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Proceedings Paper

Adaptive and efficient data compression technologies in 5G digital mobile fronthaul networks
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Paper Abstract

Digital radio-over-fiber (D-RoF) transmission technology is a favorable candidate for mobile fronthaul networks because it enables robust data transmission against noise, channel degradations, and nonlinear impairments in the link. Error-free transmission can also be achieved when the system is combined with forward error correction coding. However, the bandwidth efficiency of traditional D-RoF technology is relatively low because it applies uniform analog-to-digital converters with an ultra-high resolution and transmits the binary codes after digitization. In this work, the current progresses of 5G MFH have been reviewed and multiple digital transmission techniques have been discussed based on our previous works. The first method is about fast statistical estimation, which is computational efficient and particularly designed for wireless signals with Gaussian distributed amplitudes. Then, Lloyd algorithm will be introduced, which can re-allocate the quantization levels to achieve a best fit with the probability distributions of the wireless signal’s amplitudes. Thus, Lloyd algorithm can be applied to any kinds of wireless modulation format with a random amplitude distribution, but a trade-off needs to be considered between computational complexity and compression ratio. Differential coding-based compression techniques are also discussed, where we have proposed an adaptive low-complexity differential encoder based on least-mean-square (LMS) algorithm to further improve the compression ratio and be adaptive to the signal’s dynamic change. By jointly using Lloyd algorithm based quantizer and an LMS differential encoder, significant improvements on compression efficiency can be achieved. The Lloyd algorithm and differential coding have been jointly applied in a data-compressed mobile fronthaul testbed with a net data rate of 100 Gbit/s, which is able to comprise 45×120- MHz 5G NR carriers with lower-than 1% EVM.

Paper Details

Date Published: 31 January 2020
PDF: 10 pages
Proc. SPIE 11307, Broadband Access Communication Technologies XIV, 1130707 (31 January 2020); doi: 10.1117/12.2546740
Show Author Affiliations
Mu Xu, Cable Television Labs., Inc. (United States)
Junwen Zhang, Cable Television Labs., Inc. (United States)
Haipeng Zhang, Cable Television Labs., Inc. (United States)
Zhensheng Jia, Cable Television Labs., Inc. (United States)
Luis Alberto Campos, Cable Television Labs., Inc. (United States)

Published in SPIE Proceedings Vol. 11307:
Broadband Access Communication Technologies XIV
Benjamin B. Dingel; Katsutoshi Tsukamoto; Spiros Mikroulis, Editor(s)

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