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 Distributed Power Allocation for Parallel Broadcast Channels with Only Common Information in Cognitive Tactical Radio Networks | Hindawi Publishing Corporation EURASIP Journal on Wireless Communications and Networking Volume 2010 Article ID 172013 10 pages doi 2010 172013 Research Article Distributed Power Allocation for Parallel Broadcast Channels with Only Common Information in Cognitive Tactical Radio Networks Vincent Le Nir EURASIP Member and Bart Scheers Department of Communication Information Systems Sensors CISS Royal Military Academy 30 Avenue de la Renaissance 1000 Brussels Belgium Correspondence should be addressed to Vincent Le Nir Received 31 May 2010 Revised 27 October 2010 Accepted 20 December 2010 Academic Editor Vincent Lau Copyright 2010 V. Le Nir and B. Scheers. 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. A tactical radio network is a radio network in which a transmitter broadcasts the same information to its receivers. In this paper dynamic spectrum management is studied for multiple cognitive tactical radio networks coexisting in the same area. First we consider the problem of common rate maximization subject to a total power constraint for a single tactical radio network having multiple receivers and using parallel subchannels parallel multicast channels . Mathematical derivations show that the optimal power allocation can be found in closed form under multiple hypothesis testing. An outer loop can be used to minimize the power subject to a common rate constraint. Then we extend the iterative water-filling algorithm to the coexistence of multiple cognitive tactical radio networks without requiring any cooperation between the different networks. The power allocation is performed autonomously at the transmit side assuming knowledge of the noise variances and channel variations of the network. Simulation results showthat the proposed algorithm is very robust in satisfying these
