Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion

Zhang, Yan; Wen, Jinxiao; Yang, Guanshu; He, Zhiyuan; Wang, Jing

doi:10.3390/app9091908

Cited by 139 publications

(138 citation statements)

References 28 publications

Supporting

Mentioning

104

Contrasting

Order By: Relevance

“…The machine learning approach to path loss modeling is expected to provide a better model, which can generalize well the propagation environment since the model is being learned through training with the data collected from the environment. The prediction of propagation path loss is regarded as a regression problem, as stated in the literature [15][16][17]. In this context, path loss models have been developed by various supervised learning techniques such as support vector machine (SVM) [16,18], artificial neural network (ANN) [19][20][21][22], random forest [17], K-nearest neighbors (KNN) [17].…”

Section: Introductionmentioning

confidence: 99%

Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process

Park

Lee

et al. 2020

Sensors

106

View full text Add to dashboard Cite

Although various linear log-distance path loss models have been developed for wireless sensor networks, advanced models are required to more accurately and flexibly represent the path loss for complex environments. This paper proposes a machine learning framework for modeling path loss using a combination of three key techniques: artificial neural network (ANN)-based multi-dimensional regression, Gaussian process-based variance analysis, and principle component analysis (PCA)-aided feature selection. In general, the measured path loss dataset comprises multiple features such as distance, antenna height, etc. First, PCA is adopted to reduce the number of features of the dataset and simplify the learning model accordingly. ANN then learns the path loss structure from the dataset with reduced dimension, and Gaussian process learns the shadowing effect. Path loss data measured in a suburban area in Korea are employed. We observe that the proposed combined path loss and shadowing model is more accurate and flexible compared to the conventional linear path loss plus log-normal shadowing model.

show abstract

Section: Introductionmentioning

confidence: 99%

Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process

Park

Lee

et al. 2020

Sensors

106

View full text Add to dashboard Cite

show abstract

“…Aside from urban areas, it is also possible to classify the target region from the aerial images into different classes such as forest or village, and use a suitable path loss model [19]. Recently in [20], a survey of existing machine learning methods in literature for path loss predic-tion, including decision tree based [21] and support vector regression based [22] methods, is presented. Moreover, the use of satellite images as input to a deep neural network is utilized in [23] to predict LTE signal quality metrics, including RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality) and SINR (Signal to Interference and Noise Ratio).…”

Section: Introductionmentioning

confidence: 99%

Predicting Path Loss Distribution of an Area From Satellite Images Using Deep Learning

et al. 2020

View full text Add to dashboard Cite

Path loss prediction is essential for network planning in any wireless communication system. For cellular networks, it is usually achieved through extensive received signal power measurements in the target area. When the 3D model of an area is available, ray tracing simulations can be utilized; however, an important drawback of such an approach is the high computational complexity of the simulations. In this paper, we present a fundamentally different approach for path loss distribution prediction directly from 2D satellite images based on deep convolutional neural networks. While training process is time consuming and completed offline, inference can be done in real time. Another advantage of the proposed approach is that 3D model of the area is not needed during inference since the network simply uses an image captured by an aerial vehicle or satellite as its input. Simulation results show that the path loss distribution can be accurately predicted for different communication frequencies and transmitter heights. INDEX TERMS Path loss, deep learning, convolutional neural networks.

show abstract

“…For regression or prediction problems, the output of the random forest is the average of the outputs of all the decision trees. We have demonstrated in previous works [29]- [31] that the random forest has high accuracy in the estimation of channel qualities.…”

Section: A Prediction Model Of A2g Link Quality Based On the Random mentioning

confidence: 98%

A Two-Step Environment-Learning-Based Method for Optimal UAV Deployment

Luo

Zhang

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) can be used as low-altitude flight base stations to satisfy the coverage requirements of wireless users in various scenarios. In practical applications, since the transmitted power and energy resources of the UAVs are limited and the propagation environments are complicated and time-variant, it is challenging to control a group of UAVs to ensure coverage performance while preserving the connectivity and safety of the UAV networks. To this end, a two-step environment-learningbased method is proposed for the intelligent deployment of the UAVs. First, a machine learning algorithm is used to establish an accurate prediction model of the link qualities from the UAVs to the users under a specific scenario for the next step. Then, a modified deep deterministic policy gradient (DDPG) algorithm is employed to control the movements of the UAVs according to the predicted link qualities and to maximize the proportion of covered users. The prioritized experience replay mechanism is introduced to the standard DDPG algorithm to accelerate the deployment procedure. The coverage performance is analyzed in both the interference-free situation and the situation with co-channel interference. Simulation results have shown that the proposed method has a higher convergence speed than the standard DDPG method. Additionally, the proposed deployment method can achieve higher coverage performance and better adaptability to the dynamic environment than three commonly used methods, the random method, the K-means-based method, and the statistical-channel-model-based method. INDEX TERMS Coverage performance, environment-learning-based method, link quality, optimal deployment, unmanned aerial vehicle networks.

show abstract

Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion

Cited by 139 publications

References 28 publications

Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process

Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process

Predicting Path Loss Distribution of an Area From Satellite Images Using Deep Learning

A Two-Step Environment-Learning-Based Method for Optimal UAV Deployment

Contact Info

Product

Resources

About