Location, Location, Location: Maximizing mmWave LAN Performance through Intelligent Wireless Networking Strategies

The main objective of this dissertation is to design and evaluate intelligent techniques to maximize mmWave wireless local-area network (WLAN) performance. To meet the ever-increasing data demand of various bandwidth-hungry applications, we propose techniques to enable consistently ultra-high-rate mmWave communication in the wireless environment. However, the weak diffraction of mmWave signals makes them extremely sensitive to blockage effects caused by real-world obstacles, and this is a primary challenge to overcome for the feasibility of mmWave communications. To this end, we exploit location sensitivity to explore robust mmWave WLAN designs that expedite the full realization of ubiquitous mmWave wireless connectivity. The techniques investigated to exploit location sensitivity are the use of multiple access points (APs), controlled mobility, AP-user association mechanisms, and environment-aware prediction We first develop optimal multi-AP planning approaches to maximize line-of-sight connectivity and aggregate throughput in mmWave WLANs, and then study multi-AP association mechanisms to achieve low-overhead and blockage-robust mmWave wireless communications among multiple users and multiple APs. Furthermore, we explore the potential benefits achievable from AP mobility technology, which yields insights on the best configurations of mobile APs. We also develop an environment-aware link-quality predictor to accurately derive dynamic mmWave link quality due to static blockages and small changes in device locations, which provides a basis for the development of anticipatory networking with proactive resource-allocation schemes. In a complementary direction for evaluating the performance of mmWave networks, we develop and implement advanced features for dense wireless networks that increasingly characterize many mmWave scenarios of interest in the widely-used network simulator ns-3, including a sparse cluster-based wireless channel model that statistically models multi-path components in mmWave WLANs.Ph.D

Multi-Drone-Cell 3D Trajectory Planning and Resource Allocation for Drone-Assisted Radio Access Networks

Equipped with communication modules, drones can perform as drone-cells (DCs) that provide on-demand communication services to users in various scenarios, such as traffic monitoring, Internet of things (IoT) data collections, and temporal communication provisioning. As the aerial relay nodes between terrestrial users and base stations (BSs), DCs are leveraged to extend wireless connections for uncovered users of radio access networks (RAN), which forms the drone-assisted RAN (DA-RAN). In DA-RAN, the communication coverage, quality-of-service (QoS) performance and deployment flexibility can be improved due to the line-of-sight DC-to-ground (D2G) wireless links and the dynamic deployment capabilities of DCs. Considering the special mobility pattern, channel model, energy consumption, and other features of DCs, it is essential yet challenging to design the flying trajectories and resource allocation schemes for DA-RAN. In specific, given the emerging D2G communication models and dynamic deployment capability of DCs, new DC deployment strategies are required by DA-RAN. Moreover, to exploit the fully controlled mobility of DCs and promote the user fairness, the flying trajectories of DCs and the D2G communications must be jointly optimized. Further, to serve the high-mobility users (e.g. vehicular users) whose mobility patterns are hard to be modeled, both the trajectory planning and resource allocation schemes for DA-RAN should be re-designed to adapt to the variations of terrestrial traffic. To address the above challenges, in this thesis, we propose a DA-RAN architecture in which multiple DCs are leveraged to relay data between BSs and terrestrial users. Based on the theoretical analyses of the D2G communication, DC energy consumption, and DC mobility features, the deployment, trajectory planning and communication resource allocation of multiple DCs are jointly investigated for both quasi-static and high-mobility users. We first analyze the communication coverage, drone-to-BS (D2B) backhaul link quality, and optimal flying height of the DC according to the state-of-the-art drone-to-user (D2U) and D2B channel models. We then formulate the multi-DC three-dimensional (3D) deployment problem with the objective of maximizing the ratio of effectively covered users while guaranteeing D2B link qualities. To solve the problem, a per-drone iterated particle swarm optimization (DI-PSO) algorithm is proposed, which prevents the large particle searching space and the high violating probability of constraints existing in the pure PSO based algorithm. Simulations show that the DI-PSO algorithm can achieve higher coverage ratio with less complexity comparing to the pure PSO based algorithm. Secondly, to improve overall network performance and the fairness among edge and central users, we design 3D trajectories for multiple DCs in DA-RAN. The multi-DC 3D trajectory planning and scheduling is formulated as a mixed integer non-linear programming (MINLP) problem with the objective of maximizing the average D2U throughput. To address the non-convexity and NP-hardness of the MINLP problem due to the 3D trajectory, we first decouple the MINLP problem into multiple integer linear programming and quasi-convex sub-problems in which user association, D2U communication scheduling, horizontal trajectories and flying heights of DBSs are respectively optimized. Then, we design a multi-DC 3D trajectory planning and scheduling algorithm to solve the sub-problems iteratively based on the block coordinate descent (BCD) method. A k-means-based initial trajectory generation scheme and a search-based start slot scheduling scheme are also designed to improve network performance and control mutual interference between DCs, respectively. Compared with the static DBS deployment, the proposed trajectory planning scheme can achieve much lower average value and standard deviation of D2U pathloss, which indicate the improvements of network throughput and user fairness. Thirdly, considering the highly dynamic and uncertain environment composed by high-mobility users, we propose a hierarchical deep reinforcement learning (DRL) based multi-DC trajectory planning and resource allocation (HDRLTPRA) scheme for high-mobility users. The objective is to maximize the accumulative network throughput while satisfying user fairness, DC power consumption, and DC-to-ground link quality constraints. To address the high uncertainties of environment, we decouple the multi-DC TPRA problem into two hierarchical sub-problems, i.e., the higher-level global trajectory planning sub-problem and the lower-level local TPRA sub-problem. First, the global trajectory planning sub-problem is to address trajectory planning for multiple DCs in the RAN over a long time period. To solve the sub-problem, we propose a multi-agent DRL based global trajectory planning (MARL-GTP) algorithm in which the non-stationary state space caused by multi-DC environment is addressed by the multi-agent fingerprint technique. Second, based on the global trajectory planning results, the local TPRA (LTPRA) sub-problem is investigated independently for each DC to control the movement and transmit power allocation based on the real-time user traffic variations. A deep deterministic policy gradient based LTPRA (DDPG-LTPRA) algorithm is then proposed to solve the LTPRA sub-problem. With the two algorithms addressing both sub-problems at different decision granularities, the multi-DC TPRA problem can be resolved by the HDRLTPRA scheme. Simulation results show that 40% network throughput improvement can be achieved by the proposed HDRLTPRA scheme over the non-learning-based TPRA scheme. In summary, we have investigated the multi-DC 3D deployment, trajectory planning and communication resource allocation in DA-RAN considering different user mobility patterns in this thesis. The proposed schemes and theoretical results should provide useful guidelines for future research in DC trajectory planning, resource allocation, as well as the real deployment of DCs in complex environments with diversified users

Performance evaluation of future wireless networks: node cooperation and aerial networks

Perhaps future historians will only refer to this era as the \emph{information age}, and will recognize it as a paramount milestone in mankind progress. One of the main pillars of this age is the ability to transmit and communicate information effectively and reliably, where wireless radio technology became one of the most vital enablers for such communication. A growth in radio communication demand is notably accelerating in a never-resting pace, pausing a great challenge not only on service providers but also on researches and innovators to explore out-of-the-box technologies. These challenges are mainly related to providing faster data communication over seamless, reliable and cost efficient wireless network, given the limited availability of physical radio resources, and taking into consideration the environmental impact caused by the increasing energy consumption. Traditional wireless communication is usually deployed in a cellular manner, where fixed base stations coordinate radio resources and play the role of an intermediate data handler. The concept of cellular networks and hotspots is widely adopted as the current stable scheme of wireless communication. However in many situations this fixed infrastructure could be impaired with severe damages caused by natural disasters, or could suffer congestions and traffic blockage. In addition to the fact that in the current networks any mobile-to-mobile data sessions should pass through the serving base station that might cause unnecessary energy consumption. In order to enhance the performance and reliability of future wireless networks and to reduce its environmental footprint, we explore two complementary concepts: the first is node cooperation and the second is aerial networks. With the ability of wireless nodes to cooperate lays two main possible opportunities; one is the ability of the direct delivery of information between the communicating nodes without relaying traffic through the serving base station, thus reducing energy consumption and alleviating traffic congestion. A second opportunity would be that one of the nodes helps a farther one by relaying its traffic towards the base station, thus extending network coverage and reliability. Both schemes can introduce significant energy saving and can enhance the overall availability of wireless networks in case of natural disasters. In addition to node cooperation, a complementary technology to explore is the \emph{aerial networks} where base stations are airborne on aerial platforms such as airships, UAVs or blimps. Aerial networks can provide a rapidly deployable coverage for remote areas or regions afflicted by natural disasters or even to patch surge traffic demand in public events. Where node cooperation can be implemented to complement both regular terrestrial coverage and to complement aerial networks. In this research, we explore these two complementary technologies, from both an experimental approach and from an analytic approach. From the experimental perspective we shed the light on the radio channel properties that is hosting terrestrial node cooperation and air-to-ground communication, namely we utilize both simulation results and practical measurements to formulate radio propagation models for device-to-device communication and for air-to-ground links. Furthermore we investigate radio spectrum availability for node cooperation in different urban environment, by conductive extensive mobile measurement survey. Within the experimental approach, we also investigate a novel concept of temporary cognitive femtocell network as an applied solution for public safety communication networks during the aftermath of a natural disaster. While from the analytical perspective, we utilize mathematical tools from stochastic geometry to formulate novel analytical methodologies, explaining some of the most important theoretical boundaries of the achievable enhancements in network performance promised by node cooperation. We start by determining the estimated coverage and rate received by mobile users from convectional cellular networks and from aerial platforms. After that we optimize this coverage and rate ensuring that relay nodes and users can fully exploit their coverage efficiently. We continue by analytically quantifying the cellular network performance during massive infrastructure failure, where some nodes play the role of low-power relays forming multi-hop communication links to assist farther nodes outside the reach of the healthy network coverage. In addition, we lay a mathematical framework for estimating the energy saving of a mediating relay assisting a pair of wireless devices, where we derive closed-form expressions for describing the geometrical zone where relaying is energy efficient. Furthermore, we introduce a novel analytic approach in analyzing the energy consumption of aerial-backhauled wireless nodes on ground fields through the assistance of an aerial base station, the novel mathematical framework is based on Mat\'{e}rn hard-core point process. Then we shed the light on the points interacting of these point processes quantifying their main properties. Throughout this thesis we relay on verifying the analytic results and formulas against computer simulations using Monte-Carlo analysis. We also present practical numerical examples to reflect the usefulness of the presented methodologies and results in real life scenarios. Most of the work presented in this dissertation was published in-part or as a whole in highly ranked peer-reviewed journals, conference proceedings, book chapters, or otherwise currently undergoing a review process. These publications are highlighted and identified in the course of this thesis. Finally, we wish the reader to enjoy exploring the journey of this thesis, and hope it will add more understanding to the promising new technologies of aerial networks and node cooperation