arXiv subjects

Nho-Duc Tran

Publications and source records attributed to Nho-Duc Tran.

3 recordsLinked to original sources

arXiv2026

Predictive Triggering for Outage-Resilient Threshold Decisions over Short-Packet Links

Remote threshold decisions require more than accurate state estimates: the posterior must support reliable alarm/no-alarm decisions and, when possible, anticipate early critical decisions. We study this problem over short-packet wireless links with outage risk. We derive false-positive/false-negative feasibility conditions that define a decision-feasible region of the estimation and yield a predictive decision-update trigger. To protect predictive updates from outages, we add AoI-controlled resilience updates that both detect disruptions and maintain freshness. A two-state Markov surrogate of the thresholded process, matched to its one-step switching statistics, enables tractable long-term reliability-energy analysis. Then, we jointly optimized transmit power and AoI-controlled resilience update probabilities. Simulations show earlier, reliable decisions at competitive energy with baselines.

cs.IT↗

arXiv2026

Learning-Based Sensor Scheduling for Delay-Aware and Stable Remote State Estimation

Unpredictable sensor-to-estimator delays fundamentally distort what matters for wireless remote state estimation: not just freshness, but how delay interacts with sensor informativeness and energy efficiency. In this paper, we present a unified, delay-aware framework that models this coupling explicitly and quantifies a delay-dependent information gain, motivating an information-per-joule scheduling objective beyond age of information proxies (AoI). To this end, we first introduce an efficient posterior-fusion update that incorporates delayed measurements without state augmentation, providing a consistent approximation to optimal delayed Kalman updates, and then derive tractable stability conditions ensuring that bounded estimation error is achievable under stochastic, delayed scheduling. This conditions highlight the need for unstable modes to be observable across sensors. Building on this foundation, we cast scheduling as a Markov decision process and develop a proximal policy optimization (PPO) scheduler that learns directly from interaction, requires no prior delay model, and explicitly trades off estimation accuracy, freshness, sensor heterogeneity, and transmission energy through normalized rewards. In simulations with heterogeneous sensors, realistic link-energy models, and random delays, the proposed method learns stably and consistently achieves lower estimation error at comparable energy than random scheduling and strong RL baselines (DQN, A2C), while remaining robust to variations in measurement availability and process/measurement noise.

cs.IT↗

arXiv2025

Efficient Continual Learning in Keyword Spotting using Binary Neural Networks

Keyword spotting (KWS) is an essential function that enables interaction with ubiquitous smart devices. However, in resource-limited devices, KWS models are often static and can thus not adapt to new scenarios, such as added keywords. To overcome this problem, we propose a Continual Learning (CL) approach for KWS built on Binary Neural Networks (BNNs). The framework leverages the reduced computation and memory requirements of BNNs while incorporating techniques that enable the seamless integration of new keywords over time. This study evaluates seven CL techniques on a 16-class use case, reporting an accuracy exceeding 95% for a single additional keyword and up to 86% for four additional classes. Sensitivity to the amount of training samples in the CL phase, and differences in computational complexities are being evaluated. These evaluations demonstrate that batch-based algorithms are more sensitive to the CL dataset size, and that differences between the computational complexities are insignificant. These findings highlight the potential of developing an effective and computationally efficient technique for continuously integrating new keywords in KWS applications that is compatible with resource-constrained devices.

cs.LG↗