基於人工神經網路的載波頻率飄移以及取樣頻率偏差等化器應用在正交分頻多工-毫米波無線光纖整合

A Neural Network based joint Carrier Frequency Offset and Sampling Clock Offset Equalizer in OFDM Millimeter Wave Fiber Wireless System

曾家祥(Jia-Shiang Tseng)

指導教授 ： 馮開明

國立清華大學/電機資訊學院/光電工程研究所/碩士(2019年)

摘要

正交分頻多工(Orthogonal frequency division multiplexing, OFDM)無線電與光纖整合系統(Radio over fiber)被認為是非常具有潛力的次世代通訊架構，透過強度直接調變，將無線電波訊號載上光纖進行長距離傳輸。RoF技術將升降頻等工作集中在中央機房做處理，大幅度降低基地台複雜度以及維修成本，有利於大量基地台建設。然而在升降頻的過程都是透過而外的震盪器。而這樣子的過程難免會因為收發端的震盪器頻率不一致而導致所謂的載波偏移(Carrier frequency offset, CFO)，這個問題會導致基頻訊號產生相位轉移進而導致誤碼率上升。迴路上也會有取樣率偏差(Sampling clock offset, SCO)。與CFO相同的是，SCO也會產生相位轉移
本篇論文過單層神經網路可以在25公里光纖傳輸下，將帶有10ppm SCO與100ppm CFO進行補償。而透過深度學習則可以在100ppm SCO與200ppm CFO進行補償。在25公里光纖傳輸後外加3公尺毫米波傳輸下，單層神經網路可以補償10ppm SCO與50ppm CFO同時存在的情況，而三層神經網路則可以補償到100ppm。

關鍵字

人工神經網路 ； 載波頻率飄移 ； 取樣頻率偏差 ； 毫米波 ； 無線與光纖整合系統 ； 第五代行動通訊

並列摘要

Orthogonal frequency division multiplexing(OFDM) with Radio-over-fiber is a promising technology in next generation wireless communication, for its broadband and directly modulation and demodulation in transmitter and receiver. Through centralizing the difficult work to central office like up-conversion and down-conversion, reduce the complexity at base station(BS) then the BS can be widely built. However, the frequency of local oscillator for up-conversion and down-conversion oscillator almost can’t perfect match. The mismatch called Carrier Frequency Offset(CFO). This issue cause the base band signal got a phase which may influence the decision of the bit. The other problem is the Sampling Clock Offset(SCO) caused by the inaccurate sampling clock. Like CFO, SCO also cause the phase rotation but little different in the frequency domain.
In this paper, the signal with 10ppm SCO and 100ppm CFO under 25km fiber transmission can be compensated by single layer Neural network. 100ppm SCO and 200ppm CFO by Deep learning. In the other case, the signal with 100ppm SCO and 50ppm CFO under 25km fiber and 3m millimeter wave transmission can be compensated by Neural network, 100ppm SCO and 100ppm CFO by deep learning.

並列關鍵字

Neural Network ； Carrier Frequency Offset ； Sampling Clock Offset ； Millimeter Wave ； Fiber Wireless ； 5G

參考文獻

[1] Z. Pi and F. Khan, "An introduction to millimeter-wave mobile broadband systems," IEEE Communications Magazine, vol. 49, no. 6, pp. 101-107, 2011, doi: 10.1109/MCOM.2011.5783993.

[4] T. Pollet, P. Spruyt, and M. Moeneclaey, "The BER performance of OFDM systems using non-synchronized sampling," in 1994 IEEE GLOBECOM. Communications: The Global Bridge, 28 Nov.-2 Dec. 1994 1994, pp. 253-257 vol.1, doi: 10.1109/GLOCOM.1994.513417.

參考文獻

[2] R. v. Nee and R. Prasad, OFDM for wireless multimedia communications. Artech House, Inc., 2000.

[3] T. Pollet, M. Van Bladel, and M. Moeneclaey, "BER sensitivity of OFDM systems to carrier frequency offset and Wiener phase noise," IEEE Transactions on communications, vol. 43, no. 2/3/4, pp. 191-193, 1995.