UCLA

Mobile devices generate an enormous amount of data traffic to satisfy their computing and communications needs. To meet these demands, mobile network operators frequently need to expand their capacity, which entails significant capital costs and increased energy consumption. Motivated by this, we seek to develop cooperative systems that will bring higher communications speeds and larger computing power to mobile devices without relying on mobile network infrastructure.

In recent years, unmanned aerial vehicle (UAV) technology has garnered interest for its potential use as a communications enabler. Swarms of UAVs can be deployed as temporary relays to meet short term but high intensity communication demands from mobile users. UAV swarms can coordinate their placement to improve the capacity on the fronthaul link between users and UAVs. Algorithms for optimal placement often rely on the knowledge of channel gain across space. Hence, we developed deep learning methods for channel gain prediction across space based on measurements collected by the UAVs and 3D maps of the environment. In line with this, we also developed methods to design UAV flying paths for optimal measurement collection such that the accuracy of channel gain prediction is maximized under constraints on the distance traveled by the UAVs. Additionally, we develop a reinforcement-learning based approach that controls a UAV to directly improve the fronthaul link without relying on channel gain knowledge across space.

With the proliferation of intelligent vehicles, there is an increasing number of computationally demanding computer applications appearing in vehicular environments. Providing the computational resources to meet the demands of such applications is a critical problem. In this work, we consider a cooperative computing paradigm between intelligent vehicles of similar computing power to enable emerging vehicular applications. Vehicles cooperate with each other over vehicle-to-vehicle networks to form vehicular micro clouds that can complete computationally intensive tasks without relying on cloud or edge computing. We developed optimized resource assignment and scheduling algorithms that efficiently use vehicular computing resources for computation in emerging vehicular applications. Our proposed approaches adapt to link quality changes between vehicles and prevent congestion in vehicular networks, even in the presence of incumbent interference.