Optimal Fronthaul Quantization for Cloud Radio Positioning

TL;DR

This paper develops an optimal fronthaul quantization strategy for cloud radio positioning systems, enhancing indoor localization accuracy by balancing compression and information constraints.

Contribution

It introduces a robust optimization framework for fronthaul quantization that maximizes localization accuracy using CRB and information-theoretic bounds.

Findings

Proposed algorithm improves localization accuracy under fronthaul constraints.

Robust optimization accounts for parameter uncertainty at the CU.

Numerical results validate the effectiveness of the approach.

Abstract

Wireless positioning systems that are implemented by means of a Cloud Radio Access Networks (C-RANs) may provide cost-effective solutions, particularly for indoor localization. In a C-RAN, the baseband processing, including localization, is carried out at a centralized control unit (CU) based on quantized baseband signals received from the 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 Cram\'{e}r-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.