Search

Next-generation cellular systems will rely on millimeter wave (mmWave) bands to meet the increasing demand for wireless connectivity from end user equipment. Given large available bandwidth and small-sized antenna elements, mmWave frequencies can support high communication rates and facilitate the use of multiple-input-multiple-output (MIMO) techniques to increase the wireless capacity. However, the small wavelength of mmWave yields severe path loss and high channel uncertainty. Meanwhile, using a large number of antenna elements requires a high energy consumption and heavy communication overhead for MIMO transmissions and channel measurement. To facilitate efficient mmWave communications, in this dissertation, the challenges of energy efficiency and communication overhead are addressed. First, the use of unmanned aerial vehicle (UAV), intelligent signal reflector, and device-to-device (D2D) communications are investigated to improve the reliability and energy efficiency of mmWave communications in face of blockage. Next, to reduce the communication overhead, new channel modeling and user localization approaches are developed to facilitate MIMO channel estimation by providing prior knowledge of mmWave links. Using advance mathematical tools from machine learning (ML), game theory, and communication theory, this dissertation develops a suite of novel frameworks using which mmWave communication networks can be reliably deployed and operated in wireless cellular systems, UAV networks, and wearable device networks. For UAV-based wireless communications, a learning framework is developed to predict the cellular data traffic during congestion events, and a new framework for the on-demand deployment of UAVs is proposed to offload the excessive traffic from the ground base stations (BSs) to the UAVs. The results show that the proposed approach enables a dynamical and optimal deployment of UAVs that alleviates the cellular traffic congestion. Subsequently, a novel energy-efficient framework is developed to reflect mmWave signals from a BS towards mobile users using a UAV-carried intelligent reflector (IR). To optimize the location and reflection coefficient of the UAV-carried IR, a deep reinforcement learning (RL) approach is proposed to maximize the downlink transmission capacity. The results show that the RL-based approach significantly improves the downlink line-of-sight probability and increases the achievable data rate. Moreover, the channel estimation challenge for MIMO communications is addressed using a distributional RL approach, while optimizing an IR-aided downlink multi-user communication. The results show that the proposed method captures the statistic feature of MIMO channels, and significantly increases the downlink sum-rate. Moreover, in order to capture the characteristics of air-to-ground channels, a data-driven approach is developed, based on a distributed framework of generative adversarial networks, so that each UAV collects and shares mmWave channel state information (CSI) for cooperative channel modeling. The results show that the proposed algorithm enables an accurate channel modeling for mmWave MIMO communications over a large temporal-spatial domain. Furthermore, the CSI pattern is analyzed via semi-supervised ML tools to localize the wireless devices in the mmWave networks. Finally, to support D2D communications, a novel framework for mmWave multi-hop transmissions is investigated to improve the performance of the high-rate low-latency transmissions between wearable devices. In a nutshell, this dissertation provides analytical foundations on the ML-based performance optimization of mmWave communication systems, and the anticipated results provide rigorous guidelines for effective deployment of mmWave frequency bands into next-generation wireless systems (e.g., 6G).

Transit passenger Origin-Destination (OD) matrices are essential inputs to transit analysis, planning and operations. With increased availability of data collection technologies and acceptance and deployment of such technologies by transit agencies, large quantities of boarding and alighting data can be collected and used to enhance the ability of transit agencies to estimate OD information that was previously too expensive to obtain. This thesis investigates two transfer OD estimation methods that use boarding and alighting counts for a two-route bus transit network. A transfer OD matrix represents OD flows for origin-destination stop pairs where the origin and destination stops are contained on different routes. Both methods investigated, namely, the modified iterative proportional fitting (MIPF) method and the proportional distribution (PD) method, are illustrated on a small, hypothetical transit network with two intersecting routes. In addition, both methods are implemented on two full-scale intersecting bus routes in the Central Ohio Transit Authority (COTA). Boarding and alighting counts are provided by COTA's automatic passenger counting (APC) system, and null information is used for the required base input. Bus trips on the two routes are matched to form bus trip pairs that reflect transfers. The empirical results show that, when null base information is used, the differences between the estimates produced by the two methods are negligible compared to the differences produced in different time periods. Simulation analysis indicates that the quality of the base transfer OD matrix greatly affects the quality of the estimation results. Using a high quality base transfer OD matrix produces high quality results. Using a null base transfer OD matrix produces lower quality results than when using a high quality base transfer OD matrix, but much higher quality results than when using a low quality base transfer OD matrix. Thus, it is recommended to select the base transfer OD matrix with caution. When little confidence exists in the quality of the base transfer OD matrices, using the null base transfer OD matrices is recommended.