arXiv subjects

Anh Tuyen Le

Publications and source records attributed to Anh Tuyen Le.

3 recordsLinked to original sources

arXiv2026

Flexible Analog Self-Interference Cancellation for Waveform-Robust Monostatic ISAC

Monostatic integrated sensing and communication (ISAC) requires in-band full-duplex operation, where self-interference cancellation (SIC) must suppress the strong transmit leakage while preserving weak sensing echoes. In the analog domain, this task is complicated by transmitter impairments, which generate conjugate and nonlinear SI components, and by waveforms with widely different statistics. This paper develops a flexible analog self-interference cancellation (FASIC) framework for monostatic ISAC based on the widely linear normalized least-mean-square (WL-NLMS) algorithm. FASIC generates the cancellation signal in digital baseband, subtracts it in the radio-frequency domain, and enables flexible sensing-range control via a configurable cancellation span. Its normalized adaptation remains stable across waveforms with substantially different peak-to-average power ratios (PAPRs), for which fixed-step adaptation can become unstable. We further relate transmit-waveform statistics to analog SIC performance and predict the waveform-dependent cancellation floor. Simulations validate the analysis and demonstrate stable analog SIC of approximately $30$--$34$ dB, with the low-PAPR waveforms achieving a $3.3$ dB deeper floor owing to their smaller high-order amplitude moments. Near-field static reflections within the cancellation span are suppressed while delayed and Doppler-shifted echoes are preserved. These results establish FASIC as a waveform-robust analog SIC solution and identify waveform statistics as a key consideration for sixth-generation (6G) waveform selection.

eess.SP↗

arXiv2026

Koopman-Predictive Coefficient Tracking for Vehicle-Mounted Monostatic ISAC

This paper studies predictive coefficient tracking for digitally generated self-interference cancellation (SIC) in vehicle-mounted monostatic integrated sensing and communication (ISAC) systems. In such systems, the canceller must suppress strong transmitter leakage and nearby unintended reflections while preserving delayed sensing echoes. On mobile platforms, mechanical vibration from engines, rotors, or nearby structures can induce quasi-periodic variation in the self-interference (SI) channel. The optimal cancellation coefficients may then vary within one adaptation period, causing the applied coefficients of a conventional adaptive filter to lag behind the moving SI channel and increasing the residual SI. To address this problem, we propose a Koopman-predictive SIC (KP-SIC) module that augments a conventional adaptive filter with a predicted coefficient trajectory. KP-SIC uses recent effective SI-channel coefficient snapshots to identify structured vibration-induced dynamics and predict a sample-varying coefficient sequence for the next adaptation period. The predicted coefficients place the canceller closer to the expected time-varying optimum, while the adaptive filter corrects residual prediction errors and unstructured variation. For evaluation, the module is integrated with a widely linear normalized least mean-square (WL-NLMS) filter under a flexible analog SIC (FASIC) framework. Simulations show that KP-SIC improves SI suppression by $6.3$--$10.2$~dB over conventional FASIC-WL-NLMS once the observation window spans at least about $1.5$ vibration cycles. Compared with a non-causal performance bound, KP-SIC achieves $60$--$74\%$ of the available prediction gain.

eess.SP↗

arXiv2026

Federated Low-Rank Koopman Learning for Multivariate Time-Series Anomaly Detection in IoT Systems

Distributed IoT systems generate multivariate time-series streams for monitoring physical assets, servers, and embedded sensing platforms. Detecting abnormal temporal behavior is critical for fault diagnosis, predictive maintenance, and security. However, practical IoT anomaly detection is hindered by decentralized and non-IID data, limited bandwidth, and the constrained computation and memory of edge devices. This paper proposes FedKAD, a resource-efficient federated Koopman anomaly detection framework for distributed IoT multivariate time series. Unlike deep-learning-based anomaly detectors that require training and communicating large neural models, FedKAD learns normal temporal dynamics through lightweight sliding-window Koopman representations. Federated training is formulated as a low-rank consensus problem, where raw sensor streams and local reduced dynamics remain on device while only compact subspace variables are exchanged with the server. To optimize the shared representation under orthonormality constraints, we develop a federated Stiefel-ADMM algorithm and provide convergence and stationarity analysis under partial client participation. During inference, each client detects anomalies locally by measuring the prediction residual between observed future trajectories and the learned Koopman dynamics. Experiments on four widely used multivariate time-series anomaly detection benchmarks show that FedKAD maintains or improves detection performance compared with federated deep-learning baselines. More importantly for IoT deployment, FedKAD provides up to $2.1\times10^3$ faster training, $80\times$ lower communication, and $79\times$ lower inference latency than neural baselines, confirming its suitability for resource-constrained edge devices.

cs.LG↗