arXiv subjects

Jixuan Zhou

Publications and source records attributed to Jixuan Zhou.

4 recordsLinked to original sources

arXiv2026

AI Empowered Communication and Radar Modulation Recognition: A Survey

Automatic modulation recognition (AMR) is of vital importance for ensuring communication and radar reliability, efficient spectrum utilization and resistance to electronic interference. The development of artificial intelligence (AI) technology is reshaping the technological paradigm of AMR, promoting its transition from traditional modes relying on manual features to data-driven intelligent recognition. This change is not only reflected in the significant improvement of recognition accuracy, but also injects strong momentum into the intelligent evolution of both communication and radar systems through algorithm innovation, architecture optimization, and scenario expansion. In order to clarify the current development status and bottlenecks of AMR, and to find breakthrough directions, we make a comprehensive survey of recent AI-based technologies for AMR in this paper, including model-based machine learning (ML) methods and data-driven deep learning (DL) methods. We first investigate the modulation types used in current communication and radar systems. Next, we summarize the typically used features in the field of AMR, and discuss their inherent advantages and disadvantages. Then, we introduce the basic AI models for AMR and conduct a hierarchical investigation of AMR methods for communication and radar. Finally, based on existing research works, we highlight open issues and propose future research directions for AMR.

eess.SP↗

arXiv2026

A Multimodal Data Fusion Attention-Empowered Generative Adversarial Network for Real Time 3D Underwater Sound Speed Field Construction

Sound speed profiles (SSPs) are crucial underwater parameters that determine the propagation patterns of acoustic signals, directly influencing the energy efficiency of underwater communication and the accuracy of positioning systems. Conventional techniques for obtaining SSPs, such as matched field processing (MFP), compressive sensing (CS), and deep learning (DL), typically depend on on-site sonar measurements, which impose stringent requirements on the deployment of underwater observation systems. To overcome this limitation and enable high-precision sound speed field reconstruction without the need for on-site underwater data collection, we propose a novel multimodal data-fusion generative adversarial network enhanced with residual attention blocks (MDF-RAGAN). This architecture integrates attention mechanisms to capture global spatial feature correlations effectively, while residual modules are employed to extract subtle perturbations in deep-ocean sound velocity distribution caused by sea surface temperature (SST) variations. Experimental results on a public real-world dataset demonstrate that the proposed model outperforms other state-of-the-art methods, achieving an estimation error of less than 0.3 m/s. Specifically, MDF-RAGAN reduces the root mean square error (RMSE) by nearly half compared to convolutional neural network (CNN) and spatial interpolation (SITP) methods, and attains a 65.8\% RMSE reduction relative to the mean profile method. These results highlight the effectiveness of multi-source fusion and cross-modal attention in enhancing the accuracy and robustness of sound speed profile reconstruction.

cs.SD↗

arXiv2025

A large, long-lived, slowly-expanding superbubble across the Perseus Arm

Stellar feedback is a crucial mechanism in galactic evolution, as demonstrated by the widespread bubbles observed with JWST. In this study, we combine data from Gaia and LAMOST to obtain a sample of young O-B2 stars with full three-dimensional velocity information. Focusing on the largest known superbubble in the Milky Way, we identify groups of O-B2 stars at its periphery, exhibiting a transverse velocity of 25.8 km/s and an expansion velocity of 6.2 km/s. Using these velocities, we calculate a crossing time t_cross ~ 20 Myr and an expansion timescale t_expansion ~ 80 Myr. We estimate a survival timescale t_survival ~ 250 Myr and a supernova interval t_SN ~ 0.1 Myr. Together with the Galactic shear timescale t_shear ~ 30 Myr, these values satisfy t_SN < t_shear < t_survival. The energy and momentum from supernovae are sufficient to sustain the bubble's growth against ambient pressure. This indicates that repeated supernovae replenish energy faster than shear and turbulent distort the cavity. Our analysis classifies the Giant Oval Cavity as a large, quasi-stationary superbubble, similar to the Phantom Bubble observed by JWST, stabilised by the interplay between stellar feedback and Galactic disk dynamics.

astro-ph.GA↗

arXiv2023

Underwater Sound Speed Profile Construction: A Review

Real--time and accurate construction of regional sound speed profiles (SSP) is important for building underwater positioning, navigation, and timing (PNT) systems as it greatly affect the signal propagation modes such as trajectory. In this paper, we summarizes and analyzes the current research status in the field of underwater SSP construction, and the mainstream methods include direct SSP measurement and SSP inversion. In the direct measurement method, we compare the performance of popular international commercial temperature, conductivity, and depth profilers (CTD). While for the inversion methods, the framework and basic principles of matched field processing (MFP), compressive sensing (CS), and deep learning (DL) for constructing SSP are introduced, and their advantages and disadvantages are compared. The traditional direct measurement method has good accuracy performance, but it usually takes a long time. The proposal of SSP inversion method greatly improves the convenience and real--time performance, but the accuracy is not as good as the direct measurement method. Currently, the SSP inversion relies on sonar observation data, making it difficult to apply to areas that couldn't be covered by underwater observation systems, and these methods are unable to predict the distribution of sound velocity at future times. How to comprehensively utilize multi-source data and provide elastic sound velocity distribution estimation services with different accuracy and real-time requirements for underwater users without sonar observation data is the mainstream trend in future research on SSP construction.

eess.SP↗