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20152025
most citedMachine Learning on Camera Images for Fast mmWave Beamforming

21 citations · 33 across the 9 of their papers we have counts for

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5 papers · 1 filter

eess.SP2025

ATLAS: AI-Native Receiver Test-and-Measurement by Leveraging AI-Guided Search

Mauro Belgiovine, Suyash Pradhan, Johannes Lange +2

Industry adoption of Artificial Intelligence (AI)-native wireless receivers, or even modular, Machine Learning (ML)-aided wireless signal processing blocks, has been slow. The main…

eess.SP2022★ 1 cited

Neural Network-based OFDM Receiver for Resource Constrained IoT Devices

Nasim Soltani, Hai Cheng, Mauro Belgiovine +10

Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are used for communication links in many current and emerging Internet of Things (IoT) applications, including the…

eess.SP2022★ 2 cited

Going Beyond RF: How AI-enabled Multimodal Beamforming will Shape the NextG Standard

Debashri Roy, Batool Salehi, Stella Banou +7

Incorporating artificial intelligence and machine learning (AI/ML) methods within the 5G wireless standard promises autonomous network behavior and ultra-low-latency reconfiguratio…

eess.SP2021★ 21 cited

Machine Learning on Camera Images for Fast mmWave Beamforming

Batool Salehi, Mauro Belgiovine, Sara Garcia Sanchez +3

Perfect alignment in chosen beam sectors at both transmit- and receive-nodes is required for beamforming in mmWave bands. Current 802.11ad WiFi and emerging 5G cellular standards s…

eess.SP2018

ORACLE: Optimized Radio clAssification through Convolutional neuraL nEtworks

Kunal Sankhe, Mauro Belgiovine, Fan Zhou +3

This paper describes the architecture and performance of ORACLE, an approach for detecting a unique radio from a large pool of bit-similar devices (same hardware, protocol, physica…