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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. 

Extreme mobility has become a norm rather than an exception. However, 4G/5G mobility management is not always reliable in extreme mobility, with non-negligible failures and policy conflicts. The root cause is that, existing mobility management is primarily based on wireless signal strength. While reasonable in static and low mobility, it is vulnerable to dramatic wireless dynamics from extreme mobility in triggering, decision, and execution. We devise REM, Reliable Extreme Mobility management for 4G, 5G, and beyond. REM shifts to movement-based mobility management in the delay-Doppler domain. Its signaling overlay relaxes feedback via cross-band estimation, simplifies policies with provable conflict freedom, and stabilizes signaling via scheduling-based OTFS modulation. Our evaluation with operational high-speed rail datasets shows that, REM reduces failures comparable to static and low mobility, with low signaling and latency cost. 

Cellular service providers continuously upgrade their network software on base stations to introduce new service features, fix software bugs, enhance quality of experience to users, or patch security vulnerabilities. A software upgrade typically requires the network element to be taken out of service, which can potentially degrade the service to users. Thus, the new software is deployed across the network using a rolling upgrade model such that the service impact during the roll-out is minimized. A sequential roll-out guarantees minimal impact but increases the deployment time thereby incurring a significant human cost and time in monitoring the upgrade. A network-wide concurrent roll-out guarantees minimal deployment time but can result in a significant service impact. The goal is to strike a balance between deployment time and service impact during the upgrade. In this paper, we first present our findings from analyzing upgrades in operational networks and discussions with network operators and exposing the challenges in rolling software upgrades. We propose a new framework Concord to effectively coordinate software upgrades across the network that balances the deployment time and service impact. We evaluate Concord using real-world data collected from a large operational cellular network and demonstrate the benefits and tradeoffs. We also present a prototype deployment of Concord using a small-scale LTE testbed deployed indoors in a corporate building. 

Cellular networks are constantly evolving due to frequent changes in radio access and end user equipment technologies, dynamic applications and associated traffic mixes. Network upgrades should be performed with extreme caution since millions of users heavily depend on the cellular networks for a wide range of day to day tasks, including emergency and alert notifications. Before upgrading the entire network, it is important to conduct field evaluation of upgrades. Field evaluations are typically cumbersome and can be time consuming; however if done correctly they can help alleviate a lot of the deployment issues in terms of service quality degradation. The choice and number of field test locations have significant impacts on the time-to-market as well as confidence in how well various network upgrades will work out in the rest of the network. In this paper, we propose a novel approach – Reflection to automatically determine where to conduct the upgrade field tests in order to accurately identify important features that affect the upgrade. We demonstrate the effectiveness of Reflection using extensive evaluation based on real traces collected from a major US cellular network as well as synthetic traces.