no code implementations • 3 May 2024 • Nuria González-Prelcic, Musa Furkan Keskin, Ossi Kaltiokallio, Mikko Valkama, Davide Dardari, Xiao Shen, Yuan Shen, Murat Bayraktar, Henk Wymeersch
Future wireless networks will integrate sensing, learning and communication to provide new services beyond communication and to become more resilient.
no code implementations • 25 Nov 2023 • Murat Bayraktar, Nuria González-Prelcic, Hao Chen
Specifically, we introduce a generalized eigenvalue-based precoder design that considers the downlink user rate, the radar gain, and the SI suppression.
no code implementations • 26 Aug 2023 • Yun Chen, Nuria González-Prelcic, Takayuki Shimizu, Hongshen Lu, Chinmay Mahabal
In this paper, we propose first a mmWave channel tracking algorithm based on multidimensional orthogonal matching pursuit algorithm (MOMP) using reduced sparsifying dictionaries, which exploits information from channel estimates in previous frames.
no code implementations • 30 Jun 2023 • Yun Chen, Nuria González-Prelcic, Takayuki Shimizu, HongSheng Lu
One strategy to obtain user location information in a wireless network operating at millimeter wave (mmWave) is based on the exploitation of the geometric relationships between the channel parameters and the user position.
no code implementations • 31 Oct 2022 • Joan Palacios, Nuria González-Prelcic
Greedy sparse recovery has become a popular tool in many applications, although its complexity is still prohibitive when large sparsifying dictionaries or sensing matrices have to be exploited.
no code implementations • 17 Oct 2022 • Hongxiang Xie, Joan Palacios, Nuria González-Prelcic
Low overhead channel estimation based on compressive sensing (CS) has been widely investigated for hybrid wideband millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems.
no code implementations • 24 Aug 2022 • Joan Palacios, Nuria González-Prelcic, Cristian Rusu
Greedy approaches in general, and orthogonal matching pursuit in particular, are the most commonly used sparse recovery techniques in a wide range of applications.
no code implementations • 7 Apr 2022 • Joan Palacios, Nuria González-Prelcic, Cristian Rusu
Compressive approaches provide a means of effective channel high resolution channel estimates in millimeter wave MIMO systems, despite the use of analog and hybrid architectures.
no code implementations • 4 Apr 2022 • Yun Chen, Joan Palacios, Nuria González-Prelcic, Takayuki Shimizu, HongSheng Lu
High resolution compressive channel estimation provides information for vehicle localization when a hybrid mmWave MIMO system is considered.
no code implementations • 24 Mar 2022 • Murat Bayraktar, Joan Palacios, Nuria González-Prelcic, Charlie Jianzhong Zhang
RIS-aided millimeter wave wireless systems benefit from robustness to blockage and enhanced coverage.
no code implementations • 12 Jan 2022 • Andrew Graff, Yun Chen, Nuria González-Prelcic, Takayuki Shimizu
Then, a deep network is used to translate features of these radar spatial covariances into features of the communication spatial covariances, by learning the intricate mapping between radar and communication channels, in both line-of-sight and non-line-of-sight settings.
no code implementations • 16 Nov 2021 • Yun Chen, Andrew Graff, Nuria González-Prelcic, Takayuki Shimizu
In this paper, we obtain prior information to speed up the beam training process by implementing two deep neural networks (DNNs) that realize radar-to-communication (R2C) channel information translation in a vehicle-to-infrastructure (V2I) system.
no code implementations • 16 Nov 2021 • Joan Palacios, Nuria González-Prelcic, Carlos Mosquera, Takayuki Shimizu
Beamforming gain is a key ingredient in the performance of LEO satellite communication systems to be integrated into cellular networks.
no code implementations • 13 Sep 2021 • Carlos Baquero Barneto, Taneli Riihonen, Sahan Damith Liyanaarachchi, Mikko Heino, Nuria González-Prelcic, Mikko Valkama
We then also propose new transmitter and receiver beamforming solutions for purely analog beamforming based JCAS systems that maximize the beamforming gain at the sensing direction while controlling the beamformed power at the communications direction(s), cancelling the SI as well as eliminating the potential reflection from the communication direction and optimizing the combined radar pattern (CRP).
no code implementations • 27 Feb 2021 • Yuyang Wang, Nitin Jonathan Myers, Nuria González-Prelcic, Robert W. Heath Jr
We design fully-connected layers to optimize channel acquisition and beam alignment.
no code implementations • 11 May 2020 • Yuyang Wang, Nitin Jonathan Myers, Nuria González-Prelcic, Robert W. Heath Jr
Furthermore, based on the CS channel measurements, we develop techniques to update and learn such channel AoD statistics at the BS.