arXiv subjects

Thuan Van Le

Publications and source records attributed to Thuan Van Le.

5 recordsLinked to original sources

arXiv2026

OTFS-Enabled Delayed SINR-Feedback Power Control for Reliable and Fair High-Mobility UAV Communications

This paper develops a power control framework driven by delayed signal-to-interference-plus-noise ratio (SINR) feedback for orthogonal time frequency space (OTFS) unmanned aerial vehicle (UAV) communications operating under high mobility, with reliability and fairness as the primary design targets.A base station with a uniform linear array serves several UAVs on a common OTFS frame, while the path delays, Doppler shifts, and inter-UAV interference are determined by the three-dimensional propagation geometry and the base-station array response rather than by a postulated coupling model. In place of instantaneous channel state information, the proposed controller refreshes the transmit-power vector from delayed SINR measurements alone, which matches the practical limitations of fast-fading aerial links. A prediction-smoothing-projection rule mixes a reliability share, a fairness share and a spectral-efficiency share, each normalized separately, so that the utility weights control the closed loop directly. Simulations show that the effective SINR of OTFS changes 37.8% less per frame than that of orthogonal frequency division multiplexing (OFDM) at 70 m/s under the same numerology, and that the resulting controller raises the average minimum SINR by 1.21 dB over equal power allocation at 40 m/s while lifting Jain's fairness index from 0.833 to 0.970, at a sum-rate cost of 24.6% that the utility weights keep under the designer's control. The margin of OTFS over OFDM within the same controller widens from 0.18 dB at 10 m/s to 0.81 dB at 90 m/s, showing that the waveform contribution to the usefulness of stale SINR feedback increases with mobility.

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arXiv2026

Mobility- and Feedback-Aware Multi-Level Conflict-Triggered Hybrid Beamforming for Multi-User mmWave UAV Systems

This paper investigates hybrid beamforming for multi-user large multiple-input multiple-output millimeter-wave unmanned aerial vehicle (UAV) downlink systems under mobility-induced channel aging and delayed beam-training feedback. Analog beam selection from compact delayed reports is a partial-observation decision, while additional candidate evaluations consume processing time and reduce the useful payload interval. We propose a mobility- and feedback-aware multi-level refinement strategy, termed MLR-TG, to improve robustness without always-on candidate search. Candidate subsets are ranked by a predicted net utility constructed from quantized complex coefficients of the reported codewords and the UAV mobility state, while the transmission regularized zero-forcing precoder is computed once from pilot-estimated effective channel state information (CSI) after analog selection. The refinement level is adaptively selected according to conflict severity and aging sensitivity. The selection rule is a two-statistic approximation of predicted-utility maximization, employs a system-size-invariant conflict score, and is calibrated offline on training data disjoint from evaluation. Simulations on a three-dimensional air-to-ground model with UAV attitude dynamics and common channel trajectories show that MLR-TG reduces system outage probability by 26.7% and improves the 5th-percentile user rate by 53.9% relative to greedy sector beamforming, while net spectral efficiency remains within 0.96%. Compared with always-on global top-3 refinement, MLR-TG improves net spectral efficiency by 5.5% while evaluating 77.9% fewer candidates, and remains within 3.4% of a noncausal-CSI level oracle in net spectral efficiency while requiring 86.9% fewer feedback bits than full-CSI reporting.

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arXiv2026

Feedback-Efficient Beam-User Association for Near-Field mmWave Hybrid Beamforming Systems

Near-field multiuser hybrid beamforming (HBF) requires joint angle-distance codebooks whose size, and hence reporting overhead, grows with the array aperture. For the extremely large array considered here, reporting one quality metric per codeword already incurs more overhead than full channel state information (CSI) feedback. This letter develops a feedback-efficient beam--user equipment (UE) association framework. The focusing codebook is sampled at beam-depth spacing within an effective beamfocusing Rayleigh distance (EBRD)-aware focusing region, with one far-field codeword per angular direction beyond that region, so that its radial law and size follow from the array geometry. Each UE probes this codebook but reports only its M strongest candidates, and the base station associates UEs and beams with a proportional-fair metric that consumes the leakage terms carried by this report together with the codeword correlations it knows, thereby allowing co-angular UEs to be multiplexed by focal distance. Simulations show that M=3 suffices: the proposed scheme stays within 0.6% of an optimistic full-metric reporting reference while using 0.8% of its feedback, a 99.11% reduction with respect to full-CSI feedback, and the interference-aware metric contributes up to 18.7% of the sum spectral efficiency over its interference-blind counterpart.

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arXiv2026

Hybrid Beamforming in Non-Terrestrial Networks: Architectures, Design Challenges, and Opportunities

Hybrid analog-digital beamforming (HBF) has emerged as a key enabling technology for non-terrestrial networks (NTNs), where large antenna arrays are required to compensate for severe propagation loss but fully digital beamforming is often impractical due to radio-frequency (RF) chain cost, power consumption, and payload limitations. Compared with terrestrial networks, NTN platforms such as low Earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) impose distinctive HBF design challenges, including high mobility, Doppler effects, sparse line-of-sight-dominant channels, stringent on-board energy budgets, and, for UAVs, the additional coupling between beamforming and controllable platform placement or trajectory. This survey provides a systematic review of HBF techniques for NTN systems, with emphasis on LEO satellite and UAV communications. We first introduce common HBF architectures, signal models, channel representations, analog and digital precoder designs, and learning-aided approaches that form the shared technical foundation of existing works. We then survey both platforms under a common set of five categories, which cover system architecture and precoding design, time-varying beam management, network-level cooperation and scheduling, sensing capability and reconfigurable surfaces, and security and multiple access. Their platform-specific content differs most sharply in the second one, since the dominant time-varying mechanism is traffic-driven beam hopping on an LEO payload but mobility-aware beam tracking on a UAV. Finally, we discuss open research challenges and future directions toward scalable, robust, and hardware-efficient HBF in next-generation NTNs.

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arXiv2026

Artificial Intelligence for Spatially Reconfigurable Antennas: Movable, Fluid, and Pinching Antenna Systems

Recently, sixth-generation (6G) wireless networks have moved beyond fixed-array designs toward antenna architectures that can adapt their spatial configuration to specific environmental conditions. Movable antenna, fluid antenna, and pinching antenna systems represent this principle in different ways, but they share a common vision: exploiting spatial flexibility as an additional degree of freedom (DoF) to improve communication, sensing, security, and resource efficiency. These new techniques, however, also bring challenging problems, as antenna configuration must be jointly considered with channel acquisition, beamforming, mobility, and network resource management. Therefore, artificial intelligence (AI) has become an important tool for learning fast and adaptive control policies for these highly coupled systems. In this survey, we provide a unified review of AI for spatially reconfigurable antenna systems. We first introduce the basic principles of movable, fluid, and pinching antennas, which is followed by a summary of the latest AI-enabled designs according to their primary optimization objectives. Furthermore, we compare the roles of deep learning (DL), deep reinforcement learning (DRL), multi-agent reinforcement learning (MARL), graph learning, Transformers, large language models (LLMs), and structure-guided learning across different antenna architectures. Finally, we discuss open challenges and future directions toward scalable, robust, and hardware-aware intelligent reconfigurable antenna networks.

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