On the likelihood-based approach to modulation classification

Hameed, F.; Dobre, Octavia A.; Popescu, Dimitrie C.

doi:10.1109/twc.2009.12.080883

Cited by 350 publications

(133 citation statements)

References 19 publications

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“…At present, AMR is implemented using the three types of methods listed in Figure 1: (1) The first method is based on the likelihood ratio test (LRT), such as the average LRT proposed by ElMahdy et al [4], the general LRT proposed by Panagiotou et al [5], and the hybrid LRT proposed by Hameed et al [6]. The disadvantage of this type of algorithm is that the assumption that the symbols are independent and identically distributed is usually not true in reality, and the complexity increases with an increase in the number of unknown parameters.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Deep Learning-Based Automatic Modulation Classifier on FPGA SDR Platform

Tang

2018

Electronics

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Intelligent radios collect information by sensing signals within the radio spectrum, and the automatic modulation recognition (AMR) of signals is one of their most challenging tasks. Although the result of a modulation classification based on a deep neural network is better, the training of the neural network requires complicated calculations and expensive hardware. Therefore, in this paper, we propose a master-slave AMR architecture using the reconfigurability of field-programmable gate arrays (FPGAs). First, we discuss the method of building AMR, by using a stack convolution autoencoder (CAE), and analyze the principles of training and classification. Then, on the basis of the radiofrequency network-on-chip architecture, the constraint conditions of AMR in FPGA are proposed from the aspects of computing optimization and memory access optimization. The experimental results not only demonstrated that AMR-based CAEs worked correctly, but also showed that AMR based on neural networks could be implemented on FPGAs, with the potential for dynamic spectrum allocation and cognitive radio systems.

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Section: Introductionmentioning

confidence: 99%

Implementation of Deep Learning-Based Automatic Modulation Classifier on FPGA SDR Platform

Tang

2018

Electronics

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“…The other trend is signal statistical features [2]. In order to improve PCC for modulation classification, [3][4][5][6] established different Maximum Likelihood (ML) functions. This method was optimized to estimate parameters, such as modulation mode, carrier frequency, phase, and channel response.…”

Section: Introductionmentioning

confidence: 99%

“…To solve this likelihood function, the QAM signal was verified to obtain lower complexity. In [4], a hybrid ML function was proposed. Instantaneous signal feature was obtained to reduce the complexity.…”

Section: Introductionmentioning

confidence: 99%

“…Different ML functions and Bayesian decision networks must be evaluated carefully. According to different modulation types, literature [3][4][5][6][7] proposed different likelihood ratio functions, which could obtain the theoretical optimal result under the Gaussian white noise condition. However, multipath fading channel would seriously impact on modulation classification.…”

Section: Introductionmentioning

confidence: 99%

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Modulation Classification Based on Extensible Neural Networks

Yang

2017

Mathematical Problems in Engineering

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A deep learning architecture based on Extensible Neural Networks is proposed for modulation classification in multipath fading channel. Expanded Neural Networks (ENN) are established based on energy natural logarithm model. The model is set up using hidden layers. Modulation classification based on ENN is implemented through the amplitude, phase, and frequency hidden network, respectively. In order to improve Probability of Correct classification (PCC), one or more communication signal features are extracted using hidden networks. Through theoretical proof, ENN learning network is demonstrated to be effective in improving PCC using amplitude, phase, and the frequency weight subnetwork, respectively. Compared with the traditional algorithms, the simulation results show that the proposed ENN has higher PCC than traditional algorithm for modulation classification within the same training sequence and Signal to Noise Ratio (SNR).

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“…The idea is that the receiver tries to blindly identify the AMC parameters from the data collected from the channel. For instance in [3]- [5] novel schemes for blind classification of modulation formats have been proposed. In [6]- [9], blind identification of encoder…”

Section: Introductionmentioning

confidence: 99%

Fast Blind Recognition of Channel Codes

Moosavi

Larsson

2014

IEEE Trans. Commun.

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Abstract-We present a fast algorithm that, for a given input sequence and a linear channel code, computes the syndrome posterior probability (SPP) of the code, i.e., the probability that all parity check relations of the code are satisfied. According to this algorithm, the SPP can be computed blindly, i.e., given the soft information on a received sequence we can compute the SPP for the code without first decoding the bits. We show that the proposed scheme is efficient by investigating its computational complexity.We then consider two scenarios where our proposed SPP algorithm can be used. The first scenario is when we are interested in finding out whether a certain code was used to encode a data stream. We formulate a statistical hypothesis test and we investigate its performance. We also compare the performance of our scheme with that of an existing scheme. The second scenario deals with how we can use the algorithm for reducing the computational complexity of a blind decoding process. We propose a heuristic sequential statistical hypotheses test to use the fact that in real applications, the data arrives sequentially, and we investigate its performance using system simulations.

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On the likelihood-based approach to modulation classification

Cited by 350 publications

References 19 publications

Implementation of Deep Learning-Based Automatic Modulation Classifier on FPGA SDR Platform

Implementation of Deep Learning-Based Automatic Modulation Classifier on FPGA SDR Platform

Modulation Classification Based on Extensible Neural Networks

Fast Blind Recognition of Channel Codes

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