Enhanced Independence among NOMA users through Machine Learning Classification for 5G Downlink IFoF Network

Autor: Chen, Hung-Ru, 陳弘儒
Rok vydání: 2018
Druh dokumentu: 學位論文 ; thesis
Popis: 107
The concept of Non-Orthogonal Multiple Access (NOMA) will become one of the wireless access technologies for 5G after 2020. By allocating multiple Orthogonal Frequency-Division Multiplexing (OFDM) with different powers and superimposing them to form a NOMA which is delivered to multiple base stations, the spectrum efficiency of the system will be largely improved. The receiver used to apply the Successive Interference Cancellation (SIC) for NOMA demodulation, but the base station closest to the transmitter must conduct the demodulation by relying on messages from other base stations. Such an approach leads to problems with the fairness of the system. We use Machine Learning (ML) to replace SIC for demodulation to improve system security. This paper simulates a Radio over Fiber (RoF) network architecture. In the experiment, the optical IF signal of NOMA-OFDM is employed. Two types of ML algorithms are proposed, namely K-means clustering (K-means) and artificial neural network (Artificial Neural Network, ANN), and compared with SIC. In the experiment, the scenarios of two base stations and three base stations are measured. The former uses a power ratio of 6 dB to distribute powers between NOMA signals respectively for a B2B and 25-km fiber transmissions, the short-distance transmission between different base stations (BS) and central office (CO). The latter uses a power ratio of 1:4:16, and then the NOMA signal individually transmits via 0.1 km, 4.2 km, and 14.7 km of fiber. By comparing the bit error rates (RERs) obtained by SIC demodulation and the BER obtained by demodulating with different ML algorithms, the better BER can be obtained by using ML than using SIC, while NN can get better BER than K-means.
Databáze: Networked Digital Library of Theses & Dissertations
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