物理与电子工程学院2018学术论坛之四十三
报告时间：2018年11月27日（周二）11:00-12:00
报告地点：理学实验楼718
题目：Spatial Deep Learning for Wireless Scheduling
报告人：郁炜 教授（加拿大多伦多大学）
邀请人：周发升 博士
报告人简介：郁炜教授为加拿大工程院院士，IEEE Fellow。他于1997年在加拿大滑铁卢大学计算机工程与数学专业获得学士学位，1998年和2002年在美国斯坦福大学电子工程专业分别获得硕士和博士学位。郁炜自2002年起在加拿大多伦多大学电子与计算机工程系任教，现为该系正教授与信息理论与无线通信领域的加拿大讲座研究员，他主要研究领域包括：信息论、优化、无线通信和宽带接入网络。郁炜教授目前担任IEEE信息论学会理事，IEEE Transactions on Wireless Communications领域编委，他曾是IEEE通信学会杰出讲师以及IEEE Transactions on Information Theory等著名期刊编委。郁炜教授曾两次获得IEEE信号处理学会最佳论文奖，多次入选高被引学者名单。
报告摘要：
The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task. In this talk, we first propose a novel fractional programming method to solve this problem, then point out that the traditional optimization approach of first estimating all the interfering channel strengths then optimizing the scheduling based on the model is not always practical, because channel estimation is resource intensive, especially in dense networks. To address this issue, we investigate the possibility of using a deep learning approach to bypass channel estimation and to schedule links efficiently based solely on the geographic locations of transmitters and receivers. This can be accomplished both by supervised learning using locally optimal schedules generated from fractional programming for randomly deployed device-to-device networks as training data and by unsupervised learning. In both cases, we use a novel neural network architecture that takes the geographic spatial convolutions of the interfering or interfered neighboring nodes as input over multiple feedback stages to learn the optimum solution. The resulting neural network gives good performance for sum-rate maximization and is capable of generalizing to larger deployment areas and to deployments of different link densities. Further, we propose a novel approach of utilizing the sum-rate optimal scheduling heuristics over judiciously chosen subsets of links to provide fair scheduling across the network, thereby showing the promise of using deep learning to solve discrete optimization problems in wireless networking.
