On the blind decision of modulation type in impaired AWGN channel environment

Abstract

We propose a new modulation classification method that utilizes likelihood function of received signal in an impaired AWGN (Additive White Gaussian Noise) channel environment. The proposed method utilizes the likelihood under the assumption that each modulated signal is sent, but the direct use of the ML (Maximum Likelihood) method is not considered for high computational complexity and weakness to channel impairment such as phase offsets and frequency offsets. The proposed method has lower computational complexity than does the ML method. Moreover, the proposed method is robust to the channel impairment such as phase offsets and frequency offsets. The correct classification probabilities of the proposed method and the ML method are given for an AWGN channel with phase offsets and frequency offsets, which are simulated with extensive Monte-Carlo simulation. As shown in simulation results, a more accurate classification performance both in phase offset environment and in frequency offset environment can be achieved with the low computational complexity of the proposed method. Log-Likelihood Ratio) test, this method is the approximation at a low SNR and it is hard to get a threshold value for general QAM modulation. In this paper, we propose a low complexity digital modu- lation classification method based on the likelihood function of the received signal in an AWGN (Additive White Gaussian Noise) channel environment with phase offsets and frequency offsets. The proposed method is similar to the ML method (2), (3) in the sense that it utilizes the likelihood function of the received signal, but it has lower computational complexity than the ML method and it is less sensitive to phase offsets and frequency offsets than the ML method. This paper is organized as follows. In Section II, we give the signal model used in this paper. This section also gives a previous modulation classification method with this signal model. Section III provides the new modulation classification method based on the ML method. In Section IV, we give some numerical simulation results and discussion to verify the performance of the proposed method. Section V concludes the paper.