國立臺北大學通訊工程學系前身為國立台北大學通訊工程研究所， 於2004年成立，位於南港軟體園區、中研院及新竹科學園區間， 三峽交流道旁，為台灣高科技產業的中心點。本系主要發展通訊 高科技產業，強調獨立思考及創新設計能力之訓練，並要求學生 參與研究計畫，以驗證所學和獲取經驗，同時培養理論與實務兼 備、人文與科技整合的高級人才。發展方向為通訊系統設計、通 訊晶片設計、網路交換、數位訊號處理、多媒體通訊等相關技術。 近年積極參與工程科技教育認證(IEET)，並於2010年通過認證。 目前設有通訊工程學士班、碩士班、資通科技產業碩士專班、及國際學生碩士班。

專題研討(113/10/25)－黃浩儒資深應用工程師 (台灣羅德史瓦茲有限公司 Rohde & Schwarz Taiwan Limited)

題目：淺談6G行動通訊與AI

主講人：黃浩儒資深應用工程師 (台灣羅德史瓦茲有限公司 Rohde & Schwarz Taiwan Limited)

時間：113年10月25日(星期五13：00- 15：00)

地點：臺北大學三峽校區音律電機資訊大樓電1F02

Abstract

  1. 6G Overview

This section will introduce 6G roadmap and research areas from Test & Measurement perspective.

  1. The role of AI/ML in future wireless communication

Artificial intelligence (AI) in the form of machine learning (ML) accompanies the user of a state-of-the-art wireless device daily. It has achieved tremendous success in image identification, video recognition and natural language processing, to name just a few examples. However, for the next generation of wireless communication, aka 6G, researchers propose AI/ML models and algorithms that natively drive the air interface by replacing individual or even multiple blocks of the signal processing chain. An AI-native interface with self-optimizing transceivers would, at least in theory, provide significant performance gains even under extreme radio channel conditions. Yet this potential revolution also implies substantial challenges from a design and testing perspective. This section will introduce the attendees to the ongoing fundamental research, discuss related challenges and how test and measurement solutions can accompany this research.

  • How AI/ML is used in today’s 5G mobile networks
  • The current status of the fundamental research for an AI-native air interface in 6G
  • How AI/ML outperforms the classical signal processing approach, and the potential performance gains
  • What role test & measurement solutions play in this context
  1. Will AI/ML revolutionize 6G?

Over the last few years, researchers and key industry players have been investigating the native support of AI/ML-based models and algorithms for signal processing in the future 6G air interface.

The initial focus will be on the receiver part as the concept of a neural receiver is introduced. Simulations show a performance gain compared to traditional concepts, but open questions remain: Will the use of AI/ML revolutionize the next generation of wireless communications? What is required to achieve the full potential of AI/ML-based signal processing, and what is the role of test and measurement solutions within the broader context? This section will address these exciting questions and provide important insights.

  • The current status of the 3GPP Release 18 study item on AI/ML for the air interface
  • A summary of ongoing fundamental research for an AI-native air interface in the future 6G wireless communication standard
  • The concept of a neural receiver and how it may outperform the classical signal processing approach used in today’s 4G and 5G networks
  • The next step after the neural receiver
  • The role of test & measurement solutions within the broader context