Performance Analysis and Learning Algorithms in Advanced Wireless Networks

Over the past decade, wireless data traffic has experienced an exponential growth, especially with multimedia traffic becoming the dominant traffic, and such growth is expected to continue in the near future. This unprecedented growth has led to an increasing demand for high-rate wireless communications.Key solutions for addressing such demand include extreme network densification with more small-cells, the utilization of high frequency bands, such as the millimeter wave (mmWave) bands and terahertz (THz) bands, where more bandwidth is available, and unmanned aerial vehicle (UAV)-enabled cellular networks. With this motivation, different types of advanced wireless networks are considered in this thesis. In particular, mmWave cellular networks, networks with hybrid THz, mmWave and microwave transmissions, and UAV-enabled networks are studied, and performance metrics such as the signal-to-interference-plus-noise ratio (SINR) coverage, energy coverage, and area spectral efficiency are analyzed. In addition, UAV path planning in cellular networks are investigated, and deep reinforcement learning (DRL) based algorithms are proposed to find collision-free UAV trajectory to accomplish different missions. In the first part of this thesis, mmWave cellular networks are considered. First, K-tier heterogeneous mmWave cellular networks with user-centric small-cell deployments are studied. Particularly, a heterogeneous network model with user equipments (UEs) being distributed according to Poisson cluster processes (PCPs) is considered. Distinguishing features of mmWave communications including directional beamforming and a detailed path loss model are taken into account. General expressions for the association probabilities of different tier base stations (BSs) are determined. Using tools from stochastic geometry, the Laplace transform of the interference is characterized and general expressions for the SINR coverage probability and area spectral efficiency are derived. Second, a distributed multi-agent learning-based algorithm for beamforming in mmWave multiple input multiple output (MIMO) networks is proposed to maximize the sum-rate of all UEs. Following the analysis of mmWave cellular networks, a three-tier heterogeneous network is considered, where access points (APs), small-cell BSs (SBSs) and macrocell BSs (MBSs) transmit in THz, mmWave, microwave frequency bands, respectively. By using tools from stochastic geometry, the complementary cumulative distribution function (CCDF) of the received signal power, the Laplace transform of the aggregate interference, and the SINR coverage probability are determined. Next, system-level performance of UAV-enabled cellular networks is studied. More specifically, in the first part, UAV-assisted mmWave cellular networks are addressed, in which the UE locations are modeled using PCPs. In the downlink phase, simultaneous wireless information and power transfer (SWIPT) technique is considered. The association probability, energy coverages and a successful transmission probability to jointly determine the energy and SINR coverages are derived. In the uplink phase, a scenario that each UAV receives information from its own cluster member UEs is taken into account. The Laplace transform of the interference components and the uplink SINR coverage are characterized. In the second part, cellular-connected UAV networks is investigated, in which the UAVs are aerial UEs served by the ground base stations (GBSs). 3D antenna radiation combing the vertical and horizontal patterns is taken into account. In the final part of this thesis, deep reinforcement learning based algorithms are proposed for UAV path planning in cellular networks. Particularly, in the first part, multi-UAV non-cooperative scenarios is considered, where multiple UAVs need to fly from initial locations to destinations, while satisfying collision avoidance, wireless connectivity and kinematic constraints. The goal is to find trajectories for the cellular-connected UAVs to minimize their mission completion time. The multi-UAV trajectory optimization problem is formulated as a sequential decision making problem, and a decentralized DRL approach is proposed to solve the problem. Moreover, multiple UAV trajectory design in cellular networks with a dynamic jammer is studied, and a learning-based algorithm is proposed. Subsequently, a UAV trajectory optimization problem is considered to maximize the collected data from multiple Internet of things (IoT) nodes under realistic constraints. The problem is translated into a Markov decision process (MDP) and dueling double deep Q-network (D3QN) is proposed to learn the decision making policy

What are gels and how to reduce them?

Gels are a common quality problem in plastic films. Gel formation in polyolefin film is a phenomenon that is difficult to predict, reproduce and solve. 1 Gels are not only an aesthetic concern, but also could affect bag integrity and raise concerns of extractables/leachables in biopharmaceutical industry. Gels could be caused by un-melted resins, cross linked/oxidized polymers, additives, air bubbles, moisture and foreign contaminates. It is important to identify the root cause of gels and take appropriate actions to reduce them. If the wrong counteraction is applied, the problem can intensify. Currently, biopharmaceutical industry lacks a standard for particle/black spec specification in single-use systems. This presentation overviews the mechanism of gels, how process conditions and screw designs affect gel formation and strategies to reduce gels. Gel identification techniques and acceptance standards used in other industries are discussed. 1. The use of polymer processing AIDS to reduce gel formation in polyolefin plastomer extrusion.; Woods, Susan S.; Amos, Stephen E.; (Dyneon LLC, Oakdale, MN 55128, USA). Polym., Laminations Coat. Conf.; Volume 2; 1998; 675-68

The Effects On Population Health Status of Using Dedicated Property Taxes To Fund Local Public Health Agencies

Background: In the United States, a dedicated property tax describes the legal authority given to a local jurisdiction to levy and collect a tax for a specific purpose. We investigated for an association of locally dedicated property taxes to fund local public health agencies and improved health status in the eight states designated as the Mississippi Delta Region. Methods: We analyzed the difference in health outcomes of counties with and without a dedicated public health tax after adjusting for a set of control variables using regression models for county level data from 720 counties of the Mississippi Delta Region. Results: Levying a dedicated public health tax for counties with per capita income above $28,000 is associated with improved health outcomes of those counties when compared to counties without a dedicated property tax for public health. Alternatively, levying a dedicated property tax in counties with lower per capita income is associated with poor health outcomes. Conclusions: There are both positive and negative consequences of using dedicated property taxes to fund public health. Policymakers should carefully examine both the positive association of improved health outcomes and negative impact of taxation on poor populations before authorizing the use of dedicated local property tax levies to fund public health agencies

Multi-Agent Double Deep Q-Learning for Beamforming in mmWave MIMO Networks

Beamforming is one of the key techniques in millimeter wave (mmWave) multi-input multi-output (MIMO) communications. Designing appropriate beamforming not only improves the quality and strength of the received signal, but also can help reduce the interference, consequently enhancing the data rate. In this paper, we propose a distributed multi-agent double deep Q-learning algorithm for beamforming in mmWave MIMO networks, where multiple base stations (BSs) can automatically and dynamically adjust their beams to serve multiple highly-mobile user equipments (UEs). In the analysis, largest received power association criterion is considered for UEs, and a realistic channel model is taken into account. Simulation results demonstrate that the proposed learning-based algorithm can achieve comparable performance with respect to exhaustive search while operating at much lower complexity.Comment: To be published in IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) 202