arXiv subjects

Mingjiang Wu

Publications and source records attributed to Mingjiang Wu.

3 recordsLinked to original sources

arXiv2026

Beam-focusing Analysis for Modular XL-arrays: Effect of Time Synchronization Errors

For near-field communications, it is a hardware-efficient means to form an extremely large-scale array (XL-array) by concatenating multiple modular arrays (also referred to as subarrays). In this letter, we aim to investigate the effect of time synchronization errors among transmissions of different subarrays on the beam-focusing performance. To this end, we first characterize the beam pattern function when the transmit beamforming is designed based on maximum ratio transmission (MRT) under the premise of perfect time synchronization. As this function is highly difficult for analysis, we then consider a typical case with two subarrays. Interestingly, we show that for this case, the beam-focusing effect still persists even in the presence of time synchronization errors, while the focused location is deviated from the user location with an angle offset upper-bounded by 1/M, where M denotes the number of antennas in each subarray. Subsequently, for the general case with multiple subarrays, despite analytical intractability, we numerically show that time synchronization errors give rise to an imbricated (instead of focused) beam pattern. This may significantly degrade multi-user communication performance in practice due to the reduced desired signal power and increased inter-user interference.

cs.IT↗

arXiv2026

Mitigating Mixed-field Interference in Near-field and Far-field Communications: An Antenna Selection Approach

In mixed near-field and far-field systems, the nonorthogonality between near-field and far-field channels may cause severe inter-user interference and hence degrade rate performance, when the analog beamforming is designed based on the low-complexity full-array maximum ratio transmission (MRT). To tackle this issue, we propose in this paper an antenna selection-based transmission framework to effectively suppress mixed-field interference without mechanically altering antenna structures. To this end, an optimization problem is formulated to maximize the sum-rate of mixed-field systems, by jointly designing antenna selection and power allocation under the MRT-based analog beamforming. As the problem is non-convex and generally difficult to solve optimally, we first consider a typical two-user scenario to obtain useful insights. Interestingly, we analytically show that the strong mixed-field interference can be substantially mitigated by deactivating only a small portion of antennas, yet without compromising array gains too much. Moreover, an inherent tradeoff is revealed in antenna selection between interference suppression and array-gain enhancement, based on which a suboptimal number of deactivated antennas for achieving the maximum sum-rate is obtained. Next, for the general multi-user case, we develop an efficient penalty dual decomposition (PDD)-based two-layer framework to obtain its high quality solution by using the block coordinate descent (BCD) and successive convex approximation (SCA) techniques. To further reduce the computational complexity, a low-complexity antenna deactivation strategy is proposed capitalizing on an interference suppression criterion. Last, numerical results demonstrate that the proposed scheme achieves a favorable trade-off between interference suppression and array gain loss, hence achieving significant performance gains over various baseline schemes.

eess.SP↗

arXiv2026

BeamVLM for Low-altitude Economy: Generative Beam Prediction via Vision-language Models

For low-altitude economy (LAE), fast and accurate beam prediction between high-mobility unmanned aerial vehicles (UAVs) and ground base stations is of paramount importance, which ensures seamless coverage and reliable communications. However, existing deep learning-based beam prediction methods lack high-level semantic understanding of dynamic environments, resulting in poor generalization. On the other hand, the emerging large language model (LLM) based approaches show promise in enhancing generalization, but they typically lack rich environmental perception, thereby failing to capture fine-grained spatial semantics essential for precise beam alignment. To tackle these limitations, we propose in this correspondence a novel end-to-end generative framework for beam prediction, called BeamVLM, which treats beam prediction as a vision question answering task capitalizing on powerful existing vision-language models (VLMs). By projecting raw visual patches directly into the language domain and judiciously designing an instructional prompt, the proposed BeamVLM enables the VLM to jointly reason over UAV trajectories and environmental context. Last, experimental results on real-world datasets demonstrate that the proposed BeamVLM outperforms state-of-the-art methods in prediction accuracy and also exhibits superior generalization for other scenarios such as vehicle-to-infrastructure (V2I) beam prediction.

cs.NI↗