52 citations · 178 across the 19 of their papers we have counts for
8 papers · 1 filter
NARRAS: Edge-Triggered Distributed Inference for CSI-Based Localization in Vehicular IoT Networks
Rodrigo Oliver, Ricardo Vazquez Alvarez, Alejandro Lancho +1
CSI-based localization with spatially distributed antenna arrays exposes a basic resource trade-off. Each array can provide a rich view of the channel, but forwarding observations…
Extremum-Based Joint Compression and Detection for Distributed Sensing
Amir Weiss, Alejandro Lancho
We study joint compression and detection in distributed sensing systems motivated by emerging applications such as IoT-based localization. Two spatially separated sensors observe n…
Learning to Separate RF Signals Under Uncertainty: Detect-Then-Separate vs. Unified Joint Models
Ariel Rodrigez, Alejandro Lancho, Amir Weiss
The increasingly crowded radio frequency (RF) spectrum forces communication signals to coexist, creating heterogeneous interferers whose structure often departs from Gaussian model…
RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge
Alejandro Lancho, Amir Weiss, Gary C. F. Lee +4
We address the critical problem of interference rejection in radio-frequency (RF) signals using a data-driven approach that leverages deep-learning methods. A primary contribution…
A Bilateral Bound on the Mean-Square Error for Estimation in Model Mismatch
Amir Weiss, Alejandro Lancho, Yuheng Bu +1
A bilateral (i.e., upper and lower) bound on the mean-square error under a general model mismatch is developed. The bound, which is derived from the variational representation of t…
On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signals
Gary C. F. Lee, Amir Weiss, Alejandro Lancho +2
We study the single-channel source separation problem involving orthogonal frequency-division multiplexing (OFDM) signals, which are ubiquitous in many modern-day digital communica…
Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals
Alejandro Lancho, Amir Weiss, Gary C. F. Lee +4
We study the potential of data-driven deep learning methods for separation of two communication signals from an observation of their mixture. In particular, we assume knowledge on…
Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation
Gary C. F. Lee, Amir Weiss, Alejandro Lancho +4
We study the problem of single-channel source separation (SCSS), and focus on cyclostationary signals, which are particularly suitable in a variety of application domains. Unlike c…