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We design and implement LDRP , a device-based, standard-compliant solution to latency diagnosis and reduction in mobile networks without root privilege. LDRP takes a data-driven approach and works with a variety of latency-sensitive applications. After identifying elements in LTE uplink latency, we design LDRP that can infer the critical parameter used in data transmission and infer them for diagnosis. In addition, LDRP designates small dummy messages, which precede uplink data transmissions, thus eliminating latency elements due to power-saving, scheduling, etc. It imposes proper timing control among dummy messages and data packets to handle various conflicts. We achieve the latency diagnosis and reduction without requiring root privilege and ensure the latency is no worse than the legacy LTE design. The design of LDRP is also applicable for 5 G. The evaluation shows that, LDRP infers the latency with at most 4% error and reduces the median LTE uplink latency by a factor up to 7.4× (from 42 to 5 ms) for four apps over 4 mobile carriers. 

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. 

Multi-carrier cellular access dynamically selects a preferred wireless carrier by leveraging the availability and diversity of multiple carrier networks at a location. It offers an alternative to the dominant single-carrier paradigm, and shows early signs of success through the operational Project Fi by Google. In this paper, we study the important, yet largely unexplored, problem of inter-carrier switching for multi-carrier access. We show that policy conflicts can arise between inter- and intra-carrier switching, resulting in oscillations among carriers in the worst case akin to BGP looping. We derive the conditions under which such oscillations occur for three categories of popular policy, and validate them with Project Fi whenever possible. We provide practical guidelines to ensure loop-freedom and assess them via trace-driven emulations.