Optimal fronthaul quantization for cloud radio positioning

Seongah Jeong, Osvaldo Simeone, Alexander Haimovich, Joonhyuk Kang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Wireless positioning systems that are implemented by means of a cloud radio access network (C-RAN) may provide cost-effective solutions, particularly for indoor localization. In a C-RAN, baseband processing, including localization, is carried out at a centralized control unit (CU) based on quantized baseband signals received from the radio units (RUs) over finite-capacity fronthaul links. In this paper, the problem of maximizing the localization accuracy over fronthaul quantization/compression is formulated by adopting the Cramér-Rao bound (CRB) on the localization accuracy as the performance metric of interest and information-theoretic bounds on the compression rate. The analysis explicitly accounts for the uncertainty of parameters at the CU via a robust, or worst-case, optimization formulation. The proposed algorithm leverages the Charnes-Cooper transformation and difference-of-convex (DC) programming and is validated via numerical results.

Original languageEnglish (US)
Article number7104164
Pages (from-to)2763-2768
Number of pages6
JournalIEEE Transactions on Vehicular Technology
Issue number4
StatePublished - Apr 2016

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Automotive Engineering


  • Cloud Radio Access Networks (C-RANs)
  • Cramer-Rao bound (CRB)
  • localization
  • quantization


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