Advanced
Nonlinearity Detection and Compensation in Radio over Fiber Systems Using a Monitoring Channel
Nonlinearity Detection and Compensation in Radio over Fiber Systems Using a Monitoring Channel
Journal of information and communication convergence engineering. 2015. Sep, 13(3): 167-171
Copyright © 2015, The Korean Institute of Information and Commucation Engineering
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/li-censes/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • Received : June 15, 2015
  • Accepted : August 05, 2015
  • Published : September 30, 2015
Download
PDF
e-PUB
PubReader
PPT
Export by style
Article
Author
Metrics
Cited by
TagCloud
About the Authors
Sung-Man Kim
sungman@ks.ac.kr

Abstract
A radio over fiber (RoF) system is a kind of analog optical transmission system and considered as a strong candidate for the next-generation fronthaul link in the future mobile network. In RoF systems, nonlinearity compensation is essential to increase the link capacity. In this paper, we propose a nonlinearity detection and compensation scheme using a monitoring channel in RoF systems. A monitoring channel is added at the transmitter site and used for transmitting a reference signal in an RoF transmission. The nonlinearity in the RoF transmission is detected by comparing the received monitoring signal and the original reference signal at the receiver site. Finally, the nonlinearity is compensated at the receiver by giving the reverse function of the detected nonlinearity. Our results show that the proposed scheme can almost remove the error vector magnitude degradation induced by the nonlinearity in the RoF system.
Keywords
I. INTRODUCTION
In fourth-generation (4G) mobile networks such as longterm evolution advanced (LTE-Advanced) or mobile world interoperability for microwave access (WiMAX) [1] , base stations composed of a central digital unit (DU) and remote radio units (RUs) are widely used since they have several advantages such as deployment flexibility and low outside installation cost [2] . In the base station systems, the common public radio interface (CPRI) [3] , open base station architecture initiative (OBSAI) [4] , or open radio interface (ORI) [5] is currently used as the link between the DU and the RU. This network is also called a centralized (or cloud) radio access network (C-RAN) [6] . In such an interface, the analog radio signals are sampled, quantized, and then transmitted through a digital optical fiber transmission. This is sufficient to support several radio channels having a bandwidth of 20 MHz [7] . However, future mobile networks are likely to have wider channel bandwidths for higher channel throughput and each RU will need to support several sectors and more than 8 × 8 multiple-input multipleoutput (MIMO) schemes [8] . Hence, to support the future mobile networks, tens of CPRI or OBSAI interfaces of >5 Gb/s will be required.
Therefore, the radio over fiber (RoF) technology has been proposed to support the increased link capacity between the DU and the RU more economically [9 - 15] . In an RoF system, analog radio signals are transmitted as analog optical transmission. In some RoF systems, multiple analog radio signals are transmitted using several subcarrier frequencies [11] . Although RoF is a promising technology, the signal quality can deteriorate easily because of the nonlinearity in the RoF transmission. This usually limits the performance of RoF systems [16] and makes it difficult to meet the error vector magnitude (EVM) requirement of the LTE-Advanced standard [17] . Thus far, to overcome the nonlinearity in RoF systems, several approaches have been proposed [18 - 20] . However, the previous methods have focused only on the nonlinearity of the transmitter or a specific nonlinear effect.
In this paper, we propose a nonlinearity detection and compensation scheme using a monitoring channel in RoF systems. The proposed method can detect and compensate for the whole nonlinearity from the transmitter to the receiver. The proposed scheme uses a monitoring channel to send a reference signal to monitor the nonlinearity in the RoF transmission periodically. At the receiver, the transmitted monitoring channel is compared with the original reference signal so as to detect the nonlinearity. Then, the detected nonlinearity is compensated using an inverse function at the receiver. The proposed scheme can reduce the EVM degradation induced by the nonlinearity in RoF systems.
II. PROPOSED ROF SYSTEM
Fig. 1 (a) shows a typical RoF scheme. In the transmitter part, each channel is modulated with a different radio signal and a different subcarrier frequency. The radio channels are multiplexed using frequency division multiplexing (FDM) and modulate a laser without an analog-to-digital conversion. The output of the laser is an analog optical signal and is transmitted in an optical fiber channel. In the receiver part, each channel is demodulated after band-pass filtering (BPF) with the center frequency of each subcarrier.
PPT Slide
Lager Image
(a) Typical RoF system and (b) the proposed RoF system using a monitoring channel. RoF: radio over fiber, BPF: band-pass filtering.
Fig. 1 (b) shows the proposed RoF scheme using a monitoring channel to detect and compensate for the nonlinearity of the RoF system. In the proposed RoF system, a monitoring channel is added in the transmitter and is modulated with a reference signal. In this study, a simple sine wave is assumed to be the reference signal. In the transmitter, the radio channels and the monitoring channel are multiplexed using FDM. The FDM analog signal is transmitted through an optical fiber transmission system that includes a laser driver, a laser, and an optical fiber. Even though only a monitoring channel is assumed in this study, several monitoring channels can be used in the application depending on the frequency band.
The FDM analog signal containing the radio channels and the monitoring channel is distorted by the nonlinearity in the RoF transmission. At the receiver part, the monitoring channel is demodulated and compared with the original reference signal, which is the same as the reference signal at the transmitter. As a result, the nonlinear function in the transmission can be detected.
After detecting the nonlinear function in the RoF transmission, a reverse function of the detected nonlinearity is applied to the compensator of the radio channels. Each radio channel is demodulated after the compensation.
III. SIMULATION CONDITIONS
To investigate the performance of the proposed RoF scheme, computer simulations are conducted with a commercial simulator (VPItransmissionMaker; VPIphotonics Inc, Norwood, MA, USA). Table 1 shows the simulation conditions for ordinary radio channels. In the simulation, five orthogonal frequency division multiplexing (OFDM) radio channels with subcarrier frequencies of 2.0, 2.1, 2.2, 2.3, and 2.4 GHz, respectively, are assumed. The signal bandwidth of each radio channel is 20 MHz; the fast Fourier transform (FFT) size of the radio channels is 64; and the quadrature amplitude modulation (QAM) order of the radio channels is 16. The signal pattern is a pseudo-random binary sequence (PRBS).
Simulation conditions of radio channels
PPT Slide
Lager Image
FFT: fast Fourier transform, OFDM: orthogonal frequency division multiplexing, QAM: quadrature amplitude modulation.
Table 2 shows the parameters of the inserted monitoring channel. To avoid frequency beating with the ordinary radio channels, a subcarrier frequency of 500 MHz is assumed. As a reference signal, a 1-MHz sine wave is modulated in the subcarrier through amplitude modulation. The amplitude of the monitoring channel is adjusted to cover the maximum amplitude of the FDM radio channels. At the receiver, the monitoring channel is demodulated after BPF with a center frequency of 500 MHz. To emulate the nonlinearity in the RoF system, a third-order polynomial nonlinear function is inserted into the transmitter laser. The nonlinearity inserted into the laser is plotted as a solid line in Fig. 2 .
Simulation conditions of the monitoring channel
PPT Slide
Lager Image
Simulation conditions of the monitoring channel
PPT Slide
Lager Image
The inserted nonlinearity (solid line) and the detected nonlinearity by the monitoring channel (dashed line).
IV. RESULTS AND DISCUSSION
The ordinary radio channels and the monitoring channel are distorted by the nonlinear function. To detect the nonlinear function, the received monitoring signal at the receiver is compared with the reference signal. Fig. 3 shows the raw data of the relationship of the received monitoring signal (y-axis) and the reference signal (x-axis). To eliminate the noise of the raw data shown in Fig. 3 , the data are averaged. Finally, the nonlinearity can be detected without noise after the averaging process and is plotted as a dashed line in Fig. 2 . Here, we observe that the nonlinearity detected by the monitoring channel is almost the same as the inserted nonlinearity. Then, the inverse function of the nonlinearity can be made by switching the x-axis and the yaxis of the detected nonlinearity. This is shown in Fig. 4 . The inverse function is applied to the compensator, and the received radio signals are compensated according to the compensating function.
PPT Slide
Lager Image
Raw data of the input-output relation of the reference signal (xaxis) and the received monitoring signal (y-axis).
PPT Slide
Lager Image
Compensating function for the radio channels.
Fig. 5 shows the signal constellation of the radio channel at a 2.2-GHz subcarrier when the nonlinearity does not exist, the nonlinearity exists, a monitoring channel is added, and the nonlinearity is compensated after removing the monitoring channel. Table 3 summarizes the EVM results of the radio channel depending on the RoF system conditions in Fig. 5 . Before the nonlinearity is inserted into the RoF system, the EVM is only 1.4%. After the nonlinearity is inserted, the EVM increases to 2.8%. When the monitoring channel is added to detect the nonlinearity, the EVM increases to 15.3%. Because the monitoring channel severely increases the EVM of the radio channel, it should be removed after detecting the nonlinearity. After compensating for the nonlinearity, the EVM becomes 1.5%, which is almost the same as in the case without the nonlinearity (1.4%).
PPT Slide
Lager Image
Results of the signal constellation when (a) the nonlinearity does not exist, (b) the nonlinearity exists, (c) a monitoring channel is added, and (d) the nonlinearity is compensated after removing the monitoring channel.
Results of nonlinearity compensation
PPT Slide
Lager Image
ROF: radio over fiber, EVM: error vector magnitude.
These results show that the signal quality degradation caused by the nonlinearity in the RoF system can be detected and compensated effectively. However, because the monitoring channel deteriorates the signal quality of radio channels, we think that the monitoring channel should be used for a short time to detect the nonlinearity and should be removed after detecting the nonlinearity. The monitoring channel can be used periodically to detect the change in the nonlinearity because the nonlinearity in RoF systems can be changed with time.
V. CONCLUSION
Nonlinearity reduction is one of the major issues in RoF systems. In this paper, we have proposed an RoF scheme using a monitoring channel to detect and compensate for the nonlinearity in an RoF system. The transmitted monitoring signal is compared with the reference signal to detect the nonlinearity. The inverse function of the detected nonlinearity is used as the compensating function at the receiver. Our simulation results have shown that the EVM degradation induced by the nonlinearity can be almost removed by the proposed scheme. The proposed scheme is also useful to detect and compensate for the change in nonlinearity with time.
Acknowledgements
This research were supported by Basic Science Research Programs through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2010-0022318), and by the Fusion Research Program for Green Technologies through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2012M3C1A1048865).
BIO
Sung-Man Kim
received his B.S., M.S., and Ph.D. in Electrical Engineering from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, in 1999, 2001, and 2006, respectively. His main interests during the M.S. and Ph.D. courses included performance monitoring in optical fiber communication systems. From 2006 to 2009, he was a senior engineer at Network R&D Center, Samsung Electronics, Suwon, Korea, where he engaged in the research and development of mobile WiMAX. Since 2009, he has been a faculty member at th e Department of Electronic Engineering, Kyungsung University, Busan, Korea. His current research interests include optical fiber communications, mobile communications, visible light communications, and passive optical networks.
References
Kim S. M. 2010 “Synchronization method of broadcast messages for beamforming performance in mobile WiMAX networks,” International Journal of KIMICS 8 (3) 277 - 280
Kim S. M. 2013 “Limits of digital unit-remote radio unit distance and cell coverage induced by time division duplex profile in mobile WiMAX systems,” International Journal of Communication Systems 26 (2) 250 - 258    DOI : 10.1002/dac.1356
Common public radio interface [Internet] Available:
Open base station architecture initiative [Internet] Available:
Open radio interface [Internet] Available:
Chih-Lin I. , Huang J. , Duan R. , Cui C. , Jiang J. X. , Li L. 2014 “Recent progress on C-RAN centralization and cloudification,” IEEE Access 2 1030 - 1039    DOI : 10.1109/ACCESS.2014.2351411
Wake D. , Pato S. , Pedro J. , Lopez E. , Gomes N. J. , Monteiro P. P. “A comparison of remote radio head optical transmission technologies for next generation wireless systems,” in Proceeding of LEOS Annual Meeting Belek-Antalya, Turkey 2009 442 - 443
Truong K. T. , Heath R. W. 2013 “Effects of channel aging in massive MIMO systems,” Journal of Communications and Networks 15 (4) 338 - 351    DOI : 10.1109/JCN.2013.000065
Morant M. , Prat J. , Llorente R. 2014 “Radio-over-fiber optical polarization-multiplexed networks for 3GPP wireless carrier-aggregated MIMO provision,” Journal of Lightwave Technology 32 (20) 3721 - 3727    DOI : 10.1109/JLT.2014.2317591
Sauer M. , Kobyakov A. , George J. 2007 “Radio over fiber for picocellular network architectures,” Journal of Lightwave Technology 25 (11) 3301 - 3320    DOI : 10.1109/JLT.2007.906822
Cho S. H. , Park H. , Chung H. S. , Doo K. H. , Lee S. , Lee J. H. “Cost-effective next generation mobile fronthaul architecture with multi-IF carrier transmission scheme,” in Proceeding of Optical Fiber Communication Conference San Francisco, CA 2014 paper Tu2B.6
Gordon G. S. D. , Crisp M. J. , Penty R. V. , Wilkinson T. D. , White I. H. 2014 “Feasibility demonstration of a mode-division multiplexed MIMO-enabled radio-over-fiber distributed antenna system,” Journal of Lightwave Technology 32 (20) 3521 - 3528    DOI : 10.1109/JLT.2014.2313649
Hong M. K. , Han S. K. , Lee S. H. 2007 “Linearization of DFB LD by using cross gain modulation of reflective SOA in radio-overfiber link,” Journal of the Optical Society of Korea 11 (4) 158 - 161    DOI : 10.3807/JOSK.2007.11.4.158
Choi Y. K. , Shin S. Y. 2008 “Super-high speed photo detection through frequency conversion for microwave on optical network,” International Journal of KIMICS 6 (4) 439 - 443
Kim J. T. 2008 “Performance analyses of fiber-optic wireless communication using direct detection,” International Journal of KIMICS 6 (1) 91 - 93
Wang J. , Zhou X. , Xu Y. , Wang W. 2008 “Performance improvement of OFDM-ROF system with clipping and filtering technique,” IEEE Transactions on Consumer Electronics 54 (2) 296 - 299    DOI : 10.1109/TCE.2008.4560089
2013 3GPP TS 36.104 - Base Station (BS) radio transmission and reception (v.12.0.0), 3GPP
Roselli L. , Borgioni V. , Zepparelli F. , Ambrosi F. , Comez M. , Faccin P. , Casini A. 2003 “Analog laser predistortion for multiservice radio-over-fiber systems,” Journal of Lightwave Technology 21 (5) 1211 - 1223    DOI : 10.1109/JLT.2003.810931
Kanesan T. , Ng W. P. , Ghassemlooy Z. , Lu C. 2014 “Investigation of optical modulators in optimized nonlinear compensated LTE RoF system,” Journal of Lightwave Technology 32 (10) 1944 - 1950    DOI : 10.1109/JLT.2014.2312321
Sauer M. , Kobyakov A. , Ruffin A. B. 2007 “Radio-over-fiber transmission with mitigated stimulated Brillouin scattering,” IEEE Photonics Technology Letters 19 (19) 1487 - 1489    DOI : 10.1109/LPT.2007.903765