相關主題
商品描述
An exploration of connected intelligent edge, artificial intelligence, and machine learning for B5G/6G architecture
Artificial Intelligence for Future Networks illuminates how artificial intelligence (AI) and machine learning (ML) influence the general architecture and improve the usability of future networks like B5G and 6G through increased system capacity, low latency, high reliability, greater spectrum efficiency, and support of massive internet of things (mIoT).
The book reviews network design and management, offering an in-depth treatment of AI oriented future networks infrastructure. Providing up-to-date materials for AI empowered resource management and extensive discussion on energy-efficient communications, this book incorporates a thorough analysis of the recent advancement and potential applications of ML and AI in future networks.
Each chapter is written by an expert at the forefront of AI and ML research, highlighting current design and engineering practices and emphasizing challenging issues related to future wireless applications.
Some of the topics explored in Artificial Intelligence for Future Networks include:
- Signal processing and detection, covering preprocess and level signals, transform signals and extract features, and training and deploying AI models and systems
- Channel estimation and prediction, covering channel characteristics, modeling, and classic learning-aided and AI-aided estimation techniques
- Resource allocation, covering resource allocation optimization and efficient power consumption for different computing paradigms such as Cloud, Edge, Fog, IoT, and MEC
- Antenna design using AI, covering basics of antennas, EM simulator/optimization algorithms, and surrogate modeling
Identifying technical roadblocks and sharing cutting-edge research on developing methodologies, Artificial Intelligence for Future Networks is an essential reference on the subject for professionals and researchers involved in the field of wireless communications and networks, along with graduate and PhD students in electrical and computer engineering programs of study.
商品描述(中文翻譯)
《連接智能邊緣、人工智慧與機器學習在B5G/6G架構中的探索》
《未來網路的人工智慧》闡明了人工智慧（AI）和機器學習（ML）如何影響未來網路的整體架構，並透過提升系統容量、降低延遲、高可靠性、更高的頻譜效率以及支持大規模物聯網（mIoT）來改善其可用性。
本書回顧了網路設計與管理，深入探討以AI為導向的未來網路基礎設施。提供最新的AI驅動資源管理材料，並廣泛討論能源效率通訊，本書包含了對ML和AI在未來網路中最近進展及潛在應用的全面分析。
每章均由在AI和ML研究前沿的專家撰寫，突顯當前的設計與工程實踐，並強調與未來無線應用相關的挑戰性問題。
《未來網路的人工智慧》中探討的一些主題包括：
- 信號處理與檢測，涵蓋預處理和級別信號、轉換信號和特徵提取，以及訓練和部署AI模型和系統
- 頻道估計與預測，涵蓋頻道特性、建模，以及經典學習輔助和AI輔助的估計技術
- 資源分配，涵蓋資源分配優化和不同計算範式（如雲端、邊緣、霧計算、物聯網和MEC）的高效能耗
- 使用AI的天線設計，涵蓋天線基礎知識、電磁模擬器/優化算法和替代建模
《未來網路的人工智慧》識別技術障礙並分享開創性研究，開發方法論，是無線通訊和網路領域專業人士及研究人員的重要參考資料，亦適合電機與計算機工程學程的研究生和博士生。
作者簡介
Mohammad Abdul Matin is a Professor in the Department of Electrical and Computer Engineering at North South University (NSU).
Sotirios K Goudos is a Professor in the Department of Physics at the Aristotle University of Thessaloniki and the director of the ELEDIA@AUTH lab member of the ELEDIA Research Center Network.
George K. Karagiannidis is a Professor with the Electrical and Computer Engineering Department and the Head of the Wireless Communications and Information Processing (WCIP) Group at Aristotle University in Thessaloniki, Greece.
作者簡介(中文翻譯)
Mohammad Abdul Matin 是北南大學 (North South University, NSU) 電機與計算機工程系的教授。
Sotirios K Goudos 是塞薩洛尼基亞里士多德大學 (Aristotle University of Thessaloniki) 物理系的教授，也是 ELEDIA 研究中心網絡的 ELEDIA@AUTH 實驗室主任。
George K. Karagiannidis 是希臘塞薩洛尼基亞里士多德大學電機與計算機工程系的教授，並擔任無線通信與信息處理 (Wireless Communications and Information Processing, WCIP) 小組的負責人。
