arXiv subjects

Shichuan Chen

Publications and source records attributed to Shichuan Chen.

9 recordsLinked to original sources

arXiv2026

Strangeon Ergostars

The nature of the central engine powering short gamma-ray bursts (sGRBs) in binary neutron star (BNS) mergers remains a key open question in the era of multi-messenger astronomy. The ergostar hypothesis, that a rapidly rotating star with an ergoregion can act as a powerful energy source, offers an alternative explanation to the black hole-accretion disk paradigm. In this work, however, we examine this hypothesis using a phenomenological EOS of strangeon matter, i.e., condensed matter with nucleon-like units for three flavors of quarks. By constructing a large suite of uniformly rotating equilibrium models, we systematically investigate the parameter space of the stable ergostars and calculate their maximum extractable energy. We demonstrate that strangeon matter supports a vast and robust parameter space for dynamically stable ergostars, even without requiring differential rotation. We find that the extractable rotational energy from these configurations can be on the order of $0.01 M_\odot$, representing a massive energy reservoir, even when accounting for baryonic mass variations (e.g., mass ejection and particle capture during the Penrose process). Our results suggest that BNS merger remnants composed of exotic matter could play a crucial, previously underestimated role in high-energy astrophysics.

astro-ph.HE↗

arXiv2024

Free Energy of Anisotropic Strangeon Stars

Can pulsar-like compact objects release further huge free energy besides the kinematic energy of rotation? This is actually relevant to the equation of state of cold supra-nuclear matter, which is still under hot debate. Enormous energy is surely needed to understand various observations, such as $γ-$ray bursts, fast radio bursts and soft $γ-$ray repeaters. In this paper, the elastic/gravitational free energy of solid strangeon star is revisited for strangeon stars, with two anisotropic models to calculate in general relativity. It is found that huge free energy (> $10^{46}$ erg) could be released via starquakes, given an extremely small anisotropy ($(p_{\rm t}-p_{\rm r})/p_{\rm r} \sim 10^{-4}$, with $p_{\rm t}$/$p_{\rm r}$ the tangential/radial pressure), implying pulsar-like stars could have great potential of free energy release without extremely strong magnetic fields in solid strangeon star model.

astro-ph.HE↗

arXiv2024

Black hole-neutron star mergers with massive neutron stars in numerical relativity

We study the merger of black hole-neutron star (BH-NS) binaries in numerical relativity, focusing on the properties of the remnant disk and the ejecta, varying the mass of compactness of the NS and the mass and spin of the BH. We find that within the precision of our numerical simulations, the remnant disk mass and ejecta mass normalized by the NS baryon mass ($\hat{M}_{\rm{rem}}$ and $\hat{M}_{\rm{eje}}$, respectively), and the cutoff frequency $f_{\rm{cut}}$ normalized by the initial total gravitational mass of the system at infinite separation approximately agree among the models with the same NS compactness $C_{\rm{NS}}=M_{\rm{NS}}/R_{\rm{NS}}$, mass ratio $Q=M_{\rm{BH}}/M_{\rm{NS}}$, and dimensionless BH spin $\chi_{\rm{BH}}$ irrespective of the NS mass $M_{\rm{NS}}$ in the range of $1.092$--$1.691\,M_\odot$. This result shows that the merger outcome depends sensitively on $Q$, $\chi_{\rm BH}$, and $C_{\rm{NS}}$ but only weekly on $M_{\rm{NS}}$. This justifies the approach of studying the dependence of NS tidal disruptions on the NS compactness by fixing the NS mass but changing the EOS. We further perform simulations with massive NSs of $M_{\rm{NS}}=1.8M_{\odot}$, and compare our results of $\hat{M}_{\rm{rem}}$ and $\hat{M}_{\rm{eje}}$ with those given by existing fitting formulas to test their robustness for more compact NSs. We find that the fitting formulas obtained in the previous studies are accurate within the numerical errors assumed, while our results also suggest that further improvement is possible by systematically performing more precise numerical simulations.

astro-ph.HE↗

arXiv2021

SigNet: A Novel Deep Learning Framework for Radio Signal Classification

Deep learning methods achieve great success in many areas due to their powerful feature extraction capabilities and end-to-end training mechanism, and recently they are also introduced for radio signal modulation classification. In this paper, we propose a novel deep learning framework called SigNet, where a signal-to-matrix (S2M) operator is adopted to convert the original signal into a square matrix first and is co-trained with a follow-up CNN architecture for classification. This model is further accelerated by integrating 1D convolution operators, leading to the upgraded model SigNet2.0. The simulations on two signal datasets show that both SigNet and SigNet2.0 outperform a number of well-known baselines. More interestingly, our proposed models behave extremely well in small-sample learning when only a small training dataset is provided. They can achieve a relatively high accuracy even when 1\% training data are kept, while other baseline models may lose their effectiveness much more quickly as the datasets get smaller. Such result suggests that SigNet/SigNet2.0 could be extremely useful in the situations where labeled signal data are difficult to obtain. The visualization of the output features of our models demonstrates that our model can well divide different modulation types of signals in the feature hyper-space.

eess.SP↗

arXiv2020

DemodNet: Learning Soft Demodulation from Hard Information Using Convolutional Neural Network

Soft demodulation is a basic module of traditional communication receivers. It converts received symbols into soft bits, that is, log likelihood ratios (LLRs). However, in the nonideal additive white Gaussian noise (AWGN) channel, it is difficult to accurately calculate the LLR. In this letter, we propose a demodulator, DemodNet, based on a fully convolutional neural network with variable input and output length. We use hard bit information to train the DemodNet, and we propose log probability ratio (LPR) based on the output layer of the trained DemodNet to realize soft demodulation. The simulation results show that under the AWGN channel, the performance of both hard demodulation and soft demodulation of DemodNet is very close to the traditional methods. In three non-ideal channel scenarios, i.e., the presence of frequency deviation, additive generalized Gaussian noise (AGGN) channel, and Rayleigh fading channel, the performance of channel decoding using the soft information LPR obtained by DemodNet is better than the performance of decoding using the exact LLR calculated under the ideal AWGN assumption.

eess.SP↗

arXiv2020

DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer

A canonical wireless communication system consists of a transmitter and a receiver. The information bit stream is transmitted after coding, modulation, and pulse shaping. Due to the effects of radio frequency (RF) impairments, channel fading, noise and interference, the signal arriving at the receiver will be distorted. The receiver needs to recover the original information from the distorted signal. In this paper, we propose a new receiver model, namely DeepReceiver, that uses a deep neural network to replace the traditional receiver's entire information recovery process. We design a one-dimensional convolution DenseNet (1D-Conv-DenseNet) structure, in which global pooling is used to improve the adaptability of the network to different input signal lengths. Multiple binary classifiers are used at the final classification layer to achieve multi-bit information stream recovery. We also exploit the DeepReceiver for unified blind reception of multiple modulation and coding schemes (MCSs) by including signal samples of corresponding MCSs in the training set. Simulation results show that the proposed DeepReceiver performs better than traditional step-by-step serial hard decision receiver in terms of bit error rate under the influence of various factors such as noise, RF impairments, multipath fading, cochannel interference, dynamic environment, and unified reception of multiple MCSs.

eess.SP↗

arXiv2019

Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios

Spectrum sensing is a key technology for cognitive radios. We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification. We normalize the received signal power to overcome the effects of noise power uncertainty. We train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new signals. We also use transfer learning strategies to improve the performance for real-world signals. Extensive experiments are conducted to evaluate the performance of this method. The simulation results show that the proposed method performs better than two traditional spectrum sensing methods, i.e., maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based method. In addition, the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals. Furthermore, the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning. Finally, experiments under colored noise show that our proposed method has superior detection performance under colored noise, while the traditional methods have a significant performance degradation, which further validate the superiority of our method.

eess.SP↗

arXiv2019

Deep Learning for Cooperative Radio Signal Classification

Radio signal classification has a very wide range of applications in cognitive radio networks and electromagnetic spectrum monitoring. In this article, we consider scenarios where multiple nodes in the network participate in cooperative classification. We propose cooperative radio signal classification methods based on deep learning for decision fusion, signal fusion and feature fusion, respectively. We analyze the performance of these methods through simulation experiments. We conclude the article with a discussion of research challenges and open problems.

eess.SP↗

arXiv2019

Deep Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification

Radio signal classification has a very wide range of applications in the field of wireless communications and electromagnetic spectrum management. In recent years, deep learning has been used to solve the problem of radio signal classification and has achieved good results. However, the radio signal data currently used is very limited in scale. In order to verify the performance of the deep learning-based radio signal classification on real-world radio signal data, in this paper we conduct experiments on large-scale real-world ACARS and ADS-B signal data with sample sizes of 900,000 and 13,000,000, respectively, and with categories of 3,143 and 5,157 respectively. We use the same Inception-Residual neural network model structure for ACARS signal classification and ADS-B signal classification to verify the ability of a single basic deep neural network model structure to process different types of radio signals, i.e., communication bursts in ACARS and pulse bursts in ADS-B. We build an experimental system for radio signal deep learning experiments. Experimental results show that the signal classification accuracy of ACARS and ADS-B is 98.1% and 96.3%, respectively. When the signal-to-noise ratio (with injected additive white Gaussian noise) is greater than 9 dB, the classification accuracy is greater than 92%. These experimental results validate the ability of deep learning to classify large-scale real-world radio signals. The results of the transfer learning experiment show that the model trained on large-scale ADS-B datasets is more conducive to the learning and training of new tasks than the model trained on small-scale datasets.

cs.LG↗