Southwest University

Selected work

Representative Papers

An Evolutionary Game With the Game Transitions Based on the Markov Process

Jan 01, 2024IEEE Transactions on Systems, Man, and Cybernetics: Systems

Traditional evolutionary game models neglect the role of individual psychological variability in shaping collective cooperation dynamics. Method: We propose a novel evolutionary game framework integrating Markovian game-state switching with reputation-guided neighbor selection on complex networks—first embedding stochastic game transitions into networked evolutionary dynamics and coupling them with a reputation-based mechanism for strategy updating. Contribution/Results: Theoretical analysis and numerical simulations reveal that both the game-switching rate and the reputation weight exert dual critical control over cooperation emergence. Increasing either parameter significantly enhances cooperative behavior, and high cooperation levels remain robust even in large-scale networks. This model establishes a computationally tractable theoretical paradigm for investigating the coevolution of psychological states, behavioral strategies, and network structure.

22 citationsRead paper

Open Data in the Digital Economy: An Evolutionary Game Theory Perspective

Jun 01, 2024IEEE Transactions on Computational Social Systems

Existing research on open data–driven sustainable development overlooks the critical role of data intermediaries—particularly regulatory entities—in governing data ecosystems. Method: This study constructs a tripartite evolutionary game model integrating data providers, users, and regulators—the first systematic incorporation of regulators into open data literature—to characterize multi-stakeholder co-evolutionary dynamics. It employs replicator dynamics analysis, numerical simulation, and sensitivity testing to examine nonlinear interactions among regulatory incentives, user costs, and data value. Contribution/Results: The analysis identifies multiple evolutionarily stable strategies (ESS) and quantifies threshold effects: regulatory reward-penalty intensity and users’ data mining capability exhibit nonlinear, bifurcation-like impacts on cooperation rates. Findings provide empirically grounded theoretical foundations for designing incentive-compatible platform mechanisms and evidence-based data governance policies to advance sustainable development through open data.

13 citationsRead paper

Efficient UAV trajectory prediction: A multi-modal deep diffusion framework

Jan 26, 2026

This work addresses the challenge of insufficient accuracy in predicting trajectories of unauthorized drones in low-altitude airspace, which stems from the limited information provided by single-sensor systems. To overcome this limitation, the authors propose a multimodal deep diffusion framework that fuses point clouds from LiDAR and millimeter-wave radar. The approach employs structurally aligned dual-branch encoders to extract modality-specific features and introduces a bidirectional cross-attention mechanism to achieve semantic alignment and complementary fusion of geometric structures and dynamic reflectivity characteristics. A tailored loss function and post-processing strategy are further integrated to enhance prediction performance. Evaluated on the MMAUD dataset, the proposed method achieves a 40% improvement in trajectory prediction accuracy over baseline models, demonstrating the effectiveness and practicality of the multimodal fusion strategy.

1 citationsRead paper

Hybrid Near-Far Field 6D Movable Antenna Design Exploiting Directional Sparsity and Deep Learning

Jun 18, 2025

Conventional far-field channel models exhibit insufficient accuracy for 6D metamaterial antenna (6DMA) systems operating in hybrid near-field/far-field scenarios, while joint optimization of antenna pose and beamforming incurs prohibitively high computational complexity. Method: This paper proposes: (1) a generalized hybrid-field channel model integrating planar- and spherical-wave propagation characteristics to accurately capture coexisting near- and far-field effects across the metasurface; (2) a low-overhead full-state channel mapping algorithm leveraging directional sparsity to significantly reduce channel acquisition overhead; and (3) an end-to-end deep reinforcement learning framework for unified optimization of 6DMA pose (position and orientation) and transmit beamforming. Results: Experiments demonstrate a substantial reduction in channel modeling error, over 60% decrease in training overhead, and a 3.2× improvement in spectral efficiency compared to flexible antenna systems, validating the proposed model’s accuracy and algorithmic efficacy.

1 citationsRead paper