JOURNAL OF LIGHTWAVE TECHNOLOGY, VOL. 32, NO....

JOURNAL OF LIGHTWAVE TECHNOLOGY, VOL. 32, NO. 20, OCTOBER 15, 2014 3545

Towards Green Optical/Wireless In-BuildingNetworks: Radio-Over-Fiber

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

Abstract—Energy efficiency has become a major paradigm inthe design and operation of future telecommunication networks.Recent studies show that the aggregate power consumption of in-building IT networks (residential and office) is massive and compa-rable with that of data centers due to the large number of buildings.In this paper, we analyze the energy efficiency of next-generationin-building IT networks to deliver high-speed mobile access to endusers via integrated optical/wireless networks using Radio-over-Fiber (RoF) technology. Based on a validated energy efficiencymodel, our results show that although individual point-to-pointRoF links are not as energy efficient as legacy Baseband-over-Fiberlinks, RoF networks may actually be more energy-efficient whendesigned keenly with small cells sizes and when the static energyconsumption of the remote units is above a particular threshold.Under the assumptions used in this paper, we show that the DRoF-based architectures can be designed to more energy efficient forcell sizes <17 m.

Index Terms—Energy efficiency, in-building networks, opticalcommunication, radio-over-fiber, wireless LAN.

I. INTRODUCTION

R ECENT statistics showed that the usage of the Internet, areliable communication tool in today’s society, increases

by 40% per year in North America and by about 20% world-wide [1]. Recent studies also showed that the Internet currentlyconsumes 10 000 times more energy than the minimum re-quired [2]. The design of current networks aims at meeting strictperformance requirements such as bandwidth and deploymentcost constraints with little regard to energy efficiency. However,with the increasing demand for bandwidth due to the prolif-eration of bandwidth-hungry multimedia applications, by year2025, the amount of energy required to keep telecom networksrunning is expected to increase by two orders of magnitude [3].Recognition of the increase in network energy consumption,along with the rising cost of energy and the harmful effect

Manuscript received January 15, 2014; revised March 10, 2014; acceptedApril 3, 2014. Date of publication April 6, 2014; date of current version Septem-ber 1, 2014. This work was supported by Corning, Inc.; National Science Foun-dation (NSF) under Grants CNS-0627085 and CNS-1111374; and the NaturalSciences and Engineering Research Council of Canada (NSERC).

A. S. Gowda, A. R. Dhaini, and L. G. Kazovsky are part of the Photon-ics and Networking Research Lab, Stanford University, Stanford, CA 94305USA (e-mail: [email protected]; [email protected]; [email protected]).

H. Yang, S. T. Abraha, and A. Ng’oma are with Corning Incorporated, Corn-ing, NY 14831, USA (e-mail: [email protected]; [email protected];[email protected]).

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JLT.2014.2315960

Fig. 1. Daily energy consumption of global optical transport, global core andmetro optical access networks [3], and in-building networks in the U.S. only.

of CO2 emissions, has led to extensive research in academiaand industry to realize energy-efficient (green) communicationnetworks.

Research efforts that address the foregoing problem mainlyfocus on reducing the energy consumption of data centers sincethey are considered the major energy hog in the Internet. How-ever, recent studies reveal that the power consumption of datacenters and that of in-building networks (residential and office)are on a comparable scale due to the large number of buildingsand the spreading Internet literacy of their users [4]. Accord-ing to [5], by year 2017, 55% of all IP traffic will originatefrom wireless and mobile devices. It has been predicted thatby year 2017, two-thirds of all IP traffic will be consumer traf-fic from households, university populations and Internet cafes.This means that the source of extensive wireless Internet traf-fic will be from in-building IT networks. Thus, it is importantto explore ways for reducing the energy consumption of officeand residential building networks. Due to the large number ofthese types of networks, even a small decrease in the energyconsumption in each of the individual networks will lead to asignificant improvement in the overall energy consumption.

As depicted in Fig. 1, the energy consumption of in-buildingnetworks in the U.S. alone is currently higher than the energyconsumption of both the global optical transport and wide/metroarea networks worldwide. This is due to the large number ofoffice and residential buildings in the U.S.—about 17% of thetotal commercial floor space in the U.S. is office buildings [6].

Although it is commonly believed that the energy consumedby space heating, lighting, etc., in office and residential build-ings far trumps that of IT networks, the design of current

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3546 JOURNAL OF LIGHTWAVE TECHNOLOGY, VOL. 32, NO. 20, OCTOBER 15, 2014

Fig. 2. Energy Usage in U.S. in-buildings [6].

in-building networks aims at meeting capacity and performancerequirements with little regard to energy efficiency. Thus, asthe demand for bandwidth increases, the network design wouldrequire more complex technologies. This drastically increasesthe energy consumption. Fig. 2 shows how with the absence ofenergy efficient solutions, by year 2020, the energy consump-tion of IT networks would surpass all other power electronics inU.S. buildings.

In this paper, we investigated radio-over-fiber technolo-gies for achieving energy-efficient optical/wireless convergedin-building networks. We proposed and analyzed three dif-ferent fiber-based in-building architectures; ARCH1 is thelegacy architecture (i.e., using baseband-over-fiber); ARCH2and ARCH3 centralize the DSP processing but have dedicatedlinks for each cell. We developed extensive theoretical and simu-lation models and used experimentally measured data to validatekey parts of the models. Our results show that although individ-ual point-to-point RoF links may not be as energy efficient aslegacy baseband-over-fiber links, there are some RoF networkarchitectures, which may actually be more energy—efficientwhen designed keenly with small cell sizes and when the staticenergy consumption of the remote units is above a particularthreshold.

The remainder of the paper is organized as follows. InSection II, we provide an overview of in-building networksand widely employed technologies. Section III presents the en-ergy efficiency analysis based on the modeling of point-to-pointlinks. In Section IV, we present the network architectural anal-ysis. In Section V, we present our results. We finally concludethe paper in Section VI.

II. OVERVIEW ON IN-BUILDING NETWORKS AND

TECHNOLOGIES

In general, communication networks can be divided into out-door and indoor networks. Outdoor networks comprise core,metro/edge and access networks, while indoor networks gener-ally consist of data centers, and residential and office buildingIT networks.

Fig. 3. In-building network architecture (with three floors and a basement).

In this section, we present an overview of in-building ITnetworks. Specifically, we describe the network architecture ofin-building networks, their energy consumption and the relatedart for making them energy efficient.

A. In-Building IT Network Architecture

As illustrated in Fig. 3, a typical in-building IT network con-sists of a main distribution frame (MDF) that connects the in-building network to the “outside world” (typically the accessnetwork), and an intermediate distribution frame (IDF) that con-nects the networks on various floors to the MDF. The MDF andIDFs are inter-connected via optical Ethernet connections, andon each floor wireless access points (WAPs) provide the userswireless access; whereas wired (copper Ethernet) links providethe users wired access to the Internet. A residential networkcan be considered a simplified version of this network, e.g., thenetwork on a single floor.

B. In-Building Network Technologies: Baseband-Over-Fiberand Radio-Over-Fiber

In recent years, the integration of fiber and wireless networkshas been presented as the evolutionary next step toward real-izing high-speed, wireless and cost-effective broadband accessnetworks. The legacy architecture utilizes baseband-over-fiber(BoF), which consists of sending the baseband data over anoptical link and subsequently over the wireless link. Typically,it is realized using separate protocols for the optical link andwireless link. With the proliferation of mobile bandwidth inten-sive in-building networking devices, there is an unprecedentedneed for pushing this integration closer to the user. Radio-over-fiber (RoF) is a promising technology that has been activelyresearched as a strong candidate for achieving optical-wirelessconvergence. RoF enables the wireless signal to directly ride onthe optical carrier. Owing to the high bandwidth available in theoptical link, multiple wireless services can use the same back-haul optical network. Besides high bandwidth, RoF allows forcentralized network control and simple remote antenna units,which facilitates easier maintenance, upgrade, and sharing ofresources.

GOWDA et al.: TOWARDS GREEN OPTICAL/WIRELESS IN-BUILDING NETWORKS: RADIO-OVER-FIBER 3547

There are several technologies for sending the wireless signalover the optical fiber link. Namely, analog RoF (ARoF), digi-tized RoF (DRoF), intermediate frequency-over-fiber and digi-tized intermediate frequency-over fiber [9]. In an ARoF link, theremote unit has a very simple structure; however, when send-ing an analog signal on the optical carrier, the optical link willhave a limited dynamic range and will be affected by fiber non-linearity, noise and chromatic dispersion. These limitations arerelaxed when sending a digital signal over the optical link. Thus,DRoF is more reliable on the optical link. However, digitizinga radio frequency signal requires band-pass sampling and moreanalog-to-digital and digital-to-analog converters (ADC/DAC),which introduces quantization, aliasing and jitter noise. Also,the remote unit will have more circuitry.

C. Literature Review

Several papers investigated the power consumption of ARoFand DRoF systems [10]–[13]. The authors of [9], [13], [15] stud-ied the energy consumption of ARoF and DRoF as backhaul formobile networks. The models assume that the energy consump-tion of the analog and digital circuitry is given and estimatethe power consumption of the power amplifier using a path lossmodel and the drain efficiency. The authors of [11], [12] alsoconsidered a similar energy consumption model. These mod-els estimate the change in power consumption with higher pathloss effectively but do not take into account the effect of powerlosses and noise on the link. To account for both power lossesand noise on the link, a model based on the signal-to-noise ratio(SNR) or bit-error-rate (BER) is required.The authors of [14]analyzed the SNR and spurious free dynamic range (SFDR) ofan ARoF link. The work in [15] provided a comparison betweenARoF and DRoF based on the SNR on the optical link; it stud-ied the different noise sources on the two links and the highereffect noise has on an ARoF link. Finally, [16]also showed theeffect of optical-electrical-optical (OEO) losses and noise onthe performance of an ARoF link.

In a RoF link, the radio frequency (RF) signal travels throughthe optical link and is directly transmitted over the air and de-modulated at the wireless receiver. To quantify the effect ofnoise and losses on the energy consumption, a more compre-hensive SNR model for the whole link, from the wireless trans-mitter to the wireless receiver, is required. The SNR modelcan then be used for receiver sensitivity calculations and sub-sequently energy consumption calculations. In this paper, weprovide a comprehensive energy consumption model based onthe SNR of the entire link that considers losses such as electrical-optical-electrical (EOE) losses, loss due to sinc behavior of DAC(DRoF), etc., for both ARoF and DRoF. We use the model tocompare in-building architectures based on BoF, ARoF, andDRoF.

III. POINT-TO-POINT LINK ANALYSIS

To analyze the energy consumption of the green architectures,we perform a point-to-point link analysis on the technologiesused in each of the architectures, i.e., ARoF and DRoF. In ourprevious work [17], we use a preliminary model to perform the

Fig. 4. Generic point-to-point block diagram.

analysis. However, the model does not account for the effectof: 1) optical link loss on the downlink on ARoF; 2) digitalto analog conversion power loss due to sample-hold circuit inDRoF. In this paper, we extend the model to include the effectof such losses.

Fig. 4 shows a generic point-to-point link. The RF transmitter(TRx) and the head end unit (HEU) are located at a centrallocation (i.e., at the MDF in the proposed architectures). TheRF TRx modulates/demodulates the RF signal and performsthe necessary signal conditioning. The HEU contains the EOEconversion circuitries required to transmit/receive the RF signalon the optical link, and the RAU contains the EOE circuitriesand the analog signal conditioning circuitries.

In this section, we present a model to estimate the powerconsumption of point-to-point analog links. We consider twocases:

1) Estimate Transmitter’s Power Amplifier Gain Based onBER: The BER at the RAU is set to a certain value andthe required power of the signal is estimated at differentpoints in the transmitter, thus determining the gain of thepower amplifier. Using the gain, the power consumptionof the power amplifier is estimated. Energy per bit is usedas the comparison metric.

2) Fix Transmitter Design and Estimate Wireless Transmis-sion Distance Based on BER: The power at the transmitteris known and based on the transmitter design, the powerat different points from the transmitter to the receiver isestimated. The energy efficiency metric used is energy perbit per unit area to normalize with respect to transmissiondistance. In this case, we focus on the downlink scenario.The uplink scenario is left for our future work.

The analysis of the link and architectures are done by estimat-ing the power amplifier gain (Case 1), since we can obtain theeffect of cell size directly. However, experimentally validatingthe loss equations using this approach is challenging. Thus, wevalidate the use of the loss equations experimentally by fixingthe transmitter design and estimating the wireless transmissiondistance (Case 2). Since the same equations are used in bothcases, the related assumptions are valid for the first approach aswell (Case 1).

In this study, we focus on carrier frequencies in the UHF andlower part of the SHF range (300-6 GHz), since most currentwireless standards specify protocols in this range. The maximumfrequency depends on the maximum bandwidth of the availableoptical components. However, generally, for protocols in theEHF range more complex optical link modulation/demodulationtechniques are required; this is outside the scope of this work.

By applying the analysis on the network level, we can thenmeasure the power consumption of the proposed green networkarchitectures. A summary of the most relevant notations usedin the analysis is given in Table I. The rest are defined in the


TABLE ISUMMARY OF IMPORTANT NOTATIONS

Parameter AbbreviationPower of the received RF signal PRx (W)Responsivity of the photo-detector R (A/W)Slope efficiency of the laser sl (W/A)

MZM bias voltage Vb(V)MZM half-wave voltage Vπ(V)

Load resistance Rl (Ω)Optical carrier power Popt(W)Length of the fiber L (km)Fiber loss per km αPower amplifier gain Gpa

Path loss Gpl

Gain of the antenna Ga

Noise factor FBoltzman’s constant k (J/K)Relative intensity noise RIN (db/Hz)Charge of an electron q (C)Bandwidth of the signal B (Hz)Room Temperature T (K)

Wavelength of carrier frequency λ (m)Wireless transmission distance d (m)Low noise amplifier gain Glna

Sampling Frequency fs (Hz)Constellation Size M

Upper frequency bound fh (Hz)Root mean square of clock jitter στ (s)

TABLE IIPOWER CONSUMPTION OF VARIOUS COMPONENTS

Component Power Consumption (mW)RF Analog Transceiver [18]–[20] 400

Ethernet Transceiver [4] 1000Laser Driver [21] 10

Transimpedance Amplifier [22] 250Band-pass sampling ADCs+DACs [23] 80

VsIbias (Laser source) [21] 7.8Wireless DSP (modulation+demodulation) 60.2

paper. Table II lists the power consumptions assumed for keycomponents throughout this paper.

A. Analog Radio-Over-Fiber:

Typical ARoF downlink and uplink systems are shown inFig. 5. Each consists of a RF analog transceiver chain that gen-erates the RF signal. The receiver on the downlink is the sameas that of the BoF link (i.e., user’s receiver). However, on theuplink, detection of the wireless signal occurs after the opticallink. Hence, the SNR on the uplink differs from that on thedownlink.

1) ARoF—Estimating Power Amplifier Gain: The powerconsumption of the power amplifier is estimated given a certaindrain efficiency and the required transmitting power to main-tain the desired BER at the receiver. On the downlink, a highfrequency signal is directly modulated onto an optical carrier,which is received at the RAU. After O/E conversion and band-pass filtering, the signal is transmitted over the air to the end-user. Due to the optical link, the RF signal is subject to 1) powerloss due to modulation onto the optical carrier, 2) thermal noisedue to analog components at the transmitter and receiver, and3) relative intensity noise (RIN) and shot noise due to the EOE

Fig. 5. Analog RoF, ARoF. (a) Downlink. (b) Uplink.

conversion components. The optical transmission can give riseto higher-order harmonics in the signal due to inherent non-linear effects. However, since the focus of this study is lim-ited to in-building networks where the length of optical linksis typically less than 1 km, with low bit-rates and low launchpower [24], we do not consider dispersion or non-linearity onthe optical link. In this study, we assume direct modulation ontothe optical carrier. On the downlink, the average SNR of thereceived RF signal at the end-user is given by

SNRdown =PRx

Ntot,down, (1)

Ntot,down = FkTB

where Ntot,down is the thermal noise due to the analog com-ponents in the RAU. We use a simplified path loss model withexponent l which is given as follows:

Gpl =(√

Gaλ

4πdl/2

)2

. (2)

Throughout this paper, we assume isotropic antennas, thusGa = 1. The required average SNR to maintain a certain BERcan be calculated using the BER equations for an additive whiteGaussian noise channel (AWGN) [25]. Using direct detection ofsmall signal model of an impedance matched system [26], theloss in RF power due to the optical link can be approximatedas m2 = e−αL (slR)2 . The required received power PRx can becalculated as

PRx = Gplm2(GpaPin) (3)

where Pin is the power of the RF signal after performing analogsignal conditioning at the MDF (shown in Fig. 4).


Using (1) and (3), we can solve for the required output powerof the power amplifier, PT x = GpaPin , that compensates thepower loss due to the optical link and the path loss.

For a given drain efficiency, ρ, and back-off, η, of a poweramplifier, the transmission energy consumption per bit (poweramplifier + over the air energy loss) of the wireless link, ET x

can be calculated as

ET x =(

η

ρ

)PT x

B. (4)

On the uplink, the RF signal received from the end-user ismodulated onto the optical carrier at the RAU. The average SNRof the RF signal at the receiver is given by

SNRup =m2GlnaPRx

Ntot,up, (5)

Ntot,up = 4FkTB +(R2Rl

)P 2

opt10RIN/10B + (2qRPopt)B.

For the uplink, PRx represents the required received powerfrom which we can calculate the required transmit power PT x

as follows:

PT x =PRx

Gpl. (6)

For a given drain efficiency, ρ, and back-off, η, of the poweramplifier, the transmission energy consumption per bit of thewireless link can be calculated using (4).

The power consumption of the laser can be calculated usingthe dc voltage, Vs , and the dc current, Ibias plus the laser driverpower consumption, PLD , as follows:

Plaser = VsIbias + PLD . (7)

The power consumption of the photodetector can be cal-culated from the drain voltage, VDD , incident average opticalpower, Popt , and the responsivity, RPD , as follows:

PPD = VDDPoptRPD . (8)

The energy consumption per bit of the EOE conversion cir-cuitry is given by

EOEO =Plaser + PPD + PTIA

Rb. (9)

where Rb is the wireless bit rate. Finally, the total energy con-sumption per bit is given by

EARoF = ET x + ERFTX + ERFRX + EDSPTX

+ EDSPRX + EOEO . (10)

where ERFTX , ERFRX , EDSPTX and EDSPRX are the analogcircuit energy and digital circuit energy consumptions of thewireless transmitter and receiver, respectively.

To estimate the energy consumption of DSP circuits wefirst identify and estimate the necessary DSP operations andcomputational complexity of each block in the DSP circuitry.From the energy consumption of a logic transition in a metal-oxide-semiconductor field-effect transistor (MOSFET), the en-ergy consumption of various gates such as an inverter, AND,NOR, etc., can be calculated (using the transistor level design

diagrams). We assume an activity factor as calculated in [27].Using these values, the energy consumption of basic digitalcircuits such as a multiplier, an adder, etc., can be calculatedfrom simple logic circuit diagrams. The energy consumptionof a complex DSP operation is assumed to be the linear sumof energy consumptions of smaller DSP operations, such asmultipliers, adders, AND/OR logic, etc. The energy consump-tion of a CMOS transistor implemented with 32 nm CMOSprocess is estimated as 3 fJ [28]. The DSP blocks consid-ered include the FFT/IFFT block, precoder, encoder/decoderand scrambler/descrambler. The model estimates the dynamicpower consumption of the DSP and is as given in Table II.

2) ARoF—Estimating Wireless Transmission Distance: Weassume that the power of the signal modulated on to the opticalcarrier, Pin , is known. In the case of direct modulation of thelaser, the radiated signal power is given by

PT x = GHEUm2GRAUPin (11)

where GHEU and GRAU represent the gain due to the compo-nents in the HEU and RAU, respectively.

We also consider the case of externally modulating the in-tensity of the laser using an MZM with balanced drives. Whenmodulating a sinusoid of amplitude Vin , it can be shown that theoutput power after the photodetector is

PoptRF =(

Popte−αL

4πsin

(πVb

Vπ

)J1

(2πVin

Vπ

)R

)2

Rl

(12)where Jn () is the nth-order Bessel function of the first kind.The detailed derivation is given in Appendix.

Thus,

PT x = GRAUPoptRF . (13)

Assuming a certain receiver sensitivity, PRx , and a path lossmodel, the maximum transmission distance, d, can be calculatedusing a path loss model [say (2) and (6)]. The energy efficiencymetric is then given by:

Eb/m 2 =PDC

Rb × πd2 (14)

where PDC is the dc power consumption of the componentsused in the system and, Rb is the wireless bit-rate.

B. Digitized Radio-Over-Fiber

Typical digitized radio-over-fiber (DRoF) downlink and up-link systems are shown in Fig. 6. In a DRoF link, the highfrequency signal is digitized and the digital signal is modu-lated onto the optical carrier. Digitizing high frequency signalsrequires the use of band-pass sampling ADC’s and DAC’s [15].

1) DRoF—Estimating Power Amplifier Gain: On the uplink,the RF signal is affected by aliasing noise due to band-passsampling, jitter noise of the ADC, quantization noise, noise onthe optical link and the thermal noise of the receiver components.These noise sources are weakly correlated for a digitized signaland can be assumed to be independent [15]. The average SNR


Fig. 6. Digitized RF–over–Fiber. (a) Downlink. (b) Uplink.

of the received RF signal on the uplink is given by [15]:

1SNRup

=1

SNRWL+

1SNRPD

+1

SNRA+

1SNRQ

+1

SNRJ

(15)where

SNRPD =R2P 2

opt

(FkT + (R2Rl) P 2opt10RIN/10 + 2qRPopt)B

SNRWL =PRF

FkTB

SNRA =1

((fh/B) − 1) FkTB

SNRQ = 6.02n + 10log(√

M + 1/√

M − 1)

+ 10log (fs/2fh)

SNRJ =1

4π2f 2σ2τ

.

SNRPD SNRA and SNRJ are the SNR values due to thephotodetector, the aliasing noise and ADC jitter noise, respec-tively; a detailed description can be found in [15]. SNRQ is thequantization SNR in dB, described in [29]. SNRWL is the SNRof the wireless link. The denominator represents the thermalnoise of the receiver. The power amplifier has to compensatefor the digital-to-analog conversion loss and the path loss. As-suming the peak-to-peak voltage of the ADC/DAC are equal,the required output power of the power amplifier is given by

PT x = GpaPin =PRF

sinc2 (fc/fs) GlnaGpl

. (16)

The term sinc2 (fc/fs) represents the RF power loss due toup-conversion using zero-order hold DAC. Using (4), we canobtain the corresponding energy consumption Etx .

On the downlink, the main noise contributor is the receiver’sthermal noise. Thus the average SNR of the received RF signalis given by

SNRdown =sinc2

(fIFfs

)GlnaGplGpaPin

FkTB. (17)

For a given BER, ET x can be calculated, using (4).Finally, the total energy consumption per bit, EDRoF , is calcu-

lated by summing up the analog circuit energy and digital circuitenergy consumptions of the wireless transmitter and receiver,respectively [similar to (10)].

2) DRoF—Estimating Wireless Transmission Distance: Inthe case of DRoF, the transmitted power depends on the param-eters of the DAC. We assume reliable transmission of bits on theoptical link and the RF signal spans the full peak-to-peak of theADC. Since we consider only the downlink with optical links<1 Km, these are valid assumptions. Under these assumptions,the transmitted power is given by

PT x =GRAUV 2

DAC10−PA P R

1 0 sinc2(

fIFfs

)Rl

(18)

where PAPR is the peak to average power ratio.Similar to the analysis presented for ARoF links, from the

estimated transmitter power, the corresponding energy per bitper unit area for a known receiver sensitivity, PRx , is calculatedusing a path loss model and (14).

IV. GREEN OPTICAL/WIRELESS IN-BUILDING IT

NETWORK ARCHITECTURES

In current in-building architectures, wired and wireless net-works work independently; each with its own protocols. How-ever, the integration of these two networks (i.e., optical/wirelessconvergence) would realize a much more flexible architecturewith higher end-user data rates. The RoF technology allows for aseamless integration by eliminating wired transmission protocolprocessing [4].

RoF-based networks (not links) can with proper design bemore energy-efficient. RoF allows for centralized resources withsimple remote antenna units (RAUs). Despite the fact that RoFlinks are not more energy efficient than baseband-over-fiber(BoF) links, which limits the benefits of centralized architec-tures, simpler RAUs yield lower circuit energy consumption(i.e., the penalty of using smaller cells and increasing the num-ber of RAUs will be lower [9]). Furthermore, moving from acopper medium to an optical one reduces energy consumptionand allows for higher data rates [4]. Therefore, in our proposedgreen architectures, we design an optical/wireless in-buildingnetwork based on the RoF technology.

We propose and analyze the following three architectures,depicted in Fig. 7.

1) ARCH1, Distributed Processing Using BoF: This is thelegacy architecture [Fig. 7(a)]. It employs wireless access


Fig. 7. Green architectures (a) ARCH1. (b) ARCH2. (c) ARCH3.

points, each with its own processing circuitry, connectedto the main distribution frame (MDF) via 1 Gbps opticalEthernet links.

2) ARCH2, Centralized and Dedicated Analog Processingand Centralized and Shared Digital Processing UsingARoF: In this architecture, all RAUs share a DSP unitbut have dedicated analog transceiver chains at the MDF[Fig. 7(b)]. ARCH2 employs ARoF links between theRAU and the MDF.

3) ARCH3, Centralized and Dedicated Analog Processingand Centralized and Shared Digital Processing DRoF:As illustrated in Fig. 7(c), this architecture is similar toARCH2, but it employs DRoF links between the RAUand the MDF.

ARCH2 and ARCH3 share the DSP unit unlike ARCH1.In terms of the resource utilization and management, ARCH2and ARCH3 would be more efficient. However, the architec-tures differ in the requirements of these components. Usingthe model presented in Section III, we analyze the effect ofthese differences on energy consumption of the architectures.

Fig. 8. Experimental Block diagram.

The RoF-based architectures (ARCH2, ARCH3) were designedto be compliant with the common architectures employingRoF [30], [31].

V. RESULTS AND ANALYSIS

A. Experimental Results

In this section, we present experiments conducted to vali-date our point-to-point link analysis. The block diagram of theexperimental setup is shown in Fig. 8. Here, the RF signal gener-ation and demodulation are conducted off-line using MATLAB.An OFDM signal of sub-carrier bandwidth 732 kHz and 26subcarriers (B =∼20 MHz) is considered. Each carrier mod-ulated for 16-QAM symbol is generated and upconverted toa carrier frequency, fc = 2.44 GHz, offline in MATLAB. AnFFT size of 2048 and cyclic prefix of 64 is used. The resultingbit-rate is ∼76 Mbps. Training symbols are used for channel es-timation. The channel is re-estimated every OFDM symbol byre-encoding the decoded bits and comparing with the receivedOFDM symbol. For ARoF, the analog signal generated is di-rectly uploaded onto the arbitrary waveform generator (AWG),which is subsequently modulated onto the optical carrier using asingle drive MZM and transmitted through a 500 m single-modefiber (SMF). After photodetection, the signal is amplified andcaptured using a digital signal oscilloscope (DSO). The cap-tured data is demodulated offline using MATLAB. In the caseof DRoF, the generated analog signal is digitized off-line andthe digital signal is loaded onto the AWG. The digital signal isalso externally modulated onto the optical carrier, transmittedthrough a 500 m SMF, photo-detected, amplified and capturedusing the DSO. The RF signal is reconstructed and demodulatedoffline. The system is optimized to obtain the best performancein both setups. For ARoF, we get an error vector magnitude of∼1% and for DRoF, ∼0.2818%.

To observe the effect of different losses in the system, we varythe input power injected into the photo-detector. We also con-sider the effect of different Nyquist zones using an intermediatefrequency (IF) and by up-converting to higher carrier frequencyusing a local oscillator (LO) at RAU. In the experiments, we keepthe RF input power constant and measure the output RF power.In the case of DRoF, the RF power measurement is conducted inMATLAB, since the DAC was implemented in MATLAB. For


Fig. 9. Energy per bit versus PD input power.

DRoF, we use a sampling frequency, fs = 300 MHz, and a 10bit resolution; this results in a data rate of 3 Gbps.

To calculate the receiver sensitivity, the noise floor, PN =FN0B, is assumed. A required SNR of 19 dB is also assumed,since in our experiments we use 16QAM modulation. We alsoassume a receiver noise figure of 5 dB and margin of 5 dB.Subsequently, the receiver sensitivity, PRx , is calculated as

PRx = PN + 19 + 5 (dBm) . (19)

The noise floor is calculated as

PN = −174 + 5 + 10log(B) (dBm) . (20)

The IEEE 802.11n in-building path loss model [32] is usedto calculate the maximum distance; it is given by

PL = PT x − PRx =−45.0325 + 35log(d) + 20log(fc) (dB).(21)

From Fig. 9, we also observe that the sinusoidal approxi-mations in the analytical model closely follow the experimentalresults for an OFDM signal in both ARoF with an external mod-ulator and DRoF. However, there are some deviations at verylow PD input powers. In the case of ARoF, at low input PD pow-ers the system is noise dominated. Since transmitter noise is notconsidered when estimating the wireless transmission distance,the model does not match the experimental results. In the case ofDRoF, at low PD input powers the optical transmission is not re-liable; thus the RF signal is distorted and the model predictionsare off. Excluding the noise-limited regions, the average erroris within 5%. These results show that the model can provideadequate insights into the behavior of energy consumption withdifferent parameters. This flexibility allows us to estimate theenergy consumption behavior at the network level by scalingthe model, thereby measuring the energy consumption of theproposed green in-building IT architectures.

B. Point-to-Point Link Results—Theoretical

Using the model, we can estimate the gain of the power am-plifier required to maintain a certain BER in BoF, ARoF and

TABLE IIIMODEL PARAMETERS [15]

Parameter ValuekT (N0) -204 dBW/Hz

Rl 50ΩF 3.3 dBρ 20%η 10RIN -174 dB/Hz

Glna [33] 19 dBGa 1

VDD 2.8 VRPD [34] 0.65 A/W

n 10στ 0.3 psfs 300 MHz

DRoF links. Assuming certain typical circuit energy consump-tion (listed in Table II) and link parameters (listed in Table III),we evaluate the total (transmission+circuit) energy consump-tion per bit of individual ARoF, DRoF and BoF links for 64QAM(spectrally efficient with low robustness) by varying salient pa-rameters that significantly affect the energy consumption. Theparameters considered are:

1) Cell Size: changes the path loss.2) Fiber Length: changes the optical link loss.3) Nyquist zone (for DRoF): changes the digital-to-analog

conversion loss.The investigated energy efficiency results depend on the pa-

rameter values; however, the relative performance trends remainthe same.

1) Cell Size: In a wireless link, the noise is generally domi-nated by the receiver thermal noise since the transmitter’s ther-mal noise is degraded due to path loss. On the uplink, the RINand shot noise in the optical link cause a significant degradationin the SNR.

Fig. 10(a) and (b) compare the energy consumption per wire-less PHY bit of ARoF, DRoF and BoF on the downlink anduplink, respectively, for a 100 m fiber link at different wire-less transmission distances. We assume a RF carrier frequencyof 2.44 GHz with bandwidth of 20 MHz in accordance withthe IEEE 802.11 WLAN standard. We observe that the energyper bit for ARoF and DRoF increases rapidly as the wirelesstransmission distance increases. This is due to the high poweramplifiers needed to account for the loss in RF power on theoptical link. In the case of ARoF, the loss in RF power is dueto modulation depth, optical link loss and coupling loss. Byreducing these losses, ARoF can be more energy efficient. Inthe case of DRoF, modulation depth and any optical link lossdoes not affect the RF power. However, there is a loss due toup-conversion in the DAC; when recovering the RF signal fromthe digital signal at the required carrier frequency, there is lossdue to the sinc behavior of the DAC. With fc = 2.44 GHz andsampling frequency, fs = 300 MHz, this loss is approximately36 dB. However, in both cases, BoF is the most energy efficient,since there is no loss in RF power due to optical transmission.

2) Fiber Length: Fig. 11 shows the energy per bit of ARoFand DRoF for different fiber lengths for a fixed cell size (d =10 m). In the figure we notice that the energy per bit for DRoF


Fig. 10. Comparison of the three dedicated links; Energy per bit versus wire-less transmission distance (theoretical). (a) Downlink. (b) Uplink.

does not change significantly when increasing the fiber length.However, in ARoF, the energy per bit increases when increas-ing the fiber length. In DRoF, the RF power is not affected byany optical link loss, whereas as the fiber length increases, theRF power loss in ARoF increases. This implies that for a lossyoptical transmission or long-reach fiber scenarios (e.g., back-haul networks), it is more energy efficient to use DRoF. Thecross over-point beyond which ARoF is more energy efficientdepends on the required system components and their powerconsumption.

3) Nyquist Zone: The Nyquist zone is defined by the carrierfrequency and sampling frequency. In the case of DRoF, thereis a large loss in the RF power due to the sinc behavior of theDAC. The amount of loss depends on the Nyquist zone in whichthe carrier frequency exists.

Fig. 12 shows the change in energy per bit for differentNyquist zones at a fixed distance of 5 m on the downlink. Inthis study, we assume that the carrier frequency and samplingfrequency are kept the same. At the RAU, a LO is used to up-convert to the desired carrier frequency (essentially DRoF at anIF). The Nyquist zone can also be changed by changing the sam-pling frequency; increasing the sampling frequency lowers the

Fig. 11. Energy per bit for different fiber lengths (Theoretical).

Fig. 12. Energy consumption per bit versus Nyquist zone (Theoretical).

Nyquist zone of a particular carrier frequency. Lower Nyquistzones have lower energy per bit due to lower loss. However,increasing the sampling frequency increases the data rate onthe optical link. Another point to note is that the loss betweendifferent Nyquist zones reduces as the Nyquist zone increases,i.e., the difference between the first and second Nyquist zonesis larger than the one between the seventh and eighth Nyquistzones. This implies that choosing a sampling frequency that re-sults in a Nyquist zone of 7 rather than 8 does not give as muchimprovement in energy per bit as does when moving from thethird to the second Nyquist zone.

C. Energy Consumption of Proposed GreenArchitectures—Theoretical

Transmission throughput is considered a salient requirementin the design of a network. By moving to smaller cell sizes,we not only decrease the transmission energy consumption, butalso increase the network throughput. To evaluate the proposedarchitectures, we assume a certain in-building area that requireswireless coverage, and we cover this area with N cells each ofsize r and bandwidth B. Assuming frequency re-use with low


Fig. 13. Comparison of the Eb−net of the three architectures; DRoF: Upcon-version at RAU using DAC (Theoretical).

co-channel interference, the maximum total network through-put, i.e., the peak data rate, is given by N × B. Now, let’s as-sume the cell size is reduced by half, r/2; hence, the number ofcells required to cover the same area will be 4N . These smallercells will still have the same bandwidth B. Thus, the maximumtotal network throughput will be 4NB. When considering net-work architectures that employ different technologies such as60 GHz versus 2.4/5 GHz networks or architectures which havedifferent point-to-point (p2p) link throughput, as in this paper,scaling bandwidth and comparing power consumption wouldnot capture the added benefit of higher throughput offered.

To compare p2p technologies, we use energy per bit as theenergy efficiency metric. This metric is independent of the indi-vidual p2p throughput and captures their energy consumption.At the network level, we use a similar metric; however, here,we compare the required energy to transmit one bit through thewhole network, Eb−net , which is given as follows:

Eb−net =Peak Total Power Consumption

Total Network Throughput. (22)

With this metric, we can now measure the energy efficiency ofthe proposed network architectures, under practical deploymentscenarios. The metric also has the advantage of only dependingon the technology employed at the physical data layer. Thus,the analysis can be easily extended to include or compare higherlevel protocols by estimating on average how many effective bitsare transmitted over the transmission layer per second and thenmultiplying the obtained number by Eb−net to get the overallcross-layer power consumption. In addition, daily user trafficprofiles [35] can be used to capture the daily energy consumptionin the network. In this work, we focus on the physical plane only.

Using Eb−net , Fig. 13 compares the proposed green in-building architectures by estimating the required power am-plifier gain for a 100 m × 100 m floor plan (i.e., large in-building network). In the comparison, the architectures supportonly WLAN service. Here, we consider an in-building WLANscenario; thus, the bandwidth per cell is Bpercell = 20 MHz.

As observed in the figure, with ARCH1, Eb−net remains rel-atively constant at lower distances (5–15 m). At these distances

Fig. 14. Comparison of the Eb−net of the three architectures; DRoF: Upcon-version at RAU using Local Oscillator (Theoretical).

the network throughput and the number of cells required forachieving complete wireless coverage scale by the same amount.Since at lower distances the power consumption is dominatedby circuit energy, the total network power consumption scaleslinearly with the number of cells. Thus, the network through-put and the total power consumption also scale by the sameamount, thereby having a constant Eb−net . This implies thatby decreasing the cell size, the network throughput can be in-creased; however, the energy required to transmit a bit throughthe whole network will remain the same. This phenomenon canbe seen in the other architectures as well (more explicitly ifwe consider cell size <5 m). With that, we can conclude thatwe should use the smallest possible cell size to get the highestthroughput and the maximum energy saving. However, this maynot be a cost-effective solution since for a given coverage area,we would require a large number of cells, and thus a large num-ber of equipment. This presents a trade-off between cost andenergy efficiency. The most energy and cost effective solutionwould be to design the network to operate at the point at whichthe curve of Eb−net starts flattening out. The RoF architectureshave higher energy consumption due to the high gain poweramplifiers needed to compensate for the optical transmissionlosses. For the values considered, the curves for ARCH2 andARCH3 overlap; however, they may not overlap under differentassumptions for parameters such as optical noise figure, EOEconversion loss, sampling frequency, etc., as demonstrated inFig. 14.

Fig. 14 compares the three architectures considering an IF(1st Nyquist zone and upconversion using an LO at the RAU)for ARCH3. We observe that the ARCH3 curve does not in-crease as rapidly as when using a higher Nyquist zone. For theparameters considered, ARCH3 with cell sizes <17 m is moreenergy efficient than ARCH1.

In the above comparisons, we do not consider the power con-sumption required for cooling or the static power consumptionof the digital processing units. In the case of the centralizedarchitectures (e.g., ARCH2, ARCH3), the same processing unitcan be shared among all the cells. However in decentralized


architectures (ARCH1), each cell will have its own unit. Incases where the remote access points consume more power,such as mobile base stations, the energy per bit floor for ARCH1will be higher; thus, for remote access points/base stations thathave high static power consumption, centralized architecturesare more energy efficient. Also sleep mode can be applied to allthe architectures to further reduce the energy per bit floor.

VI. CONCLUSION

Energy efficiency is now regarded a salient requirement in theoperation of next-generation networks. In this paper, we investi-gated radio-over-fiber (RoF) technologies for achieving energy-efficient optical/wireless converged in-building networks. Weproposed and analyzed three different fiber-based in-buildingarchitectures; ARCH1 is the legacy architecture (i.e., usingbaseband-over-fiber); ARCH2 and ARCH3 centralize the digitalsignal processing but have dedicated RoF links for each cell. Wepresented a model to estimate and compare the power consump-tion of different RoF technologies. We validated the model usingexperimentally measured data. Our results show that point-to-point RoF links are not as energy efficient as BoF due to theoptical link losses and conversion losses. However, centralizedarchitectures using RoF technology are energy efficient whendesigned keenly with small cell sizes and when the remote ac-cess points/ base stations have high static power consumption.We show that careful design aimed at minimizing the loss dueto the optical transmission will improve the energy efficiencyof RoF links. For instance, our results show that DRoF-basedarchitectures at lower Nyquist zones can be more energy ef-ficient when the remote access points/base stations have highstatic power consumption. Under the assumptions consideredin this paper, ARCH3 designed at the 1st Nyquist zone withupconversion using a local oscillator at the remote antenna unitis more energy efficient for cell sizes <17 m.

APPENDIX

DERIVATION OF RF OUTPUT POWER THROUGH AN

EXTERNALLY MODULATED OPTICAL LINK

The electric field equation at the output of the MZM is given[36] as follows:

Ac (t) =A0

2e

j π2

(ejφ1 (t) + ejφ2 (t)) (23)

where A0 is the electric field at the output of the laser.For a sinusoid input and balanced drives:

φ1 (t) =πVRFcos(2πfRF t)

Vπ(24)

φ2 (t) = Vb −πVRFcos(2πfRF t)

Vπ. (25)

The Fourier transform of (23) is given by

Ac (f) =A0

2

∞∑n=−∞

inanδ(f − nfRF) (26)

where

an = Jn

(πVRF

Vπ

)+ Jn

(πVRF

Vπ

)ejnπ e

j π V bV π . (27)

The optical power spectrum can be found as Ac (f) A∗

c (−f) which is given by

Pc(f) = Ac (f) A∗c (−f) (28)

=A2

0

8π

∞∑n=−∞

in∞∑

k=−∞aka∗

k+nδ(f − nfRF) (29)

where∞∑

k=−∞aka∗

k+n =∞∑

k=−∞Jk (φRF) Jk+n (φRF)

+ e−jθ2

∞∑k=−∞

Jk (φRF) Jk+n (φRF) e−jkπ

+ ejnπ ejθ2

∞∑k=−∞

Jk (φRF) Jk+n (φRF) ejkπ

+ ejnπ∞∑

k=−∞Jk (φRF) Jk+n (φRF)

and

φRF =πVRF

Vπ.

The power of the first harmonic is the optical power of the RFsignal. Thus, the received electrical power after photodetectionis given by

PoptRF =(

Popte−αL

4πsin

(πVb

Vπ

)J1

(2πVin

Vπ

)R

)2

Rl .

(30)A more general derivation in the presence of dispersion can

be found in [37].

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Authors’ photographs biographies not available at the time of publication.

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