RT Journal Article
T1 Energy-Efficient Cooperative Spectrum Sensing based on Stochastic Programming in Dynamic Cognitive Radio Sensor Networks     Normal
A1 Kaschel, Hector
A1 Toledo, Karel
A1 Torres Gómez, Jorge
A1 Fernández-Getino García, María Julia
AB Nowadays, Cognitive Radio Sensor Networks (CRSN) arise as an emergent technology to deal with the spectrum scarcity issue and the focus is on devising novel energy-efficient solutions. In static CRSN, where nodes have spatial fixed positions, several reported solutions are implemented via sensor selection strategies to reduce consumed energy during cooperative spectrum sensing. However, energy-efficient solutions for dynamic CRSN, where nodes are able to change their spatial positions due to their movement, are nearly reported despite today's growing applications of mobile networks. This paper investigates a novel framework to optimally predict energy consumption in cooperative spectrum sensing tasks, considering node mobility patterns suitable to model dynamic CRSN. A solution based on the Kataoka criterion is presented, that allows to minimize the consumed energy. It accurately estimates -with a given probability-the spent energy on the network, then to derive an optimal energy-efficient solution. An algorithm of reduced-complexity is also implemented to determine the total number of active nodes improving the exhaustive search method. Proper performance of the proposed strategy is illustrated by extensive simulation results for pico-cells and femto-cells in dynamic scenarios.
PB IEEE
SN 2169-3536
YR 2021
FD 2021
LK https://hdl.handle.net/10016/32681
UL https://hdl.handle.net/10016/32681
LA eng
NO This work was supported in part by the DICYT Project, Direction of Research, Development and Innovation, Universidad de Santiago de Chile, USACH, under Grant 061813KC, in part by the CONICYT-PFCHA/Doctorado Nacional/2016-21160292, and in part by the Spanish National Project TERESA-ADA (MINECO/AEI/FEDER, UE) under Grant TEC2017-90093-C3-2-R.
DS e-Archivo
RD 9 sept. 2024