Machine Learning-Based Radio Coverage Prediction in Urban Environments

Mohammadjafari, Sanaz; Roginsky, Sophie; Kavurmacioglu, Emir; Çevik, Mücahit; Ethier, Jonathan; Bener, Ayşe

doi:10.1109/tnsm.2020.3035442

Cited by 30 publications

(12 citation statements)

References 19 publications

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“…At the same specification of BS, the extent of mobile network coverage will vary according to its operating environment [48]. The transmitted signal will be reflected more frequently in urban areas compared to suburban and open areas.…”

Section: ) Environment Category (𝑪𝑪𝑬𝑬𝑨𝑨𝑪𝑪𝑪𝑪)mentioning

confidence: 99%

Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

et al. 2023

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Nowadays, the dependency on high-performance digital mobile connectivity is not limited to human usage but also the intelligent objects increasingly deployed to serve the needs of Internet of Things (IoT) applications. However, the current network planning technique limitation has constrained the real potential of mobile digital connectivity development. This situation has hindered sustainable Internet-oriented economic and technological development. The 3 rd generation partnership project (3GPP), through its specification release 18 (Rel.18), has included and leveraged the potential capabilities of machine learning (ML) technologies in advanced mobile network planning. The main objective is to enhance mobile network planning performance and reduce complexity. To materialize this aim, we propose a novel ML-based Online coverage Estimator (MLOE) tool developed based on Random Forest (RF) ML algorithm. It uses seven unique features to predict the mobile network performance through reference signal received power (RSRP). Accordingly, the results showed that MLOE outperformed traditional empirical techniques and previous works. The final trained RF algorithm has achieved an outstanding root mean square error (RMSE) of 2.65 dB and a coefficient of determination (𝑅𝑅 2 ) of 0.93. With the dynamic and fast-growing mobile technology, MLOE has been deployed on an online platform using MATLAB ® Web App Server, which offers a modular and scalable architecture.

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Section: ) Environment Category (𝑪𝑪𝑬𝑬𝑨𝑨𝑪𝑪𝑪𝑪)mentioning

confidence: 99%

Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

et al. 2023

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“…Many researchers are trying to make breakthroughs in channel modeling by machine learning (ML) [3]. Because of its generalizable architecture, machine learning is widely used in almost every branch of science and technology.…”

Section: Introductionmentioning

confidence: 99%

Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model

Zhang¹,

He²

2023

Preprint

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As an emerging approach, deep learning plays an increasingly influential role in channel modeling. Traditional ray tracing (RT) methods of channel modeling tend to be inefficient and expensive. In this paper, we present a superresolution (SR) model for channel characteristics. Residual connection and attention mechanism are applied to this convolutional neural network (CNN) model. Experiments prove that the proposed model can reduce the noise interference generated in the SR process and solve the problem of low efficiency of RT. The mean absolute error of our channel SR model on the PL achieves the effect of 2.82 dB with scale factor 2, the same accuracy as RT took only 52% of the time in theory. Compared with vision transformer (ViT), the proposed model also demonstrates less running time and computing cost in SR of channel characteristics.

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“…ML-based propagation models can be used as alternatives to empirical or computational methods for generating the data required to select the location of transmitters. These models can be trained with simulated data, often provided by RT simulations [15], [16], or measurements [17], [18]. Once trained, they can rapidly and accurately predict the RSS levels for a variety of transmitter and receiver locations, geometries, frequencies and other quantities of interest [19]- [21].…”

Section: Introductionmentioning

confidence: 99%

Fast Selection of Indoor Wireless Transmitter Locations with Generalizable Neural Network Propagation Models

Seretis,

Xu,

Sarris

2023

Preprint

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<p>The continuous emergence of new wireless communication systems increases the need for intelligent planning prior to their deployment. This planning phase includes determining the position and transmit power of wireless access points, to meet quality of service objectives along with electromagnetic compatibility standards. How to effectively place a set of transmitters in an environment is a long-standing problem that has been widely studied over the years. This process has mainly relied on expensive measurement campaigns, low-fidelity empirical models, or high-fidelity but time-consuming simulations. Recent advances in scientific machine learning create new possibilities for overcoming the standard dichotomy between speed and accuracy.<br> In this paper, we train a deep neural network (U-Net) to predict received signal strength levels for a variety of different geometries and for positions of multiple transmitters. Then, we leverage the computational efficiency of the trained model to determine the position of access point transmitters in new geometries. This approach dramatically accelerates the process of selecting the position of access points, meeting multiple optimization objectivesin an efficient manner. </p>

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Machine Learning-Based Radio Coverage Prediction in Urban Environments

Cited by 30 publications

References 19 publications

Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

Machine Learning-Based Online Coverage Estimator (MLOE): Advancing Mobile Network Planning and Optimization

Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model

Fast Selection of Indoor Wireless Transmitter Locations with Generalizable Neural Network Propagation Models

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