1. 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.

2. Analysis and Detection of RIS-based Spoofing in Integrated Sensing and Communication (ISAC)

Integrated sensing and communication (ISAC) is a key feature of next-generation 6G wireless systems, allowing them to achieve high data rates and sensing accuracy. While prior research has primarily focused on addressing communication safety in ISAC systems, the equally critical issue of sensing safety remains largely under-explored. In this paper, the possibility of spoofing the sensing function of ISAC in vehicle networks is examined, whereby a malicious reconfigurable intelligent surface (RIS) is deployed to compromise the sensing functionality of a roadside unit (RSU). For this scenario, the requirements on the malicious RIS'' phase shifts design and number of reflecting elements are analyzed. Under such spoofing, the practical estimation bias of the vehicular user (VU)''s Doppler shift and angle-of-departure (AoD) for an arbitrary time slot is analytically derived. Moreover, from the attacker''s view, a Markov decision process (MDP) is formulated to optimize the RIS'' phase shifts design. The goal of this MDP is to generate complete and plausible fake trajectories by incorporating the concept of spatial-temporal consistency. To defend against this sensing spoofing attack, a signal temporal logic (STL)-based neuro-symbolic attack detection framework is proposed and shown to learn interoperable formulas for identifying spoofed trajectories.

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

This letter considers multiple reconfigurable intelligent surfaces (RISs)-aided multiuser downlink systems with the goal of optimizing the transmitter precoding and RIS phase shift matrix to maximize spectrum efficiency. A practical coupling effect is considered between the reflecting amplitude and phase shift for the reflecting elements and their corresponding reflectivities. However, with coupled optimization variables, the formulated optimization problem is non-convex. Therefore, deep deterministic policy gradient (DDPG) based deep reinforcement learning (DRL) is proposed to solve the optimization problem. A practical scenario was simulated, whereby the proposed DDPG models were trained considering the random and fixed number of users. The simulation results show that the proposed DDPG, despite being comparably complex, outperforms the two optimization-based algorithms.

4. Green Learning for STAR-RIS mmWave Systems with Implicit CSI

In this paper, a green learning (GL)-based precoding framework is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided millimeter-wave (mmWave) MIMO broadcasting systems. Motivated by the growing emphasis on environmental sustainability in future 6G networks, this work adopts a broadcasting transmission architecture for scenarios where multiple users share identical information, improving spectral efficiency and reducing redundant transmissions and power consumption. Different from conventional optimization methods, such as block coordinate descent (BCD) that require perfect channel state information (CSI) and iterative computation, the proposed GL framework operates directly on received uplink pilot signals without explicit CSI estimation. Unlike deep learning (DL) approaches that require CSI-based labels for training, the proposed GL approach also avoids deep neural networks and backpropagation, leading to a more lightweight design. Although the proposed GL framework is trained with supervision generated by BCD under full CSI, inference is performed in a fully CSI-free manner. The proposed GL integrates subspace approximation with adjusted bias (Saab), relevant feature test (RFT)-based supervised feature selection, and eXtreme gradient boosting (XGBoost)-based decision learning to jointly predict the STAR-RIS coefficients and transmit precoder. Simulation results show that the proposed GL approach achieves competitive spectral efficiency compared to BCD and DL-based models, while reducing floating-point operations (FLOPs) by over four orders of magnitude. These advantages make the proposed GL approach highly suitable for real-time deployment in energy-and hardware-constrained broadcasting scenarios.

5. Agentic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networks

In this paper, we present an agentic double deep Q-network (DDQN) scheduler for licensed/unlicensed band allocation in New Radio (NR) sidelink (SL) networks. Beyond conventional reward-seeking reinforcement learning (RL), the agent perceives and reasons over a multi-dimensional context that jointly captures queueing delay, link quality, coexistence intensity, and switching stability. A capacity-aware, quality of service (QoS)-constrained reward aligns the agent with goal-oriented scheduling rather than static thresholding. Under constrained bandwidth, the proposed design reduces blocking by up to 87.5% versus threshold policies while preserving throughput, highlighting the value of context-driven decisions in coexistence-limited NR SL networks. The proposed scheduler is an embodied agent (E-agent) tailored for task-specific, resource-efficient operation at the network edge.

6. 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 difficult to manage product quality. In this article, 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 deep learning (DL)-based model. The two most common defect categories of DIP are examined: \\text {1)} surface defects and \\text {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 size dataset for training and testing. Four varieties of the you only look once (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.

7. 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 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.

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

In recent decades, the rising aging population has increased the importance of fall detection (FD) systems as assistive technology. To enhance accuracy, deep learning (DL) techniques are widely applied to these systems. A typical FD system involves placing edge devices (EDs) on individuals to collect real-time data, which is transmitted to a cloud center (CC) or processed by the ED itself. However, this architecture faces challenges such as limited ED model size and data transmission latency to the CC. Mobile edge computing (MEC) has been explored to overcome these challenges, which allows computations at MEC servers deployed between EDs and the CC. A multilayer mobile edge computing (MLMEC) framework is proposed to manage the tradeoff between accuracy and latency. MLMEC divides the architecture into multiple stations, each equipped with a neural network model. If the front-end equipment lacks deterministic detection capability, data are transmitted to a station with robust back-end computing for detection. The knowledge distillation (KD) approach is employed to improve front-end detection accuracy. The KD approach allows high-power back-end stations to provide additional learning experiences, enhancing precision and reducing latency and processing load. Simulation results demonstrate improved accuracy with the KD approach (11.65% average improvement for the SisFall dataset and 2.78% for the FallAllD dataset). The proposed MLMEC with the KD approach outperforms without the KD approach regarding data latency rate by 54.15% for the FallAllD dataset and 46.67% for the SisFall dataset. In summary, the MLMEC FD system exhibits improved accuracy and reduced latency.

9. 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.