52 citations · 178 across the 19 of their papers we have counts for
25 papers
Flow-based Spectral Kernel Learning for Nonstationary Attention
Zi Yang, Ying Li, Alejandro Lancho +3
Kernelized attention provides a useful perspective for understanding self-attention as a similarity-based aggregation mechanism, but existing approaches often rely on fixed or stat…
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…
Advancing AI Challenges for the United States Department of the Air Force
Christian Prothmann, Vijay Gadepally, Jeremy Kepner +35
The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers…
A Vector-Quantized Foundation Model for Patient Behavior Monitoring
Rodrigo Oliver, Josué Pérez-Sabater, Leire Paz-Arbaizar +4
Foundation models have achieved remarkable success across various domains, yet their adoption in healthcare remains limited. While significant advances have been made in medical im…
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…
Coded Orthogonal Modulation for the Multi-Antenna Multiple-Access Channel
Alexander Fengler, Alejandro Lancho, Yury Polyanskiy
This study focuses on (traditional and unsourced) multiple-access communication over a single transmit and multiple () receive antennas. We assume full or partial channel state…
Score-based Source Separation with Applications to Digital Communication Signals
Tejas Jayashankar, Gary C. F. Lee, Alejandro Lancho +3
We propose a new method for separating superimposed sources using diffusion-based generative models. Our method relies only on separately trained statistical priors of independent…
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 the Advantages of Asynchrony in the Unsourced MAC
Alexander Fengler, Alejandro Lancho, Krishna Narayanan +1
In this work we demonstrate how a lack of synchronization can in fact be advantageous in the problem of random access. Specifically, we consider a multiple-access problem over a fr…
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…
Efficient evaluation of the error probability for pilot-assisted URLLC with Massive MIMO
A. Oguz Kislal, Alejandro Lancho, Giuseppe Durisi +1
We propose a numerically efficient method for evaluating the random-coding union bound with parameter on the error probability achievable in the finite-blocklength regime by a…
Finite-Blocklength Results for the A-channel: Applications to Unsourced Random Access and Group Testing
Alejandro Lancho, Alexander Fengler, Yury Polyanskiy
We present finite-blocklength achievability bounds for the unsourced A-channel. In this multiple-access channel, users noiselessly transmit codewords picked from a common codebook…
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…
Cell-Free Massive MIMO for URLLC: A Finite-Blocklength Analysis
Alejandro Lancho, Giuseppe Durisi, Luca Sanguinetti
We present a general framework for the characterization of the packet error probability achievable in cell-free Massive multiple-input multiple output (MIMO) architectures deployed…
Unsourced Multiple Access With Random User Activity
Khac-Hoang Ngo, Alejandro Lancho, Giuseppe Durisi +1
To account for the massive uncoordinated random access scenario, which is relevant for the Internet of Things, Polyanskiy (2017) proposed a novel formulation of the multiple-access…
On Joint Detection and Decoding in Short-Packet Communications
Alejandro Lancho, Johan Östman, Giuseppe Durisi
We consider a communication problem in which the receiver must first detect the presence of an information packet and, if detected, decode the message carried within it. We present…
Cell-free Massive MIMO with Short Packets
Alejandro Lancho, Giuseppe Durisi, Luca Sanguinetti
In this paper, we adapt to cell-free Massive MIMO (multiple-input multiple-output) the finite-blocklength framework introduced by Östman et al. (2020) for the characterization of t…
Massive Uncoordinated Access With Random User Activity
Khac-Hoang Ngo, Alejandro Lancho, Giuseppe Durisi +1
We extend the seminal work by Polyanskiy (2017) on massive uncoordinated access to the case where the number of active users is random and unknown a priori. We define a random-acce…
URLLC with Massive MIMO: Analysis and Design at Finite Blocklength
Johan Östman, Alejandro Lancho, Giuseppe Durisi +1
The fast adoption of Massive MIMO for high-throughput communications was enabled by many research contributions mostly relying on infinite-blocklength information-theoretic bounds.…
Short-Packet Transmission over a Bidirectional Massive MIMO link
Johan Östman, Alejandro Lancho, Giuseppe Durisi
We consider the transmission of short packets over a bidirectional communication link where multiple devices, e.g., sensors and actuators, exchange small-data payloads with a base…
Saddlepoint Approximations for Short-Packet Wireless Communications
Alejandro Lancho, Jöhan Ostman, Giuseppe Durisi +2
In recent years, the derivation of nonasymptotic converse and achievability bounds on the maximum coding rate as a function of the error probability and blocklength has gained atte…