Analysis Of Automatic Modulation Recognition AMR TechniqueAnd Its Adaptibility To Software Defined Radio SDR For Low SNR

Abstract

Software radio technology is expected to play an important role in the development of Fourth Generation (4G) wireless communication systems. The signal identification process, an intermediate step between signal interception and demodulation, is a major task of an intelligent receiver. Automatic Modulation Classification (AMC) or Automatic Modulation Recognition (AMR) employed in SDR is the process of deciding based on observations of the received signal, what modulation is being used at the transmitter. The AMR problems are blind in nature i.e. the signals captured by an AMR receiver have no prior knowledge. Blind modulation classification deals with identification of modulation formats from received signal without the knowledge of the type of modulation transmitted. The problem becomes more challenging in real world scenarios when there are synchronization errors such as frequency offset and timing offset, and multi-path fading. newlineMaximum classification algorithms work only in the presence of Additive White Gaussian Noise (AWGN). Few algorithms work under conditions of both AWGN and fading environment, still the number of class problems and the lower limit of SNR vary for all. Most of the existing algorithms assume prior knowledge of some parameter such as carrier frequency symbol rate etc. Very few algorithms are completely blind in nature. newlineTen Modulated signals 2ASK, 4ASK, 2PSK, 4PSK 2FSK, 4FSK and 16 QAM, GMSK, 64QAM and 256 QAM were generated. Raised cosine filter was used for pulse shaping. Channel conditions were modeled by simulating AWGN and multipath Rayleigh fading channel effect. The conditions adaptable to SDR were taken into consideration in designing the classifier. Pre-processing and features subset selection was used to reduce the complexity and to improve the classifier s performance. Preprocessing task included denoising and equalization of received signals. Carrier Frequency Estimation (CFE) was done using cyclostationary analysis. newlineInstantaneous features such as amplitude, phase and frequency w