Sep 2026· IEEE Antennas and Wireless Propagation Letters· Vol 25, pp. 3656-3660· 0 citations· 27 references
Abstract
Path loss (PL) prediction is critical for wireless networks and most data traffic originates indoors. Effective network planning and base station deployment in high-frequency bands of 5G/6G systems necessitate addressing the accuracy–efficiency tradeoff. We propose a deep learning framework that combines dilated convolutions with spatial attention mechanisms to enhance PL prediction accuracy. Our model adaptively extracts multiscale environmental features while focusing on site-specific PL through attention-driven localization. Experimental results demonstrate superior performance over conventional models. In addition, the proposed architecture eliminates input size constraints and enables direct fine-tuning with measurement data, offering practical advantages for facilitating real-world deployment.
Upper mid-band communication is a promising technology for future wide-area 6G networks. In this band, accurate path loss (PL) prediction is essential for coverage evaluation and network deployment. Existing AI-based PL prediction methods mainly use geometric environmental representations, but usually ignore the electr...
Heng-Hao Xu, Yan Zhang, Jiu-Peng Song et al.· 2026 IEEE/CIC International...· 0 citations
Accurate and efficient pathloss map prediction is essential for wireless network planning and optimization in the beyond 5th generation mobile communication (B5G) systems. Recent learning-based approaches have demonstrated promising performance by employing convolutional encoder-decoder architectures with atrous convol...
Mao-Dong Chen, Miao Liu, Yang Peng et al.· 2026 IEEE/CIC International...· 0 citations
Experimental results demonstrate that the proposed training strategy enables faster convergence and improved adaptation compared with training from scratch, and reduces training time under different dataset configurations, verifying its effectiveness and efficiency for adaptive RSS-based indoor localization.
Low Power Wide Area Networks like LoRa are increasingly deployed for smart city applications, requiring accurate path loss prediction for effective network planning. Traditional (empirical) propagation models often exhibit limited accuracy in these scenarios. We investigate machine learning models for LoRa path loss pr...
Robert Bitterling, Christian Nettersheim, Jörn Hees et al.· 0 citations
In recent deep learning-based path loss prediction methods, path loss map generation is commonly formulated as an image-to-image translation task. Most state-of-the-art methods in this field incorporate convolutional layers into their deep learning architectures. However, standard convolutional layers often struggle to...
Jelena D. Mladenovic, Aleksandar N. Pajovic, N. Neskovic et al.· IEEE Access· 0 citations
Deep learning models for radio map prediction are trained and tested at the same carriers and cannot serve unseen frequencies. We propose a two-stream network learning a residual over an analytic prior, fusing environment and physics streams by cross-attention. Evaluated at the query frequency, this free-space and knif...
Sajjad Hussain· 0 citations
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