mobile network resource sharing options: performance comparisons

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, ACCEPTED FOR PUBLICATION 1

Mobile Network Resource Sharing Options:Performance Comparisons

Jignesh S. Panchal, Member, IEEE, Roy D. Yates, Fellow, IEEE, and Milind M. Buddhikot, Member, IEEE

Abstract—Resource sharing among mobile network operatorsis a promising way to tackle growing data demand by in-creasing capacity and reducing costs of network infrastructuredeployment and operation. In this work, we evaluate sharingoptions that range from simple approaches that are feasible inthe near-term on traditional infrastructure to complex methodsthat require specialized/virtualized infrastructure. We build asimulation testbed supporting two geographically overlapped 4GLTE macro cellular networks and model the sharing architec-ture/process between the network operators. We compare Capac-ity Sharing (CS) and Spectrum Sharing (SS) on traditional infras-tructure and Virtualized Spectrum Sharing (VSS) and VirtualizedPRB Sharing (VPS) on virtualized infrastructure under light,moderate and heavy user loading scenarios in collocated andnoncollocated E-UTRAN deployment topologies. We also studythese sharing options in conservative and aggressive sharingparticipation modes. Based on simulation results, we concludethat CS, a generalization of traditional roaming, is the bestperforming and simplest option, SS is least effective and thatVSS and VPS perform better than spectrum sharing with addedcomplexity.

Index Terms—Resource sharing, capacity sharing, spectrumsharing, virtualization, whitespace, 4G, LTE.

I. INTRODUCTION

W ITH the growing popularity of smartphones, notepadsand other wireless-ready devices, mobile cellular net-

works are experiencing dramatic increases in traffic [1]. Var-ious estimates indicate that mobile data usage will increase13-fold between 2012 and 2017 [1]. This impending growthwill require dramatic increases in mobile network capacity. Tomeet the anticipated capacity demand in the existing modelof fixed resources including static-licensed spectrum, mobilenetwork operators, in the context of 4G Long Term Evolution(LTE) [3], are exploring many techniques such as higherdata rate broadband air interface technologies with MIMO[2] and femto/small cells [4] [5]. These techniques increasethe capital and operating expenditures and complexity ofthe network. Alternatively, increasing capacity by acquiringadditional resource in terms of new static-licensing spectrumalso requires large upfront investment. This motivates theparadigm of resource sharing, where resources are pooled anddynamically shared for common benefits. Efficient resource

Manuscript received October 14, 2012; revised February 26, 2013; acceptedMay 13, 2013. The associate editor coordinating the review of this paper andapproving it for publication was X. Dong.

J. Panchal is with Verizon (e-mail: [email protected]).R. Yates is with WINLAB, E&CE Dept., Rutgers University (e-mail:

[email protected]). His work was supported in part by NSF grantFIA-1040735.

M. Buddhikot is with Alcatel-Lucent Bell Labs (e-mail:[email protected]).

Digital Object Identifier 10.1109/TWC.2013.071913.121597

sharing improves network performance by mitigating suddenand sporadic loading surges, reducing outages and providinga better end-user experience. It can also increase network ca-pacity and RF coverage with fewer base stations, help in fasterservice introduction [6]. However, there are many commercial,legal and technical challenges to overcome in order to fullyrealize mobile network resource sharing. The goals of thiswork are to focus on details of technical challenges, provideunique comparative guidelines on various sharing options interms of their performance based on simulation results andtheir complexity and feasibility of implementation.

This paper focuses on inter-operator sharing of cellular re-sources including capacity, spectrum and base stations. The ca-pacity and spectrum sharing use traditional mobile network in-frastructure and are relatively simple. Inter-operator roaming,where subscribers of one operator get access to the networkof another operator, is the most prevalent sharing techniquesupported by standard bodies and is currently implementedin existing mobile networks. Advances in software definedradio (SDR) technologies [7]–[9] have generated significantinterest in dynamic spectrum allocation (DSA) [10]–[14] andbase station virtualization [15]–[21] to enable spectrum andhardware sharing respectively. Spectrum sharing is a form ofphysical resource sharing [34] and includes coordinated dy-namic spectrum access [11], spectrum reuse [12] and dynamicspectrum allocation (DSA) [13].

Base station sharing requires virtualized infrastructure andis complex to realize. Virtualization has been explored asa future internet (wireline network) design approach [15]and with advancements in SDR and cognitive radio (CR)technologies, it was further extended into Radio/Wirelessnetwork virtualization by GENI [16], VINI/Planetlab [17] andothers for experimental testbeds to support large numbers ofsimultaneous experiments. It was also promoted as a flexibleand cost effective solution for rapid deployments of newwireless access technologies [18].

Inter-operator base station sharing based on virtualizationhas also appeared in the form of Multi-RAN base station[19], Virtual Base Station [21] and others [22]–[24]. Most im-portantly, [25]–[29] show increasing interest and willingnessby operators, regulatory and standard bodies to explore andsupport resource sharing, especially as networks evolve to 4Gand beyond. Moreover, various aspects of the inter-operatorresource sharing have been studied by [31]–[34]. A simpleanalytical model for dynamic inter-operator resource sharinghas been developed to make real-time sharing decisions [33].Also, [31] and [32] have shown capacity gain using a simplenoncollocated base station layout and a rudimentary round-

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2 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, ACCEPTED FOR PUBLICATION

SE

eNB eNB eNB eNB eNB eNB

Neighborhood - X Neighborhood - Y

Decision & Condition

MME-2

Coordination & Management

Negotiation

Database

SHx

S1-MME

Loading Updates

Resource Supply

Response

Resource Borrowing

Request

E-UTRAN-2

MME-1

SHx

Activation & Deactivation

EPC-2

PLMN-2

S1-MME

PLMN-1

EPC-1

E-UTRAN-1

S1-U

SE

Fig. 1. Resource sharing architecture and process.

robin scheduler model for time-slot sharing among operators.This paper is an in-depth investigation of inter-operator

sharing that compares performance of multiple sharing optionsusing detailed models in a system-level 4G LTE simulationtestbed. We develop and model realistic sharing processes andarchitecture that are compatible with the standardized LTEnetwork. We introduce a new sharing entity (SE) to mediateand coordinate the sharing process among the participatingoperators. A steering of mobile devices from one operatornetwork to another to facilitate capacity sharing (CS) betweenthe operators is modeled on a mobile roaming process betweentwo LTE networks. We realize an inter-operator spectrumsharing (SS) option in LTE by incorporating an on-the-fly spectrum division and reconfiguration process which ispresently not in the 3GPP/LTE standard [3]. The spectrumdivision and reconfiguration divides the original spectrum,carves out a supplied spectrum block while reconfiguresa retained spectrum for immediate use. Finally, we modelbase station sharing in LTE using a Multi-operator Virtualevolved Node-B (MoV-eNB) and propose two new sharingoptions, virtualized spectrum sharing (VSS) and virtualizedPRB sharing (VPS), to share spectrum locally within the MoV-eNB at two different granularities among operators. Note thata PRB (Physical Resource Block) is the LTE physical layertime-frequency resource unit of 1 ms duration and 180 KHzbandwidth.

In this paper, we briefly discuss cellular resource sharingscenarios, architecture, process and options in the context ofLTE in Section II. We develop a simulation testbed, discussedin Section III, to evaluate and compare the performance of no-sharing (NS), CS and SS options on traditional infrastructureand VSS and VPS on virtualized infrastructure under light,moderate and heavy user loading scenarios in collocated and

UE UE

Sector Sector Sectors Sectors

UE UE

Sector Sector

MME MME

UE UE

SE

PLMN-1 PLMN-2

Radio Channel

Traffic Generator

Ovld Ovld

SHx SHx

S10

S1-MME S1-MME

Scheduler

C1 = 5 MHz

Scheduler C2 = 5 MHz

S1-MME S1-MME

Fig. 2. Resource sharing system model.

noncollocated E-UTRAN deployment topologies. We studythe performance of these sharing options in conservativeand aggressive sharing participation modes and expose theirsensitivity to these modes in Section IV. Finally, conclusionsare summarized in Section V.

II. CELLULAR RESOURCE SHARING

In this section, we briefly discuss cellular resource sharingscenarios, architecture, process and options; more details canbe found in [35]. We envision three types of participantstaking part in cellular network resource sharing: establishedoperators such as Verizon and T-Mobile, third parties, whoare not cellular operators but are owners/brokers of cellularresources (e.g. a DTV operator subleasing spectrum to cellu-lar operators) and content/service providers like Google andVonage, who are neither operators nor owners but borrowersof resources in order to operate their own cellular networks.These participants create three distinct scenarios: i) resourcesharing among established operators, ii) established operatorsborrowing resources from third parties and iii) exchange ofresources among all three types of participants. In this paper,we primarily focus on the inter-operator resource sharingscenario.

Figure I shows two collocated LTE networks, denoted asPLMN-1 and PLMN-2 managed by two different operators.In each network, the Mobility Management Entity (MME)is a sharing control and decision maker for each operator.It initiates participation in resource sharing activities andcommunicates to a Sharing Entity (SE) on a newly defined IP-based SHx1 control plane interface. The SE is an independententity owned and operated by either a third party or analliance of cellular operators. It coordinates sharing agree-ments between the MMEs. Since there can be more than twocellular networks operating in the area, the sharing architecture

1SH: Prefix indicating sharing interface


PANCHAL et al.: MOBILE NETWORK RESOURCE SHARING OPTIONS: PERFORMANCE COMPARISONS 3

Collocated

PLMN-1 and PLMN-2

Channel G

ain (dB)

NonCollocated

PLMN-1 and PLMN-2

Fig. 3. Collocated and noncollocated topologies and channel gain (without shadowing).

depicted in Figure I can be generalized to MMEs of multipleoperators connected to the SE. Typically, there will be one SEmanaging all neighborhoods in a cellular network coveragearea referred to as a market. Deployed cellular resourcesincluding E-UTRAN, i.e. collection of eNBs, and spectrumto serve these neighborhoods depend on the market area andcustomer density.

We define a generic, distributed sharing process common toall sharing options to dynamically create, modify, renew andterminate sharing agreements. This facilitates activation anddeactivation of network interfaces and configuration updatesof network entities such as eNB. As illustrated in Figure I, thesharing process is divided into four tasks: sharing databaseconfiguration at SE, resource demand decision making atMMEs based on sector-loading information, sharing coordi-nation by the SE including processing of demand requestsfrom the MMEs and finally, sharing functionality activation atresource supply sectors.

The capacity and spectrum sharing options use traditionalnetwork infrastructure. Traditional (inter-operator) roaming isa simple form of capacity sharing where an operator shares itsnetwork capacity by providing wireless services to subscribersof other operators who lack cell sites in that area. Here, in theCS option, we broaden roaming into an open-network strategywhere roaming is not restricted to certain areas but is possibleanywhere, even among operators with collocated facilities.While the SS option can facilitate sharing of various typesof spectrum, including licensed, unlicensed and whitespace,among operators, we focus on inter-operator licensed spectrum

sharing. Spectrum subleasing from one LTE operator to otheris enabled by a process of spectrum division and sharing oneNBs having software configurable RF front-ends.

The mobile network virtualization promises multiple per-sonality network elements in terms of virtual ownership bymultiple operators. That means multiple cellular network run-ning virtually (i.e. logically) and concurrently (at the sametime) within one physical network equipment or hardware.In this work, we explore virtualization in terms of virtualsector (V-sector) to realize the hardware sharing in mobilenetworks. We introduce a MoV-eNB platform/architecture forLTE E-UTRAN to enable hardware sharing. The MoV-eNBlogically encapsulates multiple eNBs. These logical eNBsare software modules running on the shared/common hard-ware. The MoV-eNB is capable of dynamically activatingand deactivating logically-independent V-sectors operated bydifferent operators on one physical sector hardware platform.Also, the virtualization of sectors facilitates spectrum sharingamong operators and further improves network efficiency,performance and save money for operators. In this work, theMoV-eNB is used for an alternative localized spectrum sharingschemes referred to VSS and VPS.

III. SIMULATION TESTBED

This section provides an overview of our simulation testbedincluding E-UTRAN topologies, sharing models and perfor-mance evaluation metrics; more details can be found in [35].



1 2 3 m 25 12

114

Sect

or

PRB

57

58

s

112,,

Cmt

0157,3,

Ct

(a) Carrier C1

12,25,

Cst

02,,

Csmt

1 2 3 m 25 12

114

Sect

or

PRB

57

58

s

(b) Carrier C2

Fig. 4. PRB activity maps at TTI t across all 114 sectors for carriers (a) C1 = 5 MHz and (b) C2 = 5 MHz.

A. System

The system model, shown in Figure 2 and simulated inMATLAB, includes two LTE networks, PLMN-1 and PLMN-2, operated by two different operators in a geographic market.The model incorporates UEs, traditional and virtual sectors,a subset of entities in the Evolved Packet Core (EPC) ofboth networks including two MMEs, and a Sharing Entity(SE) to coordinate sharing. The model simulates only thosefunctionalities of the LTE network elements necessary tosupport the research goal. The SE is a new element, notpart of the current LTE standard, to facilitate sharing betweenthe two operators/PLMNs. The S1-MME and S10 interfaces,already part of the LTE standard, are simulated to providecommunication links that carry sharing and inter-operator(roaming) handover messages among the sectors and betweenthe MMEs. The newly defined SHx (dashed line) interface issimulated to carry messages between the SE and the MMEs.Message processing plus propagation delays of 20 ms onaverage between two entities with standard deviation of 5 msis implemented on these interfaces. The S1-MME connectionsfrom sectors of PLMN-1 to the MME of PLMN-2 and sectorsof PLMN-2 to the MME of PLMN-1 are needed for VSS andVPS, where sectors shared by both PLMNs require backhaulconnections to both PLMN-1 and PLMN-2 core networks.There is no direct communication link, such as an X2 interface[3], between sectors of PLMN-1 and sectors of PLMN-2. Thedotted lines shown in Figure 2 are not interfaces but representsoftware/simulation links from traffic generator to sectors ofboth PLMNs. We consider scenarios with an equal numberof eNBs deployed by each operator. The inter-eNB distancebetween two eNBs of the same operator is set to 1.5 km.There are three sectors (cells) per eNB. Each operator has 19eNBs and 57 sectors. Thus, there are a total of 38 eNBs and114 sectors/cells between the two operators in the market.Typically, a market is a collection of neighborhoods whereneighborhoods are served by one or more sectors. Sectors ofPLMN-1 operate on 5 MHz spectrum designated as carrier C1

and Sectors of PLMN-2 on 5 MHz spectrum designated ascarrier C2. These 114 sectors (38 eNBs) can be configured astraditional or virtualized. In traditional infrastructure, sectorshave one to one relationship with PLMN, i.e. traditionalsectors 1 to 57 belong to PLMN-1 and 58 to 114 to PLMN-

2. On other hand, in virtualized infrastructure, a virtualizedsector can have one to many relationships, i.e. it can be partof one or both PLMNs.

We consider two topological scenarios (sector layouts) asshown in Figure 3. In the first scenario, the sector antennasof both operators are collocated having similar orientationsand thus comparable RF coverage or footprint in the market.In the second scenario, the sector antennas are not collocatedand thus both operators have different RF footprint coveringneighborhoods in the market. Each sectors antennas are ori-ented in a clover-leaf pattern. The channel gain ρu,s betweenUE u and sector s includes pathloss, log-normal distributedshadowing with standard deviation of 10 dB and correlationfactor of 0.7 between eNBs. It also includes antenna gains ofsector s and UE u, and cable and other losses between thesector antenna and amplifier. The sector antenna gain of 17dBi is used. Fast fading is not simulated.

Only the Physical Downlink Shared Channel (PDSCH)is simulated. The PDSCH is divided into multiple physicalresource blocks (PRBs). A transmit time interval (TTI) is theone sub-frame (1 ms) long minimum unit of time-frequencyscheduling. An entire carrier bandwidth (5 MHz correspondingto 25 PRBs) is assigned to PDSCH and used to schedule User-Plane (U-plane) data going to the UEs. Each sector tracks itsPRB usage per TTI to estimate per PRB cumulative powercoming from all interfering sectors at UE. Figure 4 showsan example of PRB usage/activity maps across 114 sectorson carriers C1 and C2. At TTI t, αCx

t,m,s is the binary activityindicator of PRB m at sector s of carrier Cx. The active PRBs,shown in black in Figure 4, have αCx

t,m,s = 1; otherwise theactivity indicator is zero. Important to note in Figure 4 thatsectors of one PLMN are not allowed to use spectrum of theother PLMN and thus active PRBs on C1 are limited to sectors1 to 57 and active PRBs on C2 are limited to sectors 58 to114. However, in the special case of spectrum sharing (SS),sectors of one PLMN can acquire/use part of the spectrumfrom the other operator. In that case, sectors 1-57 of PLMN-1may also be allowed to use PRBs of carrier C2 and similarlysectors 58-114 of PLMN-2 may also be allowed to use PRBsof carrier C1.

Using the PRB activity maps, the total interference powerexperienced by UE u served by sector v, on PRB m of carrier



(a) (b)

Fig. 5. Prospective supply sectors of (a) demand sector 1 and (b) demand sector 58 in noncollocated topology.

Cx, at TTI t is

ICxt,m,u,v =

114∑

s=1,s�=v

αCxt,m,sρu,sP (1)

where P is the sector transmitting power per PRB and ρu,s isthe channel gain from interfering sector s to UE u. The DLSINR experienced by UE u on PRB m of carrier Cx at TTIt is

γCxt,m,u,v =

ρu,vP

ICxt,m,u,v + η̄

(2)

where η̄ is the PRB noise floor at UE. Averaging the per PRBDL SINR over the allowed PRBs, the averaged wide band DLSINR at UE u served by sector v on carrier Cx at TTI t is

γ̄Cxt,m,u,v =

1

NCxv

∑

m∈ϕCxv

γCxt,m,u,v (3)

where ϕCxv is the set of allowed/available PRBs to be used

by the serving sector v on carrier Cx and NCxv is the number

of PRBs in the set ϕCxv . The sector uses look-up, illustrated

in [35, Tables 4 and 5], to determine the spectral efficiencyrt,u,v (in bits/PRB) from γ̄Cx

t−1,u,v for UE u.Each sector has a proportional-fair downlink (DL) scheduler

to server its connected UEs. Under the no-sharing option, aserving sector v has a two-stage scheduler that serves oneoperator on a single carrier and has a set of PRBs ϕCx

v ={1, 2 . . .25} available for use in every TTI. However, when aserving sector acquires additional spectrum via SS, it employsa two-stage single-operator multi-carrier scheduler using morethan 25 PRBs spanning across carriers C1 and C2 every TTI.By contrast, a serving sector activating V-sectors in VSS orVPS will have a two-stage multi-operator scheduler havingless than 25 PRBs available per operator every TTI. Additionaldetails on these specific schedulers pertaining to these sharingoptions and their available/allowed to use PRBs per TTI areprovided in Section III-B.

However, many details are common across all schedulertypes implemented for NS, CS SS, VSS and VPS. In the first(pre-selection) stage of the scheduler, UEs with a non-zero bitbacklog are pre-selected. In the second stage, each schedulerprioritizes the pre-selected UEs based on the proportional fairratio rt,u,v/R̃t,u,v, where R̃t,u,v is averaged bits transmitted

per TTI to UE u by serving sector v in TTI t. The highest-ratio UE is scheduled to be served first and then the secondhighest UE and so on until no additional PRBs remain tobe utilized. For each selected UE, the scheduler determinesits PRB-need in the given TTI based on its averaged wideband DL SINR and number of queued bits. Here, the goalof the scheduler is to empty the queue of the selected UEin the given TTI. Thus, the scheduler determines size (bits)of a PDSCH packet greater or equal to the UE’s queue size.Then, the scheduler uses bits/PRB look-up to calculate theUEs PRB-need. If the number of bits queued for a selectedUE exceeds the maximum packet size possible for given wideband DL SINR, the scheduler assigns all PRBs in the TTI fortransmission to the UE. The remaining bits of the UE are stillqueued for the next transmission opportunity. On other hand,after emptying the first UE queue, if there are still one or morePRBs available in the given TTI, the scheduler selects thenext UE for service. The process continues until all selectedUEs for the TTI are served or all available/allowed PRBs areused. These per UE PRB assignments are randomly/uniformlydistributed across all PRBs.

The spectral efficiency and TBS look-up tables are modeledconservatively and produce close to zero block error rate(BLER) per transmission. As there are effectively no retrans-missions in the simulation, PHY layer Hybrid ARQ (HARQ)functionality is not implemented. The MAC, RLP and PDCPlayers are also not implemented in simulation. Table I showsother simulation parameters.

The traffic generator produces DL traffic packets for eachUE. The packets are queued at each sector to be scheduled andtransmitted over-the-air to each UE. We use a two-state on/offtraffic model. The state machines of the UEs are maintainedindependently. In the ON state, 400-bit fixed length packetsarrive in a UE’s queue in each TTI. In the OFF state, thereare no packet arrivals. The traffic generator stays in a stateaccording to geometrically distributed waiting time with meanof 10 TTIs. On average, for each UE, there can be five 400-bitpackets queued in an average ON-OFF cycle of 20 TTIs whichresults in an average source rate of 100 kbps per UE. Uponarrival in the queue, each packet is time stamped. A maximumwaiting time of a packet in the queue is 100 ms; if a sectorscheduler can’t deliver the packet to its destination UE within100 ms, the packet is discarded from the queue. Each sector



TABLE ISIMULATION PARAMETERS

Path Loss Model 114.2 + 35.2× log10(dkm)

Log Normal Shadowing 10.0 dB Std. Dev.PRBs in 5 MHz FDD channel 25Useful Channel Bandwidth 180 KHz × 25 = 4.5 MHzThermal Noise per PRB -121.4 dBmSector Antenna Gain 17 dBiSector Tx Antennas 2Sector Antenna Height 30 mSector Tx Power Per PRB 26.8 dBm (0.48 W)Max Sector Tx Power per An-tenna

40dBm (10 W)

UE Rx Antennas 2UE Antenna Height 1.5 mUE Noise Figure 10 dBUE Noise Floor per PRB -111.4 dBmUE Antenna Gain 2 dBChannel Model AWGN-Thermal Noise (No fading)

keeps track of per-UE packet drop counts to compute averageper user packet drop probability performance metric.

Each sector computes its average PRB utilization per carrierM̄Cx

t,s based on its instantaneous PRB usage over the past 100TTIs:

M̄Cxt,s =

1

100

t∑

j=t−100

MCx

j,s (4)

where MCx

j,s is an instantaneous PRB usage indicator on carrierCx by sector s at TTI j, and periodically sends to its MMEon the S1-MME interface. Each MME estimates loading ofeach sector under its control based on received average PRButilization and declares whether the sector is in overload (i.e.heavily loaded). We define Ψ̄Cx

t,s = M̄Cxt,s /N

Cxs as the average

PRB utilization factor of sector s at TTI t on carrier Cx

having NCxs PRBs. If Ψ̄Cx

t,s ≥ θovld, where θovld is an overloadthreshold, the sector s is declared to be in overload. TheMMEs trigger sharing requests to the SE, if the sectors areexperiencing heavy loading and need additional resources toease the loading.

The SE processes the resource demand requests from theMMEs and consults the sharing database to find appropriatesharing partners in the form of one or more resource supplysectors. Figure 5 shows two examples of a prospective supplysector identification process in a noncollocated topology whensectors 1 and 58, rendered in black, are in overload. In thiscase, the SE selects sharing partners, rendered in gray, fromcoverage-overlapping neighbor. Specifically, sectors 57, 58, 70and 72 of PLMN-2 are chosen as supply sectors for sector 1of PLMN-1 and neighbor sectors 1, 7, 8, 9 and 12 of PLMN-1are chosen as supply sectors for sector 58 of PLMN-2. TheSE keeps track of active sharing agreements and associatedsupply and demand sectors. For simplification, we curtail theSEs responsibility by limiting a sector to be a borrower or asupplier of a resource but not both.

To introduce dynamics in the system in terms of the overallresource utilization, we introduce variable UE density acrossthe sectors. Specifically, given the UE density factor f ≥ 1,where higher f indicates heavier sector loading, the numberof UEs placed in the RF coverage area of a given sector

s is a discrete uniform (Umin, Umax = fUmin) distributedrandom number variable, independent of the number in anyother sector, where Umin and Umax are minimum and max-imum number of UEs per sector respectively. The variableUE density introduces spatial variability in demands for thecellular resources including capacity, spectrum and othersacross neighborhoods and also between operators. All UEs arekept stationary but the spatial variability in UE density acrossneighborhoods can be attributed to the UEs coming in andmoving out of the sectors/neighborhoods and thus indirectlysimulates mobility. Each placed UE knows its subscribedPLMN id and preferred access carrier. A UE selects themaximum channel gain sector as its serving sector. The defaultrule is that the UEs subscribed to PLMN-1 are restricted toaccess C1 on sectors 1 to 57. Similarly, UEs subscribed toPLMN-2 are restricted to access C2 on sectors 58 to 114. Butin resource sharing scenarios (detailed in the next subsection),the rule is relaxed, enabling the UEs to access or handover toa non-subscribed PLMN/carrier.

B. Sharing Models

In this subsection, we discuss sharing models including CSand SS on traditional infrastructure as well as VSS and VPSon virtualized infrastructure. So far, we have used subscript sto designate a sector and v for a serving sector. Here, weintroduce additional types of sectors within traditional andvirtualized infrastructures. Neutral sectors, not participatingin sharing, are also designated as s, whereas supply sectors,who are supplying resources, and demand sectors, who aredemanding/borrowing resources, are designated as ss and sdrespectively. The sharing process begins when a heavily loadeddemand sector sd requests additional resources from a lightlyor moderately loaded resource supply sector ss belonging tothe other PLMN.

Capacity Sharing (CS): In CS, if a supply sector ssof PLMN-1 is not in overload (i.e. Ψ̄C1

t,ss < θovld ) andfurthermore Ψ̄C1

t,ss < θsup, where θsup is the supply threshold,then MME of the supply sector allows a portion of the supplysector capacity to be used by a demand sector of PLMN-2.The demand sector sd uses the borrowed capacity by steeringone or more of its serving UEs to the supply sector. Thesteering process is similar to inter-operator Inter-FrequencyHandover (IFHO) but with better controller and coordinationwhile transferring UEs between two networks. We assume thatthe inter-operator IFHO data interruption period experiencedby the UEs steered from the demand sectors (on carrier C2) ofPLMN-2 to supply sectors of PLMN-1 on carrier C1 is 80 mslong. A UE will be a candidate for steering if its current DLSINR from its serving sector on C2 is poor and the supplysector offers an improvement in channel gain on C1. Aftera successful steering of a UE, the supply sector waits for100 ms. After the wait, if Ψ̄C1

t,ss < θovld, the supply sectoragain signals the demand sector and allows one more UEto be steered, otherwise the steering process stops. This waitand allow rule makes CS response well-controlled in avoidingsupply sector overload. In CS, all sectors have a two-stagescheduler having maximum 25 PRBs available per TTI andserving both subscribed and unsubscribed/steered UEs.



C1= 5MHz

C11= 1.4MHz

Supply Sector ss = 1

C1= 5MHz C2 = 5MHz

C2 = 5MHz C11= 1.4MHz C12= 3MHz C12= 3MHz C12= 3MHz

…

…

Supply Sector ss = n Demand Sector sd

Spectrum Division and Reconfiguration

Spectrum transfer

Spectrum sub-leasing

Fig. 6. Spectrum division of C1 = 5 MHz, retention and reconfiguration of C11 = 1.4 MHz at supply sectors ss and transfer of subleased C12 = 3 MHzto demand sector sd.

Spectrum Sharing (SS): If Ψ̄C1t,ss < θsup, the supply sector

ss of PLMN-1 can supply a portion of its own carrier C1 to aheavily loaded demand sector sd of PLMN-2. In LTE, spec-trum flexibility allows multiple carrier bandwidths including1.4, 3, 5, 10, 15 and 20MHz. Here, the supply sector havingC1 = 5 MHz (25 PRBs) has the option of either releasing 3MHz with 15 PRBs or 1.4 MHz with 6 PRBs while retainingeither 1.4 MHz (6 PRBs) or 3 MHz (15 PRBs) respectively.Similar to the supply sector overload prevention strategy inCS, we reduce the possibility of supply sector overload dueto excessive subleasing by exercising caution in determiningthe maximum bandwidth (BW) that can be released for subleasing. The supply sector is allowed to release BW C12 tothe demand sector and retain C11, if (M̄C1

t,ss/NC11ss ) < θovld.

Otherwise, the supply sector has to either further reduce sizeof C12 or deny the sharing request from the demand sector.In addition, similar to CS, a post-sharing (reactive) overloadcontrol algorithm is implemented to manage supply sectoroverload. More details on the algorithm are provided underVSS. For the collocated topology, co-channel interference canbe managed by having a sector serve as a supply sector onlyfor its collocated counterpart sector. On other hand, for thenoncollocated topology as shown in Figure 5, each demandsector has multiple RF coverage overlapping neighbor sectorsin other PLMN as prospective spectrum supplying sectors.Also, as an additional restriction to avoid excessively highco-channel interference, all of those supplying sectors have torelease equal amounts and fixed locations of spectrum to thedemand sector.

Let’s assume, based on M̄C1t,ss , the supply sector ss can

sublease up to a maximum bandwidth Cmax12,ss to the demand

sector sd. Thus, it informs Cmax12,ss and possible locations

of the subleased spectrum to the SE. For example, if thesupply sector ss can sublease up to Cmax

12,ss = 3 MHz outof available C1 = 5 MHz, there are two possible locationsof the subleased spectrum: 3 MHz spectrum blocks from theleft edge of 5 MHz or the right edge of the 5 MHz (asillustrated in Figure 6). If there are multiple supply sectors,the SE determines C12 = minss∈ξsd

Cmax12,ss and a spectrum

location common/fixed across all supplying sectors to be theavailable subleased spectrum and informs to all supplyingsectors in a subleased spectrum release request. Here, ξsd isa set of supply sectors selected by the SE for the demandsector sd. Upon reception of the release request, the supplysectors initiate a 40 ms spectrum division and reconfiguration

C1= 5MHz

C11= 1.4MHz

Supply Sector ss

C12= 3MHz

Virtualization, Spectrum Division and allocation

Owned V-sector vP1 Leased V-sector vP2

Fig. 7. Activation of V-sectors vP1 and vP2 on supply sector ss andspectrum division of C1 = 5 MHz, allocation of C11 = 1.4 MHz to vP1 andC12 = 3 MHz to vP2.

process [35] in which the UEs are put in a suspend state andthe spectrum C1 is divided, retaining C11 and releasing C12.As illustrated in Figure 6, the fixed location of releasing C12

across all supplying sectors also fixes the location of retainedspectrum C11 across all supplying sectors and safeguardsagainst potential inter-operator co-channel interference. Uponreception of C12 release messages from all supply sectors, theSE informs the demand sector sd of the availability of C12

for consumption. With subleased spectrum C12, the demandsector sd now has a combined non-continuous spectrum C+

with total NC+

sd> 25 PRBs including NC2

sdPRBs on its own

spectrum C2 plus NC12sd PRBs on the borrowed spectrum C12

as shown in Figure 6. The demand sector activates the multi-carrier scheduler upon reception of additional spectrum. Themulti-carrier scheduler creates two identical instances of theDL scheduler, one for carrier C2 and the other for carrier C12.All UEs currently served by the demand sector sd are allowedto be scheduled on C2, but only the selected UEs are allowedto be scheduled on the borrowed spectrum C12.

A UE u is selected by the demand sector sd for theborrowed carrier C12, if the UE’s DL SINR is poor (i.e. < 6dB) on C2 but better on C12. The demand sector sd is declaredto be in overload, if Ψ̄C+

t,sd ≥ θovld. Sector-schedulers of thesupply sectors have reduced spectrum C− with NC−

ss < 25PRBs available every TTI. With the reduced PRBs, the supplysector ss is declared to be in overload, if Ψ̄C−

t,ss ≥ θovld.All neutral sectors have a two-stage single-carrier schedulerhaving maximum 25 PRBs available per TTI.

Virtualized Spectrum Sharing (VSS): In VSS, MME of aheavily loaded demand sector requests activation of a virtual



sector (V-sector) on the lightly or moderately loaded supplysectors. For example, a supply sector ss of PLMN-1 is lightlyloaded and willing to activate a V-sector of PLMN-2 to helpout the heavily loaded demand sector sd of PLMN-2. Thelightly loaded supply sector reconfigures itself and activatestwo V-sectors namely vP1 and vP2, divides its own carrier C1

into two non-continuous carriers, C11 and C12, as illustratedin Figure 7. Similar to SS, the decision to enable VSS onsupply sector ss and the determination of C11 and C12 alsorequire Ψ̄C1

t,ss < θsup and (M̄C1t,ss/N

C11ss ) < θovld respectively.

Before activating vP2, the supply sector initiates a 40 mslong spectrum division and reconfiguration process that putsits UEs into the suspend state, divides spectrum C1, allocatingC11 to vP1 and C12 to vP2. For C1 = 5 MHz having 25 PRBresources, C12 can be either 3 MHz with 15 PRBs or 1.4MHz with 6 PRBs and respectively retained spectrum C11

either 1.4 MHz (6 PRBs) or 3 MHz (15 PRBs). Here, sincePLMN-1 is the owner of the supply sector ss, the V-sector vP1

is called an owner V-sector and subsequently vP2 is called aleased V-sector. Once the leased V-sector vP2 is active andoperational, UEs who are subscribed to PLMN-2 and activein the neighborhood see vP2 as part of their home networkand are allowed to perform IFHO to vP2 by their currentserving sector. Note that the IFHO data interruption periodexperienced by the UEs is 80 ms. A UE u subscribed toPLMN-2 and served by sector v of PLMN-2 is allowed toIFHO to vP2 if v is in overload, the channel gain from theIFHO-target-supply sector ss to u is the highest among allsectors in the supply PLMN, the channel gain from ss to UEu is greater than that from v, and u’s current DL SINR oncarrier C2 from sector v is below 6dB.

Each V-sector is served by an independent instance of a DLscheduler. The V-sectors, vP1 and vP2 on the supply sectorss, operate on carriers C11 and C12 having N11 and N12

available PRB resources per TTI respectively and serve UEssubscribed to PLMN-1 and PLMN-2 respectively. All non-virtualized sectors have a two-stage single-operator schedulerhaving 25 PRBs available per TTI. Every TTI, the schedulersare allowed to use those PRBs associated with their allocatedchannels/carriers.

To manage supply sector overload, a post-sharing (reactive)overload control algorithm periodically monitors PRB utiliza-tion factors Ψ̄vP1

t,ss and Ψ̄vP2t,ss of the owner V-sector vP1 and

leased V-sector vP2 respectively. If vP1 is in overload, i.e.Ψ̄vP1

t,ss ≥ θovld, the algorithm reduces the allocated spectrumchunk to leased V-sector vP2 and, at the same time, increasesthe spectrum allocated to vP1. This stops when the spectrumallocated to vP2 reaches 1.4 MHz (6 PRBs). Also, if vP2 isin overload, i.e. Ψ̄vP2

t,ss ≥ θovld, the algorithm transfers 10% ofUEs served by vP2 back to their original source sectors ofPLMN-2. The transfer stops when vP2 is serving only oneUE.

Virtualized PRB Sharing (VPS): Similar to VSS, in theVPS scheme, the supply sector ss activates the owner V-sectorvP1 and the leased V-sector vP2. But unlike VSS, there isno spectrum division and thus no spectrum reconfiguration inVPS. The activated V-sectors share a total of N1 = 25 PRBsavailable on the 5 MHz supply sector carrier C1, where N11

PRBs are allocated to vP1, N12 are to vP2 such that N11 +

Fig. 8. Average number of users per sector.

N12 = N1 and N11, N12 > 6. Similar to VSS, the decision toenable VPS on supply sector ss and the determination of N11

and N12 also require Ψ̄C1t,ss < θsup and M̄C1

t,ss/N11 < θovld

respectively. Once the V-sector vP2 is active and operational,UEs that are subscribed to PLMN-2 see vP2 as part of theirhome network and can IFHO to vP2.

Under VPS, a two-tier scheduler is implemented to serveboth V-sectors. For each V-sector, the first tier is an inde-pendent pre-selection stage. Lists of pre-selected UEs fromboth V-sectors are received by the second tier. In the secondtier, which is common to both V-sectors, all pre-selected UEsare prioritized based on their proportional fair ratios. It hasmaximum N1 = 25 PRBs available every TTI but only N11

PRBs are allowed to be used for scheduling the prioritizedUEs of PLMN-1 and remaining N12 PRBs are allowed tobe used for scheduling the prioritized UEs of PLMN-2.All non-virtualized sectors have a two-stage single-operatorscheduler having 25 PRBs available per TTI. Similar to VSS,a reactive overload control algorithm periodically monitorsPRB utilization factors Ψ̄vP1

t,ss and Ψ̄vP2t,ss of the owner V-sector

vP1 and the leased V-sector vP2. If vP1 is in overload, i.e.Ψ̄vP1

t,ss ≥ θovld, the algorithm reduces the allocated PRBs to vP2

by one and, at the same time, increases the allocated PRBsto vP1 by one. This stops when the PRBs allocated to vP2

reaches 6 PRBs. Also, if vP2 is in overload, i.e. Ψ̄vP2t,ss ≥ θovld,

the algorithm transfers 10% of UEs served by vP2 back totheir original source sectors of PLMN-2. The transfer stopswhen vP2 is serving only one UE.

IV. EXPERIMENTAL EVALUATION

A. Experiments

We conducted experiments on the simulation testbed usingconservative, moderate and aggressive supply threshold valuesin heavy, moderate to light sector loading conditions on fivedifferent sharing options on collocated and noncollocatedtopologies.



The overload threshold θovld and the supply threshold θsupare used to enable the sharing and resource distribution pro-cesses respectively. We use 90% of average PRB utilizationM̄Cx

t,s mark as overload threshold, i.e. θovld = 0.9. On a 5MHz (25 PRBs) carrier Cx, the minimum BW a supply sectorcan sublease to a demand sector is 1.4 MHz (6 PRBs) whileretaining 3 MHz (15 PRBs). To avoid the supply sector goinginto overload after subleasing 1.4MHz, its pre-supply averagePRB utilization M̄Cx

t,s needs to be less than θovld × 15 = 13.5.To meet M̄Cx

t,s < 13.5 condition, the supply threshold θsupshould be less than M̄Cx

t,s /25 = 0.54. Thus, by default, we use50% of average PRB utilization mark as the supply threshold,i.e. θsup = 0.5. We also use θsup = 0.3 and θsup = 0.8 asconservative and aggressive sharing thresholds respectively.

We start with the highest UE density factor f = 10representing heaviest sector loading. For each sector s ∈{1, 2, . . . , 114}, we determine Z10

s a number of UEs basedon a discrete uniform (Umin, Umax = fUmin) random variablewith Umin = 5 and Umax = 50 (as per Section III-A usingf = 10). Then we construct three distinct UE position-trafficprofiles for both collocated and noncollocated topologies. Aposition-traffic profile for a given topology includes an arrayof positions of all Z10

s UEs by placing them uniformly withinboundaries of each sector s of the topology and a two-dimension array of packet arrival times of all Z10

s UEs over theentire simulation duration respectively. Also, for each position-traffic profile of a topology, we construct a channel gain matrixrepresenting radio links from each of the UEs to each sectors of the topology. For other values of f ∈ {6, 7, 8, 9}, wespecify Zf

s = round(Z10s f/10) UEs to be assigned to sector

s. Then, for each f ∈ {6, 7, 8, 9}, we repeat the entire pro-cess of placing UEs, generating three distinct position-trafficprofiles and channel gain matrices for both collocated andnoncollocated topologies. Thus, for each topology, there are 15total position-traffic profiles and corresponding channel gainmatrices covering all five values of f . We also construct twodistinct sharing databases for collocated and noncollocatedtopologies to be used by the SE to identify all prospectivesupply sectors ss for each demand sector sd ∈ {1, 2, . . .114}.

For each simulation run, we choose one of the supplythresholds (θsup = 0.3, 0.5 or 0.8), a topology, a UE densityfactor f , corresponding sharing database, one of the threeposition-traffic profiles and corresponding radio channel ma-trix. Each run includes simulating NS, CS, SS, VSS and VPSoptions and repeating three times. Important to note, for afair performance comparison across the sharing options, wefix topology, user density, user placement, traffic pattern andsimulate all five sharing options. Each experiment is conductedfor 5000 TTIs. The sharing is activated and data collectionstarts after the first 2500 TTIs elapse.

B. Results

We compare the performance of No-Sharing (NS), CapacitySharing (CS), Spectrum Sharing (SS), Virtualized SpectrumSharing (VSS) and Virtualized PRB Sharing (VPS) optionsin various loading scenarios (multiple UE density factors) oncollocated and noncollocated topologies. Our comparisons arebased on the effectiveness of sharing options in (1) reducing

number of overloaded sectors and (2) improving user’s packetdrop probability.

Figure 8 shows the percentage of sectors in overload (i.e.%overloaded sectors) without activating any sharing option.It serves as a baseline for comparison. From the figure, thenumber of overloaded sectors decreases as the UE densityfactor f decreases. For UE densities up to 18 users per sectoron average, no sector goes into overload. Differences in thenumber of overloaded sectors between two PLMNs are due todifference in their UE densities. These differences encouragethe comparatively less loaded PLMN-1 to supply its surplusresources to the more loaded PLMN-2.

Figure 9a shows %overloaded sectors out of 114 sectorscovering both PLMNs and average per user packet drop proba-bility encompassing all users of both PLMNs with and withoutsharing in collocated and noncollocated topologies. Each datapoint in Figure 9a and also other figures in this section fora given topology, f , θsup, and sharing option represents asystem-level value averaged over nine runs covering threedifferent position-traffic profiles and channel gain matricesand their three repeats. For example, to calculate packet dropprobability for NS on noncollocated topology, f = 10 andθsup = 0.5, we collect each users packet drop probability forthree repeats of each position-traffic profile and channel gainmatrix combination and then per-user average them across thethree repeats. This per-user-averaged packet drop probabilitywas further averaged over all users in the two-network systemto find a system-level packet drop probability number foreach position-traffic profile and channel gain combination. Thethree distinct position-traffic profile and channel gain matrixcombinations result into three system-level packet drop prob-ability numbers. Finally, those three packet drop probabilitynumbers are averaged across all three runs to find a singlesystem-level packet drop probability number represented inthe figure for NS on noncollocated topology with parametersf = 10 and θsup = 0.5.

Further, Figure 9a shows sharing options are more effectivein curtailing overloading for lower UE density settings (i.e.f = 7, 8) compared to higher UE density (f = 9, 10)settings. For sharing to be effective, the availability of surplusresources should be in close proximity to the demand. Ifsectors of both PLMNs are in overload, they won’t havesurplus resources to share. For higher UE density, the resourcesupply is insufficient to meet the demand. On other hand, atrelatively lower UE density, there are ample sectors in PLMN-1 supplying surplus resources to nearby demand sectors inPLMN-2. From Figure 9a, CS is the most effective and SS isthe least effective option. VPS performance is comparable toCS. VSS does not perform as well as CS and VPS becauseof the loss of 0.6 MHz valuable spectrum at every supplysector due to the division of the 5 MHz LTE carrier into twoseparate LTE carriers of 3 MHz and 1.4 MHz. In this scenario,the 0.6 MHz spectrum loss is an artifact of choosing a 5MHz (pre-division) carrier bandwidth and the LTE standardnot supporting 2.5 MHz partitions. For example, if the pre-division bandwidth is 15 MHz, the spectrum can be partitionedinto 10 MHz and 5 MHz without any spectrum loss. VSSperformance with zero spectrum loss in the division processwould be comparable to VPS.



Underperforming Spectrum Sharing

Underperforming Spectrum Sharing

(a)

Supply Overload

Supply Overload

(b)

Fig. 9. With default θsup = 0.5; (a) %overloaded sectors and users averagepacket drop probability and (b) %overloaded sectors in supply PLMN-1 anddemand PLMN-2.

Similar to VSS, SS also suffers from spectrum loss but,in addition, SS is uniquely disadvantaged compared to allother sharing options because it prompts geographical transferof spectrum from supply sectors of one PLMN to demandsectors of the other PLMN whereas other options achieve re-source sharing between two PLMNs primarily via transfers ofusers/UEs from one PLMN to the other. Since present mobilenetworks are inherently designed to support transfers of usersrather than transfers of spectrum, the other sharing optionstake advantage of well-coordinated and efficiently manageduser transfers (IFHOs) with minimal impact on networkperformance whereas spectrum transfers are inefficient andimpact network performance with potential increases in co-channel interference. Geographical spectrum transfer from oneoperator sector to a sector of another operator is a complicatedprocess because of the possibility of inter-operator inter-cellinterference (ICI) on the transferred spectrum chunk.

There are many techniques, including dynamic Inter-CellInterference Control (ICIC) [30], to reduce co-channel inter-ference when the frequency reuse factor is one. But thesetechniques are designed for intra-operator (within own net-work) co-channel interference control, not for inter-operatorscenarios. Also, these dynamic control techniques requirereal-time interaction among sectors [30]. Since it is lesslikely to have established interfaces like X2 [3] betweentwo operators, traditional sectors/RANs to support real-timeinformation exchanges, such inter-operator ICIC techniques,are not likely to be realized. Thus, we chose not to modelthese techniques to support SS. However, to avoid any harmfulinter-operator co-channel interference issues without dynamicICIC, we make transfers of spectrum from one operator/sectorto another contingent upon assurance of no harmful co-channel interference between the operators. This puts thebrakes on network-wide proliferation of spectrum transferand eventually limits the effectiveness of SS. In particular,before the transfer of a spectrum chunk, all overlapping supplysectors surrounding a demand sector (as illustrated in theFigure 5 noncollocated topology) have to cease operationon the spectrum chunk. Thus, under SS, multiple supplysectors share spectrum with a demand sector. Unless all supplysectors are able to cease operation on the chuck due to theirdemand/needs, the spectrum transfer is prohibited and SS isdeemed unsuccessful. Hence, SS can create supply shortagesbecause multiple supply sectors have to release their spectrumto fulfill demand of just one demand sector. This supplyshortage is much evident in moderate and heavily loadednetworks, when available supplies are less to begin with. Insummary, the uniqueness resulting from supply shortage andthe inherited disadvantage are major factors in the relativelypoor showing of SS in moderate and heavily loaded networks,as represented by higher UE density experiments (i.e. f = 9and 10).

Figure 9a also shows how SS underperforms at f = 7 in thenoncollocated topology where more overloaded sectors wererecorded with SS compared to no-sharing (NS). Figure 9breveals the reason for the increase in %overloaded sectors withSS is that supply sectors in PLMN-1 get into overload aftersupplying surplus spectrum resources to demand sectors inPLMN-2. The cause of the supply sector overload phenomena



(a)

Supply Overload

Supply Overload

(b)

Fig. 10. %Overloaded sectors in supply PLMN-1 and demand PLMN-2(a) using θsup = 0.3 for conservative mode and (b) using θsup = 0.8 foraggressive mode.

is that a sudden reduction of available spectrum/PRB resourceswithin the supply PLMN-1 in a concentrated geographical re-gion increases PRB utilization/loading. Here, the geographicalregion is a collection of one or more neighboring/interferingsupply sectors. This increase in sector loading occurringsimultaneously in a geographical region covering multipleneighboring or facing cells/sectors increases inter-cell inter-ference and creates positive feedback which in turn furtherincreases interference/loading. Now, for a geographical regionthat is marginally away from overload, an increase in PRButilization after supplying surplus resource creates this positivefeedback, tipping the region into overload. Important to note,from Figure 9b, the issue of PLMN-1 supply-overload is notonly limited to SS but also observed for VSS and VPS whereits caused by a sudden and uncontrolled increase of servingusers at supply sectors in PLMN-1 right after the creation ofthe PLMN-2 V-sectors. The creation of PLMN-2 V-sectorson PLMN-1 supply sectors triggers IFHOs of (stationary andPLMN-1 subscribed) UEs from PLMN-2s overloaded sectorsto PLMN-2s V-sectors. But, unlike SS, overall performancewith VSS and VPS are not negative as it is offset by largerreduction in overloaded demand sectors of PLMN-1.

The supply-overload is not observed in CS primarily be-cause its wait and allow post-sharing (reactive) supply over-load control mechanism performs better than that in VSS andVPS. In particular, in light loading scenarios (f = 7, 8), CSavoids a sudden influx of users at a supply sector after en-abling the sharing. With comparable or better supply-overloadalgorithm, VSS performance can be further improved and becomparable or better than CS. In addition to the post-sharing(reactive) supply-overload control mechanism, the supply-overload can be managed by proactively minimizing theavailability of supply sectors. Next we try a proactive solutionunder both a conservative sharing mode with θsup = 0.3 tolimit number of eligible resource supplying sectors and anaggressive sharing mode with θsup = 0.8 to increase numberof eligible resource supplying sectors.

Figure 10 shows %overloaded sectors in supply and de-mand PLMNs using both aggressive and conservative supplythresholds. Figure 10b results indicate that all sharing optionsusing aggressive θsup = 0.8 perform relatively better in heavyloading (f = 9, 10) compared to the default θsup = 0.5 inFigure 9b and conservative θsup = 0.3 in Figure 10a. Inheavy loading scenarios, almost all sectors in both PLMNs arein overload except border sectors. These edge sectors, whichbecome supply sectors, don’t go into overload due to lack ofinterference (and positive feedback) and ultimately contributeto performance improvement by effectively reducing overload-ing in the demand sectors.

On other hand, for a lightly loaded (f = 7, 8) system,the same aggressive θsup = 0.8 usage results in supply-overload for SS, VSS and VPS options and eventually perfor-mance degradation for those options compared to the defaultθsup = 0.5 or conservative θsup = 0.3. This occurs in lightloading because an aggressive supply threshold θsup = 0.8aggravates the issue of supply-overload for SS, VSS and VPSby creating a larger pool of available supply sectors thatare marginally away from overload in a geographical region.Since the CS option doesn’t suffer from supply-overload, it



performs well in aggressive θsup = 0.8 mode due to theincrease in eligible resource supplying sectors compared toθsup = 0.5 and θsup = 0.3 modes. With the reductionin available supply sectors in PLMN-1, the conservativeθsup = 0.3 mode completely eliminates supply-overload issuesbut degrades performance in terms of %overloaded sectorsand users’ packet drop probability (not shown) compared toθsup = 0.5 and θsup = 0.8 for CS.

In summary, aggressive mode is better for well-controlledsupply-resource-consuming sharing options like CS. For sud-den resource consuming sharing options like SS, VSS andVPS, less aggressive supply threshold is advisable in lightlyloaded systems. Of course, more research into better post-sharing reactive approaches for SS, VSS and VPS includingterminating sharing if a supply sector goes into overload willbe helpful in eliminating or reducing the supply-overload.

C. Comparison of Sharing Options

Based on %overloaded sectors and users’ packet drop prob-ability statistics, we observe that CS performs better than otheroptions. We also note that VPS would perform comparablyto CS with more effective supply-overload control algorithm.VPS offers PRB level sharing among V-sectors of differentPLMNs and doesn’t suffer from spectrum loss. However, CS isthe simplest form of resource sharing that can be implementedrelatively easily on existing traditional cellular infrastructureand is cheaper than other sharing options that require so-phisticated technologies including software configurable RFfront-end and virtualization-capable infrastructure hardware.Since sharing options on virtualized hardware platforms arelikely to be costlier to implement than those on traditionalinfrastructure, the simplicity of CS on traditional infrastructuremay provide a better cost-benefit tradeoff relative to VPS onvirtualized infrastructure.

Like VPS, VSS also requires virtualization capable infras-tructure but, unlike VPS, VSS requires spectrum division ofa 5 MHz band and thus suffers from the loss of 0.6 MHzspectrum or 4 PRBs. VSS performance without spectrum losswould be comparable to VPS. SS is the least effective option.Two factors contributing to SS’s poor performance includ-ing the spectrum loss and inherently conservative spectrumsupply to mitigate co-channel interference. SS is unique inthat surplus spectrum resources have to be geographicallytransferred from supply PLMN to demand PLMN. In othersharing options, the surplus resource stays at its originallocation and is consumed by the demanding PLMN viauser steering and IFHO processes. This disadvantage and theconsequent co-channel interference limitations make SS theworst performing and least capable option in inter-operatorcellular/macro resource sharing scenario.

V. CONCLUSIONS

We studied two LTE cellular networks on collocated andnoncollocated antenna topologies with total of 114 sectors, twoMMEs, and a sharing entity (SE). We implemented basic shar-ing process involving the SE, the MMEs and the sectors. Wemodeled CS and SS options on traditional infrastructure andVSS and VPS on virtualized infrastructure. We defined sharing

parameters including overload threshold, supply thresholdand performance metrics and ran simulation experiments toquantify and compare performance of the sharing options inheavy, moderate to light sector loading conditions.

Based on the experimental results, we concluded that CS, anextension of roaming on traditional infrastructure, performedthe best. It is the simplest and the most preferable inter-operator sharing option for LTE cellular networks. VSS andVPS options on virtualized infrastructure also performed wellbut are significantly more complex to implement. From theperspective of system and service deployments, there arebenefits for operators to employ virtualized infrastructuresharing. However, we show in this work that virtualization isnot essential for the performance improvements derived frominter-operator resource sharing. In particular, simpler sharingmechanisms in which users effectively roam to systems withavailable capacity would generally perform equally or betterthan more complex spectrum reallocation and base stationvirtualization techniques. However, VSS and VPS virtualiza-tion techniques would be more suitable for implementationon advanced dynamic spectrum access (DSA) based cellularsystems. SS was found to be the least effective inter-operatorsharing option. It can be more effective with dynamic inter-operator co-channel interference control or in sharing betweencellular/macro and micro/femto networks as well as inter-wireless service-network SS scenarios, e.g. a DTV operatorsubleasing whitespace/broadcast spectrum to cellular opera-tors. The simulation results also showed low effectiveness ofinter-operator sharing options for higher UE density factors.The inter-operator sharing options were effective only whenthere were large differences in resource utilization between thetwo networks. Their effectiveness also depended on locationof the available surplus resources. For higher UE densityscenarios in which mobile networks are heavily loaded andsurplus resources are scarce, inter-wireless-service-networkresource sharing (e.g. dynamically accessing unlicensed/Wi-Fi or DTV spectrum) is likely to be more effective than theintra-service, inter-operator sharing examined in this work.Simulation and modeling of inter-operator resource sharingbetween cellular and femto networks, inter-wireless-service-network SS on traditional infrastructure and VSS, VPS andother advanced DSA techniques on virtualized infrastructureare good candidates for future study.

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[34] E. A. Jorswieck, L. Badia, T. Fahldieck, D. Gesbert, S. Gustafsson, M.Haardt, K.-M. Ho, E. Karipidis, A. Kortke, E. G. Larsson, H. Mark,M. Nawrocki, R. Piesiewicz, F. Romer, M. Schubert, J. Sykora, P.Trommelen, B. van den Ende, and M. Zorzi, “Resource sharing inwireless networks: the SAPHYRE approach,” in Proc. 2010 FutureNetw. Mobile Summit, pp.1–8.

[35] J. S. Panchal, “Inter-operator resource sharing in 4G LTE cellularnetworks,” Ph.D. thesis, ECE Department, Rutgers University, NJ,2011. Available: http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000063566.

Jignesh S. Panchal is a Distinguished Member ofTechnical Staff (DMTS) in advanced technology andnetwork strategy group under corporate CTO organi-zation at Verizon. He received his B.E. in Electronicsand Communication Engineering from L.D. Collegeof Engineering, Gujarat University, India in 1996and M.S. and Ph.D. degrees in 1998 and 2011 fromRutgers University, New Brunswick, New Jerseyall in Electrical and Computer Engineering. He hasco-authored several papers and filed more than 10patents. At Verizon, he is involved in spectrum and

3GPP/LTE-Advanced feature evaluation and future wireless technology as-sessment. His research interests include cellular resource scheduling, spectrumsharing and future cellular network design and architecture.

Roy D. Yates received the B.S.E. degree in 1983from Princeton and the S.M. and Ph.D. degrees in1986 and 1990 from MIT, all in Electrical Engineer-ing. Since 1990, he has been with the Wireless In-formation Networks Laboratory (WINLAB) and theECE department at Rutgers University. He presentlyserves as an Associate Director of WINLAB anda Professor in the ECE Dept. He also serves asan associate editor of the IEEE TRANSACTIONS

ON INFORMATION THEORY. He is a co-author ofthe text Probability and Stochastic Processes: A

Friendly Introduction for Electrical and Computer Engineers, publishedby John Wiley and Sons. He is a 2011 IEEE Fellow and a recipient ofthe 2003 IEEE Marconi Prize Paper Award in Wireless Communicationsand the 2011 Rutgers University Teacher-Scholar award. His research inwireless networks includes interference mitigation, secret communication, andspectrum regulation.

Milind M. Buddhikot is a Distinguished Memberof Technical Staff (DMTS) in Alcatel-Lucent BellLabs. His research interests are in the areas ofsystems, software, protocols and security for dy-namic spectrum access networks, network virtual-ization and green networks. Milind holds a Doctorof Science (D. Sc.) in computer science (1998) fromWashington University in St. Louis and a Masterof Technology (M. Tech.) (1988) in communicationengineering from Indian Institute of Technology(I.I.T.), Mumbai. He has authored more than 30

papers and holds nine patents. He has served as an Associate Editor ofIEEE/ACM TRANSACTIONS ON NETWORKING (TON) (2003-2009) andElsevier’s Computer Networks Journal (2003-2007).


mobile network resource sharing options: performance comparisons

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