Wen-Xuan Long

Wen-Xuan Long, Shengyu Ye, Marco Moretti et al.

The sixth-generation (6G) wireless systems are expected to adopt extremely large aperture arrays (ELAAs), novel antenna architectures, and operate in extremely high-frequency bands to meet growing data demands. ELAAs significantly increase the number of antennas, enabling finer spatial resolution and improved beamforming. At high frequencies, ELAAs shift communication from the conventional far-field to near-field regime, where spherical wavefronts dominate and the channel response depends on both angle and distance, increasing channel dimensionality. Conventional far-field channel estimation methods, which rely on angular information, struggle in near-field scenarios due to increased pilot overhead and computational complexity. This paper presents a comprehensive survey of recent advances in near-field channel estimation. It first defines the near- and far-field boundary from an electromagnetic perspective and discusses key propagation differences, alongside a brief review of ELAA developments. Then, it introduces mainstream near-field channel models and compares them with far-field models. Major estimation techniques are reviewed under different configurations (single/multi-user, single/multi-carrier), including both direct estimation and RIS-assisted cascaded estimation. These techniques reveal trade-offs among estimation accuracy, complexity, and overhead. This survey aims to provide insights and foundations for efficient and scalable near-field channel estimation in 6G systems, while identifying key challenges and future research directions.

Jiabei Fan, Rui Chen, Wen-Xuan Long et al.

Orbital angular momentum (OAM) at radio-frequency provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectral efficiencies. However, classical phase gradient-based OAM mode detection methods require perfect alignment of transmit and receive antennas, which greatly challenges the practical application of OAM communications. In this paper, we first show the effect of non-parallel misalignment on the OAM phase structure, and then propose the OAM mode detection method based on uniform circular array (UCA) samples learning for the more general alignment or non-parallel misalignment case. Specifically, we applied three classifiers: K-nearest neighbor (KNN), support vector machine (SVM), and back-propagation neural network (BPNN) to both single-mode and multi-mode OAM detection. The simulation results validate that the proposed learning-based OAM mode detection methods are robust to misalignment errors and especially BPNN classifier has the best generalization performance.