Efficient Integration of 5g and Beyond Heterogeneous Networks (5G及未來異質網路的高效整合)
Wu, Zi-Yang, Ismail, Muhammad, Kong, Justin
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商品描述
This book discusses the smooth integration of optical and RF networks in 5G and beyond (5G+) heterogeneous networks (HetNets), covering both planning and operational aspects. The integration of high-frequency air interfaces into 5G+ wireless networks can relieve the congested radio frequency (RF) bands. Visible light communication (VLC) is now emerging as a promising candidate for future generations of HetNets. Heterogeneous RF-optical networks combine the high throughput of visible light and the high reliability of RF. However, when implementing these HetNets in mobile scenarios, several challenges arise from both planning and operational perspectives. Since the mmWave, terahertz, and visible light bands share similar wave propagation characteristics, the concepts presented here can be broadly applied in all such bands.
To facilitate the planning of RF-optical HetNets, the authors present an algorithm that specifies the joint optimal densities of the base stations by drawing on stochastic geometry in order to satisfy the users' quality-of-service (QoS) demands with minimum network power consumption. From an operational perspective, the book explores vertical handovers and multi-homing using a cooperative framework. For vertical handovers, it employs a data-driven approach based on deep neural networks to predict abrupt optical outages; and, on the basis of this prediction, proposes a reinforcement learning strategy that ensures minimal network latency during handovers. In terms of multi-homing support, the authors examine the aggregation of the resources from both optical and RF networks, adopting a two-timescale multi-agent reinforcement learning strategy for optimal power allocation.
Presenting comprehensive planning and operational strategies, the book allows readers to gain an in-depth grasp of how to integrate future coexisting networks at high-frequency bands in a cooperative manner, yielding reliable and high-speed 5G+ HetNets.
商品描述(中文翻譯)
本書討論了在5G及其後續版本（5G+）的異構網絡（HetNets）中，光學和射頻網絡的順暢整合，涵蓋了規劃和運營方面的內容。將高頻空中介面整合到5G+無線網絡中可以減輕射頻（RF）頻段的擁擠。可見光通信（VLC）現在正成為未來一代HetNets的有前途的候選技術。異構的RF-光學網絡結合了可見光的高吞吐量和RF的高可靠性。然而，在移動場景中實施這些HetNets時，從規劃和運營的角度都會面臨一些挑戰。由於毫米波、太赫茲和可見光頻段具有相似的波傳特性，這裡介紹的概念可以廣泛應用於所有這些頻段。
為了促進RF-光學HetNets的規劃，作者們提出了一種算法，通過借鑒隨機幾何的方法來指定基站的聯合最優密度，以滿足用戶的服務質量（QoS）要求並實現最小的網絡功耗。從運營的角度來看，本書探討了使用合作框架進行垂直切換和多重連接。對於垂直切換，它採用基於深度神經網絡的數據驅動方法來預測突然的光學中斷；並且基於這個預測，提出了一種強化學習策略，確保切換期間網絡延遲最小。在多重連接支持方面，作者們研究了來自光學和RF網絡的資源聚合，採用了兩個時間尺度的多智能體強化學習策略來進行最優功率分配。
通過提供全面的規劃和運營策略，本書使讀者能夠深入了解如何以合作方式在高頻段整合未來共存的網絡，從而實現可靠且高速的5G+ HetNets。
作者簡介
Zi-Yang Wu received his B.S. degree in Electronic Science and Technology and his M.S. degree in Circuits and Systems from Northeastern University, Shenyang, China, in 2014 and 2016, respectively, where he is currently pursuing his Ph.D. degree in Electrical and Electronic Engineering. He was also a joint Ph.D. student at the Department of Electrical and Computer Engineering, Texas A&M University, College Station, USA, under a full scholarship granted by the State Scholarship Fund of China, from 2018 to 2019.
Muhammad Ismail received his B.Sc. and M.Sc. degrees in Electrical Engineering (Electronics and Communications) from Ain Shams University, Cairo, Egypt, in 2007 and 2009, respectively, and his Ph.D. degree in Electrical and Computer Engineering from the University of Waterloo, Canada, in 2013. He is currentlyan Assistant Professor at the Computer Science Department, Tennessee Tech University, USA. He was a co-recipient of the Best Paper Awards in the IEEE ICC 2014, the IEEE Globecom 2014, the SGRE 2015, the Green 2016, and the IEEE Technical Committee on Green Communications and Networking (TCGCN) Best Paper Award at the IEEE ICC 2019.
Justin Kong received his B.S. and Ph.D. degrees in Electrical Engineering from Korea University, Seoul, South Korea, in 2009 and 2015, respectively. From 2015 to 2018, he was a Postdoctoral Research Fellow at Nanyang Technological University, Singapore, and he was a Postdoctoral Research Associate with Texas A&M University from 2018 to 2019. In 2019, he joined the US Army Research Laboratory, where he is currently a Postdoctoral Fellow. He received the Bronze Prize in the Samsung Humantech Paper Contest in 2012 and 2013, and was a recipient of the IEEE TCGCN Best Paper Award at the IEEE ICC 2019.
Erchin Serpedin received his specialization degree in Transmission and Processing of Information from Ecole Superieure D'Electricite (SUPELEC), Paris, in 1992, his M.Sc. degree from the Georgia Institute of Technology in 1992, and his Ph.D. degree from the University of Virginiain 1999. He is currently a Professor at the Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA, and an IEEE fellow. He has served as an Associate Editor for 12 journals and as the Technical Chair for six major conferences.
Jiao Wang received his Ph.D. degree in Pattern Recognition and Intelligent Systems from Northeastern University, Shenyang, China, in 2006. He was a Research Fellow at Microsoft Asia in 2007. He is currently a Professor at the College of Information Science and Engineering, Northeastern University. He also serves as Director of the Institute of Advanced Learning and Advanced Intelligent Decision Making at Northeastern University, and as Director of the Machine Game Committee of the China Association of Artificial Intelligence (CAAI).
作者簡介(中文翻譯)
Zi-Yang Wu於2014年和2016年分別獲得中國沈陽東北大學電子科學與技術學士學位和電路與系統碩士學位，目前正在該校攻讀電氣與電子工程博士學位。他也是2018年至2019年間在美國德克薩斯州A&M大學電機與計算機工程系的聯合博士生，獲得中國國家獎學金全額資助。
Muhammad Ismail於2007年和2009年分別獲得埃及開羅阿因沙姆斯大學電氣工程（電子與通信）學士和碩士學位，並於2013年獲得加拿大滑鐵盧大學電氣與計算機工程博士學位。他目前是美國田納西科技大學計算機科學系的助理教授。他曾獲得IEEE ICC 2014、IEEE Globecom 2014、SGRE 2015、Green 2016以及IEEE ICC 2019的最佳論文獎。
Justin Kong於2009年和2015年分別獲得韓國首爾高麗大學電氣工程學士和博士學位。從2015年到2018年，他在新加坡南洋理工大學擔任博士後研究員，並在2018年至2019年期間在德克薩斯州A&M大學擔任博士後研究員。2019年，他加入美國陸軍研究實驗室，目前是博士後研究員。他曾於2012年和2013年獲得三星人才科技論文競賽銅獎，並獲得IEEE ICC 2019的TCGCN最佳論文獎。
Erchin Serpedin於1992年從法國巴黎高等電力學院（SUPELEC）獲得信息傳輸和處理專業學位，並於同年獲得喬治亞理工學院碩士學位，1999年獲得維吉尼亞大學博士學位。他目前是美國德克薩斯州A&M大學電機與計算機工程系的教授，也是IEEE的會士。他曾擔任12個期刊的副編輯和六個重要會議的技術主席。
Jiao Wang於2006年從中國沈陽東北大學獲得模式識別和智能系統博士學位，並於2007年在微軟亞洲擔任研究員。他目前是東北大學信息科學與工程學院的教授，同時擔任該校高級學習和高級智能決策研究所所長，以及中國人工智能學會機器遊戲委員會主任。
以上。
