Asset Details
Do you wish to reserve the book?
Deep Learning Architectures for Accurate Millimeter Wave Positioning in 5G
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Deep Learning Architectures for Accurate Millimeter Wave Positioning in 5G
Do you wish to request the book?
Deep Learning Architectures for Accurate Millimeter Wave Positioning in 5G
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Deep Learning Architectures for Accurate Millimeter Wave Positioning in 5G
2020
Overview
The introduction of 5G’s millimeter wave transmissions brings a new paradigm to wireless communications. Whereas physical obstacles were mostly associated with signal attenuation, their presence now adds complex, non-linear phenomena, including reflections and scattering. The result is a multipath propagation environment, shaped by the obstacles encountered, indicating a strong presence of hidden spatial information within the received signal. To untangle said information into a mobile device position, this paper proposes the usage of neural networks over beamformed fingerprints, enabling a single-anchor positioning approach. Depending on the mobile device target application, positioning can also be enhanced with tracking techniques, which leverage short-term historical data. The main contributions of this paper are to discuss and evaluate typical neural network architectures suitable to the beamformed fingerprint positioning problem, including convolutional neural networks, hierarchy-based techniques, and sequence learning approaches. Using short sequences with temporal convolutional networks, simulation results show that stable average estimation errors of down to 1.78 m are obtained on realistic outdoor scenarios, containing mostly non-line-of-sight positions. These results establish a new state-of-the-art accuracy value for non-line-of-sight millimeter wave outdoor positioning, making the proposed methods very competitive and promising alternatives in the field.
