arXiv subjects

Po-Heng Chou

Publications and source records attributed to Po-Heng Chou.

At least 19 recordsLinked to original sources

arXiv2026

Spatial-Temporal Learning-Based Distributed Routing for Dynamic LEO Satellite Networks

This paper proposes a spatial-temporal learning-based routing framework for dynamic low Earth orbit (LEO) satellite networks. The routing problem is formulated as a partially observable Markov decision process (POMDP), where each satellite independently selects its next hop using network-state observations and destination information. A graph attention network (GAT) extracts spatial features from the dynamic satellite topology and queue states, while a long short-term memory (LSTM) network captures their temporal evolution. The resulting representation is integrated into a deep Q-network (DQN) for destination-aware next-hop selection without a separate traffic-prediction stage. Simulations in a 45-satellite LEO constellation under normal and bursty traffic demonstrate packet delivery performance comparable to a graph neural network (GNN)-enhanced routing baseline, although the proposed method incurs higher delay and queue occupancy. Ablation studies further demonstrate the complementary benefits of spatial and temporal learning.

cs.NI↗

arXiv2026

Capacity-Aware Multi-UE Handover Optimization for LEO Satellite Networks Using Dueling DDQN

Frequent handovers and finite satellite capacity make multi-user mobility management challenging in low Earth orbit (LEO) satellite networks. This paper develops a capacity-aware multi-user handover framework based on a double deep Q-network (DDQN) with a dueling architecture. Both training and evaluation employ sequential user equipment (UE) processing with shared satellite occupancy, while action masking excludes invisible or capacity-infeasible satellites. The state incorporates satellite visibility, achievable link quality, remaining capacity, previous association, short-term visibility, and time information. A fixed multi-criteria reward balances throughput, blocking, handover cost, connection persistence, and short-term visibility, while expert trajectories are used only for replay-buffer initialization. Simulations under different UE densities and satellite capacities show that the proposed framework maintains high average throughput per UE and low blocking rates with moderate handover frequency. Ablation results show that removing the remaining-capacity vector has only a limited effect on performance when sequential occupancy tracking and feasible-action masking are retained.

cs.IT↗

arXiv2026

Ray-Tracing-Parameter-Based Lightweight Hybrid Precoding via Cascade XGBoost for Wideband Massive MIMO-OFDM Systems

Hybrid precoding reduces the radio-frequency (RF) hardware burden in wideband massive MIMO-OFDM systems, but conventional optimization and deep neural network (DNN)-based approaches can incur high online complexity. This paper proposes XGB-SVD, a lightweight hybrid precoding framework that maps compact ray-tracing path parameters directly to analog beamforming codeword indices using cascade eXtreme Gradient Boosting (XGBoost). The cascade captures sequential beam-selection dependencies and transmit--receive propagation correlation, while the digital precoder and combiner are obtained from the reduced-dimensional equivalent channel using singular value decomposition (SVD). Evaluations over five DeepMIMO scenarios spanning sub-6-GHz and millimeter-wave bands show that XGB-SVD consistently outperforms the considered DNN baselines and approaches simultaneous orthogonal matching pursuit (SOMP) in several environments. Cross-scenario results further indicate improved transfer from propagation-diverse training environments, while XGB-SVD provides lower online computational and training costs than the DNN baselines.

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arXiv2026

Green Learning-Based Signal-Level Detection of RIS Spoofing Attacks in Vehicular ISAC Systems

Integrated sensing and communication (ISAC) enables wireless infrastructure to jointly support communication and environmental sensing, but also introduces new physical-layer security threats. A malicious reconfigurable intelligent surface (RIS) can manipulate its time-varying reflection phases to generate spoofing echoes and distort the sensing results of a roadside unit (RSU). Existing trajectory-level detectors rely on reconstructed sensing parameters and vehicle trajectories, making their performance dependent on upstream estimation accuracy. In this paper, we propose a signal-level Green Learning (GL) framework that directly detects RIS spoofing from matched-filtered RSU echoes. The proposed method combines two-stage Subspace Approximation with Adjusted Bias (SAAB), Discriminant Feature Test (DFT), and eXtreme Gradient Boosting (XGBoost) for spatial-temporal representation learning, feature selection, and classification, respectively. Simulation results show 99.335\% detection accuracy for a 32-element malicious RIS at $-80$ dBm receiver noise power. GL remains competitive with Long Short-Term Memory (LSTM) and bidirectional LSTM (BiLSTM) baselines while requiring only 0.1119 million floating-point operations (FLOPs), and also generalizes to previously unseen RIS sizes.

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arXiv2026

Reduced-Feedback Hybrid Precoding for Wideband mmWave MIMO-OFDM Systems via Hierarchical Interpolation

In this paper, we propose a feedback-efficient hybrid precoding framework for wideband millimeter-wave (mmWave) multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. To reduce the hardware cost of radio-frequency (RF) chains and the feedback burden associated with frequency-selective digital precoding, frequency-flat analog precoders and combiners are first constructed from the dominant angle-of-arrival (AoA) and angle-of-departure (AoD) directions associated with the strongest propagation paths. For digital precoding, a shared quantized codebook is designed using the Lloyd algorithm, and a binary-search-based hierarchical interpolation method is developed to identify codeword switching locations between adjacent pilot subcarriers. By avoiding exhaustive codeword comparisons over all intermediate subcarriers, the proposed method reduces online search complexity and enables compact digital-precoder feedback. For the considered system configuration, the proposed method achieves a worst-case feedback reduction of approximately 98.34% relative to full-grid per-subcarrier feedback. Simulation results further demonstrate competitive spectral efficiency and bit error rate (BER) performance, low computational cost, and robustness under imperfect effective channel state information (CSI).

cs.IT↗

arXiv2026

Measurement-Driven Early Warning for Resilient 5G NSA Railway Networks

This paper presents a measurement-driven study of early warning for reliability breakdown events in 5G non-standalone (NSA) railway networks. Using 10 Hz metro-train measurement traces with serving- and neighbor-cell indicators, we benchmark six representative learning models, including CNN, LSTM, XGBoost, Anomaly Transformer, PatchTST, and TimesNet, under multiple observation windows and prediction horizons. Rather than proposing a new prediction architecture, this study develops a measurement-driven benchmark to quantify the feasibility and operating trade-offs of seconds-ahead reliability prediction in 5G NSA railway environments. Experimental results show that learning models can anticipate radio link failure (RLF)-related reliability breakdown events seconds in advance using lightweight radio features available on commercial devices. The presented benchmark provides insights for sensing-assisted communication control and offers an empirical foundation for integrating sensing and analytics into future mobility control.

cs.NI↗

arXiv2026

Fairness-Guided DQN-Based TXOP Control for NR-U/Wi-Fi Coexistence

New Radio operating in unlicensed spectrum (NR-U) and Wi-Fi employ different contention parameters and channel-occupancy durations, which can produce severe coexistence imbalance. This correspondence develops a fairness-guided deep Q-network (DQN) that adjusts the NR-U transmission opportunity (TXOP) using the observed per-node throughput ratio. Two reward designs, Q1 and Q2, expose complementary operating regimes. Q1 promotes strict per-node throughput balance, whereas Q2 admits a broader inter-radio-access-technology fairness region and rewards throughput improvement in an intermediate fairness band. The policies are compared with the default Third Generation Partnership Project (3GPP) listen-before-talk (LBT), an adapted multi-armed-bandit method, a utility-fairness-based tabular Q-learning comparator, and a default-referenced fixed-TXOP baseline. Experiments across four priority classes and asymmetric populations report aggregate throughput, user-level Jain fairness, and concave throughput utility over 20 independent trials. The results characterize the cost of strict fairness, efficiency recovery under moderate relaxation, and transfer from symmetric to asymmetric deployments.

cs.NI↗

arXiv2026

Lightweight Time-Frequency Vibration Sensing for Consumer Device Condition Monitoring

Consumer rotating devices, including washing machines, air conditioners, electric fans, robotic vacuum cleaners, and home fitness equipment, increasingly rely on vibration sensing for intelligent condition monitoring and predictive maintenance. Ensuring reliable operation in these resource-constrained appliances requires efficient, lightweight artificial intelligence (AI) solutions capable of processing non-stationary signals under real-world operational interference. This paper proposes a lightweight AI-assisted vibration sensing framework that employs continuous wavelet transform (CWT) and Hilbert-Huang transform (HHT) to convert one-dimensional (1-D) vibration time-series signals into two-dimensional (2-D) time-frequency representations. These representations are subsequently analyzed using lightweight deep learning architectures, specifically MobileNetV3-Small, ConvNeXt-Tiny, and the You Only Look Once version 11 classification model (YOLOv11-cls). Public bearing benchmark datasets, including Case Western Reserve University (CWRU), Paderborn University (PU), and Intelligent Maintenance Systems (IMS), are utilized as representative rotating components to investigate model generalization. The proposed framework is designed with eventual embedded deployment in consumer electronics in mind. Experimental results demonstrate that CWT combined with YOLOv11-cls achieves high diagnostic accuracy, while MobileNetV3-Small provides an attractive low-latency alternative for computationally constrained edge platforms. Conventional random splitting is also compared with dataset-specific structured isolation. The results highlight the importance of avoiding data leakage and quantify tradeoffs in classification accuracy, parameter count, floating-point operations (FLOPs), model footprint, preprocessing cost, and central processing unit (CPU) inference latency.

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arXiv2026

CWT-Enhanced Vibration Sensing With Time-Frequency Region Localization Using YOLO

This letter presents a CWT-enhanced vibration sensing framework for bearing fault monitoring through localized time-frequency region detection on continuous wavelet transform (CWT) spectrograms. Vibration signals are transformed into CWT spectrograms to improve the observability of weak and non-stationary fault signatures, and YOLOv9, YOLOv10, and YOLOv11 are employed to detect and identify localized fault-related energy regions in the time-frequency domain. Experiments on the CWRU, PU, and IMS datasets show that the proposed framework improves the detectability and robustness of fault-related sensing patterns compared with conventional time-series models, modern vision backbones, and short-time Fourier transform (STFT)-based representations, achieving mean average precision (mAP) values up to 99.4%, 97.8%, and 99.5%, respectively. In addition, the localized region detection framework provides a more interpretable relationship between time-frequency energy distributions and characteristic bearing fault frequencies. These results demonstrate an effective and generalizable approach for interpretable vibration sensing in noisy industrial environments.

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arXiv2026

DRL-Based Beam Positioning for LEO Satellite Constellations with Weighted Least Squares

This paper investigates a lightweight deep reinforcement learning (DRL)-assisted weighting framework for CSI-free multi-satellite positioning in LEO constellations, where each visible satellite provides one serving beam (one pilot response) per epoch. A discrete-action Deep Q-Network (DQN) learns satellite weights directly from received pilot measurements and geometric features, while an augmented weighted least squares (WLS) estimator provides physics-consistent localization and jointly estimates the receiver clock bias. The proposed hybrid design targets an accuracy-runtime trade-off rather than absolute supervised optimality. In a representative 2-D setting with 10 visible satellites, the proposed approach achieves sub-meter accuracy (0.395m RMSE) with low computational overhead, supporting practical deployment for resource-constrained LEO payloads.

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arXiv2025

NGGAN: Noise Generation GAN Based on the Practical Measurement Dataset for Narrowband Powerline Communications

To effectively process impulse noise for narrowband powerline communications (NB-PLCs) transceivers, capturing comprehensive statistics of nonperiodic asynchronous impulsive noise (APIN) is a critical task. However, existing mathematical noise generative models only capture part of the characteristics of noise. In this study, we propose a novel generative adversarial network (GAN) called noise generation GAN (NGGAN) that learns the complicated characteristics of practically measured noise samples for data synthesis. To closely match the statistics of complicated noise over the NB-PLC systems, we measured the NB-PLC noise via the analog coupling and bandpass filtering circuits of a commercial NB-PLC modem to build a realistic dataset. To train NGGAN, we adhere to the following principles: 1) we design the length of input signals that the NGGAN model can fit to facilitate cyclostationary noise generation; 2) the Wasserstein distance is used as a loss function to enhance the similarity between the generated noise and training data; and 3) to measure the similarity performances of GAN-based models based on the mathematical and practically measured datasets, we conduct both quantitative and qualitative analyses. The training datasets include: 1) a piecewise spectral cyclostationary Gaussian model (PSCGM); 2) a frequency-shift (FRESH) filter; and 3) practical measurements from NB-PLC systems. Simulation results demonstrate that the generated noise samples from the proposed NGGAN are highly close to the real noise samples. The principal component analysis (PCA) scatter plots and Fréchet inception distance (FID) analysis have shown that NGGAN outperforms other GAN-based models by generating noise samples with superior fidelity and higher diversity.

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arXiv2025

Sustainable LSTM-Based Precoding for RIS-Aided mmWave MIMO Systems with Implicit CSI

In this paper, we propose a sustainable long short-term memory (LSTM)-based precoding framework for reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) MIMO systems. Instead of explicit channel state information (CSI) estimation, the framework exploits uplink pilot sequences to implicitly learn channel characteristics, reducing both pilot overhead and inference complexity. Practical hardware constraints are addressed by incorporating the phase-dependent amplitude model of RIS elements, while a multi-label training strategy improves robustness when multiple near-optimal codewords yield comparable performance. Simulations show that the proposed design achieves over 90% of the spectral efficiency of exhaustive search (ES) with only 2.2% of its computation time, cutting energy consumption by nearly two orders of magnitude. The method also demonstrates resilience under distribution mismatch and scalability to larger RIS arrays, making it a practical and energy-efficient solution for sustainable 6G wireless networks.

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arXiv2025

Automated Defect Detection for Mass-Produced Electronic Components Based on YOLO Object Detection Models

Since the defect detection of conventional industry components is time-consuming and labor-intensive, it leads to a significant burden on quality inspection personnel and makes it difficult to manage product quality. In this paper, we propose an automated defect detection system for the dual in-line package (DIP) that is widely used in industry, using digital camera optics and a deep learning (DL)-based model. The two most common defect categories of DIP are examined: (1) surface defects, and (2) pin-leg defects. However, the lack of defective component images leads to a challenge for detection tasks. To solve this problem, the ConSinGAN is used to generate a suitable-sized dataset for training and testing. Four varieties of the YOLO model are investigated (v3, v4, v7, and v9), both in isolation and with the ConSinGAN augmentation. The proposed YOLOv7 with ConSinGAN is superior to the other YOLO versions in accuracy of 95.50\%, detection time of 285 ms, and is far superior to threshold-based approaches. In addition, the supervisory control and data acquisition (SCADA) system is developed, and the associated sensor architecture is described. The proposed automated defect detection can be easily established with numerous types of defects or insufficient defect data.

cs.CV↗

arXiv2025

MECKD: Deep Learning-Based Fall Detection in Multilayer Mobile Edge Computing With Knowledge Distillation

The rising aging population has increased the importance of fall detection (FD) systems as an assistive technology, where deep learning techniques are widely applied to enhance accuracy. FD systems typically use edge devices (EDs) worn by individuals to collect real-time data, which are transmitted to a cloud center (CC) or processed locally. However, this architecture faces challenges such as a limited ED model size and data transmission latency to the CC. Mobile edge computing (MEC), which allows computations at MEC servers deployed between EDs and CC, has been explored to address these challenges. We propose a multilayer MEC (MLMEC) framework to balance accuracy and latency. The MLMEC splits the architecture into stations, each with a neural network model. If front-end equipment cannot detect falls reliably, data are transmitted to a station with more robust back-end computing. The knowledge distillation (KD) approach was employed to improve front-end detection accuracy by allowing high-power back-end stations to provide additional learning experiences, enhancing precision while reducing latency and processing loads. Simulation results demonstrate that the KD approach improved accuracy by 11.65% on the SisFall dataset and 2.78% on the FallAllD dataset. The MLMEC with KD also reduced the data latency rate by 54.15% on the FallAllD dataset and 46.67% on the SisFall dataset compared to the MLMEC without KD. In summary, the MLMEC FD system exhibits improved accuracy and reduced latency.

cs.LG↗

arXiv2025

YOLO-Based Defect Detection for Metal Sheets

In this paper, we propose a YOLO-based deep learning (DL) model for automatic defect detection to solve the time-consuming and labor-intensive tasks in industrial manufacturing. In our experiments, the images of metal sheets are used as the dataset for training the YOLO model to detect the defects on the surfaces and in the holes of metal sheets. However, the lack of metal sheet images significantly degrades the performance of detection accuracy. To address this issue, the ConSinGAN is used to generate a considerable amount of data. Four versions of the YOLO model (i.e., YOLOv3, v4, v7, and v9) are combined with the ConSinGAN for data augmentation. The proposed YOLOv9 model with ConSinGAN outperforms the other YOLO models with an accuracy of 91.3%, and a detection time of 146 ms. The proposed YOLOv9 model is integrated into manufacturing hardware and a supervisory control and data acquisition (SCADA) system to establish a practical automated optical inspection (AOI) system. Additionally, the proposed automated defect detection is easily applied to other components in industrial manufacturing.

cs.CV↗

arXiv2025

DNN-Based Precoding in RIS-Aided mmWave MIMO Systems With Practical Phase Shift

In this paper, the precoding design is investigated for maximizing the throughput of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems with obstructed direct communication paths. In particular, a reconfigurable intelligent surface (RIS) is employed to enhance MIMO transmissions, considering mmWave characteristics related to line-of-sight (LoS) and multipath effects. The traditional exhaustive search (ES) for optimal codewords in the continuous phase shift is computationally intensive and time-consuming. To reduce computational complexity, permuted discrete Fourier transform (DFT) vectors are used for finding codebook design, incorporating amplitude responses for practical or ideal RIS systems. However, even if the discrete phase shift is adopted in the ES, it results in significant computation and is time-consuming. Instead, the trained deep neural network (DNN) is developed to facilitate faster codeword selection. Simulation results show that the DNN maintains sub-optimal spectral efficiency even as the distance between the end-user and the RIS has variations in the testing phase. These results highlight the potential of DNN in advancing RIS-aided systems.

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arXiv2025

Deep Reinforcement Learning-Based Precoding for Multi-RIS-Aided Multiuser Downlink Systems with Practical Phase Shift

This study considers multiple reconfigurable intelligent surfaces (RISs)-aided multiuser downlink systems with the goal of jointly optimizing the transmitter precoding and RIS phase shift matrix to maximize spectrum efficiency. Unlike prior work that assumed ideal RIS reflectivity, a practical coupling effect is considered between reflecting amplitude and phase shift for the RIS elements. This makes the optimization problem non-convex. To address this challenge, we propose a deep deterministic policy gradient (DDPG)-based deep reinforcement learning (DRL) framework. The proposed model is evaluated under both fixed and random numbers of users in practical mmWave channel settings. Simulation results demonstrate that, despite its complexity, the proposed DDPG approach significantly outperforms optimization-based algorithms and double deep Q-learning, particularly in scenarios with random user distributions.

cs.IT↗

arXiv2025

Capacity-Net-Based RIS Precoding Design without Channel Estimation for mmWave MIMO System

In this paper, we propose Capacity-Net, a novel unsupervised learning approach aimed at maximizing the achievable rate in reflecting intelligent surface (RIS)-aided millimeter-wave (mmWave) multiple input multiple output (MIMO) systems. To combat severe channel fading of the mmWave spectrum, we optimize the phase-shifting factors of the reflective elements in the RIS to enhance the achievable rate. However, most optimization algorithms rely heavily on complete and accurate channel state information (CSI), which is often challenging to acquire since the RIS is mostly composed of passive components. To circumvent this challenge, we leverage unsupervised learning techniques with implicit CSI provided by the received pilot signals. Specifically, it usually requires perfect CSI to evaluate the achievable rate as a performance metric of the current optimization result of the unsupervised learning method. Instead of channel estimation, the Capacity-Net is proposed to establish a mapping among the received pilot signals, optimized RIS phase shifts, and the resultant achievable rates. Simulation results demonstrate the superiority of the proposed Capacity-Net-based unsupervised learning approach over learning methods based on traditional channel estimation.

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