11月19日 「AI in Wireless Communications and Bio-Medical Engineering」系列演講

時間：2019/11/19 9:00-12:00

把事件新增至行事曆

地點：交通大學 工程四館 ED816

講者：

PROF. GEOFFREY LI / GEORGIA TECH, USA

LI-CHUN WANG / NCTU, TAIWAN

報名網址：https://forms.gle/Uid7BCGTamfJjhao6

DEEP LEARNING BASED WIRELESS RESOURCE ALLOCATION

【Abstract】
Judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless network performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve it to a certain level of optimality. However, as wireless networks become increasingly diverse and complex, such as high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning represents a promising alternative due to its remarkable power to leverage data for problem solving. In this talk, will present our research progress in deep learning based wireless resource allocation. Deep learning can help solve optimization problems for resource allocation or can be directly used for resource allocation. We will first present our research results in using deep learning to solve linear sum assignment problems (LSAP) and reduce the complexity of mixed integer non linear programming (MINLP), and introduce graph embedding for wireless link scheduling. We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with application in vehicular networks.DEEP LEARNING IN PHYSICAL LAYER COMMUNICATIONS

DEEP LEARNING IN PHYSICAL LAYER COMMUNICATIONS

【Abstract】
It has been demonstrated recently that deep learning (DL) has great potentials to break the bottleneck of the conventional communication systems. In this talk, we present our recent work in DL in physical layer communications. DL can improve the performance of each individual (traditional) block in the conventional communication systems or jointly optimize the whole transmitter or receiver. Therefore, we can categorize the applications of DL in physical layer communications into with and without block processing structures. For DL based communication systems with block structures, we present joint channel estimation and signal detection based on a fully connected deep neural network, model drive DL for signal detection, and some experimental results. For those without block structures, we provide our recent endeavors in developing end-to-end learning communication systems with the help of deep reinforcement learning (DRL) and generative adversarial net (GAN). At the end of the talk, we provide some potential research topics in the area.

MACHINE LEARNING INTERFERENCE MANAGEMENT

【Abstract】
In this talk, we discuss how machine learning algorithms can address the performance issues of high-capacity ultra-dense small cells in an environment with dynamical traffic patterns and time-varying channel conditions. We introduce a bi-adaptive self-organizing network (Bi-SON) to exploit the power of data-driven resource management in ultra-dense small cells (UDSC). On top of the Bi-SON framework, we further develop an affinity propagation unsupervised learning algorithm to improve energy efficiency and reduce interference of the operator deployed and the plug-and-play small cells, respectively. Finally, we discuss the opportunities and challenges of reinforcement learning and deep reinforcement learning (DRL) in more decentralized, ad-hoc, and autonomous modern networks, such as Internet of things (IoT), vehicle-to-vehicle networks, and unmanned aerial vehicle (UAV) networks.scheduling. We will then discuss how to use deep reinforcement learning directly for wireless resource allocation with application in vehicular networks.