Modulation recognition for real HF signals

Abstract

High-frequency (HF) communications is undergoing resurgence despite advances in long-range satellite communication systems. Defense agencies are using the HF spectrum for backup communications, as well as for spectrum surveillance applications. Spectrum management organizations are monitoring the HF spectrum to control and enforce licensing. These activities usually require systems capable of determining the location of a source of transmissions, separating valid signals from interference and noise, and recognizing signal modulation. Our ultimate aim is to develop robust modulation recognition algorithms for real HF signals, that is, signals propagating by multiple ionospheric modes with cochannel signals and non-Gaussian noise.One aspect of modulation recognition is the extraction of signal identifying features. This paper continues our work of applying various feature parameters to real HF signals and gives guidance on which features show potential for use in robust recognition of HF modulation types in the presence of HF noise and multi-path. It also defines a measure of mean separation distance between modulation types based on an entropy parameter, and discusses the probability density function of HF noise.

Semi-blind algorithms for automatic classification of digital modulation schemes

“…The current work is also limited to phase amplitude modulation only. For future works, we propose to investigate (i) modulation classification in fading and non-Gaussian channels; (ii) development of a universal modulation classifier; and (iii) physical evaluation of algorithms through hardware implementation and verification [28]. …”

An Empirical Study of the Probability Density Function of Hf Noise

“…Separating the true HF noise from all interfering signals is difficult. Therefore this paper, which continues work presented earlier [13][14][15][16], uses an empirical method to show that the probability density function (PDF) of HF noise is generally not Gaussian. The resulting implication is that common assumptions for theoretical communication models do not necessarily apply to the HF band.…”

Data Recovery through Modulation Identification in Dense Wireless Networks

“…In [7] , authors present a work related to the application of various feature parameters on real HF signals and gives guidance on which features show potential for use in robust recognition of HF modulation types in the presence of HF noise and multi-path. It also defines a measure of mean separation distance between modulation types based on an entropy parameter, and discusses the probability density function of HF noise.…”