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Mobile Network coverage prediction Using Multi-Modal Model Based on Deep Neural Networks and Semantic Segmentation

SENSORS 2024年第16期24卷 5178页

作者： Zeng, Sheng Ji, Yuhang Chen, Weiwei Yan, Liping Zhao, Xiang Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China

A coverage prediction model helps network operators find coverage gaps, plan base station locations, evaluate quality of service, and build radio maps for spectrum sharing, interference management, localization, etc. Existing coverage prediction models rely on the height and transmission power of the base station, or the assistance of a path loss model. All of these increase the complexity of large-scale coverage predictions. In this paper, we propose a multi-modal model, DNN-SS, which combines a DNN (deep neural network) and SS (semantic segmentation) to perform coverage prediction for mobile networks. Firstly, DNN-SS filters the samples with a geospatial-temporal moving average filter algorithm, and then uses a DNN to extract numerical features. Secondly, a pre-trained model is used to perform semantic segmentation of satellite images of the measurement area. Thirdly, a DNN is used to extract features from the results after semantic segmentation to form environmental features. Finally, the prediction model is trained on the dataset consisting of numerical features and environmental features. The experimental results on campus show that for random location prediction, the model achieves a RMSE (Root Mean Square Error) of 1.97 dB and a MAE (Mean Absolute Error) of 1.41 dB, which is an improvement of 10.86% and 10.2%, respectively, compared with existing models. For the prediction of a test area, the RMSE and MAE of the model are 4.32 dB and 3.45 dB, respectively, and the RMSE is only 0.22 dB lower than that of existing models. However, the DNN-SS model does not need the height, transmission power, and antenna gain of the base station, or a path loss model, which makes it more suitable for large-scale coverage prediction.

关键词： coverage prediction DNN multi-modal model satellite map sematic segmentation

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Hybrid Kriging and multilayer perceptron neural network technique for coverage prediction in cellular networks

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2020年第6期35卷 682-706页

作者： Mezhoud, Naima Oussalah, Mourad Zaatri, Abdelouahab Hammoudi, Zoheir Univ Freres Mentouri Constantine Fac Technol Sci Dept Elect Constantine Algeria Univ Bejaia Fac Technol Elect Engn Lab LGEB Bejaia Algeria Univ Oulu Ctr Ubiquitous Comp Comp Sci POB 4500 Oulu 91004 Finland Univ Freres Mentouri Constantine Fac Technol Sci Dept Mech Engn Constantine Algeria

coverage prediction is a crucial issue in wireless network planning and design. Typically, coverage prediction techniques start by model identification given a set of measurements at specified locations. Then, this model is used to predict the signal strength at other locations using some machine-learning like approach. However, when the drive tests (or test-beds) become costly because of some environmental constraints, the performance of the machine learning-based model is questionable due to the insufficiency of the training dataset. In this context, we suggest exploiting the geostatistical interpolation technique named Ordinary Kriging to enrich the training data set. For this purpose, a set of received signal strengths from wireless transmitters has been collected at known locations through cellular network technology, which are then used to generate an enriched dataset according to the Ordinary Kriging interpolation technique. The results show that the hybrid Ordinary Kriging and machine learning model significantly enhances path loss accuracy and offers a new setting for data reproducibility. A database is constructed from received signal strength measurements which is not enough to produce accurate results when it is used to train a feed forward neural network for the task of coverage prediction. A geospatial interpolated technique named Kriging is used in order to produce a big amount of data. The neural network is trained by both of the original and the interpolated datasets. The coverage prediction results more reproducible and accurate than the results without the interpolation stage. [GRAPHICS] .

关键词： coverage prediction cellular network hybridisation neural networks Kriging technique

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Design of Power Wireless Private Network coverage prediction System

Design of Power Wireless Private Network Coverage Prediction...

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3rd International Conference on Mechatronics Engineering and Information Technology(ICMEIT 2019)

作者： Qianke Ai Wenjiang Feng Weinong Wu Yuxiang Liu Guojin Liu Taotao Bao ICT Branch of Chongqing Electric Power Corp College of Communication Engineering of Chongqing University

In this paper, according to the practical needs of the electric power wireless network coverage prediction, a propagation model based on real data and revised statistical prediction model(SPM) is proposed to predict the spatial field intensity distribution of the base station and improve the rationality and efficiency of the network planning and construction. The optimal parameters of SPM model parameters are obtained by least square method. On this basis, the coverage prediction with geographic information is proposed. The simulation results show that compared with SPM and LAP model, the proposed scheme can effectively analyze the signal loss intensity around the base station at any location in the map, so as to solve the signal planning problem of the base station.

关键词： radio wave propagation model coverage prediction geographic information system

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Branch coverage prediction in automated testing

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JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS 2019年第9期31卷 e2158-e2158页

作者： Grano, Giovanni Titov, Timofey, V Panichella, Sebastiano Gall, Harald C. Univ Zurich Dept Informat Binzmuhlestr 14 Zurich Switzerland

Software testing is crucial in continuous integration (CI). Ideally, at every commit, all the test cases should be executed, and moreover, new test cases should be generated for the new source code. This is especially true in a Continuous Test Generation (CTG) environment, where the automatic generation of test cases is integrated into the continuous integration pipeline. In this context, developers want to achieve a certain minimum level of coverage for every software build. However, executing all the test cases and, moreover, generating new ones for all the classes at every commit is not feasible. As a consequence, developers have to select which subset of classes has to be tested and/or targeted by test-case generation. We argue that knowing a priori the branch coverage that can be achieved with test-data generation tools can help developers into taking informed decision about those issues. In this paper, we investigate the possibility to use source-code metrics to predict the coverage achieved by test-data generation tools. We use four different categories of source-code features and assess the prediction on a large data set involving more than 3'000 Java classes. We compare different machine learning algorithms and conduct a fine-grained feature analysis aimed at investigating the factors that most impact the prediction accuracy. Moreover, we extend our investigation to four different search budgets. Our evaluation shows that the best model achieves an average 0.15 and 0.21 MAE on nested cross-validation over the different budgets, respectively, on EVOSUITE and RANDOOP. Finally, the discussion of the results demonstrate the relevance of coupling-related features for the prediction accuracy.

关键词： automated software testing coverage prediction machine learning software testing

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prediction Method and Verification of Inland Waters VHF coverage Effect Based on ITU-R P.1546-6 Modified Model 11

Prediction Method and Verification of Inland Waters VHF Cove...

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11th International Conference on Communications, Circuits and Systems (ICCCAS)

作者： Yi, Zhongli Zhan, Junyun Kong, Chen Wang, Shanshan Xu, Bin Zhao, Jinyu Wang, Fuzhai Minist Transport Transport Planning & Res Inst Beijing Peoples R China China Mobile Commun Grp Shandong Co Ltd Jinan Branch Jinan Peoples R China NanJing Maritime Safety Adm PRC Nanjing Peoples R China

ISBN: (纸本)9781665469890

Combined with the actual conditions of VHF communication system in inland waters, the distance, frequency and antenna height of the radio wave coverage effect calculation model proposed in Recommendation ITU-R P.1546-6 are modified to obtain the VHF path loss function curve adapted to inland waters. The results of the real ship test show that the modified radio wave coverage effect prediction model can predict the coverage effect of VHF communication system better in inland waters, and has high promotion and application value.

关键词： VHF Recommendation ITU-R P.1546-6 coverage prediction inland waters free space Transmission loss

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Uncertainty estimation using boundary prediction for medical image super-resolution

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COMPUTER VISION AND IMAGE UNDERSTANDING 2025年 256卷

作者： Dey, Samiran Basuchowdhuri, Partha Mitra, Debasis Augustine, Robin Saha, Sanjoy Kumar Chakraborti, Tapabrata Indian Assoc Cultivat Sci Kolkata India Indian Inst Engn Sci & Technol Kolkata India Uppsala Univ Uppsala Sweden Jadavpur Univ Kolkata India Alan Turing Inst London England UCL London England

Medical image super-resolution can be performed by several deep learning frameworks. However, as the safety of each patient is of primary concern, having models with a high degree of population level accuracy is not enough. Instead of a one size fits all approach, there is a need to measure the reliability and trustworthiness of such models from the point of view of personalized healthcare and precision medicine. Hence, in this paper, we propose a novel approach to predict a range of super-resolved (SR) images that any generative super-resolution model may yield for a given low-resolution (LR) image using residual image prediction. Providing multiple images within the suggested lower and upper bound increases the probability of finding an exact match to the high-resolution (HR) image. To further compare models and provide reliability scores, we estimate the coverage and uncertainty of the models and check if coverage can be improved at the cost of increasing uncertainty. Experimental results on lung CT scans from LIDC-IDRI and Radiopedia COVID-19 CT Images Segmentation datasets show that our models, BliMSR and MoMSGAN, provide the best HR and SR coverage at different levels of residual attention with a comparatively lower uncertainty. We believe our model agnostic approach to uncertainty estimation for generative medical imaging is the first of its kind and would help clinicians decide on the trustworthiness of any super-resolution model in a generalized manner while providing alternate SR images with enhanced details for better diagnosis for each individual patient.

关键词： Uncertainty estimation coverage prediction Residual image learning Super-resolution Medical images

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Radio Environment Map Construction Based on Privacy-Centric Federated Learning

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IEEE ACCESS 2024年 12卷 28109-28121页

作者： Khan, Shafi Ullah Garcia, Carla E. Hwang, Taewoong Koo, Insoo Univ Ulsan Dept Elect Elect & Comp Engn Ulsan 680749 South Korea Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT L-1359 Luxembourg Luxembourg

In today's digital age, coverage prediction is essential for optimizing wireless networks and improving user experience. While numerous path loss models and advanced machine learning algorithms have been developed to achieve high prediction performance, they predominantly operate within a centralized learning paradigm. While effective, this conventional approach often suffers from scalability and privacy limitations that are critical to the successful deployment of wireless maps. Conversely, in this paper, we propose a novel decentralized approach based on a federated learning long short-term memory (LSTM) model to accurately predict network coverage in indoor environments. The proposed FedLSTM is a method that allows multiple users, or clients, to train the model without sharing their personal data directly with a central server. In an experimental setup, we used real data collected from numerous clients moving along different paths. The FedLSTM model is evaluated in terms of root mean square error (RMSE), mean absolute error (MAE), and R2. Furthermore, compared to a centralized counterpart, FedLSTM shows a slight increase in RMSE from 2.4 dBm to 2.5 dBm and an increase in MAE from 1.7 dBm to 1.9 dBm. In addition, we evaluate the proposed FedLSTM considering variations in the number of participating clients and the number of local training epochs. The results show that even devices with limited computational power can meaningfully contribute to the training of the federated model, with fewer epochs achieving competitive results. Graphical analyses of the radio environment maps (REMs) generated by both FedLSTM and the centralized LSTM highlight their similarities. However, FedLSTM provides client privacy while reducing communication overhead and server strain.

关键词： Radio environment map (REM) coverage prediction received signal strength indicator (RSSI) LiDAR sensor federated learning

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Development of Testability prediction Models Considering Complexity Diversity for C Programs

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IEEE ACCESS 2023年 11卷 98469-98485页

作者： Choi, Hyun-Jae Chae, Heung-Seok Pusan Natl Univ Dept Elect & Comp Engn Pusan 46241 South Korea

Testability prediction can help developers identify software components that require significant effort to ensure software quality, plan test activities, and recognize the need for refactoring to reduce the test effort. Previous studies have predicted code coverage as a measure of testability based on software metrics. However, these studies have primarily used object-oriented software with simple code structures. Industrial software developed using C is often more complex than the object-oriented software used in these studies. Models trained primarily on low-complexity training data may have insufficient training for the testability of high-complexity software. In this study, we developed a testability prediction model for C programs by considering the complexity diversity. We analyzed the impact of the complexity of the training/test data on the testability prediction model for C programs. The results showed that the model with the best performance achieves an MAE of 7.436 and an R-2 of 0.813. Moreover, the results demonstrated that as the complexity diversity of the training data decreased, MAE increased from 5.203 to 6.361, and R-2 decreased from 0.809 to 0.725. Furthermore, the performance of the model trained with low complexity-diversity deteriorated as the complexity level of the test data increased, with MAE increasing from 3.498 to 6.631, and R-2 decreasing from 0.841 to 0.687. Additionally, in the correlation analysis between the model performance and the difference in the complexity of the training and test data, a strong correlation was observed, with MAE of 0.898 and R-2 of -0.848.

关键词： Measurement Predictive models Object oriented modeling Software testing Complexity theory Training data C languages coverage prediction metrics regression software testability testing

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Directive mmWave radio channel modeling in a ship hull

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INTERNATIONAL JOURNAL OF MICROWAVE AND WIRELESS TECHNOLOGIES 2022年第6期14卷 701-712页

作者： De Beelde, Brecht Lopez, Andres Almarcha Plets, David Yusuf, Marwan Tanghe, Emmeric Joseph, Wout Univ Ghent IMEC Ghent Belgium Barcelona Supercomp Ctr Barcelona Spain

Wireless connectivity has been realized for multiple environments and different frequency bands. However, little research exists about mmWave communication in industrial environments. This paper presents the 60 GHz double-directional radio channel for mmWave communication in a ship hull for Line-of-Sight (LOS) and non-Line-of-Sight (NLOS) conditions. We performed channel measurements using the Terragraph channel sounder at different locations in the ship hull and fitted LOS path loss to a one-slope path loss model. Path loss and root-mean-square delay spread of the LOS path is compared to the reflected path with lowest path loss. NLOS communication via this first-order reflected path is modeled by calculating the path distance and determining the reflection loss. The reflection losses have a considerable contribution to the signal attenuation of the reflected path. The channel models are implemented in an indoor coverage prediction tool, which was extended with a ray launching algorithm and validated by comparison with an analytical electromagnetic solver. The results show that the mmWave radio channel allows high-throughput communication within a ship hull compartment, even when no LOS path between the transmitter and receiver is present.

关键词： 60 GHz channel modeling coverage prediction mmWave propagation ray launching ship hull

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Propagation Modeling of Unmanned Aerial Vehicle (UAV) 5G Wireless Networks in Rural Mountainous Regions Using Ray Tracing

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DRONES 2024年第7期8卷 334-334页

作者： Ali, Shujat Abu-Samah, Asma Abdullah, Nor Fadzilah Kamal, Nadhiya Liyana Mohd Univ Kebangsaan Malaysia Dept Elect Elect & Syst Engn Bangi 43600 Selangor Malaysia Univ Kuala Lumpur Malaysian Inst Aviat Technol Unmanned Aerial Syst Res Lab Dengkil 43900 Selangor Malaysia

Deploying 5G networks in mountainous rural regions can be challenging due to its unique and challenging characteristics. Attaching a transmitter to a UAV to enable connectivity requires a selection of suitable propagation models in such conditions. This research paper comprehensively investigates the signal propagation and performance under multiple frequencies, from mid-band to mmWaves range (3.5, 6, 28, and 60 GHz). The study focuses on rural mountainous regions, which were empirically simulated based on the Skardu, Pakistan, region. A complex 3D ray tracing method carefully figures out the propagation paths using the geometry of a 3D environment and looks at the effects in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. The analysis considers critical parameters such as path loss, received power, weather loss, foliage loss, and the impact of varying UAV heights. Based on the analysis and regression modeling techniques, quadratic polynomials were found to accurately model the signal behavior, enabling signal strength predictions as a function of distances between the user and an elevated drone. Results were analyzed and compared with suburban areas with no mountains but more compact buildings surrounding the Universiti Kebangsaan Malaysia (UKM) campus. The findings highlight the need to identify the optimal height for the UAV as a base station, characterize radio channels accurately, and predict coverage to optimize network design and deployment with UAVs as additional sources. The research offers valuable insights for optimizing signal transmission and network planning and resolving spectrum-management difficulties in mountainous areas to enhance wireless communication system performance. The study emphasizes the significance of visualizations, statistical analysis, and outlier detection for understanding signal behavior in diverse environments.

关键词： millimeter wave propagation measurements channel modeling 5G mobile communications signal propagation rural mountainous area ray tracing signal behavior coverage prediction

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