Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Arash Behboodi"'
Tree-based demappers for multiple-input multiple-output (MIMO) detection such as the sphere decoder can achieve near-optimal performance but incur high computational cost due to their sequential nature. In this paper, we propose the perturbed linear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::040bd27c46c8ed87513f4b15c55f79a1
http://arxiv.org/abs/2209.00529
http://arxiv.org/abs/2209.00529
Hybrid analog-digital (HAD) architecture is widely adopted in practical millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems to reduce hardware cost and energy consumption. However, channel estimation in the context of HAD i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::06186eba057567e61c1ddfa332c172cf
http://arxiv.org/abs/2205.05202
http://arxiv.org/abs/2205.05202
Autor:
Shreya Kadambi, Arash Behboodi, Joseph B. Soriaga, Max Welling, Roohollah Amiri, Srinivas Yerramalli, Taesang Yoo
We present a neural network architecture for jointly learning user locations and environment mapping up to isometry, in an unsupervised way, from channel state information (CSI) values with no location information. The model is based on an encoder-de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::341350742d77b7c27f38ff0eda62ca93
Publikováno v:
Compressed Sensing in Information Processing ISBN: 9783031097447
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4559bdf3f6e7703210bb1bba0924ecef
https://doi.org/10.1007/978-3-031-09745-4_8
https://doi.org/10.1007/978-3-031-09745-4_8
Autor:
Hamed Pezeshki, Fabio Valerio Massoli, Arash Behboodi, Taesang Yoo, Arumugam Kannan, Mahmoud Taherzadeh Boroujeni, Qiaoyu Li, Tao Luo, Joseph B. Soriaga
Analog beamforming is the predominant approach for millimeter wave (mmWave) communication given its favorable characteristics for limited-resource devices. In this work, we aim at reducing the spectral efficiency gap between analog and digital beamfo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d79dac937c73bc4d15398496f0693cf
Publikováno v:
IEEE internet of things journal
Low-power wide-area network (LPWAN) multi-radio access technology (RAT) devices promise enabling Internet of Things (IoT) use-cases that simultaneously require high coverage and data rates, and low energy consumption. For such devices, active probing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c4a51c6c7a9291d2c5805b76ae0b9ac
https://doi.org/10.1109/jiot.2019.2935804
https://doi.org/10.1109/jiot.2019.2935804
Publikováno v:
IEEE Transactions on Signal Processing. 67:6078-6091
Despite the tremendous success of deep neural networks in various learning problems, it has been observed that adding intentionally designed adversarial perturbations to inputs of these architectures leads to erroneous classification with high confid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b54689faf25152b356dc14d978c9fe97
https://doi.org/10.1109/tsp.2019.2943232
https://doi.org/10.1109/tsp.2019.2943232
We propose Hypernetwork Kalman Filter (HKF) for tracking applications with multiple different dynamics. The HKF combines generalization power of Kalman filters with expressive power of neural networks. Instead of keeping a bank of Kalman filters and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::261a8d8afa17d0b365dcec236490e7b7
http://arxiv.org/abs/2109.12561
http://arxiv.org/abs/2109.12561
Publikováno v:
Sampling theory, signal processing, and data analysis 19(2), 11 (2021). doi:10.1007/s43670-021-00006-2
Sampling theory, signal processing, and data analysis 19(2), 11 (2021). doi:10.1007/s43670-021-00006-2
Published by Birkhäuser, [Cham]
Published by Birkhäuser, [Cham]
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::32d77cc7bf84aac2ae2309b606266d3b
https://doi.org/10.1007/s43670-021-00006-2
https://doi.org/10.1007/s43670-021-00006-2
Publikováno v:
Fundamentals of Data Analytics ISBN: 9783030568306
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08663568b01b52c78b165bd35cd40bdd
https://doi.org/10.1007/978-3-030-56831-3_4
https://doi.org/10.1007/978-3-030-56831-3_4