arXiv subjects

Mingjun Du

Publications and source records attributed to Mingjun Du.

4 recordsLinked to original sources

arXiv2024

Misaligned Over-The-Air Computation of Multi-Sensor Data with Wiener-Denoiser Network

In data driven deep learning, distributed sensing and joint computing bring heavy load for computing and communication. To face the challenge, over-the-air computation (OAC) has been proposed for multi-sensor data aggregation, which enables the server to receive a desired function of massive sensing data during communication. However, the strict synchronization and accurate channel estimation constraints in OAC are hard to be satisfied in practice, leading to time and channel-gain misalignment. The paper formulates the misalignment problem as a non-blind image deblurring problem. At the receiver side, we first use the Wiener filter to deblur, followed by a U-Net network designed for further denoising. Our method is capable to exploit the inherent correlations in the signal data via learning, thus outperforms traditional methods in term of accuracy. Our code is available at https://github.com/auto-Dog/MOAC_deep

eess.SP↗

arXiv2021

Characterization of Collective Behaviors for Directed Signed Networks

This paper targets at exploring how to characterize collective behaviors of directed signed networks. The right eigenvector of the Laplacian matrix associated with zero eigenvalue is further investigated and its mathematical expression is proposed. It is shown that the right eigenvector plays an important role in determining the collective behaviors of directed signed networks. Furthermore, algebraic criteria are introduced for collective behaviors of directed signed networks, such as bipartite consensus, interval bipartite consensus and bipartite containment tracking. In addition, a simulation example is given to the correctness of our developed theoretical results.

math.GR↗

arXiv2019

Distributed Averaging Problems over Directed Signed Networks

This paper aims at addressing distributed averaging problems for signed networks in the presence of general directed topologies that are represented by signed digraphs. A new class of improved Laplacian potential functions is proposed by presenting two notions of any signed digraph: induced unsigned digraph and mirror (undirected) signed graph, based on which two distributed averaging protocols are designed using the nearest neighbor rules. It is shown that with any of the designed protocols, signed-average consensus (respectively, state stability) can be achieved if and only if the associated signed digraph of signed network is structurally balanced (respectively, unbalanced), regardless of whether weight balance is satisfied or not. Further, improved Laplacian potential functions can be exploited to solve fixed-time consensus problems of signed networks with directed topologies, in which a nonlinear distributed protocol is proposed to ensure the bipartite consensus or state stability within a fixed time. Additionally, the convergence analyses of directed signed networks can be implemented with the Lyapunov stability analysis method, which is realized by revealing the tight relationship between convergence behaviors of directed signed networks and properties of improved Laplacian potential functions. Illustrative examples are presented to demonstrate the validity of our theoretical results for directed signed networks.

math.OC↗

arXiv2019

Convergence Behavior Analysis of Directed Signed Networks Subject to Nonidentical Topologies

This paper addresses the behavior analysis problems for directed signed networks that involve cooperative-antagonistic interactions among agents. Of particular interest is to explore the convergence behaviors of directed signed networks with agents of mixed first-order and second-order dynamics. Further, the agents are subject to nonidentical topologies represented by two different signed digraphs that have a strongly connected union. It is shown that when considering signed networks subject to sign-consistent nonidentical topologies, polarization (respectively, neutralization) can be achieved if and only if the union of two signed digraphs is structurally balanced (respectively, unbalanced). By comparison, signed networks can always be guaranteed to become neutralized in the presence of sign-inconsistent nonidentical topologies.

math.DS↗