IEEE International Conference on Communications
20-24 May 2019 // Shanghai, China
Empowering Intelligent Communications


The Workshop will be held on Friday May 24th, and will last the entire day, with the morning focused on application of Machine Learning to the lower layers of the network while the afternoon will be devoted to the application of Machine Learning to the Network Optimization. It will feature an invited talk from two of the main experts of the field, Professor Kaibin Huang, from University of Hong Kong and Professor Alejandro Ribeiro, from University of Pennsylvania. 

The detailed program of the workshop can be found below.

ML4COM Keynote 1: The Equivalence of Optimal Resource Allocation in Wireless Communications and Unsupervised Learning by Prof. Ribeiro (9:00-09:50, ROOM 3D) 

Abstract: Optimal resource allocation problems in wireless communications systems are mathematically equivalent to unsupervised learning problems. Both can be formulated as functional optimization problems. Their only difference is that unsupervised learning problems optimize for a single cost whereas wireless communications systems typically try to balance several quality of service constraints. This motivates the adaptation of learning methods to work in the dual domain. We will discuss the design of algorithms for learning in the dual domain as well as their fundamental properties. We will discuss the advantages of neural network formulations and discuss the adaptation of convolutional processing to networked settings.

ML4COM S1 Deep Learning Based Beamforming (9:50-10:30, ROOM 3D)

Wenchao Xia,Gan Zheng, Yongxu Zhu, Jun Zhang,
Jiangzhou Wang,
and Athina P. Petropulu
Deep Learning Based Beamforming Neural Networks in Downlink MISO Systems
10:10-10:30 Carles Anton-Haro and Xavier Mestre Machine and Deep Learning-based Beam Selection for Hybrid Beamforming with Partial CSI


ML4COM S2 Machine Learning Transceiver (11:00-12:20, ROOM 3D)


Johannes Schmitz, Caspar von Lengerke, Nikita Airee, Arash Behboodi, and Rudolf Mathar

A Deep Learning Wireless Transceiver with Fully Learned Modulation and Synchronization
11:20-11:40 Yu Chou, Fang Liu, and Yuanan LiuS A Deep Neural Network Method For Automatic Modulation Recognition In OFDM With Index Modulation
11:40-12:00 Zhicai Zhang, Ru Wang, F. Richard Yu, Fang Fu, Qiao Yan, and Qi Jiao QoE Aware Transcoding for Live Streaming in SDN-based Cloud-aided HetNets: An Actor-Critic Approach
12:00-12:20 Seonho Kim, Minji So, Namyoon Lee, and SongNam Hong Semi-Supervised Learning Detector for MU-MIMO Systems with One-bit ADCs


ML4COM Keynote 2: Realizing an Intelligent Edge: Communication Meets Learning by Prof. Huang (14:00-14:50, ROOM 3D)

Abstract: The popularity of mobile devices and densification of wireless networks result in the availability of enormous data and computational resources distributed at the network edge. To leverage the data and resources, a new machine-learning paradigm, called edge learning, has emerged where learning algorithms are deployed at the edge for providing fast and intelligent services to mobile users. While computing speeds are advancing rapidly, the communication latency is becoming the bottleneck of fast edge learning. Attempts to overcome the bottleneck have led to the emergence of a new paradigm in wireless communication, “communication efficient edge learning”, which departs from the classic principle of “rate-maximization” and focuses on “fast intelligence acquisition”. In this talk, I will overview new design challenges in the area and highlight some recent advancements in different directions including resource allocation, gradient quantization, feature transmission, wireless data labelling, active data acquisition, and over-the-air computation.

ML4COM S4 Multi-Agent Reinforcement Learning in Wireless Networks (14:50-15:30, ROOM 3D)


Jingjing Cui, Yuanwei Liu, and Arumugam Nallanathan

The Application of Multi-Agent Reinforcement Learning in UAV Networks
15:10-15:30 Shaoyang Wang, Tiejun Lv, and Xuewei Zhang Multi-Agent Reinforcement Learning-Based User Pairing in Multi-Carrier NOMA Systems


ML4COM S4 Machine Learning for Network Management (16:00-17:40, ROOM 3D)


Lingchao Guo, Zhaoming Lu, Xiang Ming Wen, Zhihong He, Wei Zheng, and Shuang Zhou

Contact-free In-home Health Monitoring System with Commodity Wi-Fi
16:20-16:40 Xu Pan, Ting Jiang, Xudong Li, Xue Ding, Yangyang Wang, and Yanan Li Dynamic Hand Gesture Detection and Recognition with WiFi Signal Based on 1D-CNN
16:40-17:00 Humphrey Rutagemwa, Kareem E. Baddour, and Bo Rong Hierarchical Meta-learning Models with Deep Neural Networks for Spectrum Assignment
17:00-17:20 Boxiang He, Fanggang Wang, Yu Liu, and Shilian Wang Specific Emitter Identification via Multiple Distorted Receivers   
17h20-17h40 Huiwei Xia, Xin Wei, Yun Gao, and Haibing Lv Traffic Prediction Based on Ensemble Machine Learning Strategies with Bagging and LightGBM



Innovation Exhibitors