Opportunistic Third-Party Backhaul for Cellular Wireless Networks

Transcription

1 1 Opportunstc Thrd-Party Backhaul for Cellular Wreless Networks Russell Ford, Student Member, IEEE, Changkyu Km, Student Member, IEEE, Sundeep Rangan, Senor Member, IEEE arxv: v1 cs.ni 4 May 213 Abstract Wth hgh capacty ar nterfaces and large numbers of small cells, backhaul the wred connectvty to base statons s ncreasngly becomng the cost drver n cellular wreless networks. One reason for the hgh cost of backhaul s that capacty s often purchased on leased lnes wth guaranteed rates provsoned to peak loads. In ths paper, we present an alternate opportunstc backhaul model where thrd partes provde base statons and backhaul connectons and lease out excess capacty n ther networks to the cellular provder when avalable, presumably at sgnfcantly lower costs than guaranteed connectons. We descrbe a scalable archtecture for such deployments usng open access femtocells, whch are small plug-and-play base statons that operate n the carrer s spectrum but can connect drectly nto the thrd party provder s wred network. Wthn the proposed archtecture, we present a general user assocaton optmzaton algorthm that enables the cellular provder to dynamcally determne whch mobles should be assgned to the thrd-party femtocells based on the traffc demands, nterference and channel condtons and thrd-party access prcng. Although the optmzaton s non-convex, the algorthm uses a computatonally effcent method for fndng approxmate solutons va dual decomposton. Smulatons of the deployment model based on actual base staton locatons are presented that show that large capacty gans are achevable f adopton of thrd-party, open access femtocells can reach even a small fracton of the current market penetraton of WF access ponts. Index Terms cellular networks, 3GPP LTE, femtocells, access prcng, utlty maxmzaton. I. INTRODUCTION Cellular wreless networks have been tradtonally desgned on the premse that the wreless nterface s the bottleneck for system throughput and capacty. However, a surprsng recent trend s that backhaul, meanng the wred connectvty to the base statons, s ncreasngly becomng the domnant cost drver n many networks 1, 2. Even for comparatvely lower data rate pre-4g systems, backhaul already accounted for a sgnfcant percentage of the operatng costs (3 to 5% by some estmates 3, 4). Hgher data rate 4G systems combned wth the ncreasng adopton of a large numbers of small cell deployments 3 wll requre even greater costs n the backhaul, partcularly n markets where the operator does not have unversal fber access. Ths work presents a novel deployment model for cellular provders that would enable the rsng costs of backhaul networks to be mtgated by offloadng traffc to thrd-party Ths materal s based upon work supported by the Natonal Scence Foundaton under Grant No R. Ford (emal:rford2@students.poly.edu) and S. Rangan (emal: srangan@poly.edu) are wth the Polytechnc Insttute of New York Unversty, Brooklyn, NY. backhaul connectons. The basc premse s that backhaul servces are currently purchased wth guaranteed servce level agreements (SLAs) along dedcated lnes 5, whch come at a sgnfcant cost for operators. These SLAs must generally be provsoned for the peak data rates. However, due to varatons n loadng and channel condtons, much of the purchased capacty goes to waste. We propose that, rather than provsonng these lnks for the peak demand, cellular networks should be able to dynamcally leverage excess capacty on exstng backhaul lnks provded by thrd-party enttes. The role of ths thrd party may be played by other servces provders (.e. wrelne ISPs) or even broadband customers, the very end-users themselves. The thrd partes can provde connectvty to the operator s subscrbers through femtocells 6 8, whch are small, lowcost, cellular base statons that operate n the provder s spectrum but are connected nto the thrd party s backhaul. The network can then offload moble subscrbers to the thrd party femtocells and the cellular provder would remburse the thrd party for use of the backhaul resources (and possbly cover the one-tme cost of the femtocell as well). The key s to offload traffc opportunstcally when thrd partes have excess backhaul capacty. Snce ths capacty would only be purchased when used and snce moble traffc would generally represent only a small ncrement n average demand at most enterprses and resdences, the opportunstc capacty can presumably be purchased at much a lower cost than guaranteed lnes to base statons. In addton, sgnfcant progress has been made n makng femtocells completely self-organzng wth plug-and-play nstallaton 9, 1, mplyng that thrd-party femtocells would have mnmal operatng costs. In ths way, opportunstc backhaul wth thrd-party open-access femtocells can provde a scalable model for hgh-densty, hgh-capacty deployments at low cost. We descrbe a potental archtecture for thrd-party opportunstc backhaul model wthn the LTE/SAE framework 11 (see Secton II). Wthn ths archtecture, we consder one of the key techncal problems, namely the optmzaton of user assocaton: The cellular provder s network must dynamcally assgn moble subscrbers between the operator-controlled base statons and thrd party femtocells based on channel and nterference condtons, backhaul capacty, traffc demands and thrd party access prcng. We present a general optmzaton formulaton for ths problem usng a recently-developed methodology n 12, whch tself was based on 13. The user assocaton optmzaton problem s generally non-convex,

2 2 but followng 12, we show that the optmzaton admts a dual decomposton that enables applcaton of effcent approxmate augmented Lagrangan methods. The methodology s extremely general and enables ont optmzaton for load balancng and nterference coordnaton and can ncorporate a large class of nterference models, network topologes and prcng schemes. To evaluate the potental capacty ncrease for ths opportunstc backhaul model, we present a smple smulaton where we assume open access femtocells can be co-located at a small fracton (between 2 and 25%) of the locatons where current resdental and enterprse WF access ponts are deployed. Basng our smulatons on reported locaton data of WF access ponts and cellular base statons and ndustry standard cellular evaluaton models 14, we show that large capacty gans are possble wth offload nto thrd-party networks and we dscuss the potental costs of nvestment n the thrd-party model compared to the addtonal operator-deployed or leased lne nfrastructure requred to support an equvalent gan n system throughput. A. Related work Although femtocells have been tradtonally used for mprovng coverage n prvate resdences and enterprses 6 8, open access femtocells deployed explctly for wdearea coverage have also been consdered see, for example, Qualcomm s neghborhood small cells whtepaper 15. That work showed sgnfcant capacty gans would be possble wth open-access LTE femtocells placed at a small fracton (1%) of resdences. The analyss, however, does not explctly consder the ssues of backhaul usage and thrd-party chargng, whch are the man focus of ths paper. A comparable busness model s also found n publc WF networks such as FON, whch boasts tself as the world s largest WF network 16. FON members, called Foneros, agree to allow other Foneros to securely connect to ther custom home WF AP and, n return, gan access the mllons of other hotspots hosted by members of the communty. Under one of the avalable membershp plans, users are ncentvzed to provde relable WF servce by beng compensated perbyte of data traffc consumed by other Foneros. The FON busness model has been tremendously successful n recent years, brngng n 28 mllon Euro n revenue durng In ths work, we consder offload to cellular, rather than WF, whch has the advantage of better support for moblty and nterference management. Moreover, n the model presented, the network makes all cell selecton decsons and s responsble for payng thrd partes for offloaded traffc; thrd-party vs. provder ownershp s transparent to the moble subscrbers who see all base statons as belongng to a sngle network. A mathematcal evaluaton of prcng schemes for moble subscrbers s presented n 18, whch concludes that operators can maxmze revenue by offerng femto servces to all customers at a flat rate, that s, not as an extra value-added servce but part of the basc package. We adopt ths method of subscrber chargng n our model, whch we beleve has the added beneft of encouragng femtocell adopton. The utlty Source s (host) Internet GW Core network r1 max, c1 3 rd -party lnk r2 max, c2 Backhaul BS nbs ϵ 1...NBS ρ21 ρ12 ρ11 Access l ϵ 1...L MS nms ϵ 1...NMS Fg. 1: Heterogeneous network archtecture where operatorcontrolled cells (drawn n green) are combned wth thrd-party controlled femtocells (drawn n purple). for data consumers s often modeled by a logarthmc functon of rate (a specal case of so-elastc utlty), whch has the ratonal bass of expressng the decreasng margnal payoff of rate experenced by users Understandng the shape of the supply-sde utlty curve for thrd-party backhaul provders s not as straghtforward, however. Intutvely, a monopolst operator wants to offer a prce for backhaul capacty that maxmzes ther net utlty, whch s a functon of the average or total throughput seen by users as well as the cost of connectvty over thrd-party lnks. Although we consder the operator to be a prce taker under ths model, f the thrd-party provder s a ndvdual end-user wth leased broadband capacty, we assume ther supply-sde prce elastcty s lkely to be very nelastc snce they wll accept any prce offered for ther excess bandwdth (seeng as how they would otherwse get nothng) 21. Ths dynamc becomes sgnfcantly more complex, requrng a game-theoretc approach to analyss, when we consder a compettve market where one or more broadband ISPs offer resources to one or more moble servce provders and the prce of sad resources may be a tme-varyng functon of demand. 1 In ths paper, we forgo an analyss of compettve markets n favor of nvestgatng the relatonshp between a sngle cellular provder (wth exstng macrocellular nfrastructure) and many 3rd-party backhaul provders. We assume ths baselne model n order to demonstrate a general framework for determnng net utlty gans and couch an upper bound on the ncentve that could be offered whle stll ncreasng operator revenue. II. SYSTEM ARCHITECTURE As descrbed n the Introducton, we propose that thrd partes use open-access femtocells to provde servce to the moble subscrbers of a cellular operator. The basc network archtecture s shown n Fg. 1 whch follows the standard model of 3GPP LTE/SAE heterogeneous networks 9, 11. As shown, there are two classes of base staton cells n the proposed model: operator-controlled and thrd-party. The operator-controlled cells (set BS M ) are the standard BS nodes 1 Such mult-sded markets are the subect of 22, whch ntroduces a bddng system through whch provders compete for network resources. ρ22

3 3 connected drectly to the operator core network and managed by the operator. These are typcally macro- or mcrocells, hence the subscrpt M. The thrd party cells (set BS F ), are the open access femtocell BS nodes nstalled by a thrd party and connected to the thrd-party ISP network,.e. the Internet. In the proposed model, the thrd-party and operator frst agree on some access prcng, perhaps a cost per unt tme or unt data that the operator wll remburse the thrd-party provder when moble subscrbers connect to ther network we wll see that our optmzaton methodology can ncorporate a range of prcng models. Then, at any tme, the network can choose to assgn mobles to ether operator-controlled or thrd-party cells dependng on the channel and nterference condtons, traffc loadng and access prcng. There are several convenent features of ths deployment model: Deployment ease and scalablty: Most mportantly, the use of thrd party femtocells offers a scalable and cost effectve approach to ncrease capacty: Snce femtocells are low-cost, plug-and-play devces, thrd partes can nstall these devces themselves, thereby mmedately creatng an abundance of cell stes vrtually for free. In addton, as descrbed n the Neghborhood Small Cell concept 15, when the cellular operator also provdes a broadband resdental and enterprse ISP servce (such as Verzon s FOS or AT&T s U-Verse servce), they could nclude a femtocell wthn the WF access pont gven to the subscrber, thereby automatcally enablng femtocell capabltes n every subscber s locaton. Drect operator to thrd party economc relatonshp: The economc nteracton wth respect to access prcng can be made entrely between the cellular operator and the thrd-party provder the moble subscrber need not be nvolved. Ths arrangement s possble snce, n networks such as 3GPP LTE, for mobles n connected mode, the network makes all decsons on whch cells serve the mobles 11. Thus, the network and thrd-party provder can come to an agreement on access prcng, and then the network can decde, n each tme nstant, whether to have ts mobles served by the thrd party cells based on channel condtons, network load, and other factors. Transparency to mobles: Related to the above pont s that, from the moble staton perspectve, both classes of base staton cells thrd-party and operator-controlled are completely dentcal, potentally usng the same rado access technology and potentally operatng n the same spectrum band. 2 Thus, the thrd party vs. operator ownershp s transparent to the mobles. Ths feature s benefcal snce the moble user s really concerned wth the qualty of the connectvty and has no nterest per se n who provdes that connecton. Correctly matched ncentves: Snce the operator wll only remburse thrd partes when Open Subscrber Group (OSG) mobles are served by the thrd party cell, the operator does not need to enforce proper nstallment 2 Our nterference model presented n the followng secton addresses cochannel and separate channel deployments. and operaton of the femtocell. Thrd partes wll be naturally ncentvzed to keep ther femtocell on and wellpostoned to attract the operator to move ts moble subscrbers onto ts network to receve payment. On the other hand, f the thrd-party network s busy and cannot support the addtonal moble traffc, the thrd-party s free to shut off or cap the rate to the femtocell and the cellular operator can adust ts cell selecton decsons accordngly. As we wll see n the next secton, our user assocaton algorthm can account for backhaul lmts on the thrd party cells. Mnmal changes to exstng standards: All the hooks necessary for the proposed thrd-party offload can be handled wthn the exstng cellular standards. For example, n 3GPP LTE, the mobles already provde the network wth all the measurement reports necessary to determne the arlnk condtons and receved sgnal strengths to make the handover decsons. Also, n the current LTE network model, all traffc from the publc Internet s routed frst through a gateway 3 before beng tunneled to the base statons, whether the base statons cells are operator-controlled or operated by a thrd-party outsde the operator core network Thus, the network can both montor the exact amount of tme and data to each cell type for chargng and to measure lnk qualty. III. USER ASSOCIATION OPTIMIZATION As mentoned above, a key techncal challenge n realzng the proposed archtecture s that the cellular provder requres a good algorthm for user assocaton: the cellular provder must determne, n each tme nstant, how to assgn moble users between the thrd-party and operator-controlled cells whle accountng for channel and nterference condtons, access prces and loadng. To ths end, we use a utlty maxmzng algorthm proposed n 12 and 13 for optmzed user assocaton n heterogeneous networks, ncorporatng access prce nto the utlty functon. A. Optmzaton Formulaton Returnng to Fg. 1, let MS, = 1,... N MS denote the set of moble statons and BS, = 1,..., N BS set of base staton cells, the latter set ncludng both operator-controlled and thrd-party cells. For each MS, let Γ MS () denote the ndces such that BS can potentally serve the moble. Smlarly, let Γ BS () be the set of mobles potentally served by BS. Also, let r and w be the rate and bandwdth allocated to MS from BS for Γ MS (). Let r MS and r BS be the total rate to the MS and BS respectvely, whch must satsfy the constrants r MS r, r BS r. (1) Γ MS() Γ BS() 3 The gateway, n ths case, serves the combned functon of the LTE P- GW/S-GW. We consder these to be co-located nodes. 4 Note that whle protocols such as Selected IP Traffc Offload (SIPTO) 23 allow small cell traffc to bypass the CN and be offloaded drectly to the Internet, we do not consder ths case here snce network and transport layer moblty functons need to be handled by the CN n any case.

4 4 Absent of carrer aggregaton 24, mobles n LTE are typcally only served by one cell at a tme. If we let r be the vector of the rates r, we wll denote ths sngle path constrant as r S := {r : r = for all but one Γ MS ()}. (2) Now, followng 12, we attempt to fnd rates to maxmze some utlty functon of the form U(r MS ) = N MS =1 U (r MS ), (3) for some utlty functons U (r MS ). To account for the backhaul costs that the operator must pay the thrd party provders, we assume there s a cost of the form C(r BS ) = N BS =1 C (r BS ), (4) where C (r BS ) s the cost for the traffc on BS that the operator wll have to pay the thrd-party that owns BS. BS nodes belongng to BS M represent operator-deployed cells connected over statcally-provsoned backhaul lnks wll generally have zero cost (C (r BS ) = ), snce we consder exstng nfrastructure to be a sunk cost for the operator. Nodes n BS F are thrd-party cells and have some postve cost (C r BS ) > ) that they charge the operator. The cost functons C (r BS ) can also be used to ncorporate rate lmts on ether the thrd-party or operator-deployed cells due to fnte backhaul capacty on those cell stes. The goal s to maxmze a net utlty, U(r MS ) C(r BS ), subect to constrants on the rates. The rate lmts depend on the channel and nterference condtons, whch we model usng a lnear mxng nterference model proposed n 25. Let z be the nterference power on the lnk from BS to MS. As descrbed n 12, assumng that the base statons radate a fxed power per unt bandwdth, the vector of nterference powers must satsfy a constrant of the form z Gw, (5) where w s the vector of bandwdth allocatons and G s an approprate gan matrx. The rate on the lnks must then satsfy r w ρ (z ), (6) where ρ (z ) s the spectral effcency (rate per unt bandwdth) as a functon of the nterference level z. Also, the bandwdths must satsfy some constrants of the form w w, (7) Γ BS() where w s the total bandwdth avalable n BS. By approprate selecton of the gan matrx G, ths formulaton can ncorporate both co-channel deployments of the thrd-party and operator-controlled cells where the two types of cells use the same bandwdth and nterfere wth one another, or separate channel deployments where there s no nterference. Now let be the vector of all the unknowns = (r, r BS, r MS, w, z) T. (8) The constrants (1), (5) and (7) can be replaced by nequalty constrants represented n a matrx form A b, (9) for approprate choce of the matrx A and vector b. We can then wrte the user assocaton problem as the optmzaton max U(r MS ) C(r BS ) (1a) s.t. A b (1b) r w ρ(z) r S, (1c) (1d) where the constrant (1c) s a vector shorthand for the constrants (6). B. Dual Decomposton Algorthm The optmzaton (1) s, n general, non-convex due to the nonlnearty of the nterference rate-nterference constrants (1c) and the sngle path constrants (2). However, followng 12, we can fnd an approxmate soluton to ths problem n two steps. Frst, we ntally gnore the sngle path constrant (2). The resultng optmzaton wll produce a vector wth rates on all paths to the mobles. We then smply truncate the soluton to take the path wth the largest rate. Ths truncaton procedure s well-known n networkng theory 26 and often ntroduces lttle error, snce the multpath soluton tends to concentrate on domnant sngle paths. Unfortunately, even wth the multpath approxmaton, the optmzaton (1) wll be non-convex. However, as n 12, we can show that the optmzaton admts a separable dual decomposton. Specfcally, the Lagrangan correspondng to the optmzaton (1) wthout the sngle path constrant (1d) s gven by L(, µ) := U(r MS ) C(r BS ) µ T g(), (11) where g() s the vector of constrants ( T g() = A b, r w ρ(z)), (12) and µ s the vector of dual parameters parttoned conformably wth g() µ = (µ, µ r ) T. (13) Now central to usng dual optmzaton methods s the ablty to compute the maxma of the form (µ) := arg max L(, µ) Φ(), (14) for some augmentng functon Φ(). Usng a smlar argument as n 12, the followng lemma shows that, under the assumpton of a separable augmentng functon Φ( ), the optmzaton (14) admts a separable dual decomposton. Lemma 1: Let Φ() be any separable functon of the form Φ() = Φ(r, r BS, r MS, w, z) = φ r (r ) + φ z (w, z ) + φ BS (r BS ) + φ MS (r MS ) (15)

5 5 for some functons φ r ( ), φbs ( ), φ MS ( ) and φ z ( ). Let µ be any vector of Lagrange parameters and let (µ) be the correspondng maxma for the dual optmzaton augmented by Φ(), namely (µ) = ( r, r BS, r MS, ŵ, ẑ) := arg max L(, µ) Φ(). (16) Then, the components of (µ) are gven by the solutons to the optmzatons r arg mn φ r (r ) + (λ r + µ r )r and r BS r arg mn r MS r BS arg max r MS C (r BS U (r MS ) + φ BS ) φ MS (r BS (r MS ) + λ BS ) λ MS (ŵ, ẑ ) arg max µ r w ρ (z ) w,z λ z z λ w w φ z (w, z ), r BS r MS where the dual parameters µ are parttoned as n (13) and the parameters λ are the components λ = (λ r, λ BS, λ MS, λ w, λ z ) := A T µ, where the vector λ has been parttoned conformably wth n (8). Proof: Ths result follows mmedately from the separable structure of the obectve functon and constrants. The lemma shows that the vector optmzaton (16) separates nto a set of smple one and two-dmensonal optmzatons over the components of and can thus be computed easly for any dual parameters λ. As dscussed n 12, ths separablty property has two key consequences that follow mmedately from standard optmzaton theory 27: Frst, one can use weak dualty to effcently to compute an upper bound on the maxmum net utlty. Specfcally, the net utlty s bounded by max U() C() max L(, µ), (18) for any dual parameters µ. Moreover, the rght-hand sde of (18) s convex n µ, and snce the dual maxma s separable, one can compute ts value and gradent for any µ. Thus, one can effcently mnmze the rght hand sde of (18), provdng a computable upper bound on the net utlty. Although the dual upper bound may not be tght (.e. the optmzaton may not be strongly dual), the bound provdes a computable metrc under whch any approxmate algorthm can be compared aganst. A second consequence of a computable dual maxma s that one can effcently mplement several well-known augmented Lagrangan technques to fnd approxmate maxma to the net utlty U() C(). These methods nclude varous nexact versons of alternatng drecton method of multpler methods 28, 29 see 12 for more detals. A valuable property s that ths dual decomposton apples n very general crcumstances. In partcular, one can consder arbtrary cost and utlty functons, and SNR-to-rate mappngs ρ ( ). Thus, the framework provdes a tractable and general methodology for a large class of networks, nterference scenaros and prcng schemes wth computable upper bounds on performance. A. Evaluaton Methodology IV. SYSTEM EVALUATION To evaluate the opportunstc backhaul model, we conducted a network smulaton usng a smplfed verson of ndustrystandard models for evaluatng mult-ter networks 14, 3, 31. Our goal was to determne the gan n network capacty as a functon of the number of thrd party provders and offloaded backhaul capacty. Two deployment models were consdered: real-world and stochastc. In the real-world model, the operator-controlled BS locatons were based on actual cell stes as reported n the OpenCellId database, whch has logged data for over 5, ndvdual cells n the US 32. For the thrd-party femtocells, followng 15, we assume that the thrd party femtocells can be co-located wth current resdental and enterprse WF access ponts (APs) the theory beng that the owners of these WF APs would be the potental thrd partes to offer connectvty to the moble subscrbers. The WF AP locatons were estmated from the freely-avalable Wgle.net database. Contrbutors to the Wgle proect have compled over 88 mllon unque observatons of WF statons across the globe 33. Our smulatons consdered two test locatons: a 1km 2 area of the East Vllage n Manhattan, New York Cty representatve of a dense urban deployment and a smlar-szed area n Passac, NJ as representatve of a typcal suburban deployment. Fg. 2 s an aeral map of WF APs and cell stes n the selected area of Manhattan as reported n the OpenCellId and Wgle.net databases, plotted together n Google Earth TM 34. For the observed populaton of WF APs n the 1km 2 areas of NYC and Passac, NJ, we unformly sample a percentage of these nodes n the doman {2%, 5%, 1%, 15%, 2%}, whch represents the adopton rate of thrd-party WF owners that agree to provde backhaul servce to the cellular operator through a femtocell co-located at a WF AP. The equvalent number of nodes for both the urban and suburban locatons s gven n Table I. We mmedately see that, accordng to the databases we sampled, there are more than 2 WF APs for each cellular mcrocell n Manhattan. Ths vast number of WF APs suggests that f, even a small fracton of open-access femtocells can be co-located at current WF locatons, the cellular capacty can be massvely ncreased. For completeness, we compare our results based on realworld locatons wth ndustry-standard stochastc models 14, 31 used wdely n evaluatng cellular systems. For the urban scenaro n both the real-world and stochastc models, we take the operator-deployed nodes to be mcrocells, whch are omndrectonal transmtters wth power, bandwdth and other parameters concdng wth the 3GPP urban mcrocell model n 14, 31. Femtocells are smlarly confgured based on these

6 6 optmal user assocaton between the thrd-party and operatorcontrolled cells. In realty, we envson that ths optmzaton would be conducted n the operator s network. In conductng the optmzaton, we assume a smlar model as 35, where the spectral effcency ρ (z ) n (6) s gven by the Shannon capacty wth a loss of 3 db wth a maxmum value of 4.8 bps/hz (correspondng to 64-QAM at rate 5/6). For the utlty (3), we assume a proportonal far metrc U (r MS ) = log(r MS ). The backhaul capacty s assumed to be zero for operator-controlled cells, whle thrd-party cells charge a lnear cost so that C (r BS ) = { pr BS, BS F (.e. femtocell), BS M (.e. macro/mcrocell) (19) Fg. 2: Google Earth TM map of reported mcrocell locatons (blue balloons) and a fracton of the WF stes (green crcles) n the East Vllage, Manhattan. Snce the WF access ponts vastly outnumber the current cellular base statons (more that 2 to 1 n ths case), cellular capacty can be sgnfcantly ncreased f femtocells can be co-located at even a small fracton of the WF stes. TABLE I: Number of operator-controlled and thrd-party cells and number of UEs n the urban and suburban test cases Envronment Urban mcro-only and mcro+femto Suburban: macro-only and macro+femto Node Number of Nodes Type Mcro N M = 17 Femto N F {722, 185, 361, 5415, 722} UE N MS = 425 (25/mcro) Macro N M = 9 Femto N F {66, 165, 33, 495, 66, 825} UE N MS = 225 (25/macro) *N F computed from the adopton rate: {2%, 5%, 1%, 15%, 2%} of observed WF APs parameters and are subect to a maxmum backhaul rate n the set {1, 2, 3, 4, 5} Mbps. 5 These values for the rate constrant parameter are assgned unformly over the set of femtocells. For the suburban scenaro (for both real-world and stochastc models), we consder operator BSs to be three-waysectorzed macrocells. For the stochastc models, the denstes of the operator-controlled macro/mcrocells and thrd-party femtocells were adusted to match the denstes observed n the real-world data. Other salent parameters are gven n Table II. Note that the femtocells have an addtonal 2 db of path loss to account for the wall loss assumng the femtocell s deployed ndoors. For each of the scenaros (urban / suburban and real-world / stochastc), we follow the standard evaluaton methodology n 14, 31 to assess the downlnk capacty. Specfcally, we generate 1 random nstances of each of the networks; each nstance s called a drop. In each random drop, we run the optmzaton descrbed n Secton III to determne the where p represents the cost per unt backhaul capacty n the femtocell relatve to the utlty. The prce p wll be vared. Urban mcrocell Femtocell Suburban macrocell Global TABLE II: Network model parameters Parameter Topology Total TX power BW Antenna pattern Mcro UE path loss Mcro UE lognormal shadowng Topology Total TX power BW Antenna pattern Femto UE path loss Femto UE lognormal shadowng Backhaul max rate Topology Total TX power BW Antenna pattern Macro UE path loss Macro UE lognormal shadowng Carrer frequency Total area UE dstrbuton Moblty Traffc model Fadng Value Unform wth wrap-around 3 dbm 1 MHz (FDD DL) omn log 1 (R) (R n km) 1dB std. dev; 5% nter-ste correlaton; 1% ntra-ste correlaton Unform wth wrap-around 2 dbm 1 MHz (FDD DL) omn log 1 (R)+2dB (R n km) 8dB std. dev r max {1, 2, 3, 4, 5} Mbps unformly assgned to nodes Hexagonal (3-way sectorzed) wth wraparound 46 dbm 1 MHz (FDD DL) A() = mn{12( ) 2, A 3dB m} log 1 (R) (R n km) 8dB std. dev 2.1 GHz 1x1m Unform, 25 per macro/mcrocell constant full buffer none Lnk capacty C=W mn(log( β SNR),ρ max) β = 3dB (loss from Shannon cap.*) ρ max (max. spectral effcency) *We determne the achevable rate based on the loss from Shannon capacty, as dscussed n Backhaul rate constrant values are based on broadband servces commonly offered by many ISPs n the US.

7 7 Avg. rate gan/user urban real world 5 urban stochastc macro only Adopton rate (fracton of total observed nodes) (a) Urban scenaro Avg. cell edge rate gan/user (bottom 5% of rates) urban real world urban stochastc Adopton rate (fracton of total observed nodes) (a) Urban scenaro Avg. rate gan/user suburban real world macro only suburban hex grd/stochastc Adopton rate (fracton of total observed nodes) (b) Suburban scenaro Fg. 3: Average rate gan as a functon of the adopton rate n urban and suburban deployments. We see that f even a small fracton (5%) of WF owners agree to nstall open-access femtocells, cellular capacty can ncrease more than 2 fold n a dense urban envronment and a factor of 6 n a suburban deployment. B. Potental Capacty Gan wth Offloadng To estmate the maxmum possble capacty gan wth thrdparty offload, we frst consder the case where the thrd-party provders lease ther capacty at zero cost (.e. p = n (19)). Fg. 3 shows that the gan n mean throughput per moble as a functon of the adopton rate the fracton of WF AP locatons where open-access femtocells are co-located. We see that the total capacty can be ncreased sgnfcantly. For example, a 5% adopton yelds more than 2x ncrease n user throughput n the real-world urban settng and a 6x ncrease n the real-world suburban model. The maxmum gan n the suburban model s not as hgh as the urban settng snce the densty of thrd-party cells s lower. Also, the maxmum gans n both cases begn to saturate snce we fx the number of UEs per marcros. Therefore, addng more femtocells eventually has lttle value a phenomena also observed n 15. The fnte backhaul rates on the femtocells also lmts the gan. Fg. 4 smlarly plots the ncrease n the cell-edge throughput. As defned n 14, the cell-edge rate s the rate of the 5% percentle UE n each drop. We see smlar gans at the cell edge as the mean gans, suggestng that the gans are unformly experenced across the cell. Table III states the gan value for the urban case at the 5% adopton. Avg. cell edge rate gan/user (bottom 5% of rates) suburban real world suburban hex grd/stochastc Adopton rate (fracton of total observed nodes) (b) Suburban scenaro Fg. 4: Average 5% cell-edge rate gan as a functon of the adopton rate n urban and suburban deployments. TABLE III: Capacty and cell edge gans wth 5% femtocell adopton based on the urban real-world model. Scenaro Macro-only Macro+Femto Gan Avg. UE rate (Mbps) Avg. 5% cell edge rate (Mbps) Utlty (geometrc mean rate, Mbps) C. Addng Thrd-Party Prcng The results n the prevous subsecton assumed that the relatve cost of the thrd-party backhaul (the varable p n (19)) was zero. Of course, n realty, thrd partes wll not generally offer backhaul for free, so we need to consder the capacty gan wth non-zero prcng. Unfortunately, there s no way to drectly determne the correct relatve prce p to use n the evaluaton wthout some economc analyss relatng the potental revenue ncrease to the operator from ncreased capacty (as measured by the utlty) versus the cost to the operator of the thrd-party backhaul. However, such analyss s beyond the scope of ths study, although busness models have been consdered elsewhere, e.g. 36. What s relevant for ths work s to show that our net utlty

8 8 Cummulatve probablty m+f prce= m+f prce=1. m+f prce=4. m+f prce=8. macro only UE rate (Mbps) Fg. 5: Dstrbuton of the UE rates for mcro+femto urban realworld model wth femto prce p {, 1, 4, 8} and 5% adopton. Also, plotted s the mcro-only dstrbuton whch corresponds to the prce p =. maxmzaton optmzaton can ncorporate prcng, whatever the correct prcng s. As an llustraton, we fx the adopton rate at 5% and run the mcro+femto optmzaton for dfferent prces p n (19). 6 Fg. 5 plots the resultng rate dstrbutons across the UEs for the dfferent prces. As we would expect, as the prce s ncreased, the number of users scheduled on thrdparty lnks along wth the volume of offloaded traffc decreases as a result of the algorthm s penalzaton of such users. The optmal allocaton of resources therefore tends to nvolve these lnks less and less. As p, the rate dstrbuton approaches the dstrbuton usng only the operator-controlled mcrocells. Also, although we cannot assess the absolute economc value of femtocell offload, we can conduct the followng smple comparson: Suppose the operator wshes to ncrease capacty of ts network. We compare the followng two methods: Increase operator-controlled cells: In ths method, the operator does not use any femtocells and ncreases capacty by addng operator-controlled mcro/macrocells only,.e. tradtonal cell splttng. To smulate ths scenaro, we magne a network wth all the parameters beng the same, except that the densty of the mcro/macrocells s ncreased by some factor α 1. Increasng the densty n ths manner wll ncur a varety of costs to the operator ncludng the captal and operatng expenses of the new base statons as well as the cost of the addtonal backhaul. For the moment, we wll only consder the addtonal backhaul costs. Then, as we vary α, we can estmate the gan n network capacty as a functon of the addtonal backhaul. Femto offload: As an alternate approach, we magne that the operator adds no new mcro/macro base staton cells of ts own and reles entrely on purchasng capacty va femto offload. To smulate ths scenaro, we fx an adopton rate at some reasonable value (we assume 5%), and then ncrease network capacty by lowerng the relatve cost p n (19), from p = (where the operator 6 It should be noted that loadng prce p s untless (as far as the resource assgnment algorthm s concerned) and smply represents the weght of the penalty ncurred on net utlty. Utlty geom. mean rates (Mbps/user) Utlty geom. mean rates (Mbps/user) rd party femto offload deployng macro/mcrocells prce=1. prce=.5 prce=.25 1 macro only Avg. addtonal backhaul rate (Mbps/user) (a) Urban scenaro 3rd party femto offload deployng macro/mcrocells Avg. addtonal backhaul rate (Mbps/user) (b) Suburban scenaro prce= Fg. 6: Average utlty as a functon of addtonal backhaul, comparng addng operator-controlled mcro/macrocells only vs. relyng entrely on femtocell offload. For the femtocell offload case, we assume a 5% adopton rate and vary the amount of backhaul used on the femtocells. uses no femtocell offload) to p = (where the network purchases any capacty on femtocells wthout regard to cost). Then, we can agan measure the ncrease n network capacty as a functon of the addtonal backhaul costs, where the addtonal backhaul n ths case s on the thrdparty femtocells. The results of ths comparson are shown n Fg. 6. Plotted s the system utlty as a functon of the addtonal backhaul requred for both methods addng operator-controlled macro/mcrocells, or offloadng to femtocells on exstng backhaul. We measure capacty va the utlty. Snce we assume a proportonal far utlty, log(rms ), the utlty s equvalent to the geometrc mean rate ( rms ) 1/N MS. The geometrc mean rate s a better measure of network capacty than average rate snce t penalzes mobles wth lower rates more sgnfcantly. Nevertheless, although t s not plotted, very smlar curves would be observed wth ether arthmetc mean rate or cell edge throughput. We see from Fg. 6 that, for the urban scenaro, ncreasng the utlty requres roughly the same amount of addtonal backhaul whether deployng more operator-controlled cells or usng femtocell offload. In the suburban scenaro, the femtocell offload requres sgnfcantly more addtonal backhaul for small ncreases n utlty, but requres only modestly more addtonal backhaul for larger ncreases n capacty.

9 9 Now, as dscussed n the Introducton, t s lkely that the backhaul from femtocell offload would be sgnfcantly lower cost than the purchasng leased lnes wth guaranteed rates needed for operator-controlled macro/mcrocells. Moreover, addng operator-deployed cells ncurs addtonal costs ncludng ste acquston, nfrastructure expenses and network mantenance 36. Nevertheless, quantfyng the exact savngs would requre further economc analyss. CONCLUSIONS We have presented a model for operators to offset backhaul costs by leveragng exstng capacty from thrd-partes. In the proposed model, thrd partes nstall open-access femtocells n ther networks and the cellular operator can then opt to move subscrbers onto thrd party cells for a fee. The problem of dynamcally assgnng users between the thrd-party and operator-controlled cells s formulated as an optmzaton problem. A dual decomposton algorthm s presented that s extremely general and can ncorporate channel and nterference condtons, traffc demands, backhaul capacty and access prcng. To evaluate the model, we consdered deployments where the thrd party femtocells were co-located wth exstng WF APs. Due to the large numbers of WF APs relatve to base staton cell stes, our smulatons suggest that network capacty be sgnfcantly ncreased even f only a small fracton (say 5%) of current WF owners deploy open-access femtocells. The gans are partcularly large n dense urban areas where our data suggests there are some 2 WF APs per operator cell. Our optmzaton can also ncorporate a varety of prcng mechansms by the thrd partes, but determnng the correct prce wll need analyss beyond the scope of ths study. However, our smulatons show that, whatever s the correct prce, the addtonal backhaul to ncrease capacty s smlar for both addng more operator-controlled cells or offloadng to thrd-party femtocells. 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