127 / 2019-12-15 01:34:00
Millimeter Wave Channel Estimation from Quadratic Measurements
sparse phase retrieval; nonconvex optimization; compressed sensing; MmWave channel estimation
Draft Rejected
Lingling Li / The Chinese University of Hong Kong, Shenzhen, China
Kaihui Liu / University of Electronic Science and Technology of China, China
Liangtian Wan / Dalian University of Technology, China
Xianpeng Wang / Hainan University, China
Lu Sun / Dalian University of Technology, China
Zhengli Xing / Institute of Electronic Engineering, China Academy of Engineering Physics, China
Millimeter wave (mmWave) communication has the potential for satisfying growing demands of air interface capacity and new spectrum allocation of sub-$6$ GHz wireless networks. Recently proposed compressed sensing-based techniques which exploit the inherent sparse scattering nature of mmWave channel. However, these methods work with implicit assumptions on long-term phase coherence that are hard to implement with existing hardware. In this paper, we propose a compressive phase retrieval-based mmWave channel estimation technique which can estimate the sparse mmWave channel parameters from quadratic measurements. Compared with traditional compressed sensing-based channel estimation methods, the proposed method has low-cost hardware implementation and robust to carrier frequency offset caused by high-frequency hardware imperfections. Based on the proposed compressive phase retrieval-based model, we introduce a two-stage algorithm to estimate the mmWave channel parameters (up to the global phase) and then compute the exact solution via the anchor measurements. We provide numerical simulations to demonstrate the efficacy of the approach.
Important Date
  • Conference Date

    Jun 08

    2020

    to

    Jun 11

    2020

  • Jan 12 2020

    Draft paper submission deadline

  • Apr 15 2020

    Early Bird Registration

  • Dec 31 2020

    Registration deadline

Sponsored By
IEEE Signal Processing Society
Organized By
Zhejiang University
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