Predicting Wireless Channel Features Using Neural Networks
Open accessAbstract
We investigate the viability of using machine-learning techniques for estimating user-channel features at a large-array base station (BS). In the scenario we consider, user-pilot broadcasts are observed and processed by the BS to extract angle-of-arrival (AoA) specific information about propagation-channel features, such as received signal strength and relative path delay. The problem of interest involves using this information to predict the angle-of-departure (AoD) of the dominant propagation paths in the user channels, i.e., channel features not directly observable at the BS. To accomplish this task, the data collected in the same propagation environment are used to train neural networks. Our studies rely on ray-tracing channel data that have been calibrated against measurements from Shinjuku Square, a famous hotspot in Tokyo, Japan. We demonstrate that the observed features at the BS side are correlated with the angular features at the user side. We train neural networks that exploit different combinations of measured features at the BS to infer the unknown parameters at the users. The evaluation based on standard statistical performance metrics suggests that such data-driven methods have the potential to predict unobserved channel features from observed ones.
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Description of radio propagation channels characteristics for molecular communications is not well established in the literature; for such scenarios, an ANN based receiver design is proposed.
“Description of radio propagation channels characteristics for molecular communications is not well established in the literature [89]; for such communication scenarios, an ANN based receiver design is proposed in [90].”
From Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019) · cites this paper as [89]
The Introduction discusses millimeter-wave cellular channels, and the Problem Formulation describes neural networks that predict channel features at a base station. Neither passage addresses whether propagation-channel characteristics for molecular communications are established or proposes an ANN-based receiver for that setting. Searches for molecular communications and ANN receiver design found no supporting passage in our copy. (LLM, conf=0.77)
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Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
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- Authors
- Navabi, S., Wang, C., Bursalioglu, O. Y., & Papadopoulos, H.
- Affiliations
- University of Southern California (US)
- Venue
- 2018 IEEE International Conference on Communications (ICC)
- Publisher
- IEEE
- Published
- 2018-05
- Pages
- 1-6
- ISSN
- 1550-3607 (ISSN-L)
- OpenAlex
- W2787478036
- Cited by
- 51
- References
- 16
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 5 · 2023: 4 · 2022: 8 · 2021: 7 · 2020: 12 · 2019: 11 · 2018: 2
- FWCI
- 12.76
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Conference on Communications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Millimeter-Wave Propagation and Modeling
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Wireless Communication Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Keywords
- wireless channel prediction, angle of departure estimation, angle of arrival, artificial neural networks, received signal strength, path delay, channel calibration
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