.png){kind=icon}
.png){kind=icon}
Return
Optimal Fronthaul Quantization for Cloud Radio Positioning
.png){kind=icon}
OA
AI
DOI：10.1109/TVT.2015.2431491.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 Cramer-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.
Keywords:
Cloud radio access networks (C-RANs)
Cramer-Rao bound (CRB)
localization
quantization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.1
Papers:
1.8W
Citations:
6.6W