most citedMulti-Modal Beam Prediction Challenge 2022: Towards Generalization

11 citations · 15 across the 6 of their papers we have counts for

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eess.SP2024

A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing

Joao Morais, Sadjad Alikhani, Akshay Malhotra +2

This paper introduces a task-specific, model-agnostic framework for evaluating dataset similarity, providing a means to assess and compare dataset realism and quality. Such a frame…

eess.SP20241 cited

DeepSense-V2V: A Vehicle-to-Vehicle Multi-Modal Sensing, Localization, and Communications Dataset

Joao Morais, Gouranga Charan, Nikhil Srinivas +1

High data rate and low-latency vehicle-to-vehicle (V2V) communication are essential for future intelligent transport systems to enable coordination, enhance safety, and support dis…

eess.SP20221 cited

Device-Agnostic Millimeter Wave Beam Selection using Machine Learning

Sajad Rezaie, João Morais, Ahmed Alkhateeb +1

Most research in the area of machine learning-based user beam selection considers a structure where the model proposes appropriate user beams. However, this design requires a speci…

eess.SP202211 cited

Multi-Modal Beam Prediction Challenge 2022: Towards Generalization

Gouranga Charan, Umut Demirhan, João Morais +3

Beam management is a challenging task for millimeter wave (mmWave) and sub-terahertz communication systems, especially in scenarios with highly-mobile users. Leveraging external se…

eess.SP20222 cited

Location- and Orientation-aware Millimeter Wave Beam Selection for Multi-Panel Antenna Devices

Sajad Rezaie, João Morais, Elisabeth de Carvalho +2

While initial beam alignment (BA) in millimeter-wave networks has been thoroughly investigated, most research assumes a simplified terminal model based on uniform linear/planar arr…