"Optical communications and fiber technology are fast becoming key solutions for the increasing bandwidth demands of the 21st century. This introductory text provides practicing engineers, managers, and students with a useful guide to the latest developments and future trends of three major technologies: SONET, SDH, and ATM, and a brief introduction to legacy TDM communications systems.
There are clear explanations of: * How ATM is mapped onto SONET/SDH * The role of IP networking with ATM * Dense wavelength division multiplexing (DWDM) * The future direction of convergence of communications.
This concise book features easy-to-follow illustrations, review questions, worked examples, and valuable references. An accompanying CD-ROM provides the key figures in full color, suitable for easy cut-and-paste presentations. UNDERSTANDING SONET/SDH AND ATM is a must-read for communication professionals who want to improve their knowledge of this emerging technology."
Sponsored by: IEEE Communications Society
About the Author
About the Author Stamatios V. Kartalopoulos is currently on the staff of the Optical Networks Group of Lucent Technologies, Bell Labs Innovations, formerly known as AT&T. His research interests include ATM and SONET/SDH systems, ultrafast pattern recognition, IP and DWDM, access enterprise systems, local area networks, fiber networks, satellite systems, intelligent signal processing, neural networks, and fuzzy logic. He holds several patents of which six patents (and six pending) are in communications and optical communications systems. Dr. Kartalopoulos is the author of Understanding Neural Networks and Fuzzy Logic (IEEE Press, 1996). He has published widely on the subject of networks and optical communications systems. He represents the Communications Society to the Technical Activities Board (TAB) New Technology Directions organization, and is a member of the IEEE Press Board. He has also served as guest editor of the IEEE Communications Magazine and as an IEEE associate editor of the IEEE Transactions on Neural Networks.
