Channel Matrix Sparsity with Imperfect Channel State Information in Cloud-Radio Access Networks

TL;DR

This paper investigates the impact of imperfect channel state information on channel matrix sparsification in large-scale cloud-radio access networks, proposing a theoretical framework and algorithms to optimize performance under practical conditions.

Contribution

It extends channel sparsification techniques to scenarios with imperfect CSI, deriving a lower bound on SINR fidelity and proposing a Dinkelbach-based algorithm for optimal sparsification.

Findings

Derived a lower bound for SINR fidelity with imperfect CSI

Proposed a Dinkelbach-based algorithm for optimal channel sparsification

Validated results through simulations under practical conditions

Abstract

Channel matrix sparsification is considered as a promising approach to reduce the progressing complexity in large-scale cloud-radio access networks (C-RANs) based on ideal channel condition assumption. In this paper, the research of channel sparsification is extend to practical scenarios, in which the perfect channel state information (CSI) is not available. First, a tractable lower bound of signal-to-interferenceplus-noise ratio (SINR) fidelity, which is defined as a ratio of SINRs with and without channel sparsification, is derived to evaluate the impact of channel estimation error. Based on the theoretical results, a Dinkelbach-based algorithm is proposed to achieve the global optimal performance of channel matrix sparsification based on the criterion of distance. Finally, all these results are extended to a more challenging scenario with pilot contamination. Finally, simulation results are shown to evaluate the performance of channel matrix sparsification with imperfect CSIs and verify our analytical results.