Predicting Wireless Channel Features using Neural Networks
| Autor: | Chenwei Wang, Haralabos Papadopoulos, Shiva Navabi, Ozgun Y. Bursalioglu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Artificial neural network Computer science business.industry Computer Science - Information Theory Information Theory (cs.IT) 020206 networking & telecommunications 020302 automobile design & engineering 02 engineering and technology computer.software_genre Square (algebra) Base station Task (computing) 0203 mechanical engineering 0202 electrical engineering electronic engineering information engineering FOS: Electrical engineering electronic engineering information engineering Wireless Data mining Electrical Engineering and Systems Science - Signal Processing business computer Communication channel |
| Zdroj: | ICC |
| DOI: | 10.48550/arxiv.1802.00107 |
| Popis: | 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. Comment: 6 pages, 6 figures, to appear in 2018 IEEE International Conference on Communications (ICC) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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