Optimal Fronthaul Quantization for Cloud Radio Positioning

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.

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  • English

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  • Accession Number: 01601079
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Apr 19 2016 4:00PM