@article{xie_palacios_gonzalez-prelcic_2023, title={Hybrid mmWave MIMO Systems Under Hardware Impairments and Beam Squint: Channel Model and Dictionary Learning-Aided Configuration}, volume={22}, ISSN={["1558-2248"]}, url={https://doi.org/10.1109/TWC.2023.3246941}, DOI={10.1109/TWC.2023.3246941}, abstractNote={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. The channel sparsifying dictionaries used in prior work are built from ideal array response vectors evaluated on discrete angles of arrival/departure. In addition, these dictionaries are assumed to be the same for all subcarriers, without considering the impacts of hardware impairments and beam squint. In this manuscript, we derive a general channel and signal model that explicitly incorporates the impacts of hardware impairments, practical pulse shaping functions, and beam squint, overcoming the limitations of mmWave MIMO channel and signal models commonly used in previous work. Then, we propose a dictionary learning (DL) algorithm to obtain the sparsifying dictionaries embedding hardware impairments, by considering the effect of beam squint without introducing it into the learning process. We also design a novel CS channel estimation algorithm under beam squint and hardware impairments, where the channel structures at different subcarriers are exploited to enable channel parameter estimation with low complexity and high accuracy. Numerical results demonstrate the effectiveness of the proposed DL and channel estimation strategy when applied to realistic mmWave channels.}, number={10}, journal={IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS}, author={Xie, Hongxiang and Palacios, Joan and Gonzalez-Prelcic, Nuria}, year={2023}, month={Oct}, pages={6898–6913} }
 @article{palacios_gonzalez-prelcic_mosquera_shimizu_2022, title={A Dynamic Codebook Design for Analog Beamforming in MIMO LEO Satellite Communications}, ISSN={["1550-3607"]}, DOI={10.1109/ICC45855.2022.9882290}, abstractNote={Beamforming gain is a key ingredient in the performance of LEO satellite communication systems to be integrated into cellular networks. However, beam codebooks previously designed in the context of MIMO communication for terrestrial networks, do not provide the appropriate performance in terms of inter-beam interference and gain stability as the satellite moves. In this paper, we propose a dynamic codebook that provides a stable gain during the period of time that the satellite covers a given cell, while avoiding link retraining and extra calculation as the satellite moves. In addition, the proposed codebook provides a higher signal-to-interference-plus-noise (SINR) ratio than those DFT codebooks commonly used in cellular systems.}, journal={IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022)}, author={Palacios, Joan and Gonzalez-Prelcic, Nuria and Mosquera, Carlos and Shimizu, Takayuki}, year={2022} }
 @article{chen_palacios_gonzalez-prelcic_shimizu_lu_2022, title={Joint Initial Access and Localization in Millimeter Wave Vehicular Networks: a Hybrid Model/Data Driven Approach}, DOI={10.1109/SAM53842.2022.9827854}, abstractNote={High resolution compressive channel estimation provides information for vehicle localization when a hybrid mmWave MIMO system is considered. Complexity and memory requirements can, however, become a bottleneck when high accuracy localization is required. An additional challenge is the need of path order information to apply the appropriate geometric relationships between the channel path parameters and the vehicle, RSU and scatterers position. In this paper, we propose a low complexity channel estimation strategy of the angle of departure and time difference of arrival based on multidimensional orthogonal matching pursuit. We also design a deep neural network that predicts the order of the channel paths so only the LoS and first order reflections are used for localization. Simulation results obtained with realistic vehicular channels generated by ray tracing show that sub-meter accuracy can be achieved for 50% of the users, without resorting to perfect synchronization assumptions or unfeasible all-digital high resolution MIMO architectures.}, journal={2022 IEEE 12TH SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING WORKSHOP (SAM)}, author={Chen, Yun and Palacios, Joan and Gonzalez-Prelcic, Nuria and Shimizu, Takayuki and Lu, Hongsheng}, year={2022}, pages={355–359} }
 @article{shastri_palacios_casari_2022, title={Millimeter Wave Localization with Imperfect Training Data using Shallow Neural Networks}, ISSN={["1525-3511"]}, DOI={10.1109/WCNC51071.2022.9771668}, abstractNote={Millimeter wave (mmWave) localization algorithms exploit the quasi-optical propagation of mmWave signals, which yields sparse angular spectra at the receiver. Geometric approaches to angle-based localization typically require to know the map of the environment and the location of the access points. Thus, several works have resorted to automated learning in order to infer a device’s location from the properties of received mmWave signals. However, collecting training data for such models is a significant burden. In this work, we propose a shallow neural network model to localize mmWave devices indoors. This model requires significantly fewer weights than those proposed in the literature. Therefore, it is amenable for implementation in resource-constrained hardware, and needs fewer training samples to converge. We also propose to relieve training data collection efforts by retrieving (inherently imperfect) location estimates from geometry-based mmWave localization algorithms. Even in this case, our results show that the proposed neural networks perform as good as or better than state-of-the-art algorithms.}, journal={2022 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)}, author={Shastri, Anish and Palacios, Joan and Casari, Paolo}, year={2022}, pages={674–679} }
 @article{bayraktar_palacios_gonzalez-prelcic_zhang_2022, title={Multidimensional Orthogonal Matching Pursuit-based RIS-aided Joint Localization and Channel Estimation at mmWave}, ISSN={["2325-3789"]}, DOI={10.1109/SPAWC51304.2022.9833999}, abstractNote={RIS-aided millimeter wave wireless systems benefit from robustness to blockage and enhanced coverage. In this paper, we study the ability of RIS to also provide enhanced localization capabilities as a by-product of communication. We consider sparse reconstruction algorithms to obtain high resolution channel estimates that are mapped to position information. In RIS-aided mmWave systems, the complexity of sparse recovery becomes a bottleneck, given the large number of elements of the RIS and the large communication arrays. We propose to exploit a multidimensional orthogonal matching pursuit strategy for compressive channel estimation in a RIS-aided millimeter wave system. We show how this algorithm, based on computing the projections on a set of independent dictionaries instead of a single large dictionary, enables high accuracy channel estimation at reduced complexity. We also combine this strategy with a localization approach which does not rely on the absolute time of arrival of the LoS path. Localization results in a realistic 3D indoor scenario show that RIS-aided wireless system can also benefit from a significant improvement in localization accuracy.}, journal={2022 IEEE 23RD INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATION (SPAWC)}, author={Bayraktar, Murat and Palacios, Joan and Gonzalez-Prelcic, Nuria and Zhang, Charlie Jianzhong}, year={2022} }