By Topic

Dynamic Resource Allocation for MIMO Cognitive Networks With Low Control Traffic and Low Computational Complexity

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Lessinnes, M. ; OPERA Dept., Univ. Libre de Bruxelles, Brussels, Belgium ; Dricot, J.-M. ; De Doncker, P. ; Vandendorpe, L.
more authors

Radio spectrum scarcity hampers the development of new wireless technologies and services. Cognitive radios have been proposed to enable unlicensed (or secondary) users to borrow locally idle bands of the spectrum provided that no significant interference is created for the licensed (or primary) users. Fast adaptation to the changing spectrum availability is naturally a major requirement in such systems. This adaptation consists of detecting the spectrum occupied by the primary users, computing a new resource allocation for the secondary network, and communicating this allocation through the network. In that context, we develop a resource allocation scheme for multi-input-multi-output wireless mesh networks. The proposed algorithm combines low computational complexity and light control traffic thanks to a combination of relevant approximations in the general nonpolynomial-hard allocation problem. The allocation consists of two steps. First, a centralized carrier allocation is performed at a coordinator node based on partial knowledge of the network parameters. Then, each node locally computes its power allocation through simple water-filling algorithms. Numerical results show that compared to state-of-the-art techniques, 10% of the total throughput of the network is sacrificed to reduce the computation time and the control traffic by two orders of magnitude.

Published in:

Vehicular Technology, IEEE Transactions on  (Volume:62 ,  Issue: 4 )