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[Poster] Localized Random Sampling for Robust Compressive Beam Alignment

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Citation Author(s):
Nitin Jonathan Myers, Robert W. Heath Jr.
Submitted by:
Nitin Jonathan Myers
Last updated:
30 April 2019 - 11:30pm
Document Type:
Poster
Document Year:
2019
Event:
Presenters:
Nitin Jonathan Myers
Paper Code:
3658
 

Compressed sensing (CS)-based beam alignment is a promising solution for rapid link configuration in millimeter wave (mmWave)
systems that use large arrays. Translating CS to practical mmWave radios, however, can be challenging due to carrier frequency offset (CFO). Standard sparse recovery techniques that use random sampling strategies to acquire channel measurements can fail even if there is a slight mismatch in carrier frequencies. In this paper, we show that restricting the randomness in compressive sampling to local sets can achieve robustness to structured errors due to CFO. The proposed approach requires fewer channel measurements than comparable algorithms and has the same complexity as standard CS.

An implementation of our robust CS technique is available online at https://github.com/nitinjmyers/ICASSP2019RobustCS

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