A Physics-based and Data-driven Approach for Localized Statistical Channel Modeling

TL;DR

This paper introduces a physics-based, data-driven localized channel model for 5G that uses RSRP measurements to accurately capture local geographical effects, improving performance optimization.

Contribution

It proposes a novel localized statistical channel modeling approach that relies solely on RSRP data and formulates it as a sparse recovery problem with an efficient solution.

Findings

Effective modeling of local geographical structures.

Accurate channel path parameter estimation from RSRP.

Validated with synthetic and real measurements.

Abstract

Localized channel modeling is crucial for offline performance optimization of 5G cellular networks, but the existing channel models are for general scenarios and do not capture local geographical structures. In this paper, we propose a novel physics-based and data-driven localized statistical channel modeling (LSCM), which is capable of sensing the physical geographical structures of the targeted cellular environment. The proposed channel modeling solely relies on the reference signal receiving power (RSRP) of the user equipment, unlike the traditional methods which use full channel impulse response matrices. The key is to build the relationship between the RSRP and the channel's angular power spectrum. Based on it, we formulate the task of channel modeling as a sparse recovery problem where the non-zero entries of the sparse vector indicate the channel paths' powers and angles of departure. A computationally efficient weighted non-negative orthogonal matching pursuit (WNOMP) algorithm is devised for solving the formulated problem. Finally, experiments based on synthetic and real RSRP measurements are presented to examine the performance of the proposed method.