Energy consumption of Indoor Radio-over-FiberDistribution Links: Experimental Findings

Apurva S. Gowda⇤, Hejie Yang†, Solomon T. Abraha†, Anthony Ng’oma†, Ahmad R. Dhaini⇤, Leonid G. Kazovsky⇤and Kadir M. Albeyoglu

⇤Photonics and Networking Research Lab, Stanford University, Stanford, CA.Email: {asgowda;adhaini;l.kazovsky}@stanford.edu

†Corning Incorporated, Corning, NY.Email: {YangH4;AbrahaST}@corning.com

Abstract—Radio-over-Fiber (RoF) based network architectureswould allow centralization of signal processing and signal condi-tioning functions and simple, cost-effective remote units at the cellsite. RoF technology results in remote units with few components,however, certain aspects of the technology may inadvertentlylead to high power consuming components. In this paper, weexperimentally investigate the effect of electrical-optical-electrical(E/O/E) loss and signal bandwidth on the energy consumptionof analog and digitized RoF (ARoF and DRoF) links. We alsoanalyze the energy efficiency of multiple services on a singleRoF distribution network. Our results show that E/O/E losssignificantly degrades the energy efficiency of the ARoF links.DRoF is robust to the E/O/E loss on the optical link but is affectedby the loss due to reconstruction of the radio frequency signal athigher Nyquist zones. We also show that increasing the bandwidthimproves the energy efficiency. Finally, we demonstrate that theextra energy savings from having multiple services on a singleRoF link depends on the wireless environment.

Index Terms—Energy Efficiency, Radio-over-Fiber, optical sys-tems, wireless systems

I. INTRODUCTION

The economic advantages of delivering radio signals froma central node to a remote location have been recognizedsince the late 80s and 90s [1]. In recent years, the interestin distributing radio signals over fiber (Radio-over-Fiber, RoF)has been re-kindled. The Internet has become the backbone forglobal economic and social activities. Users demand high datarates for bandwidth hungry applications and require mobilitywith ubiquitous access. Cisco’s Visual Networking Index re-veals that, by year 2017, two-thirds of all Internet traffic willbe consumer traffic from households, universities and Internetcafes; 55% of all Internet traffic will originate from Wi-Fi andmobile devices [2]. Thus, wireless transmission at the edgeof the network is becoming increasingly popular. However,wireless networks have limited capacity, due to propagationimpairments through the air. To provide higher capacity, the in-building cell sizes are drastically reducing. As a result, remoteunits that are cheaper, lighter, consume lower power and alloweasier installation are very attractive. Such remote units areachievable using the RoF technology. Also, the transparencyof RoF to the wireless protocol implies wireless services atdifferent frequencies can be sent over the same RoF link to aremote antenna unit (RAU) servicing a particular area.

The carbon footprint of telecommunication networks is ofgrowing concern, thus the impact of new technologies onthe energy consumption of networks must be considered.Wireless transmission, used in WiFi and cellular networks,is inherently energy inefficient due to its broadcast nature.Defining an optimum cell size and centralizing network re-sources has the potential to reduce the carbon footprint ofwireless access networks [3]. Several papers investigated thepower consumption of ARoF and DRoF networks [4]–[6].The authors of [5], [6] considered models that assume theenergy consumption of analog and digital circuitries is givenand estimate the power consumption of the power amplifierby modeling the wireless environment. These models estimatechanges in power consumption with changes in cell sizeeffectively, but do not take into account the effect of powerlosses and noise on the link. The authors of [7] showed theeffect of Electrical-Optical-Electrical (E/O/E) losses, SpuriousFree Dynamic Range (SFDR) and noise on the performanceof an ARoF link. The work in [6] provided a comparisonbetween ARoF and DRoF based on the SNR on the opticallink. In order to design an energy-efficient RoF in-buildingnetwork, the following design questions must be answered:Does E/O/E loss, SFDR and noise affect the energy consump-tion of RoF point-to-point links? How does bandwidth affectenergy consumption? Is it more energy efficient to transmitmultiple services to one RAU instead of separate links tomultiple RAUs?

In this paper, we investigate the effect of E/O/E loss,wireless signal bandwidth and multiple services on the energyconsumption of an ARoF and DRoF link for an in-buildingnetwork. We present a theoretical model to evaluate theenergy efficiency of point-to-point ARoF and DRoF links. Weillustrate the effect of loss on the energy consumption of ARoFand DRoF links by sending an analog and digitized RF signalthrough an emulated optical link and compare their energyefficiency. We also discuss a few factors of the optical linkdesign that affect the E/O/E loss. We set up ARoF and DRoFlinks in our testbed and send the analog and digitized RFsignal of different bandwidths and different number of servicesthrough the link; we then compare their energy efficiency. Weuse the theoretical model to gain further insights on the effect

1

Fig. 1. Generic point-to-point block diagram. RF TRx: RFTransceiver

of loss and bandwidth on the energy consumption of the ARoFand DRoF links.

The remainder of the paper is organized as follows: SectionII illustrates the basic system model assumed and the theoret-ical model used to evaluate the energy efficiency of the ARoFand DRoF links. In Section III, we discuss the experimentaltestbed and the results. Finally, in Section IV, we present ourconclusions.

II. SYSTEM MODEL

Fig. 1 shows a generic point-to-point RoF link. The RFTransceiver (TRx) and the Optical TRx are located at a centrallocation. The RF TRx contains the digital and analog circuitsrequired to generate and decode the RF signal. The OpticalTRx contains E/O/E circuitry to modulate (on the downlink)and demodulate (on the uplink) the RF signal on to and fromthe optical carrier, respectively. The RAU is located at theremote cell site and contains the E/O/E circuitry required torecover (on the downlink) and modulate (uplink) the RF signalfrom and on to the optical signal. The User Equipment (UE)is the end user’s device equipped with a wireless transceiver.In this study, the assumptions and experiments focus on thedownlink. For a comprehensive study that includes the uplink,the uncertainty in the power of the RF signal being modulatedon to the optical carrier should be also considered; this isleft for future work. Also, the study is focused on carrierfrequencies in the range of 300MHz-6GHz, because mostcurrent wireless standards specify protocols in this range.

In the following sections we describe the energy efficiencymetric and model used to calculate the transmit power, PTx,depicted in Fig. 1. The transmit power, PTx is then used tocalculate the energy efficiency metric.

A. Energy Efficiency Metric

Consider a fixed transmitter design i.e., a pre-determinedRF TRx, Optical TRx and RAU design. Since the powerat different points from the transmitter to the receiver canbe determined, the power at the transmitter, PTx is known.The energy efficiency metric used is “Energy per bit per

unit area”. For a given receiver sensitivity, PRx, required tomaintain a certain bit-error-rate (BER) in a certain wirelessenvironment, the maximum transmission distance, d can becalculated. Then, the energy per bit per unit area is given by:

Eb/m2 =PDC

Rb ⇥ ⇡d2, (1)

where PDC is the total power consumption of the RF TRx,the Optical TRx and the RAU; Rb is the total bit rate.

Fig. 2. DAC Block Diagram; BPF: Bandpass filter

The IEEE 802.11n in-building wireless environment model[8] is used to calculate the maximum distance; it is given by:

PL = PTx�PRx = �45.0325+10Mlog(d)+20log(fc) (dB) ,(2)

where fc is the carrier frequency in MHz and M is the pathloss exponent.

The receiver sensitivity, PRx, is calculated as:

PRx = SNR+ � +N0 + Fn + 10log10(B) (dBW ) , (3)

where SNR is the required signal-to-noise ratio for a givenBER at the UE; � is the extra margin; N0 is the thermalnoise at room temperature; Fn is the noise figure of the user’sequipment, and B is the bandwidth in Hz.

B. Transmit Power

1) ARoF: For a fixed transmitter design, the power of thesignal modulated on to the optical carrier, Pin, is known. Inour experiments, we externally modulate the intensity of thelaser using an MZM with balanced drives. When modulatinga sinusoid of amplitude Vin, it can be shown that the outputpower after the photodetector is:

PoptRF =

✓Popte

�↵L

4⇡sin

✓⇡Vb

V⇡

◆J1

✓2⇡Vin

V⇡

◆RPD

◆2

Rl ,

(4)where Jn () is the nth-order Bessel function of the first kind;

Popt is the power of the optical carrier; ↵ is the loss per km onthe optical fiber; L is the length of the optical fiber in km; Vb

is the bias voltage of the MZM; V⇡ is the half-wave voltage ofthe MZM; RPD is the responsivity of the photodetector andRl is the load resistance of the photodetector.

Thus,

PTx = GRAUPoptRF . (5)

where GRAU is the gain due to various components suchas the amplifiers, filters etc., in the RAU.

2) DRoF: We assume reliable transmission of bits on theoptical link and the RF signal spans the full peak-to-peakof the ADC. These are valid assumptions since we consideronly the downlink with optical links <1km [9]. We assumeband-pass sampling to digitize the signal. With DRoF, thetransmitted power depends on the parameters of the digital-to-analog converter (DAC) since it reconstructs the RF signalfrom the bits. Fig. 2 shows the basic processing blocks ina DAC. Assuming the bandwidth of the RF signal is less

2

Fig. 3. Experimental Block diagram

than half the sampling frequency, fs, the average power atthe output of the zero-order hold (ZOH) block, Py , is givenby,

Py =

ˆ fc

+B/2

fc

�B/2

|Hi(f)|2 sinc2✓

f

fs

◆df , (6)

where Hi(f) is the frequency response of the RF signal.For the sake of simplicity, we assume that the loss is equalto loss at the carrier frequency and is constant over the entirebandwidth. Then, the loss due to the ZOH block is simplifiedto:

Loss = sinc2✓fcfs

◆. (7)

The average power at the input of the ZOH circuit, Px,depends on the peak-to-peak voltage of the DAC, VDAC , thepeak-to-average-power ratio, PAPR, of the signal, and theload resistance, Rl. Since we have assumed the signal spansthe full peak-to-peak of the ADC, Px is given by:

Px = V 2DAC

10�PAPR/10/R

l

. (8)

Assuming unity gain for the bandpass filter (BPF), thetransmitted power is given by:

PTx = GRAU

V 2DAC

10�PAPR/10sinc2(fIF

/fs

)/Rl

. (9)

The validity and % error of the models has been shown in[9].

III. EXPERIMENTAL RESULTS

The block diagram of the experimental setup is shownin Fig. 3. The wireless transmission is not implemented inthe testbed but is accounted for in the energy efficiencymodel through wireless environment model given by (2). TheRF signal generation and demodulation are conducted offlineusing MATLAB. An OFDM signal of sub-carrier bandwidth732 kHz and 26 sub-carriers (B =⇠ 20MHz) with eachcarrier modulated with a 16-QAM symbol is generated andupconverted to a carrier frequency, fc = 2.44GHz, also offlinein MATLAB. For ARoF, the analog signal generated is directly

Table I. Parameters used in analysis

Parameter ValuekT (N0) -204 dBW/Hz

Rl 50⌦Fn 3.3 dB

SNR 19 dB� 5dB

Popt 3.3 dBmRPD 0.65 A/W

M 3.5FFT size 2048

Cyclic Prefix 64

Fig. 4. Experimental Testbed

uploaded onto the Arbitrary Waveform Generator (AWG)and subsequently transmitted through the E/O/E transmissionsystem. The signal at the output of the system is captured usinga Digital Signal Oscilloscope (DSO). The captured data isdemodulated offline using MATLAB. In the case of DRoF, thegenerated analog signal is digitized off-line using a samplingfrequency, fs = 300MHz, and a bit resolution, n = 10. Thedigital signal is loaded onto the AWG as illustrated in Fig. 3.The captured signal is used to reconstruct and demodulate theRF signal offline. The system is optimized to obtain the bestperformance in both setups. Table I shows the values of theimportant parameters used in the experiments.

Fig. 4 illustrates the E/O/E transmission system in thetestbed. For ARoF, the RF signal from the AWG is intensity-modulated onto the optical carrier using a single drive MZM

Table II. Power Consumption of Various Components

Power (W) % of PDC % of PDC

(ARoF) (DRoF)PPA 2*3.303 ~74.2 ~74PLNA 0.996 ~11.2 ~11PV CO 0.1 0 ~1.2PADC 0.1023 0 ~1.2

(fs = 300MHz)PDAC 0.0258 0 ~0.3

(fs = 300MHz)Plaser 1 ~11.3 ~11PMZM 0.3 ~3.2 ~2

3

Fig. 5. Energy per bit per unit area versus Loss; NZ = NyquistZone (experimental)

and transmitted through a 500m Single-Mode Fiber (SMF).After photodetection, the signal is amplified and capturedusing a Digital Signal Oscilloscope (DSO). The captured datais demodulated offline using MATLAB. In the case of DRoF,the digital signal from the AWG is externally modulated ontothe optical carrier using the MZM, transmitted through a500m SMF, photodetected, amplified and captured using theDSO. The power consumption of various components and theircontribution to the overall power consumption are given inTable II. The method used to calculate the power consumptionof the laser, MZM, photodetector, the ADC and the DACis explained in the Appendix. We assume the ADC analogbandwidth adequately covers the entire RF signal bandwidth.

A. Effect of Loss

The effect of loss on the optical link is emulated using anelectrical amplifier and attenuator i.e., the E/O/E transmissionsystem shown in Fig. 3 comprises of a power amplifier and astep attenuator. By varying the attenuation, the loss is varied.The corresponding output power which represents the transmitpower, PTx, is measured for different losses with the sameinput RF signal power, Pin. The DC power consumption of thesystem, PDC is the power consumption of the power amplifierwhich is ~3.3W. The calculated Eb/m2 for different losses isplotted in Fig. 5. We see that the energy efficiency of DRoFis not affected by the loss or gain in the transmission system.However for ARoF, the loss affects PTx; thus high loss resultsin shorter wireless transmission distance. On the other hand,any gain in the system would translate to a higher PTx inthe case of ARoF, but in DRoF, the gain only improves thereliability of the bits sent over the system and not PTx.

In Fig. 5, we also plot the Eb/m2 of DRoF for differentNyquist zones. In DRoF, the RF signal at the desired carrierfrequency can be obtained in two ways: 1) reconstructing thesignal in the appropriate Nyquist zone, which is determinedby the carrier frequency and the sampling frequency; 2) re-constructing an intermediate frequency (IF) in a lower Nyquistzone and up-converting it using a local oscillator (LO). Whenusing the former approach, there is a large loss in the RF

Fig. 6. Energy per bit per unit area versus PD InputPower for different sampling frequencies (using model);NZ = Nyquist Zone; For sampling frequencies, fs =50, 300, 750, 5000 MHz. With a 10 bit resolutions, theyresult in optical link rates, ROL = 0.5, 3, 7.5, 50 Gbps.We assume the same optical TRx power consumption for allcases.

power due to the sinc behavior of the DAC as can be seenthrough (9). The amount of loss depends on the Nyquist zonein which the carrier frequency exists. For fc = 2.44GHz andfs = 300MHz, the RF signal in the 9th Nyquist zone mustbe reconstructed (which has a loss of ~36dB). From Fig. 3,we see that reconstructing the IFs in the lower Nyquist zones(i.e., IF) and using an LO at the RAU to upconvert to thedesired carrier frequency is more energy efficient.

The Nyquist zone can also be changed by changing thesampling frequency; increasing the sampling frequency lowersthe Nyquist zone of a particular carrier frequency. The impactof increasing the sampling frequency is shown in Fig. 6. Thefigure shows Eb/m2 calculated using the model for samplingfrequencies, fs = 50, 300, 750, 5000 MHz. The samplingfrequencies used satisfy the bandpass sampling frequencycriterion [6]. With a 10 bit resolutions, they result in opticallink rates, ROL = 0.5, 3, 7.5, 50 Gbps, respectively. Toobserve the effect of different losses in the system, we varythe input power injected into the photodetector. The energy perbit per unit area, Eb/m2 , reduces as we increase the samplingfrequency since the Nyquist zone is lower. However, opticallink rates of 50Gbps require more sophisticated links. Anoptical link rate of 7.5 Gbps is achievable using the opticalTRx used in the experiments and results in an ~80% reductionof Eb/m2 .

B. Effect of Bandwidth

RF signals of 20MHz, 40MHz and 80MHz bandwidthand the same average power are sent through the E/O/Etransmission system shown in Fig. 4 and the output poweris recorded. Fig. 7 shows the Eb/m2 of ARoF and DRoF fordifferent PD input powers. For DRoF, we plot the curves byreconstructing the RF signal in the 9th Nyquist Zone. We seethat the energy per bit reduces as the bandwidth increases in

4

Fig. 7. Energy per bit per unit area versus PD Input Power(experimental)

(a) Single service per link

(b) Multiple services per link

Fig. 8. Single-service vs Multi-service system. (a) Singleservice per link (b) Multiple service per link. Both systemshave the same total bandwidth, and same transmit power perantenna.

both cases. As we increase the bandwidth, the wireless bit rate,Rb, increases. Thus we have a lower value for Eb/m2 .

C. Multiple services on a single RoF link, assuming the same

total bandwidth

In this section, we compare two different scenarios, whichare illustrated in Figure 8, to provide four services to a certainarea. Case 1 uses four individual RoF links to carry fourservices. An example of this scenario would be a situation inwhich service providers do not want to share infrastructure.In contrast, for Case 2, there is just one single RoF linkcarrying four services, namely multiple-services on a singleRoF link. Here the service providers share their infrastructure

Fig. 9. Energy Savings for different path loss exponents (M)and for different number of services (N)

and in the following analysis we try to estimate the possibleenergy savings. With individual infrastructures (Case 1), theservice providers enjoy more independence in terms of systemupgrades and maintenance. However, total capital expenditureis high. Case 2 has a lower capital expenditure but individualmaintenance and upgrades are trickier.

We assume each service has 20 MHz bandwidth, thereforethe total system bandwidth is 80 MHz in both cases. We alsoassume that the output power of RAU is fixed to PTx bythe standard. Thus in Case 2, due to multiple services ona single RAU, transmit power per service is actually PTx/4(PTx/N for N number of services). A similar analysis canbe done by assuming a higher gain power amplifier in Case2 and comparing the two cases for equal power densities.However, the analysis is out of the scope of this paper.Meanwhile, since the total bandwidth is the same for bothcases, the total bit rate is also the same. Using (2), we can showthat d22 = d2

1/N2/M . Therefore, E1b/m2 = NPDC/NR

b

d21 and

E2b/m2 = PDC/NR

b

d22 = N2/MP

DC/NRb

d21. The energy savings

will be (1 � N (2/M�1)) ⇥ 100 %. Figure 9 plots the energysavings for different number of services and different wirelessenvironments. We see that for larger number of services andworse environments, the energy savings increases.

However, an RF signal with multiple carrier frequenciesimposes further restrictions on the possible sampling frequen-cies when using the same ADC for digitizing. Also, differentcarrier frequencies may lie in different Nyquist zones resultingin different losses for different carrier frequencies. In our anal-ysis and experiment, we considered carrier frequencies that arerelatively close to each other and could be sampled using asampling frequency of 300MHz. We use carrier frequenciesseparated by 38MHz; each with a bandwidth of 20MHz inthe 2.44GHz carrier frequency range. The restriction on thesampling frequency is due to available components in ourexperiment setup. Also in this scenario, the different serviceswill lie in the same Nyquist zone and thus, will exhibitrelatively the same loss. The energy savings when the carrierfrequencies are farther apart is left for future work.

5

Fig. 10. Energy per bit per unit area versus PD Input Powerfor different number RAUs at the remote cell

Fig. 10 plots the energy per bit per unit area versus PD inputpower for the two cases illustrated in Fig. 8. We see that theyboth have relatively the same energy efficiency. We observethat when M = 3.5, the energy efficiency of the two cases arerelatively the same. If we consider a scenario with a higherpath loss exponent, say M = 5, we see that having a singleRAU is more energy efficient. In Fig. 10, the energy savingsfor M = 3.5 is lower than in the analysis due to discrepanciesin the measurements (i.e. the average power at the output ofthe AWG was lower for Case 2 signal compared to the Case1. This lead to a lower PTx for Case 2 compared to Case 1).

IV. CONCLUSIONS

We experimentally investigated the effect of E/O/E loss,wireless signal bandwidth and multiple services on the energyconsumption of analog radio-over-fiber (ARoF) and digitizedradio-over-fiber (DRoF) point-to-point links. We found that theE/O/E loss significantly affects the energy efficiency of ARoF,but DRoF is robust to E/O/E loss on the link for the dynamicrange investigated. For DRoF, reconstructing signals at higherNyquist zones negatively affects its energy efficiency. We alsoshow that increasing the bandwidth improves the energy effi-ciency of both ARoF and DRoF links. Finally, we comparedtwo system approaches for adding extra services while keepingboth the total system bandwidth and individual antenna outputpower the same. Based on our assumptions (which didn’tinclude wireless interference), the analysis showed that therecould be a difference in energy savings between the twodifferent approaches, depending on the wireless environment.For wireless environments with large exponent factor (e.g. M= 5), sharing the link for multiple services offers at least ~34%savings in energy efficiency.

ACKNOWLEDGMENTS

This research has been partially sponsored by Corning,Inc; National Science Foundation (NSF) under grants CNS-0627085 and CNS-1111374; and the Natural Sciences andEngineering Research Council of Canada (NSERC).

APPENDIX APOWER CONSUMPTION MODELS

• DAC power consumption, PDAC : For DAC power con-sumption, we follow the method adopted in [10].

• ADC power consumption, PADC : We base our analysison the survey in [12]. However, we updated the surveywith more recent published results and took into accountthe projected power consumption scaling with Lprocess

in [11]. The updated formula is given as

PADC =(31.9e0.01019Lprocess � 31.88e0.01018Lprocess)

10150.9n�3.166+3.174

V 2DD ⇥ 250e� 9⇥ (fs +B). (10)

• MZM power consumption, PMZM : We use the modelgiven in [13].

REFERENCES

[1] Wake, D.; Johansson, D.; Moodie, D.G., "Passive picocell: a new conceptin wireless network infrastructure," Electronics Letters , vol.33, no.5,pp.404,406, 27 Feb 1997.

[2] ’Cisco Visual Networking Index: Forecast and Methodology,2012–2017’. [Online]

[3] Kazovsky, Leonid G.; Ayhan, Tolga; Gowda, Apurva S.; Dhaini, AhmadR.; Ng’oma, Anthony; Vetter, Peter, "Green in-building networks: Thefuture convergence of green, optical and wireless technologies," ICTON2013, vol., no., pp.1,5, 23-27 June 2013.

[4] Crisp, M.; Penty, R.V.; White, I.H.; Bell, A., "Wideband Radio overFiber Distributed Antenna Systems for Energy Efficient In-BuildingWireless Communications," Vehicular Technology Conference (VTC2010-Spring), 2010 IEEE 71st , vol., no., pp.1,5, 16-19 May 2010.

[5] Josse, Y.; Fracasso, B.; Castignani, G.; Montavont, N., "Energy-efficientdeployment of distributed antenna systems with radio-over-fiber links,"Online Conference on Green Communications (GreenCom), 2012 IEEE, vol., no., pp.7,13, 25-28 Sept. 2012.

[6] Yang, Y, “Investigation on digitized RF transport over fiber”, PhD thesis,Engineering, Department of Electrical and Electronic Engineering, TheUniversity of Melbourne, 2011.

[7] Fan, Jason C., and Kazovsky, L.G., “A Fiber Infrastructure for Mi-crocellular Personal Communication Systems: Infrastructure Designand Optical Dynamic Range Requirements” Thesis (Ph. D.), StanfordUniversity, 1996.

[8] V. Erceg et.al. ’IEEE P802.11 Wireless LANs: TGn Channel Models’,May 2004.

[9] Gowda, Apurva S.; Dhaini, Ahmad R.; Kazovsky, Leonid G.; Yang,Hejie; Abraha, Solomon T.; Ng’oma, Anthony, “Towards Green Opti-cal/Wireless In-Building Networks: Radio-over-Fiber”, Lightwave Tech-nology, Journal of , Aug 2014. [accepted]

[10] Shuguang Cui, A. J. Goldsmith and A. Bahai, "Energy-constrainedmodulation optimization," Wireless Communications, IEEE Transactionson, vol. 4, pp. 2349-2360, 2005.

[11] D. Kilper, "Energy Efficient Networks," in Optical Fiber CommunicationConference, OSA Technical Digest (CD) (Optical Society of America,2011), paper OWI5.

[12] E. Lauwers and G. Gielen, "Power estimation methods for analog circuitsfor architectural exploration of integrated systems," Very Large ScaleIntegration (VLSI) Systems, IEEE Transactions on, vol. 10, pp. 155-162, 2002.

[13] Tucker, Rodney S., "Green Optical Communications—Part I: EnergyLimitations in Transport," Selected Topics in Quantum Electronics, IEEEJournal of , vol.17, no.2, pp.245,260, March-April 2011.

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