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Fronthaul integration and monitoring in 5G networks

Abstract : Cloud Radio Access Network (RAN) was identified as a key enabler for 5G. Its deployment is however meeting multiple challenges notably in the fronthaul integration, the latter being the segment located between the Digital Unit and the Radio Unit generally based on CPRI. Giving its bit-rate, latency and jitter constrains, Wavelength Division Multiplexing (WDM) is the most adequate solution for its transport. However, the radio technologies recommended for 5G will drastically increase the CPRI bit-rate making its transport very challenging with low-cost WDM. This thesis deals with four main topics : The introduction of a control channel in the CPRI enables offering the WDM infrastructure monitoring and the wavelength tunability in the transceivers. The study of this control channel integration in the fronthaul link is reported in the second chapter as well as an investigation on the wireless transmission of CPRI. The use of Analog Radio over Fiber (A-RoF) can significantly improve the fronthaul spectral efficiency compared to CPRI-based fronthaul enabling, potentially, the transport of 5G interfaces. A thorough investigation on the actual gain brought by this solution is stated in the third chapter. CPRI compression based on uniform and non-uniform quantization is also a solution to enhance the CPRI spectral efficiency. The fourth chapter describes this solution and experimentally shows the achievable compression rates. Finally, establishing a new functional split in the radio equipment was considered as a promising solution for 5G. Two new interfaces have been identified for high and low layer functional splits. A theoretical and experimental study of these new interfaces is reported in the fifth chapter.
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Submitted on : Tuesday, February 13, 2018 - 4:46:07 PM
Last modification on : Tuesday, October 9, 2018 - 1:08:49 AM
Long-term archiving on: : Sunday, May 6, 2018 - 3:38:00 AM


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  • HAL Id : tel-01708493, version 1



Zakaria Tayq. Fronthaul integration and monitoring in 5G networks. Signal and Image processing. Université de Limoges, 2017. English. ⟨NNT : 2017LIMO0092⟩. ⟨tel-01708493⟩



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