The use of orthogonal frequency-division multiplexing (OFDM) by wireless standards is often preferred due to its high spectral efficiency and ease of implementation. However, data transmission via OFDM still suffers when passing through a noisy channel. In order to maximize the abilities of OFDM, channel effects must be corrected. Unfortunately, channel estimation is often difficult due to the nonlinearity and randomness present in a practical communication channel.
Recently, machine learning based approaches have been used to improve existing channel estimation algorithms for a more efficient transmission. This thesis investigates the application of artificial neural networks (ANNs) as a means of improving existing channel estimation techniques. Multi-layer feed forward neural networks (FNNs) and convolutional neural networks (CNNs) are tested on a variety of random fading channels with different signal-to-noise ratios (SNRs) via computer simulations. Compared to the conventional least squares (LS) algorithm, the approach based on CNN can reduce the bit error rate (BER) of data transmission by an average of 47.59%.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-4079 |
| Date | 01 June 2022 |
| Creators | Bednar, Joseph W |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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