Received Power Prediction Research Articles

Scenario-specific radio propagation simulation from LiDAR point cloud data for smart office scenario

Recently, the cyber-physical-system (CPS) has attracted great interest in the information and communications technology (ICT) research field for the virtual test of wireless communication systems. Since the performance of wireless communication significantly depends on the radio propagation channel characteristics, the development of accurate radio propagation simulation technology becomes essential for the purpose. This paper proposed the scenario-specific radio propagation simulation method from LiDAR point cloud data. By the continuous environment measurement by LiDAR, the positions of dynamic objects like pedestrians are tracked, as well as the site-specific propagation environment characteristics and their influences are taken into the simulation. For performance evaluation, the radio propagation measurement was conducted in the 28 GHz band in an indoor office. The result showed a significant improvement in the RMSE of received power prediction. The proposed method will be utilized for the radio channel emulation in the CPS.

A formulation-aid transfer learning-based framework in received power prediction

This study is motivated by the demand for an efficient deep learning-based model that helps us predict the future link quality for intelligent decision-making systems. In this letter, we propose a transfer learning-based approach to predict millimeter-wave future received power in an indoor environment. The model is pre-trained using formulation-aid generated data and fine-tuned using measured data. The proposed framework reduces more than 30% in root-mean-square error and 6.5% in accuracy with high training speed compared to the baseline training from scratch.

Feature Extraction in Reference Signal Received Power Prediction Based on Convolution Neural Networks

In this letter, an environmental features (EFs) extraction model is proposed for estimating reference signal received power (RSRP) accurately. Firstly, 18-D measured data is transformed into 15-D physical features (PFs). Then 15-D PFs is reduced to 14-D by performing correlation analysis. Secondly, EFs are extracted from the environmental maps (EMs) by applying Convolution Neural Networks (CNNs). Finally, several Machine Learning Regressors (MLRs) are trained to predict RSRP combining PFs and EFs as inputs. The results, in test dataset, show that prediction performance of MLRs is improved through 14-D PFs, and is further improved in nonlinear MLRs combining PFs and EFs.

Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction

The goal of this study is to improve the accuracy of millimeter wave received power prediction by utilizing camera images and radio frequency (RF) signals, while gathering image inputs in a communication-efficient and privacy-preserving manner. To this end, we propose a distributed multimodal machine learning (ML) framework, coined multimodal split learning (MultSL), in which a large neural network (NN) is split into two wirelessly connected segments. The upper segment combines images and received powers for future received power prediction, whereas the lower segment extracts features from camera images and compresses its output to reduce communication costs and privacy leakage. Experimental evaluation corroborates that MultSL achieves higher accuracy than the baselines utilizing either images or RF signals. Remarkably, without compromising accuracy, compressing the lower segment output by 16x yields 16x lower communication latency and 2.8% less privacy leakage compared to the case without compression.

Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks

This study demonstrates the feasibility of the proactive received power prediction by leveraging spatiotemporal visual sensing information toward the reliable millimeter-wave (mmWave) networks. Since the received power on a mmWave link can attenuate aperiodically due to a human blockage, the long-term series of the future received power cannot be predicted by analyzing the received signals before the blockage occurs. We propose a novel mechanism that predicts a time series of the received power from the next moment to even several hundred milliseconds ahead. The key idea is to leverage the camera imagery and machine learning (ML). The time-sequential images can involve the spatial geometry and the mobility of obstacles representing the mmWave signal propagation. ML is used to build the prediction model from the dataset of sequential images labeled with the received power in several hundred milliseconds ahead of when each image is obtained. The simulation and experimental evaluations using IEEE 802.11ad devices and a depth camera show that the proposed mechanism employing convolutional LSTM predicted a time series of the received power in up to 500 ms ahead at an inference time of less than 3 ms with a root-mean-square error of 3.5 dB.