The goal of this paper is to study the learning abilities of adaptive networks in the context of cognitive radio networks and to investigate how well they assist in allocating power and communications resources in the frequency domain. The allocation mechanism is based on a social foraging swarm model that lets every node allocate its resources (power/bits) in the frequency regions where the interference is at a minimum while avoiding collisions with other nodes. We employ adaptive diffusion techniques to estimate the interference profile in a cooperative manner and to guide the motion of the swarm individuals in the resource domain. A mean square performance analysis of the proposed strategy is provided and confirmed by simulation results. The proposed approach endows the cognitive network with powerful learning and adaptation capabilities, allowing fast reaction to dynamic changes in the spectrum. Numerical examples show how cooperative spectrum sensing remarkably improves the performance of the resource allocation technique based on swarming.

Bio-inspired decentralized radio access based on swarming mechanisms over adaptive networks / DI LORENZO, Paolo; Barbarossa, Sergio; A. H., Sayed. - In: IEEE TRANSACTIONS ON SIGNAL PROCESSING. - ISSN 1053-587X. - 61:12(2013), pp. 3183-3197. [10.1109/tsp.2013.2258342]

Bio-inspired decentralized radio access based on swarming mechanisms over adaptive networks

DI LORENZO, PAOLO;BARBAROSSA, Sergio;
2013

Abstract

The goal of this paper is to study the learning abilities of adaptive networks in the context of cognitive radio networks and to investigate how well they assist in allocating power and communications resources in the frequency domain. The allocation mechanism is based on a social foraging swarm model that lets every node allocate its resources (power/bits) in the frequency regions where the interference is at a minimum while avoiding collisions with other nodes. We employ adaptive diffusion techniques to estimate the interference profile in a cooperative manner and to guide the motion of the swarm individuals in the resource domain. A mean square performance analysis of the proposed strategy is provided and confirmed by simulation results. The proposed approach endows the cognitive network with powerful learning and adaptation capabilities, allowing fast reaction to dynamic changes in the spectrum. Numerical examples show how cooperative spectrum sensing remarkably improves the performance of the resource allocation technique based on swarming.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11573/523292
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