Physical Layer Abstraction Model for RadioWeaves

TL;DR

This paper presents a physical layer abstraction model for RadioWeaves, enabling efficient performance evaluation of various deployment scenarios by predicting SINRs and packet error rates, considering different antenna patterns and configurations.

Contribution

The paper introduces a novel physical layer abstraction model tailored for RadioWeaves, incorporating SINR prediction and EESM-based packet error rate estimation for diverse deployment parameters.

Findings

Model accurately predicts system performance metrics.

EESM effectively maps SINRs to packet error rates.

Model accommodates various antenna gain patterns.

Abstract

RadioWeaves, in which distributed antennas with integrated radio and compute resources serve a large number of users, is envisioned to provide high data rates in next generation wireless systems. In this paper, we develop a physical layer abstraction model to evaluate the performance of different RadioWeaves deployment scenarios. This model helps speed up system-level simulators of the RadioWeaves and is made up of two blocks. The first block generates a vector of signal-to-interference-plus-noise ratios (SINRs) corresponding to each coherence block, and the second block predicts the packet error rate corresponding to the SINRs generated. The vector of SINRs generated depends on different parameters such as the number of users, user locations, antenna configurations, and precoders. We have also considered different antenna gain patterns, such as omni-directional and directional microstrip patch antennas. Our model exploits the benefits of exponential effective SINR mapping (EESM). We study the robustness and accuracy of the EESM for RadioWeaves.