Empowering Wireless Network Applications with Deep Learning-based Radio Propagation Models
This work tackles performance issues in wireless network deployment for communication systems, but it appears incremental as it builds on existing propagation modeling techniques.
The paper addresses the limitations of conventional radio propagation solvers in wireless networks by integrating deep learning to enhance efficiency and reliability, aiming to foster adoption in next-generation applications.
The efficient deployment and operation of any wireless communication ecosystem rely on knowledge of the received signal quality over the target coverage area. This knowledge is typically acquired through radio propagation solvers, which however suffer from intrinsic and well-known performance limitations. This article provides a primer on how integrating deep learning and conventional propagation modeling techniques can enhance multiple vital facets of wireless network operation, and yield benefits in terms of efficiency and reliability. By highlighting the pivotal role that the deep learning-based radio propagation models will assume in next-generation wireless networks, we aspire to propel further research in this direction and foster their adoption in additional applications.