IEEE/ACM International Symposium on Quality of Service (IWQoS) 2021
Can Online Learning Increase the Reliability of Extreme Mobility Management?
Yuanjie Li (Tsinghua University, China); Esha Datta (University of California, Davis, USA); Jiaxin Ding (Shanghai Jiao Tong University, China); Ness B. Shroff (The Ohio State University, USA); Xin Liu (University of California Davis, USA)
In this paper, we formulate the exploration-exploitation tradeoff in extreme mobility as a composition of two online learning problems. Then we present BaTT, a multi-armed bandit-based online learning solution for both problems. BaTT uses ɛ-binary-search to optimize the threshold of a serving cell's signal strength to initiate the handover with a provable O(log J log T ) regret. We also devise an opportunistic Thompson sampling algorithm to optimize the sequence of target cells measured for reliable handovers. BaTT can be readily implemented using the recent Open Radio Access Network (O-RAN) framework in operational 4G LTE and 5G NR. Our analysis and empirical evaluations over a dataset from operational LTE networks on the Chinese high-speed rails show a 29.1% handover failure reduction at the speed of 200-350 km/h.
SuperClass: A Deep Duo-Task Learning Approach to Improving QoS in Image-driven Smart Urban Sensing Applications
Yang Zhang, Ruohan Zong, Lanyu Shang, Md Tahmid Rashid and Dong Wang (University of Notre Dame, USA)
SeqAD: An Unsupervised and Sequential Autoencoder Ensembles based Anomaly Detection Framework for KPI
Na Zhao, Biao Han and Yang Cai (National University of Defense Technology, China); Jinshu Su (National University of Defence Technology, China)
DeepDelivery: Leveraging Deep Reinforcement Learning for Adaptive IoT Service Delivery
Yan Li, Deke Guo and Xiaofeng Cao (National University of Defense Technology, China); Feng Lyu (Central South University, China); Honghui Chen (National University of Defense Technology, China)
LCL: Light Contactless Low-delay Load Monitoring via Compressive Attentional Multi-label Learning
Xiaoyu Wang, Hao Zhou, Nikolaos M. Freris and Wangqiu Zhou (University of Science and Technology of China, China); Xing Guo (Anhui University, China); Zhi Liu (The University of Electro-Communications, Japan); Yusheng Ji (National Institute of Informatics, Japan); Xiang-Yang Li (University of Science and Technology of China, China)
Inspired by observations of discriminative yet redundant current waveform and model sparsity, we propose LCL, a light-weight, contactless, plug-and-play solution for real-time load monitoring. The filtering module skips over unchanged input and compresses the measurements of interest using Compressed Sensing. The reconstruction-free inference module runs an attentional multi-label classification and returns all functioning appliance states directly from the compressed input. The compression module leverages model sparsity for real-time processing on edge devices. Evaluations based on our prototype deployed in real-life scenarios attest to the high QoS of LCL with a subset accuracy of 94.2% and a delay reduction of 52.2%. Our solution further filters out 96.8% of the redundant input and attains a Measurement Rate of 0.1 without noticeable impact on the performance.
GreenTE.ai: Power-Aware Traffic Engineering via Deep Reinforcement Learning
Tian Pan (Beijing University of Posts and Telecommunications, China); Xiaoyu Peng (BUPT, China); Qianqian Shi (Beijing University of Posts and Telecommunications, China); Zizheng Bian (BUPT, China); Xingchen Lin and Enge Song (Beijing University of Posts and Telecommunications, China); Fuliang Li (Northeastern University, China); Yang Xu (Fudan University, China); Tao Huang (Beijing University of Posts and Telecommunications, China)
Yanjiao Chen, Zhejiang University, China