Carrier aggregation (CA) is an important component technology in 5G and beyond. It aggregates multiple spectrum fragments to serve a mobile device. However, the current CA suffers under both high mobility and increased spectrum space. The limitations are rooted in its sequential, cell-by-cell operations. In this work, we propose CA++, which departs from the current paradigm and explores a group-based design scheme. We thus propose new algorithms that enable concurrent channel inference by measuring one or few cells but inferring all, while minimizing measurement cost via set cover approximations. Our evaluations have confirmed the effectiveness of CA++. Our solution can also be adapted to fit in the current 5G OFDM PHY and the 3GPP framework.

Extreme mobility becomes a norm rather than an exception with emergent high-speed rails, drones, industrial IoT, and many more. 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 deviseREM, Reliable Extreme Mobility management for beyond 5G cellular networks while maintaining backward compatibility to 4G/5G.REMshifts 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,REMreduces failures comparable to static and low mobility, with low signaling and latency cost.REMreduces the network failures by up to an order of magnitude, eliminates policy conflicts, and improves application performance by 31.8% - 88.3% compared to legacy 4G/5G.

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.

Failures in 5G mobile networks are becoming the norm with the ongoing global rollout. If left unattended, they affect mobile user experiences and the proper functioning of applications. In this work, we describe SEED, which offers a novel SIM-based solution to 5G failure diagnosis and handling. SEED infers failure causes by exploiting current standardized 5G error codes and decision-tree/online learning algorithms. It further takes corresponding multi-tier reset/redo actions (reset protocol operations, refresh outdated configurations, reload profiles, etc.) once the failure cause is inferred. SEED takes the operator's perspective in its design for fast deployment. SEED design works within the 5G standard framework and does not require changes on the device firmware or infrastructure hardware. Our evaluation has confirmed the viability of SEED.

This paper reports our five-year lessons of developing and using MobileInsight, an open-source community tool to enable software-defined full-stack, runtime mobile network analytics inside our phones. We present how MobileInsight evolves from a simple monitor to a community toolset with cross-layer analytics, energy-efficient real-time user-plane analytics, and extensible user-friendly analytics at the control and user planes. These features are enabled by various novel techniques, including cross-layer state machine tracking, missing data inference, and domain-specific cross-layer sampling. Their powerfulness is exemplified with a 5-year longitudinal study of operational mobile network latency using a 6.4TB dataset with 6.1 billion over-the-air messages. We further share lessons and insights of using MobileInsight by the community, as well as our visions of MobileInsight's past, present, and future.

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.

This paper investigates how to optimize the fingerprints reporting strategy to improve localization accuracy, and how the optimal strategy theory can be utilized to streamline the design of WLAN fingerprinting localization systems. In particular, we first reveal that the fingerprints reporting problem is essentially an NP-Hard size-constrained supermodular maximization problem, and then show the inapplicability of the state-of-the-art approximation algorithms to the problem. We then propose a new algorithm and show that if the number of fingerprints measurements is large enough, then the localization accuracy is at most 1 - ε times worse than the optimal value, with ε any given constant close to 0. Moreover, we demonstrate how the optimal strategy theory can be utilized to improve accuracy of location estimation by resolving the issue of similar fingerprints for both faraway and close-by locations, with an iterative algorithm developed to cross check fingerprints sampled in different locations, in order to derive the best possible result of localization. Further, we reveal the relationship between accuracy of location estimation and coverage of Wi-Fi signals in indoor spaces when planning deployment of APs. Experiment results are presented to validate our theoretical analysis.

In this paper, we call for a paradigm shift away from the wireless-access focused research efforts on 5G networked systems. We believe that the architectural limitations should share equal blame on issues of performance, reliability, and security. We thus identify architectural weakness on both sides of the mobile clients and the 4G network infrastructure. Our recent findings show that, contrary to commonly held perceptions, many design and operational issues arise not due to poor wireless link qualities. Instead, they are rooted in such architectural downsides. To address these issues, we further propose a new approach of enabling automated intelligence inside the 4G/5G network systems. We next describe our ongoing efforts along two dimensions: empowering date-driven smart clients and constructing verifiable network infrastructure. We report some early results and discuss possible next steps.

How to integrate labels from multiple labelers in order to obtain an accurate estimate of the ground truth is a major topic of crowdsourcing. One challenging issue is that, the labelers' abilities may vary significantly and the tasks distinguish each other in difficulties. Moreover, for a crowdsourcing system, task distributors have no idea in advance how many labels will be enough for each task. Consequently, an online task assignment mechanism based on the labeler expertise and question heterogeneousness becomes necessary. In this paper, we present such an online task assignment algorithm based on a probabilistic model consisting of both labeler abilities and question difficulties. We apply the online EM (Expectation Maximization) algorithm to make online estimations of system parameters, based on which we assign tasks adaptively. A series of simulation results have been demonstrated to show that our proposed scheme outperforms the conventional EM algorithm in efficiency and accuracy.

Indoor localization based on RSS fingerprinting approach has been attracting many research efforts in the past decades. Recent study presents a fundamental limit of the approach: given requirement of estimation accuracy, reliability of the user's localization result can be derived. As highly accurate indoor localization is essential to enable many location based services, a natural question to ask is: can we further improve the accuracy of the localization scheme fundamentally? In this paper, we theoretically show that the temporal correlation of the RSS can improve accuracy of the RSS fingerprinting based indoor localization. In particular, we construct a theoretical framework to evaluate how the temporal correlation of the RSS can influence the reliability of location estimation, which is based on a newly proposed radio propagation model considering the time-varying property of signals from a given Wi-Fi AP. Such a theoretical framework is then applied to analyze localization in the one dimensional physical space, which reveals the fundamental reason why performance improvement of localization can be brought by temporal correlation of the RSS. We further extend our analysis to high-dimensional scenarios. Experimental results corroborate our theoretical analysis.

Recent study on Wi-Fi RSS fingerprinting based indoor localization reveals that reporting fingerprints with respect to different set of access points (APs) results in location estimations in different levels of accuracy; however, how to find the best strategy for fingerprints reporting with reasonable computational cost, and how to exploit the finding to streamline the design of the localization system are still unknown. In this paper, we revisit the design principles of the localization system with the opportunity provided by the theory of best fingerprints reporting strategy. We first present the localization reliability bounds under the best strategy, and develop algorithms to find the best strategy in practice. We then demonstrate how the best strategy theory can be utilized to improve accuracy of location estimation by resolving the issue of similar fingerprints for both faraway and close-by locations. An iterative algorithm is developed to cross check fingerprints sampled in different locations, in order to derive the best possible result of localization. Moreover, we reveal the relationship between accuracy of location estimation and coverage of Wi-Fi signals in a region, when planning deployment of APs. Experiment results are presented to validate our analysis and design.

Lane-level vehicular localization has been regarded as a critical technology component for future vehicle navigation services. Current lane-level vehicular localization systems require dedicated devices, making the systems difficult to popularize. Moreover, most systems depend heavily on continuous accurate GPS data, which may be interfered under specific environments. Efficient lane-level map building is another problem to deal with. In this paper, we propose an integrated system with the capability of map building and lane- level localization using smartphones. This system employs a crowdsourcing-based approach to collect information from multiple sensors (including GPS, orientation sensor and acceleration sensor), such as lane changes and turns. Based on the information, a lane localization schemes is designed using the tool of machine learning. The experimental results show that the proposed system achieves high accuracy of map building and lane-level localization.