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20212026
most citedIntegrated Communications and Localization for Massive MIMO LEO Satellite Systems

48 citations · 107 across the 16 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

eess.SP2021

Iterated Posterior Linearization PMB Filter for 5G SLAM

Yu Ge, Yibo Wu, Fan Jiang +5

5G millimeter wave (mmWave) signals have inherent geometric connections to the propagation channel and the propagation environment. Thus, they can be used to jointly localize the r…

eess.SP2021★ 9 cited

Beamspace Multidimensional ESPRIT Approaches for Simultaneous Localization and Communications

Fan Jiang, Fuxi Wen, Yu Ge +3

Modern wireless communication systems operating at high carrier frequencies are characterized by a high dimensionality of the underlying parameter space (including channel gains, a…

eess.SP2021★ 9 cited

Beyond 5G RIS mmWave Systems: Where Communication and Localization Meet

Jiguang He, Fan Jiang, Kamran Keykhosravi +3

Upcoming beyond fifth generation (5G) communications systems aim at further enhancing key performance indicators and fully supporting brand new use cases by embracing emerging tech…

eess.SP2021

A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM

Yu Ge, Ossi Kaltiokallio, Hyowon Kim +6

Millimeter wave (mmWave) signals are useful for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment and the prop…

eess.SP2021

Cooperative mmWave PHD-SLAM with Moving Scatterers

Hyowon Kim, Jaebok Lee, Yu Ge +3

Using the multiple-model (MM) probability hypothesis density (PHD) filter, millimeter wave (mmWave) radio simultaneous localization and mapping (SLAM) in vehicular scenarios is sus…

eess.SP2021

Optimal Spatial Signal Design for mmWave Positioning under Imperfect Synchronization

Musa Furkan Keskin, Fan Jiang, Florent Munier +2

We consider the problem of spatial signal design for multipath-assisted mmWave positioning under limited prior knowledge on the user's location and clock bias. We propose an optima…