Resource: Seminar

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Time: 2022-6-6 10:30
Address: 4th Research Building A527-B
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [TMC 2022] STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control, Xianyang
    Abstract: The development of intelligent traffic light control systems is essential for smart transportation management. While some efforts have been made to optimize the use of individual traffic lights in an isolated way, related studies have largely ignored the fact that the use of multi-intersection traffic lights is spatially influenced, as well as the temporal dependency of historical traffic status for current traffic light control. To that end, in this article, we propose a novel Spatio-Temporal Multi-Agent Reinforcement Learning (STMARL) framework for effectively capturing the spatio-temporal dependency of multiple related traffic lights and control these traffic lights in a coordinating way. Specifically, we first construct the traffic light adjacency graph based on the spatial structure among traffic lights. Then, historical traffic records will be integrated with current traffic status via Recurrent Neural Network structure. Moreover, based on the temporally-dependent traffic information, we design a Graph Neural Network based model to represent relationships among multiple traffic lights, and the decision for each traffic light will be made in a distributed way by the deep Q-learning method. Finally, the experimental results on both synthetic and real-world data have demonstrated the effectiveness of our STMARL framework, which also provides an insightful understanding of the influence mechanism among multi-intersection traffic lights.
  2. [Sensys 2021] NELoRa: Towards Ultra-low SNR LoRa Communication with Neural-enhanced Demodulation, Kaiwen
    Abstract: Low-Power Wide-Area Networks (LPWANs) are an emerging Internet-of-Things (IoT) paradigm marked by low-power and long-distance communication. Among them, LoRa is widely deployed for its unique characteristics and open-source technology. By adopting the Chirp Spread Spectrum (CSS) modulation, LoRa enables low signal-to-noise ratio (SNR) communication. However, the standard demodulation method does not fully exploit the properties of chirp signals, thus yields a sub-optimal SNR threshold under which the decoding fails. Consequently, the communication range and energy consumption have to be compromised for robust transmission. This paper presents NELoRa, a neural-enhanced LoRa demodulation method, exploiting the feature abstraction ability of deep learning to support ultra-low SNR LoRa communication. Taking the spectrogram of both amplitude and phase as input, we first design a mask-enabled Deep Neural Network (DNN) filter that extracts multi-dimension features to capture clean chirp symbols. Second, we develop a spectrogram-based DNN decoder to decode these chirp symbols accurately. Finally, we propose a generic packet demodulation system by incorporating a method that generates high-quality chirp symbols from received signals. We implement and evaluate NELoRa on both indoor and campus-scale outdoor testbeds. The results show that NELoRa achieves 1.84-2.35 dB SNR gains and extends the battery life up to 272% (~0.38-1.51 years) in average for various LoRa configurations.


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

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