Modulation Classification Algorithm Research Articles

Modulation Classification Algorithm in Fading Channels under Blind Reception Conditions

该文针对衰落信道下调制识别算法预处理要求高的问题，提出了一种基于循环平稳性检测的调制分类算法，提升了盲接收环境下的分类效果。算法以信号的循环频率为特征，结合循环平稳性检测，实现了OFDM, PSK, FSK等常见数字调制信号的区分。算法无需各种参数估计以及同步等预处理过程，理论推导及仿真结果证明，能够实现低信噪比衰落信道条件下信号的有效区分。该算法将有助于提升衰落信道中非合作接收条件下的信号分类能力。

A Novel Modulation-Sensing Method

This article introduces the application of automatic modulation classification (AMC) algorithm for the signal sensing and detection of the primary user (PU) in cognitive radio (CR) environments. The algorithm was developed using an artificial neural network (ANN). Computer simulations of 12 combined analog and digitally modulated signals corrupted by additive white Gaussian noise (AWGN) have been carried out.

Modulation Recognition in Continuous Phase Modulation Using Approximate Entropy

Modulation recognition finds its application in today's cognitive systems ranging from civilian to military installations. Existing modulation classification algorithms include classic likelihood approaches and feature-based approaches. In this study, approximate entropy, a nonlinear method to analyze a time series, is proposed as a unique characteristic of a modulation scheme. It is projected as a robust feature to identify signal parameters such as number of symbol levels, pulse lengths, and modulation indices of a continuous phase modulated (CPM) signal. The method is then extended to classify CPM signals with differing pulse shapes, which include raised cosine and Gaussian pulses with varying roll-off factors and bandwidth-time products, respectively. This approximate entropy feature-based approach results in high classification accuracies for a variety of signals and performs robustly even in the presence of synchronization errors and carrier phase offsets. Results are presented in the form of extensive simulations.

Automatic Modulation Classification Methods for Wireless OFDM Systems in TDD Mode

This paper presents a novel automatic modulation classification (AMC) algorithm in wireless time division duplex (TDD) orthogonal frequency division multiplexing (OFDM) systems with adaptive modulation (AM). Based upon the maximum-a-posteriori (MAP) criterion, a classification method for digital quadrature amplitude modulation (QAM) schemes is developed that exploits the channel reciprocity in TDD systems and the receiver knowledge about the overall transmission data rate. The AMC algorithm enables the application of adaptive modulation in wireless OFDM systems without loss in the effective data rate caused by signaling of the bit allocation table (BAT). Numerical results show almost no performance loss in terms of the packet error ratio (PER) compared to the case with perfect knowledge of the modulation schemes if the proposed AMC technique is used.

Automatic modulation classification algorithm using higher-order cumulants under real-world channel conditions

An automatic modulation classification algorithm for application in communications via multipath fading channels, without a priori information on channel characteristics, based on normalized sixth-order cumulants is described. In comparison with existing approaches, proposed algorithm can achieve much better classification accuracy in distinguishing BPSK from complex-valued modulation techniques. Theoretical analysis is verified via extensive simulations, under real-world channel propagation conditions.

Using fuzzy clustering and TTSAS algorithm for modulation classification based on constellation diagram

The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power, carrier frequency and phase offsets, timing information, etc., blind identification of the modulation is a difficult task. This becomes even more challenging in real world.In this paper I develop a novel algorithm using Two Threshold Sequential Algorithmic Scheme (TTSAS) algorithm and pattern recognition to identify the modulation types of the communication signals automatically. I have proposed and implemented a technique that casts modulation recognition into shape recognition. Constellation diagram is a traditional and powerful tool for design and evaluation of digital modulations. In this paper, modulation classification is performed using constellation of the received signal by fuzzy clustering and consequently hierarchical clustering algorithms are used for classification of Quadrature–Amplitude Modulation (QAM) and Phase Shift Keying (PSK) modulations and also modulated signal symbols constellation utilizing TTSAS clustering algorithm, and matching with standard templates, is used for classification of QAM modulation. TTSAS algorithm used here is implemented by the Hamming neural network. The simulation results show the capability of this method for modulation classification with high accuracy and appropriate convergence in the presence of noise.

Blind Modulation Classification Algorithm for Adaptive OFDM Systems

Adaptive modulation is an efficient method to increase the spectral efficiency of OFDM based high-speed wireless data transmission systems in multipath channel. Blind modulation classification schemes play an important role in adaptive modulation systems, eliminating the need for transmitting modulation information, thereby increasing spectral efficiency. In this paper, a novel blind modulation classification algorithm is derived from the finite alphabet property of information symbols and the equivalent parallel model of OFDM systems. The performances of the proposed algorithm and M2M4P algorithm [1] are tested and compared using Monte-Carlo simulations. It is found that, the novel algorithm yields performance better than that of M2M4P algorithm and with much less complexity.

Maximum Log-Likelihood Function-Based QAM Signal Classification over Fading Channels

In this paper, we propose a modulation classification algorithm for M-ary QAM signals in Rician and Rayleigh fading channels. The developed algorithms are based on the maximum log-likelihood functi...

An asymptotic optimal algorithm for modulation classification

A suboptimal algorithm for modulation classification was proposed for classifying the modulation type of general M-ary phase-shifted keying (MPSK) signals. Yang and Soliman (see IEEE Trans. Aerosp. Electron. Syst., vol.33, no.1, p.38-45, 1997) approximated the phase probability density function of a received signal to be the Tikhonov function. Instead, we employ the exact phase density function and derive an asymptotic optimal classification algorithm. We show a structure of this proposed classifier for continuous wave (CW), binary phase-shift keying (BPSK), quaternary phase-shift keying (QPSK), and 8PSK. Besides, we give an example to demonstrate the capability of this algorithm and compare its performance to that in Yang and Soliman. It is shown that the performance is more effective.

A suboptimal algorithm for modulation classification

In this paper we present a suboptimal algorithm for modulation classification to classify the general M-ary phase-shifted keying (MPSK) signal buried in additive white Gaussian noise (AWGN). We first derive the phase density functions of MPSK signals, then develop the required statistics for modulation classification and demonstrate a classifier for CW, binary phase-shifted keying (BPSK), quadrature phase-shifted keying (QPSK), and 8PSK. The structure of the proposed classifier is flexible and is easy to expand. The performance of classifier is evaluated in terms of the probability of successful classification. An example (BPSK/QPSK case) is provided to demonstrate the capabilities of the proposed classifier. The performance is evaluated through the theoretical approach and the Monte Carlo computer simulations and is compared with that previously published in 1992. It is shown that the performance of the proposed classifier is better. Further improvement in performance can be obtained by increasing the length of observation interval.

Signal classification using statistical moments

An automatic modulation classification algorithm utilizing the statistical moments of the signal phase is developed and used to classify the modulation type of general M-ary PSK signals. It is shown that the nth moment (n even) of the phase of the signal is a monotonic increasing function of M. On the basis of this property, the authors formulate a general hypothesis test, develop a decision rule, and derive an analytic expression for the probability of misclassification. Two examples are given to demonstrate the performance of the algorithm. The algorithm is compared with the quasi-log-likelihood radio (qLLRC), square-law (SLC), and phase-based (PBC) classifiers. The algorithm is outperformed by qLLRC at low CNR but is comparable to SLC and is better than PBC. The qLLRC algorithm is only valid at CNR >