信号处理

Achieving efficient and cost-effective transmit beampattern control for integrated sensing and communication (ISAC) systems is a significant challenge. This paper addresses this by proposing a dual-function radar communication (DFRC)…

Many inverse problems and signal processing problems involve low-rank regularizers based on the nuclear norm. Commonly, proximal gradient methods (PGM) are adopted to solve this type of non-smooth problems as they can offer fast and…

Multi-frequency massive multi-input multi-output (MIMO) communication is a promising strategy for both 5G and future 6G systems, ensuring reliable transmission while enhancing frequency resource utilization. Statistical channel state…

This paper presents a Poisson multi-Bernoulli mixture (PMBM) filter for multi-target filtering based on sensor measurements that are sets of trajectories in the last two-time step window. The proposed filter, the trajectory measurement PMBM…

We propose a UNet-based foundation model and its self-supervised learning method to address two key challenges: 1) lack of qualified annotated analog layout data, and 2) excessive variety in analog layout design tasks. For self-supervised…

Constant-envelope signals are widely used in mobile edge applications and wireless communication systems for their hardware-friendly design, energy efficiency, and reliability. However, reliable detection with simple, power-efficient…

Orthogonal matching pursuit~(OMP) is a commonly used greedy algorithm for recovering sparse signals from compressed measurements. In this paper, we introduce a variant of the OMP algorithm to reduce the complexity of reconstructing a class…

Using Bayesian transfer learning, we develop a particle filter approach for tracking a nonlinear dynamical model in a dual-tracking system where intensities of measurement noise for both sensors are asymmetric. The densities for Bayesian…

Large antenna arrays and beamforming techniques are key components for exploiting the spectrum-rich FR2 bands in next-generation mobile communication networks. Given the site-specific spatio-temporal variations of the mm-wave channel,…

Nowadays, with the development of multi-sensor networks, the distributed cubature Kalman filter is one of the well-known existing schemes for state estimation, for which the influence of the non-Gaussian noise, abnormal data, and…

Integrated sensing and communication (ISAC) is crucial for low-altitude wireless networks (LAWNs), where the safety-critical demand for high-accuracy sensing creates a trade-off between precision and complexity for conventional methods. To…

Intelligent surfaces that reshape electromagnetic waves are regarded as disruptive technologies for wireless networks. However, existing designs sit at two costly extremes: dynamic reconfigurable intelligent surfaces (RISs) offer fine beam…

Machine learning (ML) has greatly advanced data-driven channel modeling and resource optimization in wireless communication systems. However, most existing ML-based methods rely on large, accurately labeled datasets with location…

The cubature Kalman filter based on minimum error entropy (MEE-CKF) offers accurate and robust performance in state of charge (SOC) estimation. However, due to the inflexibility of the minimum error entropy (MEE), this algorithm…

Accurate modeling of line-of-sight (LOS) probability is crucial for wireless channel description and coverage planning. The presence of a LOS impacts other channel characteristics such as pathloss, fading depth, delay- and angular spread,…

Very wideband apertures are needed in positioning, sensing, spectrum monitoring, and modern spread spectrum, e.g., frequency hopping systems. Vivaldi antennas are one of the prominent choices for the aforementioned systems due to their…

This paper presents a comprehensive study on the 3D positioning capabilities in distributed multiple-input multiple-output (MIMO) systems. Unlike previous studies that mainly rely on idealized isotropic antenna models, we adopt a…

The mean square error (MSE)-optimal estimator is known to be the conditional mean estimator (CME). This paper introduces a parametric channel estimation technique based on Bayesian estimation. This technique uses the estimated channel…

Recent advances in Wireless Physical Layer Foundation Models (WPFMs) promise a new paradigm of universal Radio Frequency (RF) representations. However, these models inherit critical limitations found in deep learning such as the lack of…

Channel state information (CSI)-based user equipment (UE) positioning with neural networks -- referred to as neural positioning -- is a promising approach for accurate off-device UE localization. Most existing methods train their neural…