Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Link Gain Matrix Estimation in Distributed Large-Scale Wireless Networks | Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2010 Article ID 651795 9 pages doi 2010 651795 Research Article Link Gain Matrix Estimation in Distributed Large-Scale Wireless Networks Jing Lei Larry Greenstein and Roy Yates WINLAB Department ofECE Rutgers University North Brunswick NJ 08902 USA Correspondence should be addressed to Jing Lei Received 9 June 2009 Revised 1 October 2009 Accepted 25 November 2009 Academic Editor Christian Ibars Copyright 2010 Jing Lei et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. In planning and using large-scale distributed wireless networks knowledge of the link gain matrix can be highly valuable. If the number N of radio nodes is large measuring N N - 1 2 node-to-node link gains can be prohibitive. This motivates us to devise a methodology that measures a fraction of the links and accurately estimates the rest. Our method partitions the set of transmitreceive links into mutually exclusive categories based on the number of obstructions or walls on the path then it derives a separate link gain model for each category. The model is derived using gain measurements on only a small fraction of the links selected on the basis of a maximum entropy. To evaluate the new method we use ray-tracing to compute the true path gains for all links in the network. We use knowledge of a subset of those gains to derive the models and then use those models to predict the remaining path gains. We do this for three different environments of distributed nodes including an office building with many obstructing walls. We find in all cases that the partitioning method yields acceptably low path gain estimation errors with a significantly reduced number of measurements. 1. Introduction The powerful technology .
