Massive CSI Acquisition for Dense Cloud-RANs with Spatial-Temporal Dynamics

Xuan Liu, Yuanming Shi, Jun Zhang, Khaled B. Letaief

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Dense cloud radio access networks (cloud-RANs) provide a promising way to enable scalable connectivity and handle diversified service requirements for massive mobile devices. To fully exploit the performance gains of dense cloud-RANs, channel state information of both the signal link and interference links is required. However, with limited radio resources for training, the channel estimation problem in dense cloud-RANs becomes a high-dimensional estimation problem, i.e., the number of measurements will be typically smaller than the dimension of the channel. In this paper, we shall develop a generic high-dimensional structured channel estimation framework for dense cloud-RANs, which is based on a convex structured regularizing formulation. Observing that the wireless channel possesses ample exploitable statistical characteristics, we propose to convert the available spatial and temporal prior information into appropriate convex regularizers. Simulation results demonstrate that exploiting the spatial and temporal dynamics can achieve good estimation performance even with limited training resources. The alternating direction method of multipliers algorithm is further adopted to solve the resultant large-scale high-dimensional channel estimation problems. The proposed framework thus enjoys modeling flexibility, low training overhead, and computation cost scalability.

Original languageEnglish
Pages (from-to)2557-2570
Number of pages14
JournalIEEE Transactions on Wireless Communications
Volume17
Issue number4
DOIs
Publication statusPublished - Apr 2018

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Keywords

  • ADMM
  • CSI
  • Cloud-RANs
  • high-dimensional structured estimation
  • massive device connectivity
  • spatial and temporal dynamics
  • structured regularizers

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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