# Deep Learning Based Received Signal Strength Estimation

Mohammed Mallik, Guillaume Villemaud

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Mohammed Mallik, Guillaume Villemaud. Deep Learning Based Received Signal Strength Estimation. ICMLCN 2025 - IEEE International Conference on Machine Learning for Communication and Networking, IEEE, May 2025, Barcelona, Spain. hal-05008809

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# Deep Learning Based Received Signal Strength Estimation

Mohammed Mallik and Guillaume Villemaud

Univ Lyon, INSA Lyon, CITI, F-69621 Villeurbanne, France (e-mail: firstname.lastname@insa-lyon.fr)

***Abstract***—As network densification becomes crucial for future mobile networks, deploying base stations (BS)s grows increasingly challenging due to the complex procedures in planning; moreover, poor layout can result in inadequate coverage. A coverage prediction model helps operators address these issues by identifying coverage gaps, optimizing BS placement, evaluating service quality, and creating radio maps for spectrum sharing, interference management, and localization. Current models rely on base station height, transmission power, or path loss models which are limited to oversimplified scenarios, and remain analytically intractable for more realistic network situations. In this work, we present a convolutional neural network based method to accurately predict received signal strength (RSS) and coverage maps of a network from its topology, thus overcoming the limitations of empirical path loss models. This approach uses the network topology as features in images to train and predict RSS and coverage maps. Experimental results show that our method can produce accurate RSS estimates with a Mean Absolute Error of -33 dBm and is able to generalize well to unseen environments.

***Index Terms***—Network coverage Prediction, convolutional Neural Network, autoencoders, performance

## I. INTRODUCTION

Wireless technologies have become a necessary component of contemporary living and have integrated into our everyday lives. In wireless networks, Reference Signal Received Power (RSRP) is a crucial metric for assessing network coverage and communication quality. The deployment and design of these networks depend on accurate coverage prediction models [1]. By simulating signal quality prior to deployment, these models assist in identifying possible coverage gaps, enabling operators to optimize signal performance and improve the user experience [2]. They also make it possible to create maps of the radio environment, which makes it easier to do tasks like localization, interference control, and spectrum sharing [3], [4], [5]. Despite this rapid progress, one of the critical technical challenges lies in accurately estimating Received Signal Strength (RSS) and generating precise coverage maps, which are essential for effective system design in 5G and future cellular networks.

In current mobile networks, the widely adopted source of coverage estimation data is the Minimization of Drive Test (MDT), introduced in 3GPP Release 10 and continually evolving. Despite its advancements, MDT data falls short in meeting network operators' requirements for accurate coverage estimation, primarily due to geographic data sparsity [6], [7]. Empirical path loss models [8], [9] are commonly

employed for RSS and coverage predictions due to their simple structure and low computational complexity. However, these models often lack accuracy, particularly when applied to varying environmental conditions [9]. Another prevalent approach for network performance modeling and coverage prediction is stochastic geometry [10]. While useful, these models often rely on simplifying assumptions, leading to less realistic outcomes. For instance, some studies have incorporated correlated shadowing [11], [12] but have made further simplifications, such as ignoring the path loss component. Furthermore, most stochastic geometry-based models assume the base stations are distributed according to a homogeneous Poisson point process (HPPP), which, although convenient for analysis, fails to reflect the repulsive nature of base station distributions observed in real-world cellular networks. Machine learning methods have emerged as promising alternatives for path loss and coverage prediction. Techniques such as Random Forests (RF), Support Vector Regression (SVR) Neural Networks (NN), and Convolutional Neural Networks (CNN) have been explored in various studies [13], [14], [15], [16], [17], [7], [18]. For example, in [16], [18], CNN-based models were used for instance segmentation of environmental structures like buildings to predict coverage maps. Generative adversarial networks (GANs) have also been employed for power map estimation from sparse data [14], while other works have utilized features like the height of BS, distance, and frequency, etc. of a network in their predictions [17]. However, none of these studies considered transmitter locations for coverage map predictions.

In this study, we introduce a CNN based method (noted as Auto-RSS) with an encoder-decoder architecture. Our approach predicts coverage maps and RSS values in real-world urban environments by feeding base station locations embedded as binary images to the neural network. CNN autoencoders are ideal for this case given that they can be applied for tasks such as denoising, image reconstruction, and dimensionality reduction [19], [20], [21]. The proposed deep learning model is designed to estimate coverage accurately when sparse transmitter locations are used as features making it particularly useful for urban network planning. The paper is organized as follows. Section II describes the methodology that includes problem formulation, dataset, proposed method, and model details. Experimental results are presented in Section III. The conclusion is given in Section IV.
<figure><img src="image_2.png" /><figcaption>Fig. 1. The components and training methodology of our proposed Auto-RSS approach. The encoder module's input and output layer has (1, 8, 16, 32) channels. The convolutional layers of the encoder perform convolution with kernel size (3,3), strides of (1,1), and 1 as padding. The latent representation has 36768 units. The RT simulator takes in BS locations and other parameters of the network as input and outputs the reference coverage map, which is used as the ground truth for the training. The decoder module is exactly the opposite of the encoder, having transposed convolution, and the weights are updated using mean absolute error loss by ADAM optimizer.</figcaption></figure>

## II. METHODOLOGY

### A. Auto-RSS

In the context of coverage and RSS estimation, the objective is to replicate the behavior of a traditional simulator while maintaining computational efficiency. To achieve this, a CNN autoencoder is employed to model the complex mapping of a simulator between network input parameters and output metrics, such as coverage or other key performance indicators. The encoder of the Auto-RSS maps the feature domain X into latent space and the decoder translates from the latent space to the output domain Y. Our goal is to train the CNN using the simulator output as the ground truth $\Upsilon$. In the proposed method, to estimate the coverage map image $\Upsilon'$ is defined in Eq. (1), as stated in [22]:

$$ \Upsilon' = f(\theta|x), \qquad (1) $$

where $f$ denotes the CNN autoencoder network function, $\theta$ is the set of parameters of the CNN and $x \in \mathbb{R}^{C \times H \times W}$ is the features of the network and binary transmitter images with channels $C$, and height $H$ and width $W$. The encoder maps the features $x$ into a latent space by Eq. (2):

$$ h = \sigma(\theta x + b), \qquad (2) $$

where $h$ referred as the coded latent expression of $x$, $\sigma$ is the activation function and $b$ is the bias vector. similarly, the decoder translates the latent representation to the predicted coverage maps given in Eq. (3):

$$ \Upsilon' = \sigma'(\theta' h + b'), \qquad (3) $$

where $\theta'$ and $b'$ are not usually related to encoder parameters and are initialized randomly. The objective of the model is to minimize the difference between the estimated coverage image $\Upsilon'$ and the reference coverage image $\Upsilon$ to predict RSS in selected areas in terms of individual pixel values. This makes it an optimization problem as given in Eq. (2):

$$ \arg\min_{\theta} \|\Upsilon - \Upsilon'\|. \qquad (4) $$

Substituting Eq. (1) in Eq. (2), we use ADAM optimizer to solve the problem presented in Eq. (3):

$$ \theta_{opt} = \arg\min_{\theta} E(f(\theta|x); \Upsilon), \qquad (5) $$

Finally, the loss function of the Auto-RSS becomes by substituting (3) in (4):

$$ \mathcal{L} = \frac{1}{N} \sum_{i=1}^{N} \|\Upsilon - \Upsilon'\| = \frac{1}{N} \sum_{i=1}^{N} \|\Upsilon - \sigma'(\theta'(\sigma(\theta x + b)) + b')\|, \qquad (6) $$

which is the mean absolute error (MAE) between $\Upsilon$ and $\Upsilon'$ and $N$ is the number of training samples. Fig. 1 reveals how the Auto-RSS works.

### B. Model details

The CNN model consists of encoder-decoder architecture. At a high level, the task of an encoder is to generate a low-dimensional latent representation of the input RoI. The BS image of size 256 x 256 of a RoI is fed into the encoder, then it pass through convolutional layers. The first layer of
<figure><img src="image_3.png" /><figcaption>Fig. 2. Visualization of the RoIs. This showcases the experimental 1km² areas in A) Fontaine des Jacobins, B) Mairie du 1er de Lyon, and C) Place Carnot areas, where red and blue circles show the 5G and 4G BSs locations for simulations in ray-tracing simulator. Blue circles reveal the 5G antenna locations. To generate different coverage maps from the simulator, different combinations of the 5G antennas with the 4G ones are chosen, by considering some 5G antennas are in sleep mode and rest are active. These coverage maps from the simulator are used as the labels for training the Auto-RSS.</figcaption></figure>

the encoder reduces the input to 128 × 128 by applying 8 filters of size 3 × 3, followed by a rectified linear unit (ReLU) activation to add non-linearity. after the third convolutional layer, the output tensor is flattened by a fully connected layer with a hidden layer. the output of this layer acts as a latent representation of the input image. Training is stabilized and accelerated by the use of batch normalization. The encoder's last stage is a flattening operation that creates a compact encoding of the image by transforming the feature maps into the latent space.

The decoder model is the opposite of the encoder model and uses a series of transposed convolutional layers (Conv2DTranspose) to upsample the feature maps. The first transposed convolution layer doubles the spatial dimensions to 64 × 64 × 16, followed by another transposed convolution that restores the dimensions to 128×128×8. A final transposed convolutional layer reconstructs the output back to the original input size of 256 × 256 × 1. All experimented model's details are given in Table I:

<table><caption>TABLE I
CNN PARAMETERS.</caption><thead><tr><th>Parameters</th><th>Value</th></tr></thead><tbody><tr><td>Optimizer</td><td>ADAM - Adaptive Moment</td></tr><tr><td>Kernel Size</td><td>2, 3, 4</td></tr><tr><td>Non-Linearity</td><td>ReLU</td></tr><tr><td>Encoding Layer</td><td>Conv2D, pooling</td></tr><tr><td>Decoding Layer</td><td>Transpose Conv2D</td></tr><tr><td>Loss function</td><td>Mean absolute Error</td></tr><tr><td>Epochs</td><td>1000</td></tr></tbody></table>

### III. RESULTS

To evaluate our method, 3 scenarios are considered. The performance is investigated on predicted coverage maps in three 0.8 km² areas in Lyon, France. Evaluation metrics, implementation details, and analysis are given in the following sections.

#### A. Evaluation metrics

To evaluate the performance of our systems by root mean square error (RMSE) and mean absolute error (MAE), given by:

$$ \text{RMSE} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (\Upsilon_{i} - \Upsilon'_{i})^{2}} \quad (7) $$

$$ \text{MAE} = \frac{1}{n} \sum_{i=1}^{n} (\Upsilon_{i} - \Upsilon'_{i}) \quad (8) $$

where $(\Upsilon_{i} - \Upsilon'_{i})$ denotes the error between the reference coverage $\Upsilon_{i}$ and the predicted coverage $\Upsilon'_{i}$ and $n$ is the number of samples in the dataset.

#### B. Implementation Details and Dataset

The chosen region of interest (RoI) for this study are three 0.8 km² areas within the city of Lyon, specifically covering the Fontaine des Jacobins (Fig. 2-A), Mairie du 1er Arrondissement de Lyon (Fig. 2-B), and Place Carnot (Fig. 2-C) areas. 4G and 5G BS location data for these RoIs are collected from the open-source database www.cartoradio.fr. This website contains information such as BS location, orientation of the antenna, height, azimuth, operating frequency, technology, etc. of a selected network in CSV format. The dataset has been cleaned to avoid repeated entries, outliers, and NaN values. To train and evaluate RSS accuracy, reference coverage maps are generated from open sourced Ray Tracing simulator [23] where building locations are integrated from OpenStreetMap. Realistic network configuration of the BSs is implemented in the simulator by the information obtained from www.cartoradio.fr. The training dataset was divided into train and validation sets, and for testing, we were interested only in the validation set.

Moreover, the environment (roads, buildings, etc.) has been taken from OpenStreetMap to incorporate the city topological effect by suppressing the building locations in the predicted maps $\Upsilon'_{i}$. For our proposed method, the dataset consists of
<figure><img src="image_4.png" /><figcaption>Fig. 3. Experimental results for Auto-RSS: 1st row: Predicted maps, 2nd row (Left to right): reference maps generated from RT simulator, red cross reveals the transmitter locations, (Left to right): corresponding error maps.</figcaption></figure>

3000 images. We trained our model on an Intel core i7 machine with NVIDIA RTX ADA 3500 GPU and 32GB RAM. A visual representation of the RoIs are depicted in Fig. 2.

### C. Visual Analysis

The coverage maps generated by the Auto-RSS method are presented in Figure 3. In the Place Carnot region, where 3 BSs are active, the predicted coverage closely aligns with the reference map. This is verified by the error map, which indicates minimal estimation error, typically between -20

dBm and -30 dBm, signifying negligible inaccuracies. As the number of active BSs increases in the Fontaine des Jacobins and Mairie de Lyon areas, the estimated RSS values remain consistent with the reference maps. On the other hand, the noticeable disparity in the middle left side of the Fontaine de Lyon error map indicates a localized region of larger error. The CNN model's generalization limitations may be the cause of this, and incorporating additional layers could potentially solve this issue. With 17 active BSs in the Mairie de Lyon
area, our model shows highly accurate RSS predictions, with a mean squared error (MSE) as low as $8,991.10^{-11}$ W. The error map, which shows consistently low error throughout the RoIs demonstrating Auto-RSS's superior performance.

## D. Quantitative Analysis

We present in Table II the RMSE, MAE of the proposed model in W of the predicted and actual values of RSS in the RoIs. We also show the execution time for the model to predict 430 test images.

<table><caption>TABLE II
RMSE & MAE OF THE ESTIMATED EXPOSURE VALUES</caption><thead><tr><td>Dataset</td><td>RMSE</td><td>MAE</td><td>Test time</td></tr></thead><tbody><tr><td>Test set</td><td>9,482×10<sup>-6</sup></td><td>4.246×10<sup>-7</sup></td><td>1s</td></tr></tbody></table>

As presented in Table II, the Auto-RSS model achieved a performance of RMSE $9,482 \times 10^{-6}$ W and MAE $4.246 \times 10^{-7}$ W on the test set, consistent with the visual analysis. These results demonstrate that the model can generalize effectively and accurately predict coverage maps using only transmitter locations as input features to the CNN. Moreover, this level of accuracy highlights the model's robustness in handling spatial information with minimal feature requirements. The results suggest that Auto-RSS can be a reliable tool for efficient network planning and real-time coverage optimization in wireless communication systems.

## IV. CONCLUSION

The study introduces Auto-RSS, a method leveraging CNN for estimating RSS and coverage maps in urban areas. Unlike other methods [17], [14], Auto-RSS employs an encoder-decoder architecture which enables coverage maps to be accurately predicted from transmitter positions as binary images without the need for any other information about the network. Remarkably, the method demonstrates its effectiveness in accurately predicting coverage without using network parameters such as height, azimuth, beam direction of the antenna, operating frequency, etc. Moreover, we do not need to rely on a simulator which requires time and expertise to generate coverage maps. The trained model can predict coverage with a very low prediction error of $9,482 \times 10^{-6}$ W (-20.23 dBm) in a given area rather than relying on simplistic signal propagation assumptions in less than 2 seconds, duly considering building characteristics. Future enhancements aim to improve the accuracy of the proposed method by implementing generative modeling such as conditional GAN and optimizing network planning effectively and efficiently.

## ACKNOWLEDGMENT

This work was supported by a French government grant managed by the Agence Nationale de la Recherche under the France 2030 program, reference "ANR-22-PEFT-0008".

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