124 citations · 164 across the 20 of their papers we have counts for
21 papers
Low-Complexity Maximum Likelihood Detection for Type-Based Over-the-Air Computation
Marc Martinez-Gost, Miguel Ángel Lagunas, Ana Pérez-Neira
Type-based multiple access (TBMA) is a digital over-the-air computation (OAC) scheme that exploits symbol collisions over the wireless multiple-access channel to construct a histog…
Efficient DCT-Based Estimation and Compensation of Nonlinear Channels for OFDM Systems
Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
This paper proposes a maximum-likelihood (ML) framework for estimating nonlinear frequency-selective channels in orthogonal frequency-division multiplexing (OFDM) communication sys…
Exponential Noise Robustness of Type-Based Multiple Access for Over-the-Air Computation
Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
This paper studies the robustness of type-based multiple access (TBMA) in over-the-air computation (AirComp) under nonparametric estimation, where no prior knowledge of the data di…
The DCT Neuron for Estimation and Compensation of Amplitude Distortions in OFDM Systems
Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
We present a receiver-side framework for identifying amplitude distortions in frequency-selective OFDM channels. The core novelty is the use of the DCT Neuron, a compact adaptive p…
The DCT Model as a Novel Regression Framework within a Lagrangian Formulation
Marc Martinez-Gost, Ana I. Perez Neira, Miguel Angel Lagunas
This paper introduces a unified regression framework based on the Lagrange formalism, demonstrating how polynomial and logistic regression can all be formulated within a common var…
Efficient Neural Networks with Discrete Cosine Transform Activations
Marc Martinez-Gost, Sara Pepe, Ana Pérez-Neira +1
In this paper, we extend our previous work on the Expressive Neural Network (ENN), a multilayer perceptron with adaptive activation functions parametrized using the Discrete Cosine…
Before AI Takes Over: Rethinking Nonlinear Signal Processing in Communications
Ana Pérez-Neira, Marc Martinez-Gost, Miguel Ángel Lagunas
There is an urgent reflection on traditional nonlinear signal processing methods in communications before Artificial Intelligence (AI) dominates the field. It implies a need to rea…
Robust Over-the-Air Computation with Type-Based Multiple Access
Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
This paper utilizes the properties of type-based multiple access (TBMA) to investigate its effectiveness as a robust approach for over-the-air computation (AirComp) in the presence…
Orbit-Aware Split Learning: Optimizing LEO Satellite Networks for Distributed Online Learning
Marc Martinez-Gost, Ana Pérez-Neira
This paper proposes a novel split learning architecture designed to exploit the cyclical movement of Low Earth Orbit (LEO) satellites in non-terrestrial networks (NTNs). Although e…
Expanding Over-the-Air Computation with Frequency Modulations
Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
In this study we introduce Logarithmic Frequency Shift Keying (Log-FSK), a novel frequency modulation for over-the-air computation (AirComp). Log-FSK leverages non-linear signal pr…
Predictive Demodulation for Chaotic Communications
Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
Chaotic signals offer promising characteristics for wireless communications due to their wideband nature, low cross-correlation, and sensitivity to initial conditions. Although cla…
Semantic and goal-oriented edge computing for satellite Earth Observation
Beatriz Soret, Israel Leyva-Mayorga, Antonio M. Mercado-Martínez +6
The integration of Semantic Communications (SemCom) and edge computing in space networks enables the optimal allocation of the scarce energy, computing, and communication resources…
Frequency Modulation for Task-Oriented Communications and Multiple Access
Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
In the context of task-oriented communications we advocate the development of waveforms for Federated Edge Learning (FEEL). Over-the-air computing (AirComp) has emerged as a commun…
Waveforms for Computing Over the Air
Ana Pérez-Neira, Marc Martinez-Gost, Alphan Şahin +3
Over-the-air computation (AirComp) leverages the signal-superposition characteristic of wireless multiple access channels to perform mathematical computations. Initially introduced…
Log-FSK: A Frequency Modulation for Over-the-Air Computation
Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
In this study we introduce Logarithmic Frequency Shift Keying (Log-FSK), a novel frequency modulation for over-the-air computation (AirComp). Log-FSK leverages non-linear signal pr…
LoRa Modulation for Split Learning
Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
In this paper we introduce a task-oriented communication design for split learning (SL) over a communication channel. Our approach involves the Expressive Neural Network (ENN), a n…
ENN: A Neural Network with DCT Adaptive Activation Functions
Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
The expressiveness of neural networks highly depends on the nature of the activation function, although these are usually assumed predefined and fixed during the training stage. Un…
LoRa-based Over-the-Air Computing for Sat-IoT
Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
Satellite Internet of Things (Sat-IoT) is a novel framework in which satellites integrate sensing, communication and computing capabilities to carry out task-oriented communication…
Frequency Modulation Aggregation for Federated Learning
Marc Martinez-Gost, Ana Pérez-Neira, Miguel Ángel Lagunas
Federated edge learning (FEEL) is a framework for training models in a distributed fashion using edge devices and a server that coordinates the learning process. In FEEL, edge devi…
Satellite edge computing for real-time and very-high resolution Earth observation
Israel Leyva-Mayorga, Marc M. Gost, Marco Moretti +4
In real-time and high-resolution Earth observation imagery, Low Earth Orbit (LEO) satellites capture images that are subsequently transmitted to ground to create an updated map of…
Mapping Motor Cortex Stimulation to Muscle Responses: A Deep Neural Network Modeling Approach
Md Navid Akbar, Mathew Yarossi, Marc Martinez-Gost +6
A deep neural network (DNN) that can reliably model muscle responses from corresponding brain stimulation has the potential to increase knowledge of coordinated motor control for n…