Low Earth Orbit (LEO) satellite communication is a promising approach to providing Internet connectivity to users in many remote areas. As videos are likely to account for most traffic in the LEO satellite network, as in the rest of the Internet, this work introduces a novel video-aware mobility management framework tailored for LEO satellite networks. Utilizing simulation models alongside real-world datasets, we show the importance of handoff strategy and throughput prediction algorithms in single-user and multi-user video streaming scenarios. Motivated by these observations, we propose a set of novel algorithms that can jointly choose the satellite and video bitrate to optimize the Quality of Experience (QoE). We first develop Model Predictive Control (MPC) and Reinforcement Learning (RL) based algorithms for a single user, and then extend them to accommodate multiple competing users that may share the same satellite. We introduce centralized training and distributed inference for our RL design, enabling a distributed policy informed by a global perspective. We demonstrate the effectiveness of our proposed models using trace-driven simulation and testbed experiments. We share our code and data with the research community.

The popularity of 4K videos is on the rise. However, streaming such high-quality videos over mm Wave to several users presents significant challenges due to directional communication, fluctuating channels, and high bandwidth demands. To address these challenges, this paper introduces an innovative 4K layered video multicast streaming system. We (i) develop a video quality model tailored for layered video coding, (ii) optimize resource allocation, scheduling, and beamforming based on the channel conditions of different users, and (iii) design a streaming strategy that integrates fountain code to eliminate redundancy in multicast groups, coupled with a Leaky-Bucket approach for congestion control. We implement our system on Commodity-Off- The-Shelf (COTS) WiGig devices and demonstrate its effectiveness through comprehensive testbed and emulation experiments.

Cellular service providers (CSPs) require predicting the network performance for various reasons such as analyzing the impact of planned configuration changes and large-scale events on the network. Although network configurations are widely considered as key predictors of performance, we claim that they are insufficient for accurately predicting cellular network performance. The cellular networks are impacted by unmeasured external factors (e.g., weather, called latents), therefore, the performance prediction based solely on configurations may result in confounding effects. We show that the Mobility, Access, and Traffic (MAT) metrics should be considered in addition as network performance predictors. Using a large dataset collected from a live cellular network, we validate the claim and show the benefit of using MAT metrics for accurate performance prediction.

Cellular service providers (CSPs) aim to optimize network performance and enhance user experience by tuning network configurations. However, this process often requires continuous live network testing, which incurs significant operational costs. In this paper, we focus on predicting the impact of configuration changes using historical data, thereby reducing the need for live network tests. A key challenge in developing such a model is accounting for unobserved external factors (e.g., weather, referred to as latents) that can introduce confounding effects between configurations and performance metrics. To address this, we employ intermediate network metrics, called Mobility, Access, and Traffic (MAT) metrics, which are influenced by both configurations and latents, and in turn, affect performance metrics. We introduce Configuration Impact Prediction Analysis Toolkit (CIPAT), a novel two-stage toolkit developed using a comprehensive real-world dataset from live LTE networks. Our evaluation demonstrates that CIPAT enables CSPs to predict the performance impact of proposed configuration changes with up to 86% accuracy and 85% efficacy, thereby reducing the operational costs associated with configuration tuning.

Managing network configuration and improving service experience effectively is essential for cellular service providers (CSPs). This is challenging because of cellular networks' large scale and complexity, the wide variety of configuration parameters, and the performance impact tradeoffs resulting across multiple metrics and geographical locations. This paper focuses on learning and using network contexts to recommend performance-improving configurations. While learning contexts, one must carefully account for the configuration parameter dependency, performance impact confusion that can arise due to co-occurring unrelated changes, and uneven change deployment distribution across locations. We present a new solution Chroma that addresses the above challenges. Using real-world data collected from a large operational LTE and 5G cellular service provider, we thoroughly evaluate and demonstrate the efficacy of Chroma. We successfully trial Chroma on an operational cellular network and highlight its benefits in practical settings.

Channel estimation is critical to millimeter-wave capability. Unlike sub-6 GHz WiFi, commercial-off-the-shelf 60 GHz WiFi devices adopt a single RF-chain and can only report the combined received signal strength (RSS) instead of the antenna-wise channel state information (CSI). Therefore, recovering the CSI using a limited number of RSS measurements is important but faces the following challenges: (i) solving a non-convex objective is hard and computationally heavy, (ii) the estimation error is high with insufficient RSS measurements, and (iii) channel fluctuates dynamically. To jointly tackle them, we propose 2ACE, an Accelerated and Accurate Channel Estimation approach using spectral profile-driven multiresolutional compressive sensing. Our thorough experiments show that 2ACE yields 2--8 dB reduction in CSI estimation error, 1--5 dB improvement in beamforming performance, and 5° - 10° reduction in angle-of-departure estimation error over the existing schemes.

The wireless signal propagates via multipath arising from different reflections and penetration between a transmitter and receiver. Extracting multipath profiles (e.g., delay and Doppler along each path) from received signals enables many important applications, such as channel prediction and crossband channel estimation (i.e., estimating the channel on a different frequency). The benefit of multipath estimation further increases with mobility since the channel in that case is less stable and more important to track. Yet high-speed mobility poses significant challenges to multipath estimation. In this paper, instead of using time-frequency domain channel representation, we leverage the delay-Doppler domain representation to accurately extract and predict multipath properties. Specifically, we use impulses in the delay-Doppler domain as pilots to estimate the multipath parameters and apply the multipath information to predicting wireless channels as an example application. Our design rationale is that mobility is more predictable than the wireless channel since mobility has inertial while the wireless channel is the outcome of a complicated interaction between mobility, multipath, and noise. We evaluate our approach via both acoustic and RF experiments, including vehicular experiments using USRP. Our results show that the estimated multipath matches the ground truth, and the resulting channel prediction is more accurate than the traditional channel prediction schemes.