REM-U-net: Deep Learning Based Agile REM Prediction with Energy-Efficient Cell-Free Use Case

TL;DR

This paper presents REM-U-net, a deep learning framework using u-nets for fast, accurate 3D radio environment map prediction, enhancing wireless network optimization and energy efficiency in cell-free MIMO systems.

Contribution

The paper introduces a novel, runtime-efficient 3D REM prediction method based on u-nets trained on large datasets, with improved accuracy through data preprocessing, applicable to real-world wireless networks.

Findings

Achieves an average normalized RMSE of 0.045.

Runs in approximately 14 milliseconds per prediction.

Effectively supports energy-efficient cell-free MIMO network management.

Abstract

Radio environment maps (REMs) hold a central role in optimizing wireless network deployment, enhancing network performance, and ensuring effective spectrum management. Conventional REM prediction methods are either excessively time-consuming, e.g., ray tracing, or inaccurate, e.g., statistical models, limiting their adoption in modern inherently dynamic wireless networks. Deep-learning-based REM prediction has recently attracted considerable attention as an appealing, accurate, and time-efficient alternative. However, existing works on REM prediction using deep learning are either confined to 2D maps or use a limited dataset. In this paper, we introduce a runtime-efficient REM prediction framework based on u-nets, trained on a large-scale 3D maps dataset. In addition, data preprocessing steps are investigated to further refine the REM prediction accuracy. The proposed u-net framework, along with preprocessing steps, are evaluated in the context of the 2023 IEEE ICASSP Signal Processing Grand Challenge, namely, the First Pathloss Radio Map Prediction Challenge. The evaluation results demonstrate that the proposed method achieves an average normalized root-mean-square error (RMSE) of 0.045 with an average of 14 milliseconds (ms) runtime. Finally, we position our achieved REM prediction accuracy in the context of a relevant cell-free massive multiple-input multiple-output (CF-mMIMO) use case. We demonstrate that one can obviate consuming energy on large-scale fading measurements and rely on predicted REM instead to decide on which sleep access points (APs) to switch on in a CF-mMIMO network that adopts a minimum propagation loss AP switch ON/OFF strategy.