Blind Automatic Modulation Classification Research Articles

Blind Modulation Classification for Overlapped Co-Channel Signals Using Capsule Networks

Automatic modulation classification (AMC) for single signals has been widely studied in the past two decades. Meanwhile, the demand for multi-signal AMC is also increasing; however, single-signal AMC performs poorly and few methods were proposed for multi-signal AMC. To address this problem, a blind AMC method for overlapped co-channel signals applying capsule networks is proposed. Multiple transmitters transmit signals simultaneously and signals are received by a single-antenna receiver. The method handle signals at intermediate frequency directly, and is able to detect the number of signals automatically. Numerical results demonstrate the effectiveness of our proposed method, and excellent generalization ability provided by capsule network.

Likelihood-Based Automatic Modulation Classification in OFDM With Index Modulation

In orthogonal frequency division multiplexing (OFDM) with index modulation, the modulation parameters to be classified include both the signal constellation and the number of active subcarriers. This is different from conventional OFDM schemes where only the signal constellation needs to be classified. In this paper, to solve this challenging problem, the likelihood-based automatic modulation classification (AMC) is studied. First, in the scenario with known channel state information (CSI), two classifiers based on average likelihood ratio test (ALRT) and hybrid likelihood ratio test (HLRT), respectively, are derived. Concretely, in HLRT-based classifier, energy-based detector, and log-likelihood ratio-based detector are employed to identify the active subcarriers. Second, a HLRT-based blind AMC is proposed in the scenario with unknown CSI, where the efficient implementation of expectation maximization algorithm is presented to estimate channel fading coefficients and noise variance. Finally, the effectiveness of the proposed AMC algorithms is confirmed by computer simulations.

Automatic Modulation Classification Based on Kernel Density Estimation

In this paper, we propose an efficient automatic modulation classification (AMC) scheme for a group of narrowband and digitally modulated signals such as quadrature phase-shift keying (QPSK), 16-PSK, 64-PSK, 4-quadratic-amplitude modulation (QAM), 16-QAM, and 64-QAM. The classification was performed by analyzing the probability density distribution for the real and imaginary parts of the modulated signals. To simplify the complexity of the proposed approach, we performed the classification in two stages: first, we classified the modulation between QAM and PSK signaling, and then, we determined the $M$ -ary order of the modulation by developing kernel density estimation, which is typically used in nonparametric methods for the estimation of the probability density function of a random variable with finite data samples. Simulations were carried out to evaluate the performance of the proposed scheme for flat channels. It is observed that this simple efficient technique can find applications in blind AMC, as the performance comparison with the state of the art is promising.

Robustness in Supervised Learning Based Blind Automatic Modulation Classification

Automatic modulation classification (AMC) involves extracting a set of unique features from the received signal. Accuracy and uniqueness of the features along with the appropriate classification algorithm determine the overall performance of AMC systems. Accuracy of any modulation feature is usually limited by the blindness of the signal information such as carrier frequency, symbol rate etc. Most papers do not sufficiently consider these impairments and so do not directly target practical applications. The AMC system proposed herein is trained with probable input signals, and the appropriate decision tree should be chosen to achieve robust classification. Six unique features are used to classify eight analog and digital modulation schemes which are widely used by low frequency mobile emergency radios around the globe. The Proposed algorithm improves the classification performance of AMC especially for the low SNR regime.