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22
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Introduction
Current institution
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- Research Associate
Publications
Publications (22)
We propose a beam codebook design to reduce self-interference (SI) in integrated sensing and communication (ISAC) systems. Our optimization methods, which can be applied to both tapered beamforming and phased arrays, adapt the codebooks to the SI channel such that a certain SI level is achieved. Furthermore, we derive an upper bound on the quantiza...
The D-band, spanning 110 GHz to 170 GHz, has emerged as a relevant frequency range for future mobile communications and radar sensing applications, particularly in the context of 6G technologies. This demonstration presents a high-bandwidth, real-time integrated sensing and communication (ISAC) platform operating in the upper D-band at 160 GHz. The...
In this paper, we formalize an optimization frame-work for analog beamforming in the context of monostatic integrated sensing and communication (ISAC), where we also address the problem of self-interference in the analog domain. As a result, we derive semidefinite programs to approach detection-optimal transmit and receive beamformers, and we devis...
This article presents two wireless measurement campaigns in industrial testbeds: industrial vehicle-to-vehicle (iV2V) and industrial vehicle-to-in-frastructure plus sensor (iV21+), with detailed information about the two captured datasets. iV2V covers sidelink communication scenarios between moving and stationary robots, while iV21+ is conducted at...
As cellular networks evolve towards the 6th Generation (6G), Machine Learning (ML) is seen as a key enabling technology to improve the capabilities of the network. ML provides a methodology for predictive systems, which, in turn, can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example, a s...
As cellular networks evolve towards the 6th generation, machine learning is seen as a key enabling technology to improve the capabilities of the network. Machine learning provides a methodology for predictive systems, which, in turn, can make networks become proactive. This proactive behavior of the network can be leveraged to sustain, for example,...
The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications wil...
This paper presents two wireless measurement campaigns in industrial testbeds: industrial Vehicle-to-vehicle (iV2V) and industrial Vehicle-to-infrastructure plus Sensor (iV2I+). Detailed information about the two captured datasets is provided as well. iV2V covers sidelink communication scenarios between Automated Guided Vehicles (AGVs), while iV2I+...
Commercial radar sensing is gaining relevance and machine learning algorithms constitute one of the key components that are enabling the spread of this radio technology into areas like surveillance or healthcare. However, radar datasets are still scarce and generalization cannot be yet achieved for all radar systems, environment conditions or desig...
Machine Learning (ML) methods have become state of the art in radar signal processing, particularly for classification tasks (e.g., of different human activities). Radar classification can be tedious to implement, though, due to the limited size and diversity of the source dataset, i.e., the data measured once for initial training of the Machine Le...
Abstract Radar is an attractive sensor for classifying human activity because of its invariance to the environment and its ability to operate under low lighting conditions and through obstacles. Classification for human activity finds applications in human–computer interfaces, user‐intent understanding and contextual‐aware smart homes. Moreover, fr...
Radar is an attractive sensor for human activity classification due to its invariance to the environment and its ability to operate in low lighting conditions and through obstacles. Human activity classification finds applications in human-computer interfaces, user-intent understanding and contextual-aware smart homes. Moreover, frequency-modulated...
Radar is an attractive sensor for human activity classification due to its ability to work in low lighting conditions, its invariance to the environment and its ability to operate through obstacles. Human activity classification finds applications in human-computer interfaces, user-intent understanding and contextual-aware smart homes. Radar reflec...
![Figure 5.1.: BLE consumption current. Image source: [13]](profile/Rodrigo-Hernangomez/publication/342562278/figure/fig5/AS:908136047181835@1593527914521/BLE-consumption-current-Image-source-13_Q320.jpg)
Otoacoustic Emissions (OAEs) are a technique for objective diagnosis of hearing impairment. Their application fields extend primarily to cases where the patient cannot actively cooperate in the clinical intervention, such as in hearing screening of neonates. Because of the high cost of professional quipment, smartphones arise as a newer, cheaper to...
