RadioNet: Transformer based Radio Map Prediction Model For Dense Urban Environments

Abstract

Radio Map Prediction (RMP), aiming at estimating coverage of radio wave, has been widely recognized as an enabling technology for improving radio spectrum efficiency. However, fast and reliable radio map prediction can be very challenging due to the complicated interaction between radio waves and the environment. In this paper, a novel Transformer based deep learning model termed as RadioNet is proposed for radio map prediction in urban scenarios. In addition, a novel Grid Embedding technique is proposed to substitute the original Position Embedding in Transformer to better anchor the relative position of the radiation source, destination and environment. The effectiveness of proposed method is verified on an urban radio wave propagation dataset. Compared with the SOTA model on RMP task, RadioNet reduces the validation loss by 27.3%, improves the prediction reliability from 90.9% to 98.9%. The prediction speed is increased by 4 orders of magnitude, when compared with ray-tracing based method. We believe that the proposed method will be beneficial to high-efficiency wireless communication, real-time radio visualization, and even high-speed image rendering.

A Multi - Task Learning Model for Super Resolution of Wireless Channel Characteristics

“…During the pre-train stage, homoscedastic uncertainty [15] is employed to balance the single-task losses as in (9). Not only the weights in neural networks W but also noise parameters σ are trainable and updated through standard back propagation during training.…”

“…Few works attempt to estimate several channel characteristics at the same time. As for ML methods, traditional algorithms such as Random Forests (RF), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) as well as Deep Learning (DL) methods such as Convolutional Neural Network (CNN) [8], Transformer [9] and Generative Adversarial Network (GAN) [10] are frequently employed. Also, the estimation targets are usually restricted to one or two characteristics.…”