Paper Info
Title
Interference Modulation Order Detection With Supervised Learning For Lte Interference Cancellation
Abstract
Blind detection of interference modulation order is studied in this paper. Exploiting the additivity property of cumulants for independent variables, we extend the techniques used in source automatic modulation classification to identify the interference modulation order. Using multi-class support vector machines, we show that accurate prediction performance can be achieved via supervised learning techniques. Three interference scenarios are considered - intra-cell interference, inter-cell interference with and without accurate interference channel estimates. Using numerical evaluation, we show that the proposed technique can be applied for all three scenarios without much degradation in all three cases. Furthermore, the technique is fairly invariant to changes in Signal-to-Noise Ratio (SNR) and Interference-to-Signal Ratio (ISR). These results are useful in advanced interference cancellation applications in wireless cellular communications, where the source modulation order is explicitly signaled, but not the interference.
Year
Venue
Keywords
2015
2015 IEEE 82ND VEHICULAR TECHNOLOGY CONFERENCE (VTC FALL)
interference modulation order detection, cumulants, LTE, NAICS, advanced receivers, supervised learning, machine learning, support vector machines
Field
DocType
Citations 
Modulation order,Computer science,Single antenna interference cancellation,Signal-to-noise ratio,Adjacent-channel interference,Co-channel interference,Supervised learning,Electronic engineering,Interference (wave propagation),Zero-forcing precoding
Conference
0
PageRank 
References 
Authors
0.34
3
2
Name
Order
Citations
PageRank
Tze-ping Low1816.38
Jangwook Moon200.34