Title :
Automatic Modulation Classification for adaptive Power Control in cognitive satellite communications
Author :
Tsakmalis, Anestis ; Chatzinotas, Symeon ; Ottersten, Bjorn
Author_Institution :
SnT - securityandtrust.lu, Univ. of Luxembourg, Luxembourg, Luxembourg
Abstract :
Spectrum Sensing (SS) and Power Control (PC) have been two important concepts of Cognitive Radio (CR). In this paper, a mechanism combining these two topics is proposed to allow a cognitive user, also called Secondary User (SU), to access the frequency band of a Primary User (PU) operating based on an Adaptive Coding and Modulation (ACM) protocol. The suggested SS technique considers Higher Order Statistical (HOS) features of the signal and an efficient Machine Learning (ML) detector, the Support Vector Machine (SVM), in order to constantly monitor the modulation scheme of the PU. Once the Automatic Modulation Classification (AMC) is ensured, the SU can attempt to access the frequency band of the PU and increase its transmitting power until it causes a change of the PU´s modulation scheme due to interference. When the SU detects the change of the PU´s modulation scheme, then it reduces its transmitting power to a lower level so as to regulate the induced interference. This Adaptive Power Control (APC) algorithm converges to the aforementioned interference limit and guarantees preservation of the PU link QoS.
Keywords :
adaptive codes; adaptive modulation; cognitive radio; control engineering computing; learning (artificial intelligence); modulation; power control; protocols; radiofrequency interference; satellite communication; support vector machines; telecommunication computing; telecommunication control; ACM protocol; PU link; QoS; SVM; adaptive coding and modulation protocol; adaptive power control algorithm; automatic modulation classification; cognitive satellite communications; cognitive user; higher order statistical features; interference; machine learning detector; primary user; secondary user; spectrum sensing; support vector machine; Interference; Modulation; Satellites; Sensors; Signal to noise ratio; Support vector machines; Training; Adaptive Coding and Modulation; Adaptive Power Control; Automatic Modulation Classification; Cognitive Radio; Higher Order Statistics; Spectrum Sensing; Support Vector Machine;
Conference_Titel :
Advanced Satellite Multimedia Systems Conference and the 13th Signal Processing for Space Communications Workshop (ASMS/SPSC), 2014 7th
Conference_Location :
Livorno
DOI :
10.1109/ASMS-SPSC.2014.6934549