This book proposes a unified algorithmic framework based on dual optimization techniques that have complexities that are linear in the number of subcarriers and users, and that achieve negligible optimality gaps in standards-based numerical simulations. Adaptive algorithms based on stochastic approximation techniques are also proposed, which are shown to achieve similar performance with even much lower complexity. All the algorithms proposed are clearly presented in concise block diagrams allowing the reader to implement these algorithms in the software of their choice. This book is an accessible reference for researchers and industry practitioners alike.
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This book proposes a unified algorithmic framework based on dual optimization techniques that have complexities that are linear in the number of subcarriers and users, and that achieve negligible optimality gaps in standards-based numerical simulations. Adaptive algorithms based on stochastic approximation techniques are also proposed, which are shown to achieve similar performance with even much lower complexity. All the algorithms proposed are clearly presented in concise block diagrams allowing the reader to implement these algorithms in the software of their choice. This book is an accessible reference for researchers and industry practitioners alike.
Imprint | Springer-Verlag New York |
Country of origin | United States |
Release date | November 2010 |
Availability | Expected to ship within 10 - 15 working days |
First published | 2008 |
Authors | Ian C. Wong, Brian Evans |
Dimensions | 235 x 155 x 7mm (L x W x T) |
Format | Paperback |
Pages | 122 |
Edition | Softcover reprint of hardcover 1st ed. 2008 |
ISBN-13 | 978-1-4419-4522-8 |
Barcode | 9781441945228 |
Categories | |
LSN | 1-4419-4522-9 |