Journal
WIRELESS COMMUNICATIONS & MOBILE COMPUTING
Volume -, Issue -, Pages -Publisher
WILEY-HINDAWI
DOI: 10.1155/2018/9783863
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Funding
- National Natural Science Foundation of China [61771293]
- Science and Technology Project of Guangzhou [201704030105]
- EPSRC TOUCAN project [EP/L020009/1]
- EU H2020 5G Wireless project [641985]
- EU H2020 RISE TESTBED project [734325]
- Key RAMP
- D Program of Shandong Province [2016GGX101014]
- Fundamental Research Funds of Shandong University [2017JC029]
- Taishan Scholar Program of Shandong Province
- EPSRC [EP/L020009/1] Funding Source: UKRI
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This paper proposes a procedure of predicting channel characteristics based on a well-known machine learning (ML) algorithm and convolutional neural network (CNN), for three-dimensional (3D) millimetre wave (mmWave) massive multiple-input multiple-output (MIMO) indoor channels. The channel parameters, such as amplitude, delay, azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA), are generated by a ray tracing software. After the data preprocessing, we can obtain the channel statistical characteristics (including expectations and spreads of the above-mentioned parameters) to train the CNN. The channel statistical characteristics of any subchannels in a specified indoor scenario can be predicted when the location information of the transmitter (Tx) antenna and receiver (Rx) antenna is input into the CNN trained by limited data. The predicted channel statistical characteristics can well fit the real channel statistical characteristics. The probability density functions (PDFs) of error square and root mean square errors (RMSEs) of channel statistical characteristics are also analyzed.
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