Deep Learning for Multi-Carrier Signal Reception
| Autor: | LI, ANG |
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| Rok vydání: | 2022 |
| Předmět: | |
| DOI: | 10.15126/thesis.900323 |
| Popis: | With the aim to meet the increasing demand of data rate, user capacity and quality of services of networks, orthogonal frequency-division multiplexing (OFDM) systems have been widely investigated and adopted in different communication scenarios during the past two decades, e.g., wireless local area networks (WLAN), long-term evolution (LTE) and 5G communications. It is appealing mainly in the sense that the inter-symbol interference (ISI) wireless channel is converted into a parallel of ISI-free sub-channels through Fourier transform with affordable computational complexity. Despite the tremendous success that has been achieved, this well-investigated technique faces limit, e.g., it trades the computational complexity affordability off the achieved detection performance. Recent advances in this research area lies in the development of deep learning algorithm for the design and optimisation on the multi-carrier system. This thesis investigates the deep learning for the multi-carrier signal reception technique in various multi-carrier systems. Relying on the strong nonlinear processing capability of deep learning algorithms, a series of DNN architectures are first proposed to address the multiuser frequency synchronisation problem for the OFDMA uplink system. The established DNN architectures are designed by relying on the conventional OFDMA system model. Then, a data-driven modular neural network (MNN), termed MCMNNet is proposed to address the coherent signal detection for various multiuser multicarrier system. Moreover, with the aim of effectively reducing the offline training complexity, a transfer learning approach is tailored for the deep learning algorithms presented in this thesis. The research in this thesis potentially offers the benefit of improved detection performance reduced offine training complexity to the deep learning-enabled multi-carrier receiver design. The ultimate goal is to pave the path towards better development and utilisation of deep learning for the wireless multi-carrier system design and optimisation. |
| Databáze: | OpenAIRE |
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