Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time=2021-09-17 8:40
|time=2021-09-24 8:40
|addr=Main Building B1-612
|addr=Main Building B1-612
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].

Revision as of 08:34, 24 September 2021

Time: 2021-09-24 8:40
Address: Main Building B1-612
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [MobiCom 2021] A community-driven approach to democratize access to satellite ground stations, Rong Cong
    Abstract: Should you decide to launch a nano-satellite today in Low-Earth Orbit (LEO), the cost of renting ground station communication infrastructure is likely to significantly exceed your launch costs. While space launch costs have lowered significantly with innovative launch vehicles, private players, and smaller payloads, access to ground infrastructure remains a luxury. This is especially true for smaller LEO satellites that are only visible at any location for a few tens of minutes a day and whose signals are extremely weak, necessitating bulky and expensive ground station infrastructure. In this paper, we present a community-driven distributed reception paradigm for LEO satellite signals where signals received on many tiny handheld receivers (not necessarily deployed on rooftops but also indoors) are coherently combined to recover the desired signal. This is made possible by employing new synchronization and receiver orientation techniques that study satellite trajectories and leverage the presence of other ambient signals. We compare our results with a large commercial receiver deployed on a rooftop and show a 8 dB SNR increase both indoors and outdoors using 8 receivers, costing $38 per RF frontend.
  2. [NSDI 2021] Toward Nearly-Zero-Error Sketching via Compressive Sensing, Xiong Wang
    Abstract: Sketch algorithms have been extensively studied in the area of network measurement, given their limited resource usage and theoretically bounded errors. However, error bounds provided by existing algorithms remain too coarse-grained: in practice, only a small number of flows (e.g., heavy hitters) actually benefit from the bounds, while the remaining flows still suffer from serious errors. In this paper, we aim to design nearly-zero-error sketch that achieves negligible per-flow error for almost all flows. We base our study on a technique named compressive sensing. We exploit compressive sensing in two aspects. First, we incorporate the near-perfect recovery of compressive sensing to boost sketch accuracy. Second, we leverage compressive sensing as a novel and uniform methodology to analyze various design choices of sketch algorithms. Guided by the analysis, we propose two sketch algorithms that seamlessly embrace compressive sensing to reach nearly zero errors. We implement our algorithms in OpenVSwitch and P4. Experimental results show that the two algorithms incur less than 0.1% per-flow error for more than 99.72% flows, while preserving the resource efficiency of sketch algorithms. The efficiency demonstrates the power of our new methodology for sketch analysis and design.

History

History

2024

2023

2022

2021

2020

  • [Topic] [ The path planning algorithm for multiple mobile edge servers in EdgeGO], Rong Cong, 2020-11-18

2019

2018

2017

Instructions

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