arXiv subjects

Ngo Hoang Tu

Publications and source records attributed to Ngo Hoang Tu.

12 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.

eess.SP↗

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.

eess.SP↗

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.

eess.SP↗

arXiv2026

Active STAR-RIS-Aided RSMA IoT Systems: Performance Analysis, Model-Based and Data-Driven Resource Allocation Frameworks

STAR-RISs have emerged as a promising technology for achieving full-space signal coverage with low hardware complexity and energy consumption. In parallel, RSMA offers a flexible interference management mechanism that enhances spectral efficiency and user fairness. Integrating STAR-RIS with RSMA provides strong potential for robust and energy-efficient communications; however, the resulting double-fading cascaded channels and the inherent imbalance between near and far users pose reliability and fairness challenges. This study presents comprehensive information-theoretic and optimization frameworks for active STAR-RIS-assisted RSMA Internet-of-Things systems. Closed-form expressions for the OP and EC are derived under both the presence and absence of direct links. Based on these results, asymptotic OP and EC, diversity order, and array gain are analyzed to characterize system behavior. In addition, throughput-based and spectral-based energy efficiency metrics are investigated to quantify the tradeoffs between transmission performance and power consumption. To enhance user fairness, a fairness-oriented RA framework is developed to jointly optimize power allocation and rate-splitting coefficients using successive convex approximation and block coordinate descent techniques. To further improve scalability and real-time applicability, we introduce a data-driven RA framework based on DMNN, CMNN, and MXGB. This framework approximates the optimization-based solutions with substantially reduced computational complexity. Numerical results show that the proposed MXGB model achieves the shortest execution time while maintaining performance comparable to ground truth-based benchmarks. Extensive Monte Carlo simulations validate the analytical results and demonstrate the effectiveness of the proposed frameworks.

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arXiv2026

Hierarchical Empirical-Bayes Naive Bayes: Minimax Smoothing and Calibration with AODE Extension

The Naive Bayes (NB) classifier remains a standard choice for categorical data, yet its widely used smoothing rules, such as Laplace, Lidstone, Krichevsky-Trofimov, and the $m$-estimate, all prescribe a fixed smoothing strength that ignores feature cardinality, sample size, and class imbalance, inducing a non-vanishing bias on modern high-cardinality tabular data. We propose hierarchical empirical-Bayes Naive Bayes (HEB-NB), in which each class-feature conditional probability is smoothed by a Dirichlet prior whose concentration is learned data-adaptively via Type-II maximum likelihood, enabling principled information sharing across classes while retaining closed-form inference. We further introduce HEB average one-dependence estimators (HEB-AODE), showing that the adaptive smoothing transfers cleanly to structural relaxations of NB. Theoretically, we establish a non-asymptotic $\ell_1$ error bound for HEB-NB matching the empirical-distribution minimax rate plus a vanishing data-adaptive bias, together with a matching Laplace-tight lower bound that yields a finite-sample, risk-level strict separation from Laplace. We further derive a plug-in excess Bayes-risk bound via total-variation tensorization and a population top-1 expected calibration error (ECE) corollary. Empirically, across 31 UCI and OpenML benchmarks, HEB-NB attains the best average Friedman rank on probabilistic metrics, with up to 22.1% log-loss reductions on high-cardinality datasets and consistent improvements of HEB-AODE over vanilla AODE. Combining HEB-NB with mutual-information weighting reduces top-1 ECE by 41%-70%, demonstrating substantial gains in probabilistic accuracy and calibration.

cs.LG↗

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

ATCNet-CIAM for Multi-Session Motor Imagery EEG Signal Classification

Motor imagery (MI)-based electroencephalography is widely used in non-invasive brain--computer interfaces (BCIs), but robust decoding remains challenging due to inter-subject variability and cross-session non-stationarity. This work proposes ATCNet-CIAM, an enhanced attention temporal convolutional network that integrates a lightweight channel-integrated attention module (CIAM) into the ATCNet framework to improve channel-spatial feature representation for MI decoding. The proposed model is evaluated on BCI Competition IV-2a, BCI Competition IV-2b, and the multi-day WBCIC-MI dataset under standard, within-session, and cross-session protocols. Experimental results show that ATCNet-CIAM achieves 86.32% accuracy on BCI IV-2a and 87.96% on BCI IV-2b under the standard protocol, while reaching 89.46% and 83.64% in the within-session WBCIC-MI on 2C and 3C, respectively. The proposed framework consistently improves classification stability and robustness under session-varying conditions, and ablation study confirms the complementary contribution of the proposed architectural components.

cs.CV↗

arXiv2026

RIS-Aided ISAC in Cluttered Environments

In this paper, we analyze the performance of a communication-optimized reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system operating in a cluttered environment where multiple scatterers may interfere with the different types of reflected sensing signals. The RIS phases coherently combine the direct and reflected communication paths at the user equipment, whereas the corresponding radar returns remain generally misaligned. In addition, static scatterers near the radar act as environmental clutter that affects only the sensing function. For the communication link over small-scale fading, we derive an exact ergodic-capacity expression for the no-RIS baseline, a moment-matched Gamma approximation for the RIS-assisted link, and a Jensen upper bound, all of which are interpreted as upper bounds on the rate of the underlying binary phase-shift keying waveform. For sensing, our analysis focuses on the average signal-to-clutter-plus-noise ratio (SCNR) at the direct range-Doppler cell. Specifically, we derive the average powers of the direct, RIS-related, and scatterer returns, which scale as constant, linear, linear, quadratic, and constant, respectively, with the number of RIS elements. We then weigh them by the range and slow-time leakage responses to obtain the SCNR, thereby separating RIS-induced clutter from geometry-governed environmental clutter. Range and velocity estimation are evaluated using resolution-normalized metrics. Our Monte Carlo simulation results validate the analysis and show that zero-Doppler clutter leakage dominates the SCNR.

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arXiv2026

Integration of TinyML and LargeML: A Survey of 6G and Beyond

The evolution from fifth-generation (5G) to sixth-generation (6G) networks is driving an unprecedented demand for advanced machine learning (ML) solutions. Deep learning has already demonstrated significant impact across mobile networking and communication systems, enabling intelligent services such as smart healthcare, smart grids, autonomous vehicles, aerial platforms, digital twins, and the metaverse. At the same time, the rapid proliferation of resource-constrained Internet-of-Things (IoT) devices has accelerated the adoption of tiny machine learning (TinyML) for efficient on-device intelligence, while large machine learning (LargeML) models continue to require substantial computational resources to support large-scale IoT services and ML-generated content. These trends highlight the need for a unified framework that integrates TinyML and LargeML to achieve seamless connectivity, scalable intelligence, and efficient resource management in future 6G systems. This survey provides a comprehensive review of recent advances enabling the integration of TinyML and LargeML in next-generation wireless networks. In particular, we (i) provide an overview of TinyML and LargeML, (ii) analyze the motivations and requirements for unifying these paradigms within the 6G context, (iii) examine efficient bidirectional integration approaches, (iv) review state-of-the-art solutions and their applicability to emerging 6G services, and (v) identify key challenges related to performance optimization, deployment feasibility, resource orchestration, and security. Finally, we outline promising research directions to guide the holistic integration of TinyML and LargeML for intelligent, scalable, and energy-efficient 6G networks and beyond.

cs.NI↗

arXiv2025

On Performance of IoT Networks with Coordinated NOMA Transmission: Covert Monitoring and Information Decoding

This work investigates the covertness and security performance of Internet-of-Things (IoTs) networks under Rayleigh fading environments. Specifically, a cellular source transmits covert information to cell-edge users with the assistance of an IoT master node, employing a coordinated direct and relay transmission strategy combined with non-orthogonal multiple access (NOMA). This approach not only enhances spectrum utilization but also generates friendly interference to complicate a warden's surveillance or an eavesdropper's decoding efforts. From a covertness perspective, we derive exact closed-form expressions for the detection error probability (DEP) under arbitrary judgment thresholds. We then identify the optimal judgment threshold for the worst-case scenario, at which the warden minimizes its DEP performance. Accordingly, we determine the effective region for user power allocation (PA) in NOMA transmission that satisfies the DEP constraint. From a security perspective, we derive analytical expressions for the secrecy outage probability under two eavesdropping strategies using selection combining and maximal ratio combining. Based on this analysis, we propose an adaptive PA scheme that maximizes covert rate while ensuring the quality-of-service (QoS) requirements of legitimate users, the system's minimum covertness requirements, and supporting successive interference cancellation (SIC) procedures. Furthermore, we design an adaptive PA scheme that maximizes the secrecy rate while ensuring the QoS requirements of legitimate users and SIC conditions. Numerical results demonstrate the accuracy of the analytical framework, while the proposed optimization strategies effectively adjust PA coefficients to maximize either the covert rate or the secrecy rate.

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arXiv2021

Ultra-Reliable and Low-Latency Short-Packet Communications for Multihop MIMO Relaying

This work considers the multihop multiple-input multiple-output relay network under short-packet communications to facilitate not only ultra-reliability but also low-latency communications. We assume that the transmit antenna selection (TAS) scheme is utilized at the transmit side, whereas either selection combining (SC) or maximum ratio combining (MRC) is leveraged at the receive side to achieve diversity gains. For quasi-static Rayleigh fading channels and the finite-blocklength regime, we derive the approximate closed-form expressions of the end-to-end (e2e) block error rate (BLER) for both the TAS/MRC and TAS/SC schemes. The asymptotic performance in the high signal-to-noise ratio regime is derived, from which the comparison of TAS/MRC and TAS/SC schemes in terms of the diversity order, e2e BLER loss, and SNR gap is provided. The e2e latency and throughputs are also analyzed for the considered schemes. The correctness of our analysis is confirmed via Monte Carlo simulations.

cs.IT↗

arXiv2021

Survey on Aerial Radio Access Networks: Toward a Comprehensive 6G Access Infrastructure

Current network access infrastructures are characterized by heterogeneity, low latency, high throughput, and high computational capability, enabling massive concurrent connections and various services. Unfortunately, this design does not pay significant attention to mobile services in underserved areas. In this context, the use of aerial radio access networks (ARANs) is a promising strategy to complement existing terrestrial communication systems. Involving airborne components such as unmanned aerial vehicles, drones, and satellites, ARANs can quickly establish a flexible access infrastructure on demand. ARANs are expected to support the development of seamless mobile communication systems toward a comprehensive sixth-generation (6G) global access infrastructure. This paper provides an overview of recent studies regarding ARANs in the literature. First, we investigate related work to identify areas for further exploration in terms of recent knowledge advancements and analyses. Second, we define the scope and methodology of this study. Then, we describe ARAN architecture and its fundamental features for the development of 6G networks. In particular, we analyze the system model from several perspectives, including transmission propagation, energy consumption, communication latency, and network mobility. Furthermore, we introduce technologies that enable the success of ARAN implementations in terms of energy replenishment, operational management, and data delivery. Subsequently, we discuss application scenarios envisioned for these technologies. Finally, we highlight ongoing research efforts and trends toward 6G ARANs.

cs.NI↗