Qianru Li

BIO


I am Qianru Li, pursuing Computer Science Ph.D. at UCLA. I work in Wireless Networking Group (WiNG), supervised by Prof. Songwu Lu. Before that, I received Bachelor of Science in Computer Science from Shanghai Jiao Tong University in 2016. My research interest are in the area of cellular networks and mobile systems.

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PUBLICATION


Reconfiguring Cell Selection in 4G/5G Networks IEEE ICNP'21

Qianru Li, Chunyi Peng
The 29th IEEE International Conference on Network Protocols (IEEE ICNP 2021)

In cellular networks, cell selection plays a critical role in providing and maintaining ubiquitous radio access. It follows standardized procedures with operator-specific policies pre-configured by tunable parameters. These parameters specify the criteria to determine whether and how to select new serving cell(s), thus impacting access quality and user experience. Recent studies reveal that today’s cell selection fails to offer good performance as it can. This is because it is configured for seamless connectivity, and thus performance is offered at “best effort”. In this work, we attempt to re-configure these parameters by taking performance into consideration. We first conduct a measurement study in one big city in the US to demonstrate that reconfigu- ration indeed helps improve the overall performance, without compromising connectivity. This implies that 4G/5G networks are capable of offering better performance but such potentials are under-utilized in practice. We further explore proactive reconfiguration to prevent such unnecessary performance losses. We examine technical challenges, factors and even limitations to reconfigure cell selection in a standard-compatible manner, and finally devise a simple reconfiguration algorithm based on profiling and heuristic searching to efficiently pursue promising performance gains. The evaluation over AT&T and T-Mobile in two US cities has validated its effectiveness. Performance gains outweigh losses. Reconfiguration boosts data speed in more than 30% of instances, which exceeds the ratio of losses by at least 16%; The median speed gain is at least 89.1% (up to 217 fold).

Device-Centric Detection and Mitigation of Diameter Signaling Attacks against Mobile Core IEEE CNS'21

Zhaowei Tan, Boyan Ding, Zhehui Zhang, Qianru Li, Yunqi Guo, Songwu Lu
IEEE Conference on Communications and Network Security (IEEE CNS 2021)

Deterrence of Intelligent DDoS via Multi-Hop Traffic Divergence ACM CCS'21

Yuanjie Li, Hewu Li, Zhizheng Lv, Xingkun Yao, Qianru Li, Jianping Wu
The ACM Conference on Computer and Communications Security (ACM CCS 2021)

We devise a simple, provably effective, and readily usable deterrence against intelligent, unknown DDoS threats: Demotivate adversaries to launch attacks via multi-hop traffic divergence. This new strategy is motivated by the fact that existing defenses almost always lag behind numerous emerging DDoS threats and evolving intelligent attack strategies. The root cause is if adversaries are smart and adaptive, no single-hop defenses (including optimal ones) can perfectly differentiate unknown DDoS and legitimate traffic. Instead, we formulate intelligent DDoS as a game between attackers and defenders, and prove how multi-hop traffic divergence helps bypass this dilemma by reversing the asymmetry between attackers and defenders. This insight results in EID, an Economical Intelligent DDoS Demotivation protocol. EID combines local weak (yet divergent) filters to provably null attack gains without knowing exploited vulnerabilities or attack strategies. It incentivizes multi-hop defenders to cooperate with boosted local service availability. EID is resilient to traffic dynamics and manipulations. It is readily deployable with random-drop filters in real networks today. Our experiments over a 49.8 TB dataset from a department at the Tsinghua campus network validate EID’s viability against rational and irrational DDoS with negligible costs.

Experience: A Five-Year Retrospective of MobileInsight MobiCom'21

Yuanjie Li, Chunyi Peng, Zhehui Zhang, Zhaowei Tan, Haotian Deng, Jinghao Zhao, Qianru Li, Yunqi Guo, Kai Ling, Boyan Ding, Hewu Li, Songwu Lu
The 27th Annual International Conference on Mobile Computing and Networking (ACM MobiCom'21)

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.

@inproceedings{li2021experience, title={Experience: a five-year retrospective of MobileInsight}, author={Li, Yuanjie and Peng, Chunyi and Zhang, Zhehui and Tan, Zhaowei and Deng, Haotian and Zhao, Jinghao and Li, Qianru and Guo, Yunqi and Ling, Kai and Ding, Boyan and others}, booktitle={Proceedings of the 27th Annual International Conference on Mobile Computing and Networking}, pages={28--41}, year={2021}}

iCellSpeed: Increasing Cellular Data Speed with Device-Assisted Cell Selection MobiCom'20

Haotian Deng, Qianru Li, Jingqi Huang, Chunyi Peng
The 26th Annual International Conference on Mobile Computing and Networking (ACM MobiCom'20)

In this paper, we propose iCellSpeed, an on-device solution to increase data access speed by substantiating unrealized performance potentials. We find that performance potentials are missed in today’s mobile networks, as the data speed a user device gets is much lower than what the device could get. The issue is rooted in the current cell selection practice, which misses good candidate cells that offer faster access speed, thus under-utilizing the available capabilities in mobile networks. We design iCellSpeed to facilitate network-controlled cell selection with proactive device-side assistance towards more desirable cells. Our evaluation over AT&T and Verizon confirms its effectiveness. iCellSpeed increases data access speed by more than 10 Mbps at 79% of test locations (> 25Mbps at 29% of locations, up to 80.6 Mbps). It doubles access speed at 62.5% of locations with the gain up to 28.4x.

@inproceedings{deng2020icellspeed, title={iCellSpeed: increasing cellular data speed with device-assisted cell selection}, author={Deng, Haotian and Li, Qianru and Huang, Jingqi and Peng, Chunyi}, booktitle={Proceedings of the 26th Annual International Conference on Mobile Computing and Networking}, pages={1--13}, year={2020}}

Beyond 5G: Reliable Extreme Mobility Management SIGCOMM'20

Yuanjie Li, Qianru Li, Zhehui Zhang, Ghufran Baig, Lili Qiu, Songwu Lu
ACM SIGCOMM 2020

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 delayDoppler domain. Its signaling overlay relaxes feedback via crossband 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.

@inproceedings{li2020beyond, title={Beyond 5g: Reliable extreme mobility management}, author={Li, Yuanjie and Li, Qianru and Zhang, Zhehui and Baig, Ghufran and Qiu, Lili and Lu, Songwu}, booktitle={Proceedings of the Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication}, pages={344--358}, year={2020} }

Resolving Policy Conflicts in Multi-Carrier Cellular Access MobiCom'18

Zengwen Yuan*, Qianru Li*, Yuanjie Li, Songwu Lu, Chunyi Peng, George Varghese
The 24th Annual International Conference on Mobile Computing and Networking (ACM MobiCom'18)
*Co-primary authors

Multi-carrier access dynamically selects a preferred cellular carrier, by leveraging the availability and diversity of multiple carrier networks at a location. It offers an alternative solution to the dominant single-carrier practice, and shows early signs of success through the operational Project Fi by Google. In this paper, we study an important, yet largely unexplored problem of inter-carrier switch for multi-carrier access. We show that policy con icts may arise between the inter- and intra-carrier levels, 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 that ensure loop-freedom and assess them via trace-driven emulations.

@inproceedings {yuan2018resolving, author = {Zengwen Yuan and Qianru Li and Yuanjie Li and Songwu Lu and Chunyi Peng and George Varghese}, title = {Resolving Policy Conflicts in Multi-Carrier Cellular Access}, booktitle = {The 24th ACM Annual International Conference on Mobile Computing and Networking (MobiCom'18)}, year = {2018}, month = Oct, address = {New Delhi, India}}

Supporting Mobile VR in LTE Networks: How Close Are We? SIGMETRICS'18

Zhaowei Tan, Yuanjie Li, Qianru Li, Zhehui Zhang, Zhehan Li, Songwu Lu
The 44th ACM Annual Conference of Special Interest Group on Measurement and Evaluation (SIGMETRICS'18)

Mobile virtual reality (VR) headsets (e.g., Google Cardboard and Samsung Gear VR) seek to offer “anytime, anywhere” panorama, immerse 3D experiences for users. In this work, we study the viability of supporting mobile VR over operational 4G LTE networks, where the device provides pose information to the edge servers to offload graphical processing. We find that, contrary to common perceptions, wireless bandwidth is not the latency bottleneck for medium-quality VR. Instead, the signaling operations, which facilitate wireless data delivery, constitute a bulk portion of the latency. We report findings that challenge five common beliefs on VR network latency in LTE under both static and mobile scenarios, and quantify their impact. We design LTE-VR, a client-side solution to medium-quality VR over LTE. LTE-VR leverages cross-layer design and rich side channel information to reduce various latency sources in the signaling operations. Our prototype evaluation has confirmed its viability in 4G LTE. We discuss its applicability to the upcoming 5G.

@inproceedings {tan2018vr, author = {Zhaowei Tan and Yuanjie Li and Qianru Li and Zhehui Zhang and Zhehan Li and Songwu Lu}, title = {Enabling Mobile VR in LTE Networks: How Close Are We?}, booktitle = {The 44th ACM Annual Conference of Special Interest Group on Measurement and Evaluation (SIGMETRICS'18)}, year = {2018}, month = Jun, address = {Irvine, California, USA}}

SOFTWARE & PROJECT


MobileInsight: In-phone mobile network analytics
  • Support control-plane monitoring (RRC/NAS) on MediaTek chipsets.
  • Build analyzers to extract performance metrics on data plane (PDCP/RLC/MAC/PHY).
  • WORK EXPERIENCE


    Research Intern @ HP Labs, San Jose, CA

    Jun 2020 - Sep 2020

    Design and implement algorithms to prevent internal attacks for edge-based services.

    Software Engineering Intern @ Google, Seattle, WA

    Jun 2019 - Sep 2019

    Design and evaluate algorithms to mitigate latency for Google Stadia on Wi-Fi.

    Software Engineering Intern @ Uber, Palo Alto, CA

    Jun 2018 - Sep 2018

    Explore the impact of LTE behaviors on ride-sharing traffic and build a tool for analysis.

    AWARD


    UCLA Summer Mentored Research Fellowship, 2021
    UCLA Graduate Division Fellowship, 2021
    ACM Sigmetrics Student Travel Grant, 2018

    TEACHING


    Teaching Fellow Since 2019. Courses include:

    CS 32 Introduction to Computer Science II (Advanced C++ Topics and Data Structure)
    CS 118 Computer Network Fundamentals
    CS M51A Logic Design of Digital Systems