Machine learning empowered beam management for intelligent reflecting surface assisted MmWave networks

The Intelligent Management of Machine Learning

Research in artificial intelligence (AI) and machine learning (ML) has exploded in the last decade, bringing humanity to the cusp of self-driving cars, digital personal assistants, and unbeatable game-playing robots. My research, which spans the areas of AI, Crowdsourcing, and Natural Language Processing (NLP), focuses on an area where machines are still significantly inferior to humans, despit...

Machine Learning and Cognitive Technology for Intelligent Wireless Networks

The ability to dynamically and efficiently allocate resources to meet the need of growing diversity in services and user behavior marks the future of wireless communications, giving rise to intelligent processing, which aims at enabling the system to perceive and assess the available resources, to autonomously learn to adapt to the perceived wireless environment, and to reconfigure its operatin...

Machine Learning for Intelligent Agents

Machine Learning for Intelligent Systems

Recent research in machine learning has focused on supervised induction for simple classification and reinforcement learning for simple reactive behaviors. In the process, the field has become disconnected from AI’s original goal of creating complete intelligent agents. In this paper, I review recent work on machine learning for planning, language, vision, and other topics that runs counter to ...

Performance Analysis of cooperative SWIPT System: Intelligent Reflecting Surface versus Decode-and-Forward

In this paper, we explore the impacts of utilizing intelligent reflecting surfaces (IRS) in a power-splitting based simultaneous wireless information and power transfer (PS-SWIPT) system and compare its performance with the traditional decode and forward relaying system. To analyze a more practical system, it is also assumed that the receiving nodes are subject to decoding cost, and they are on...