it yields near-optimal performance while eliminating the issue of long processing times needed for system-wide optimization. The contributions of this dissertation have the potential to cause a paradigm shift from the reactive cell-centric network design to an agile user-centric design that enables real-time optimization capabilities, ubiquitous user experience, higher system capacity and improved network-wide energy efficiency

On the Efficiency tradeoffs in User-Centric Cloud RAN

Ambitious targets for aggregate throughput, energy efficiency and ubiquitous user experience are propelling the advent of ultra-dense networks. Intercell interference and high energy consumption in an ultra-dense network are the prime hindering factors in pursuit of these goals. To address the aforementioned challenges, in this paper, we propose a novel user-centric network orchestration solution for Cloud RAN based ultra-dense deployments. In this solution, a cluster (virtual disc) is created around users depending on their service priority. Within the cluster radius, only the best remote radio head (RRH) is activated to serve the user, thereby decreasing interference and saving energy. We use stochastic geometry based approach to quantify the area spectral efficiency (ASE) and RRH power consumption models to quantity energy(EE) efficiency of the proposed user-centric Cloud RAN (UCRAN). Through extensive analysis we observe that the cluster sizes that yield optimal ASE and EE are quite different. We propose a game theoretic self-organizing network (GT-SON) framework that can orchestrate the network between ASE and EE focused operational modes in real-time in response to changes in network conditions and the operator's revenue model, to achieve a Pareto optimal solution. A bargaining game is modeled to investigate the ASE-EE tradeoff through adjustment in the exponential efficiency weightage in the Nash bargaining solution (NBS). Results show that compared to current non-user centric network design, the proposed solution offers the flexibility to operate the network at multiple folds higher ASE or EE along with significant improvement in user experience

Enhancing Downlink QoS and Energy Efficiency through a User-Centric Stienen Cell Architecture for mmWave Networks

This paper presents an analytical framework for performance characterization of a novel Stienen cell based user-centric architecture operating in millimeter wave spectrum. In the proposed architecture, at most one remote radio head (RRH) is activated within non overlapping user equipment (UE)-centric Stienen cells (S-cells) generated within the Voronoi region around each UE. Under the presented framework, we derive analytical models for the three key performance indicators (KPIs): i) SINR distribution (used as an indicator for quality of service (QoS)), ii) area spectral efficiency (ASE), and iii) energy efficiency (EE) as a function of the three major design parameters in the proposed architecture, namely UE service probability, S-cell radius coefficient and RRH deployment density. The analysis is validated through extensive Monte Carlo simulations. The simulation results provide practical design insights into the interplay among the three design parameters, tradeoffs among the three KPIs, sensitivity of each KPI to the design parameters as well as optimal range of the design parameters. Results show that compared to current non user-centric architectures, the proposed architecture not only offers significant SINR gains, but also the flexibility to meet diverse UE specific QoS requirements and trade between EE and ASE by dynamically orchestrating the design parameters

User-Centric Multi-RATs Coordination for 5G Heterogeneous Ultra-Dense Networks

This column reviews the concept of user-centric access, which permits the network to tailor a “follow me” virtual cell for a user via multipoint coordination in a UDN environment. We further discuss the feasibility of extending such a virtual cell by considering tight integration of heterogeneous RATs exposed to the user, by taking the context information and instantaneous status of the user device into account. In particular, we conceive an edge/fog computing platform capable of acquiring and jointly processing information from multiple heterogeneous RATs and the users. Such a framework may have great potential to play key roles in 5G and beyond, in terms of realizing truly flexible radio access that can be customized for each individual user in the network.This work has been partially funded by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant no. 761586)