Automatic Modulation Classification Using Convolutional Neural Network With Features Fusion of SPWVD and BJD

Zhang, Zufan; Wang, Chun; Gan, Chenquan; Sun, Shaohui; Wang, Mengjun

doi:10.1109/tsipn.2019.2900201

Cited by 167 publications

(103 citation statements)

References 31 publications

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“…The decode function Eqs. (4) and (5) are used to recover the reconstruction signalsx from measurement vector y. a (3) = f z (3) = f W (3) y + b (3) .…”

Section: Encoder and Decoder Sub-networkmentioning

confidence: 99%

“…where d = W (3) , W (4) ; b (3) , b (4) denotes the set of decoded parameters and T d (•) denotes the decoding nonlinear mapping function.…”

Section: Encoder and Decoder Sub-networkmentioning

confidence: 99%

“…Next, the loss function J SDAE (W, b) is computed by Eq. 7 parameters of encoder e = W (1) , W (2) ; b (1) , b (2) ; measurement vector y; parameters of decoder d = W (3) , W (4) ; b (3) , b (4) ; reconstruction signalsx. Measurement vector y = T e (x, e ).…”

Section: Offline Training Algorithmmentioning

confidence: 99%

“…CS method has been applied successfully in many fields, such as biomedical [2,3], image processing [4,5], communication [6,7] and sensor network [8,9], but there are still two significant challenges in the CS that need *Correspondence: gcq2010cqu@163.com 1 School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China Full list of author information is available at the end of the article to be resolved. On the one hand, due to the strong randomness of measurement matrix, it is difficult to realize measurement matrix on the hardware and the reconstruction performance is unstable.…”

Section: Introductionmentioning

confidence: 99%

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The optimally designed autoencoder network for compressed sensing

Zhang

Gan

et al. 2019

J Image Video Proc.

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Compressed sensing (CS) is a signal processing framework, which reconstructs a signal from a small set of random measurements obtained by measurement matrices. Due to the strong randomness of measurement matrices, the reconstruction performance is unstable. Additionally, current reconstruction algorithms are relatively independent of the compressed sampling process and have high time complexity. To this end, a deep learning based stacked sparse denoising autoencoder compressed sensing (SSDAE_CS) model, which mainly consists of an encoder sub-network and a decoder sub-network, is proposed and analyzed in this paper. Instead of traditional linear measurements, a multiple nonlinear measurements encoder sub-network is trained to obtain measurements. Meanwhile, a trained decoder sub-network solves the CS recovery problem by learning the structure features within the training data. Specifically, the two sub-networks are integrated into SSDAE_CS model through end-to-end training for strengthening the connection between the two processes, and their parameters are jointly trained to improve the overall performance of CS. Finally, experimental results demonstrate that the proposed method significantly outperforms state-of-the-art methods in terms of reconstruction performance, time cost, and denoising ability. Most importantly, the proposed model shows excellent reconstruction performance in the case of a few measurements.

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“…The decode function Eqs. (4) and (5) are used to recover the reconstruction signalsx from measurement vector y. a (3) = f z (3) = f W (3) y + b (3) .…”

Section: Encoder and Decoder Sub-networkmentioning

confidence: 99%

“…where d = W (3) , W (4) ; b (3) , b (4) denotes the set of decoded parameters and T d (•) denotes the decoding nonlinear mapping function.…”

Section: Encoder and Decoder Sub-networkmentioning

confidence: 99%

Section: Offline Training Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The optimally designed autoencoder network for compressed sensing

Zhang

Gan

et al. 2019

J Image Video Proc.

Self Cite

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show abstract

“…Physical information (such as objects, people, etc.) can be obtained by adopting the Internet of things search technology, and then these information will be managed and stored in an organized and ordered way to facilitate users to search [7]- [9]. The entity state information is perceived and observed by its associated sensor, and the user can search the entity by specifying the demand state [10] via its sensor.…”

Section: Introductionmentioning

confidence: 99%

Participant Selection Strategy With Privacy Protection for Internet of Things Search

Yang

Kang

et al. 2020

IEEE Access

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Nowadays, with the rapid developments of the Internet of Things, the information on physical entities has shown explosive growth. The emergence of Internet of Things search technology has facilitated the storage, organization, and management of massive entity information. Aiming at the issues of security and privacy and the target to obtain the optimal perceiving results by selecting participants to ensure the quality of service of the IoT platform, this paper proposes a participant selection strategy with privacy protection for IoT search (PSSPP). Firstly, the search task requester and the task contents are anonymized and blinded respectively, and then individual attributes of participants are mapped to hide the identity information of participants. Secondly, in order to choose the participants who meet the task requirements, the participants are preliminarily selected based on the bloom filter. Finally, the perceiving trust value and relative credibility of participants in participating tasks are dynamically evaluated. Simulation results demonstrate that the proposed scheme can reduce the attack probability of malicious users toward task requesters and participants, and rationally select the appropriate task participants. INDEX TERMS IoT search, mobile crowd sensing, participant selection, privacy-preserving, bloom filter.

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Hybrid NOMA/OMA with Buffer-Aided Relaying for Cooperative Uplink System

Quan

Wang

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Automatic Modulation Classification Using Convolutional Neural Network With Features Fusion of SPWVD and BJD

Cited by 167 publications

References 31 publications

The optimally designed autoencoder network for compressed sensing

The optimally designed autoencoder network for compressed sensing

Participant Selection Strategy With Privacy Protection for Internet of Things Search

Hybrid NOMA/OMA with Buffer-Aided Relaying for Cooperative Uplink System

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