CNN-Based Indoor Path Loss Modeling with Reconstruction of Input Images

Chen, Hong; Lee, Hyukjoon; Ma, Shengjie

doi:10.1109/ictc.2018.8539415

Cited by 6 publications

(6 citation statements)

References 4 publications

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“…Data can be generated by using ray-tracing software [15] or manually [13] produce a model with MAE loss of 6.95-11.11 dBms. Models that use a 3D vector representation and use manually gathered data for training an Artificial Neural Network (ANN), which is a promising architecture for this problem [16], [17], produce a model with MAE loss of 15.52-16.21 dBms [1]. Models trained without utilizing the floorplans; like the ANN trained by [14] which uses no floorplan and manually measured values to produce a model with MAE loss of 5.37-5.65 dBms.…”

Section: Resultsmentioning

confidence: 99%

“…These models rely on numeric data that is backed by physics as their basis for model creation. Other works aim to create models using intelligent algorithms such as machine learning and deep learning tools to learn features and estimate the link quality [8], [1], [9], [10], [11]. These works are more datadriven in the context of information and aim at detecting and learning from features of the provided data.…”

Section: Related Workmentioning

confidence: 99%

“…Several radio wave propagation phenomena such as diffraction, scattering, transmission, refraction, and reflection, have an effect on the RSS. Furthermore, in the majority of circumstances, the transmitter and the receiver are not in direct line-of-sight As a result, the RSS varies in time and location with regard to the receiver and transmitter [1]. The complex interplay among all these factors make precise link quality prediction a challenging task in wireless networks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Indoor Wireless Coverage from 3D Floor Plans Using Deep Convolutional Neural Networks

Ansari

Tiyal

Flushing

et al. 2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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With significant advancements in Machine Learning and Deep Learning, Convolutional Neural Networks (CNNs) have shown promising results in handling classification and regression problems. We present 3DRSSNet, a model that can predict the signal strength of an Access point in a 3-D environment based on the 3-D floor map of the building. Our deep CNN approach differs from previous work in that (i) it can generalize to unseen environments, and (ii) to the best of our knowledge, it is the first work to utilize 3D maps to build a signal strength prediction model and validate its results using actual measured data. The proposed neural network model can help solve problems lie optimal access point placement and blind spot detection. Experimental results show that CNNs can predict indoor radio link quality with a performance with a Mean Absolute Error of 4.4 dBm and is able to generalize well to unseen environments.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Indoor Wireless Coverage from 3D Floor Plans Using Deep Convolutional Neural Networks

Ansari

Tiyal

Flushing

et al. 2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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“…For indoor scenarios, several research works have been conducted [15][16][17][18]. In [15], the authors proposed a method based on Neural Networks (NN) to estimate the radio frequency exposure generated by Wi-Fi sources in indoor scenarios.…”

Section: Related Work 21 Machine Learning For Path Loss Estimationmentioning

confidence: 99%

Indoor Genetic Algorithm-Based 5G Network Planning Using a Machine Learning Model for Path Loss Estimation

Santana

Alonso

Nieto³

et al. 2022

Applied Sciences

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Accurate wireless network planning is crucial for the deployment of new wireless services. This usually requires the consecutive evaluation of many candidate solutions, which is only feasible for simple path loss models, such as one-slope models or multi-wall models. However, such path loss models are quite straightforward and often do not deliver satisfactory estimations, eventually impacting the quality of the proposed network deployment. More advanced models, such as Indoor Dominant Path Loss models, are usually more accurate, but as their path loss calculation is much more time-consuming, it is no longer possible to evaluate a large set of candidate deployment solutions. Out of necessity, a heuristic network planning algorithm is then typically used, but the outcomes heavily depend on the quality of the heuristic. Therefore, this paper investigates the use of Machine Learning to approximate a complex 5G path loss model. The much lower calculation time allows using this model in a Genetic Algorithm-based network planning algorithm. The Machine Learning model is trained for two buildings and is validated on three other buildings, with a Mean Absolute Error below 3 dB. It is shown that the new approach is able to find a wireless network deployment solution with an equal, or smaller, amount of access points, while still providing the required coverage for at least 99.4% of the receiver locations and it does this 15 times faster. Unlike a heuristic approach, the proposed one also allows accounting for additional design criteria, such as maximal average received power throughout the building, or minimal exposure to radiofrequency signals in certain rooms.

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“…In drive test campaigns, sophisticated software and measuring tools are used to measure the propagation characteristics. Presently, ray tracing techniques have been extended to model ANN and CNN-based path loss prediction models by combining 3D models with 2D satellite imagery to develop generic path loss prediction methods [26], [27], [28], [29] and [30]. While considering 3D digital maps, a given map's accuracy depends on how the terrain, foliage, and city buildings are portrayed; also captured detailed features like building footprints, building edges, facades, street features, vertical and horizontal, and the transmitting and receiving antennas position.…”

Section: Background and Related Workmentioning

confidence: 99%

Deterministic 5G mmWave Large-Scale 3D Path Loss Model for Lagos Island, Nigeria

Hinga

Atayero

2021

IEEE Access

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5G millimeter wave (mmWave) application in mobile connectivity to realize high-speed, reliable communication is attributed with high path loss. This paper presents a detailed 3D ray-tracing technique at 28 GHz for Lagos Island to investigate five unique path loss scenarios: path loss, free space path loss with antenna pattern, free space path loss without antenna pattern, excess path loss with antenna pattern, and the excess path loss without antenna pattern for an urban environment. The Close-In (CI) model, Floating Intercept (FI) path loss model, and a root mean square error (RMSE) are used to model and evaluate the best path loss model for Lagos Island. The average achieved FI (α ,β, σ) parameters were 189.92352, 0.1654, and 0.66948, While the average CI (η,Xσ) parameters were 2.309355 and 56.236425. From all the scenarios evaluated, the lowest path loss exponent achieved was 0.45, while the highest path loss exponent was 3.8. We have established that the FI path loss model accurately characterizes path loss for the Lagos Island environment with the lowest RMSE of 0.0359 dB and the highest RSME of 0.0997 dB. In contrast, the CI model over-predict the path loss at 28 GHz with the lowest RMSE of 0.0495 dB and the highest RMSE of 2.2547 dB. This work opens up a new area of research on mm-Wave at 28 GHz in Lagos Island, and the results obtained from this work can be used to benchmark future studies on mmWave in a similar environment.

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CNN-Based Indoor Path Loss Modeling with Reconstruction of Input Images

Cited by 6 publications

References 4 publications

Prediction of Indoor Wireless Coverage from 3D Floor Plans Using Deep Convolutional Neural Networks

Prediction of Indoor Wireless Coverage from 3D Floor Plans Using Deep Convolutional Neural Networks

Indoor Genetic Algorithm-Based 5G Network Planning Using a Machine Learning Model for Path Loss Estimation

Deterministic 5G mmWave Large-Scale 3D Path Loss Model for Lagos Island, Nigeria

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