@article{graff_chen_gonzalez-prelcic_shimizu_2023, title={Deep Learning-Based Link Configuration for Radar-Aided Multiuser mmWave Vehicle-to-Infrastructure Communication}, volume={72}, ISSN={["1939-9359"]}, url={https://doi.org/10.1109/TVT.2023.3239227}, DOI={10.1109/TVT.2023.3239227}, abstractNote={Configuring millimeter wave links following a conventional beam training protocol, as the one proposed in the current cellular standard, introduces a large communication overhead, especially relevant in vehicular systems, where the channels are highly dynamic. In this paper, we propose the use of a passive radar array to sense automotive radar transmissions coming from multiple vehicles on the road, and a radar processing chain that provides information about a reduced set of candidate beams for the links between the road-infrastructure and each one of the vehicles. This prior information can be later leveraged by the beam training protocol to significantly reduce overhead. The radar processing chain estimates both the timing and chirp rates of the radar signals, isolates the individual signals by filtering out interfering radar chirps, and estimates the spatial covariance of each individual radar transmission. 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. The communication rates and outage probabilities of this approach are compared against exhaustive search and pure radar-aided beam training methods (without deep learning-based mapping), and evaluated on multi-user channels simulated by ray tracing. Results show that: (i) the proposed processing chain can reliably isolate the spatial covariances for individual radars, and (ii) the radar-to-communications translation strategy based on deep learning provides a significant improvement over pure radar-aided methods in both LOS and NLOS channels.}, number={6}, journal={IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY}, author={Graff, Andrew and Chen, Yun and Gonzalez-Prelcic, Nuria and Shimizu, Takayuki}, year={2023}, month={Jun}, pages={7454–7468} }
 @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{chen_graff_gonzalez-prelcic_shimizu_2021, title={Radar Aided mmWave Vehicle-to-Infrastructure Link Configuration Using Deep Learning}, ISSN={["2576-6813"]}, DOI={10.1109/GLOBECOM46510.2021.9685360}, abstractNote={The high overhead of the beam training process is the main challenge when establishing mmWave communication links, especially for vehicle-to-everything (V2X) scenarios where the channels are highly dynamic. 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. Specifically, the first DNN is built to extract the information from the radar azimuth power spectrum (APS) to reconstruct the communication APS, while the second DNN exploits the information extracted from the spatial covariance of the radar channel to realize R2C covariance prediction. The achieved data rate and the similarity between the estimated and the true communication APS are used to evaluate the prediction performance. The covariance estimation method generally provides higher similarity, as the APS predictions cannot always capture the mismatch between the radar and communication APS. Compared to the beam training method which exploits directly the radar APS without an attempt to translate it to the communication channel, our proposed deep learning (DL) aided methods remarkably reduce the beam training overhead, resulting in a 13.3% and 21.9% rate increase when using the communication APS prediction and covariance prediction, respectively.}, journal={2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)}, author={Chen, Yun and Graff, Andrew and Gonzalez-Prelcic, Nuria and Shimizu, Takayuki}, year={2021} }