Decoupled Downlink and Uplink Access for Aerial Terrestrial Heterogeneous Cellular Networks

To enable reliable connectivity in highly dynamic and dense communication environments, aerial-terrestrial heterogeneous cellular networks (AT-HCNs) have been proposed as a plausible enhancement to the conventional terrestrial HCNs (T-HCNs). In dense urban scenarios, users are often located in clusters and demand high bandwidth in both downlink (DL) and uplink (UL). We investigate this scenario and model the spatial distribution of clustered users using a Matern cluster process (MCP). Based on our analysis we then argue that decoupling of DL and UL in such a setting can significantly improve coverage performance and spectral efficiency. We further obtain closed-form expressions for the system coverage probability, spectral efficiency, and energy efficiency by using the Fox H-function. The obtained results confirm the validity of the proposed analytical model. Our simulations further indicate a significant performance improvement using decoupled access and provide quantitative insights on AT-HCN system design

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

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

Clustered Jamming in Aerial HetNets with Decoupled Access

The tremendous increase in wireless connectivity demand will result in the degradation of the service quality and the scarcity of network capacity and coverage in the beyond 5 th generation era. To ensure reliable connectivity and enhance the network’s performance, the evolution of heterogeneous networks (HetNets) must incorporate aerial platforms in addition to traditional terrestrial base stations. The performance of Aerial-HetNets (A-HetNets) is largely dependent on the users’ association. The conventional user-association scheme based on downlink received power provides sub-optimal performance for the edge users. For this reason, decoupled user-association along with the reverse frequency allocation (RFA) strategy has been employed in A-HetNets. The performance of A-HetNets is also affected if wide-band jammers (WBJs) are present in the vicinity and impose jamming interference. In this paper, a two-tier A-HetNet with RFA and decoupled access is analyzed in the presence of jamming interference. The obtained results show that for a signal-to-interference ratio threshold of −20 dBm, the percentage decrease in the coverage probability of the decoupled access due to WBJ activity is up to 7.4%, 13.5%, and 19.7%, for the average number of WBJs equal to 2, 4, and 6, respectively. The performance of the decoupled access in A-HetNets is further decreased by increasing the transmit power of the WBJs while it is increased by increasing the radius of the WBJ’s cluster

Drone-Assisted Wireless Communications

In order to address the increased demand for any-time/any-where wireless connectivity, both academic and industrial researchers are actively engaged in the design of the fifth generation (5G) wireless communication networks. In contrast to the traditional bottom-up or horizontal design approaches, 5G wireless networks are being co-created with various stakeholders to address connectivity requirements across various verticals (i.e., employing a top-to-bottom approach). From a communication networks perspective, this requires obliviousness under various failures. In the context of cellular networks, base station (BS) failures can be caused either due to a natural or synthetic phenomenon. Natural phenomena such as earthquake or flooding can result in either destruction of communication hardware or disruption of energy supply to BSs. In such cases, there is a dire need for a mechanism through which capacity short-fall can be met in a rapid manner. Drone empowered small cellular networks, or so-called \quotes{flying cellular networks}, present an attractive solution as they can be swiftly deployed for provisioning public safety (PS) networks. While drone empowered self-organising networks (SONs) and drone small cell networks (DSCNs) have received some attention in the recent past, the design space of such networks has not been extensively traversed. So, the purpose of this thesis is to study the optimal deployment of drone empowered networks in different scenarios and for different applications (i.e., in cellular post-disaster scenarios and briefly in assisting backscatter internet of things (IoT)). To this end, we borrow the well-known tools from stochastic geometry to study the performance of multiple network deployments, as stochastic geometry provides a very powerful theoretical framework that accommodates network scalability and different spatial distributions. We will then investigate the design space of flying wireless networks and we will also explore the co-existence properties of an overlaid DSCN with the operational part of the existing networks. We define and study the design parameters such as optimal altitude and number of drone BSs, etc., as a function of destroyed BSs, propagation conditions, etc. Next, due to capacity and back-hauling limitations on drone small cells (DSCs), we assume that each coverage hole requires a multitude of DSCs to meet the shortfall coverage at a desired quality-of-service (QoS). Hence, we consider the clustered deployment of DSCs around the site of the destroyed BS. Accordingly, joint consideration of partially operating BSs and deployed DSCs yields a unique topology for such PS networks. Hence, we propose a clustering mechanism that extends the traditional Mat\'{e}rn and Thomas cluster processes to a more general case where cluster size is dependent upon the size of the coverage hole. As a result, it is demonstrated that by intelligently selecting operational network parameters such as drone altitude, density, number, transmit power and the spatial distribution of the deployment, ground user coverage can be significantly enhanced. As another contribution of this thesis, we also present a detailed analysis of the coverage and spectral efficiency of a downlink cellular network. Rather than relying on the first-order statistics of received signal-to-interference-ratio (SIR) such as coverage probability, we focus on characterizing its meta-distribution. As a result, our new design framework reveals that the traditional results which advocate lowering of BS heights or even optimal selection of BS height do not yield consistent service experience across users. Finally, for drone-assisted IoT sensor networks, we develop a comprehensive framework to characterize the performance of a drone-assisted backscatter communication-based IoT sensor network. A statistical framework is developed to quantify the coverage probability that explicitly accommodates a dyadic backscatter channel which experiences deeper fades than that of the one-way Rayleigh channel. We practically implement the proposed system using software defined radio (SDR) and a custom-designed sensor node (SN) tag. The measurements of parameters such as noise figure, tag reflection coefficient etc., are used to parametrize the developed framework

Performance Analysis of Hybrid UAV Networks for Probabilistic Content Caching

Caching content in small-cell networks can reduce the traffic congestion in backhaul. In this paper, we develop a hybrid caching network comprising of unmanned aerial vehicles (UAVs) and ground small-cell base stations (SBSs), where UAVs are preferred because of their flexibility and elevated platform for line-of-sight. First, we derive the association probability for the ground user affiliated with a UAV and ground SBS. Then, we derive the successful content delivery probability by considering both the inter-cell and intra-cell interference. We also analyze the energy efficiency of the hybrid network and compare it with the separate UAV and ground networks. We further propose the caching scheme to improve the successful content delivery by managing the content popularity, where the part of the caching capacity in each UAV and ground SBS is reserved to store the most popular content (MPC), while the remaining stores less popular contents. Numerical results unveil that the proposed caching scheme has an improvement of 26.6% in content delivery performance over the MPC caching which overlooks the impact of content diversity during caching

Performance analysis of hybrid UAV networks for probabilistic content caching

Caching content in small-cell networks can reduce the traffic congestion in backhaul. In this article, we develop a hybrid caching network comprising of unmanned aerial vehicles (UAVs) and ground small-cell base stations (SBSs), where UAVs are preferred because of their flexibility and elevated platform for line of sight. First, we derive the association probability for the ground user affiliated with a UAV and ground SBS. Then, we derive the successful content delivery probability by considering both the intercell and intracell interference. We also analyze the energy efficiency of the hybrid network and compare it with the separate UAV and ground networks. We further propose the caching scheme to improve the successful content delivery by managing the content popularity, where the part of the caching capacity in each UAV and ground SBS is reserved to store the most popular content (MPC), while the remaining stores less popular contents. Numerical results unveil that the proposed caching scheme has an improvement of 26.6% in content delivery performance over the MPC caching, which overlooks the impact of content diversity during caching

Analytical Performance Evaluation of Cooperative and Multi-Radio Concepts in Emerging Wireless Networks

<br/