Deep learning-based end-to-end receiver for the NOMA system
Orthogonal frequency-division multiplexing access (OFDMA) greatly improves the frequency utilization in fourth generation (4G) wireless communication system, data exchange rate and system capacity for multiple users by dividing frequency selective channel into several orthogonal subcarriers. H...
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Format: | Thesis-Master by Coursework |
Language: | English |
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Nanyang Technological University
2021
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Online Access: | https://hdl.handle.net/10356/149378 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | Orthogonal frequency-division multiplexing access (OFDMA) greatly improves the
frequency utilization in fourth generation (4G) wireless communication system, data
exchange rate and system capacity for multiple users by dividing frequency selective
channel into several orthogonal subcarriers. However, when facing the explosive
growth of communication data, more and more users in communication system and
requirement of high quality of communication service, fifth generation (5G)
communication system should be developed to solve these problems. As a key
technology for the 5G system, non-orthogonal multiplexing access (NOMA) becomes
a critical technique in next-generation communication. Compared with OFDMA,
NOMA shows advantage in spectrum efficiency, user access capability and system
capacity. However, perfect channel state information (CSI) is difficult to be obtained
under the rapidly changing channel condition in practical communication system with
traditional channel estimation method such as least square (LS) and minimum mean square error (MMSE). In addition, the high computational complexity in receiver
restricts the performance of NOMA system. Because of the ability of finding the
relationship of input and output of a system, deep learning (DL) method and neural
network (NN) are widely applied in many areas including communication system. In
this dissertation, two DL methods, i.e., back-propagation neural network (BPNN) and
recurrent neural network (RNN) are introduced to classify the received signal after
channel transmission and restore the original signal, which play a role of obtaining CSI
and decoding symbol. The traditional channel estimation and equalization methods,
i.e., LS and MMSE, are used as a comparison to evaluate the performance of NOMA
system with DL methods. Furthermore, the effect of system parameters, such as cyclic
prefix (CP) length and pilot arrangement, will be discussed to promote the NOMA
system proposed in this dissertation. Results discussed in Chapter four show that II
BPNN and RNN methods outperform conventional methods, with a lower bit-error
rate (BER) at same signal-to-noise ratio (SNR). Also, computational complexity is
reduced by using DL methods. Finally, cyclic prefix length and pilot arrangement show
an obvious effect on the performance of all the channel estimation methods. |
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