• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4664-4681
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• 2020

Modulation recognition (MR) plays a key role in cognitive radar, cognitive radio, and some other civilian and military fields. While existing methods can identify the signal modulation type by extracting the signal characteristics, the quality of feature extraction has a serious impact on the recognition results. In this paper, an end-to-end MR method based on long short-term memory (LSTM) and the gated recurrent unit (GRU) is put forward, which can directly predict the modulation type from a sampled signal. Additionally, the sliding window method is applied to fast-changing mixed-modulation signals for which the signal modulation type changes over time. The recognition accuracy on training datasets in different SNR ranges and the proportion of each modulation method in misclassified samples are analyzed, and it is found to be reasonable to select the evenly-distributed and full range of SNR data as the training data. With the improvement of the SNR, the recognition accuracy increases rapidly. When the length of the training dataset increases, the neural network recognition effect is better. The loss function value of the neural network decreases with the increase of the training dataset length, and then tends to be stable. Moreover, when the fast-changing period is less than 20ms, the error rate is as high as 50%. As the fast-changing period is increased to 30ms, the error rates of the GRU and LSTM neural networks are less than 5%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1743-1758
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• 2023

Automatic modulation classification is a critical algorithm for non-cooperative communication systems. This paper addresses the challenging problem of closed-set and open-set signal modulation classification in complex channels. We propose a novel approach that incorporates a self-learning filter and center-loss in Deep Residual Shrinking Networks (DRSN) for closed-set modulation classification, and the Opendistance method for open-set modulation classification. Our approach achieves better performance than existing methods in both closed-set and open-set recognition. In closed-set recognition, the self-learning filter and center-loss combination improves recognition performance, with a maximum accuracy of over 92.18%. In open-set recognition, the use of a self-learning filter and center-loss provide an effective feature vector for open-set recognition, and the Opendistance method outperforms SoftMax and OpenMax in F1 scores and mean average accuracy under high openness. Overall, our proposed approach demonstrates promising results for automatic modulation classification, providing better performance in non-cooperative communication systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권10호
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• pp.3458-3478
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• 2022

Automatic modulation recognition is the core algorithm in the field of modulation classification in communication systems. Our investigations show that deep learning (DL) based modulation recognition techniques have achieved effective progress for multiple-input multiple-output (MIMO) systems. However, network complexity is always an additional burden for high-accuracy classifications, which makes it impractical. Therefore, in this paper, we propose a low-complexity dimensional interactive lightweight network (DilNet) for MIMO systems. Specifically, the signals received by different antennas are cooperatively input into the network, and the network calculation amount is reduced through the depth-wise separable convolution. A two-dimensional interactive attention (TDIA) module is designed to extract interactive information of different dimensions, and improve the effectiveness of the cooperation features. In addition, the TDIA module ensures low complexity through compressing the convolution dimension, and the computational burden after inserting TDIA is also acceptable. Finally, the network is trained with a penalized statistical entropy loss function. Simulation results show that compared to existing modulation recognition methods, the proposed DilNet dramatically reduces the model complexity. The dimensional interactive lightweight network trained by penalized statistical entropy also performs better for recognition accuracy in MIMO systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2356-2376
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• 2021

In this paper, we proposed a novel modulation recognition method based on quantum elephant herding algorithm (QEHA) evolving neural network under impulse noise environment. We use the adaptive weight myriad filter to preprocess the received digital modulation signals which passing through the impulsive noise channel, and then the instantaneous characteristics and high order cumulant features of digital modulation signals are extracted as classification feature set, finally, the BP neural network (BPNN) model as a classifier for automatic digital modulation recognition. Besides, based on the elephant herding optimization (EHO) algorithm and quantum computing mechanism, we design a quantum elephant herding algorithm (QEHA) to optimize the initial thresholds and weights of the BPNN, which solves the problem that traditional BPNN is easy into local minimum values and poor robustness. The experimental results prove that the adaptive weight myriad filter we used can remove the impulsive noise effectively, and the proposed QEHA-BPNN classifier has better recognition performance than other conventional pattern recognition classifiers. Compared with other global optimization algorithms, the QEHA designed in this paper has a faster convergence speed and higher convergence accuracy. Furthermore, the effect of symbol shape has been considered, which can satisfy the need for engineering.

• IEIE Transactions on Smart Processing and Computing
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• 제1권3호
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• pp.143-151
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• 2012

A new data-driven method for the design of a blind modulation frequency filter that suppresses the slow-varying noise components is proposed. The proposed method is based on the temporal local decorrelation of the feature vector sequence, and is done on an utterance-by-utterance basis. Although the conventional modulation frequency filtering approaches the same form regardless of the task and environment conditions, the proposed method can provide an adaptive modulation frequency filter that outperforms conventional methods for each utterance. In addition, the method ultimately performs channel normalization in a feature domain with applications to log-spectral parameters. The performance was evaluated by speaker-independent isolated-word recognition experiments under additive noise environments. The proposed method achieved outstanding improvement for speech recognition in environments with significant noise and was also effective in a range of feature representations.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.119-122
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• 2005

In this paper, we investigated the recognition range according to modulation index for the ISO(International Standards Organization) 14443 13.56MHz contactless Type B RFID(Radio Frequency IDentification) system. We measured recognition range with changing modulation index step by step from 5% to 24% where we used 4 samples of Type B transponder with different resonance frequencies between 13.838MHz and 17.200MHz. While gradually increasing a distance in vertical direction from the center of the reader antenna, we measured the distance where the transponder's PUPI(Pseudo Unique PICC(Proximity IC Card) Identifier) is recognized continuously during 10 seconds and the distance where the transponder's PUPI is recognized at least two times during 5 seconds. From the measurement results, we found that the best recognition ranges were achieved when the reader had modulation index between 11% and 14%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3761-3779
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• 2022

The performance of existing recognition algorithms for binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals degrade under conditions of low signal-to-noise ratios (SNR). Hence, a novel recognition algorithm based on features in the graph domain is proposed in this study. First, the power spectrum of the squared candidate signal is truncated by a rectangular window. Thereafter, the graph representation of the truncated spectrum is obtained via normalization, quantization, and edge construction. Based on the analysis of the connectivity difference of the graphs under different hypotheses, the sum of degree (SD) of the graphs is utilized as a discriminate feature to classify BPSK and QPSK signals. Moreover, we prove that the SD is a Schur-concave function with respect to the probability vector of the vertices (PVV). Extensive simulations confirm the effectiveness of the proposed algorithm, and its superiority to the listed model-driven-based (MDB) algorithms in terms of recognition performance under low SNRs and computational complexity. As it is confirmed that the proposed method reduces the computational complexity of existing graph-based algorithms, it can be applied in modulation recognition of radar or communication signals in real-time processing, and does not require any prior knowledge about the training sets, channel coefficients, or noise power.

• 한국정보통신학회논문지
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• 제18권10호
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• pp.2409-2416
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• 2014

본 논문에서는 넌코히어런트 비동기 조건에서 PSK, QAM 등의 선형 디지털 변조 방식을 자동으로 식별하는 알고리즘을 제안한다. 디지털 변조 신호는 심볼 천이 주기간 주파수, 위상, 진폭 등의 특성이 반복적으로 변하게 된다. 이러한 특성을 이용하여 변조 방식을 식별할 수 있도록 순환 모멘트와 고차 큐뮬런트를 이용하는 방법을 제안한다. 계층적 의사 결정 트리 방식의 알고리즘 구조를 사용하여 고속으로 처리 가능하도록 구성하였으며 총 4개의 특징 추출 인자를 사용하여 식별하였다. 모의실험 결과 심볼 수 4,096개, SNR 15dB 이상에서 95% 이상의 식별 정확도를 나타내었으며, 반송 주파수와 위상 편이가 발생하더라도 신호를 분류하는데 효과적임을 확인하였다.

• 한국통신학회논문지
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• 제30권1C호
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• pp.73-81
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• 2005

본 논문에서는 미리 정의된 키 피쳐(key feature)를 수신된 변조 신호로부터 추출하여 동등 이득 조합(equal gain combining) 기법을 적용하는 자동 변조 인식 알고리즘을 제안하곡 의사 결정 이론(decision-theoretic) 알고리즘과 제안된 알고리즘의 성능을 비교, 분석하였다. 제안된 변조 인식 알고리즘은 키 피쳐 추출 단위인 세그먼트별로 미리 정의된 5 가지 종류의 키 피쳐를 추출하고, 전체 프레임에 걸쳐 평균화된 각 키 피쳐값을 결정-순서도(decision flowchart)에 적용하여 수신 신호의 변조 형식을 구분한다. 제안된 알고리즘의 성능을 검증하기 위하여 아날로그 변조 신호인 AM, FM, SSB 신호와 디지털 변조 신호인 FSK2, FSK4, PSK2, PSK4 신호를 대상으로 SNR의 변화 및 신호 수집 시간의 변화에 따른 변조 인식 성공률을 측정하였다. 그 결과 제안된 알고리즘이 기존의 의사 결정 이론 알고리즘에 거의 근접하는 성능을 나타내면서 낮은 복잡도를 나타내었다.

• ETRI Journal
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• 제38권6호
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• pp.1190-1196
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• 2016

We propose a new bandpass filter (BPF)-based online channel normalization method to dynamically suppress channel distortion when the speech and channel noise components are unknown. In this method, an adaptive modulation frequency filter is used to perform channel normalization, whereas conventional modulation filtering methods apply the same filter form to each utterance. In this paper, we only normalize the two mel frequency cepstral coefficients (C0 and C1) with large dynamic ranges; the computational complexity is thus decreased, and channel normalization accuracy is improved. Additionally, to update the filter weights dynamically, we normalize the learning rates using the dimensional power of each frame. Our speech recognition experiments using the proposed BPF-based blind channel normalization method show that this approach effectively removes channel distortion and results in only a minor decline in accuracy when online channel normalization processing is used instead of batch processing