PRECISE: Power Aware Dynamic TrafficSteering in Tightly Coupled LTE Wi-Fi

NetworksThomas Valerrian Pasca S, Himank Gupta, Bheemarjuna Reddy Tamma and Antony Franklin A

Indian Institute of Technology Hyderabad, IndiaEmail:[cs13p1002, cs16mtech01001, tbr, antony.franklin]@iith.ac.in

Abstract—LTE—Wi-Fi Radio Level Integration withIPsec Tunnel (LWIP) thrives as a complete solution toaddress the data requirement for telecom operators byeffectively utilizing unlicensed spectrum. Co-located LWIP(C-LWIP) node couples Home eNodeB (HeNB) and Wi-Fi Access Point (AP) at radio protocol stack to enable aunified control decision over both LTE and Wi-Fi links.The challenges pertaining to small cells viz., high co-tierinterference and QoS provisioning can be effectively ad-dressed by using LWIP. This paper proposes a novel PowerawaRE dynamiC traffIc StEering (PRECISE) algorithmwhich regulates transmit powers of LTE and Wi-Fi ofLWIP node in order to minimize interference across LWIPnodes in dense deployments. The PRECISE algorithmalso does flow steering across LTE and Wi-Fi interfacesby employing Multi Attribute Decision Making (MADM)technique. It thrives on ensuring Guaranteed Bit Rate(GBR) flow requirements by dynamically controlling thetransmit power across multiple LWIP nodes. Interferencemitigation sub-problem and ensuring GBR sub-problemare formulated as Mixed Integer Non-Linear Programming(MINLP) problems. The proposed PRECISE algorithmimproves the network throughput by 84% compared to3GPP Rel-12 LTE Wi-Fi interworking and 48% comparedto state-of-the-art α-optimal scheduler. It also has reducedthe number of unsatisfied GBR flows by 35% as comparedto α-optimal scheduler.

I. INTRODUCTION

High penetration of multi-featured smartphones andtablets has lead to data explosion [1]. Limited by avail-able spectrum for operation due to licensing require-ments, it has become challenging for mobile networkoperators to address the ever growing data demand.One of the solutions to meet ever-growing data demandis to offload some of the cellular network traffic ontoIEEE 802.11 based WLANs (Wi-Fi) which operateon the unlicensed spectrum. 3GPP has proposed vari-ous cellular-Wi-Fi interworking strategies from Rel.8 toRel.12, which realize interworking between cellular andWi-Fi networks through a gateway. Switching or offload-ing of downlink flows from cellular to Wi-Fi requiredchanges to the flow table at the cellular/WLAN gateway.Fast switching and dynamic flow steering across cellularand Wi-Fi links are not feasible due to high signalingoverhead and network delay involved in such operations.

To overcome these challenges, 3GPP has proposed afiner level of interworking in Rel.13 [2]. The tighter levelof interworking technologies include LTE Wi-Fi Aggre-gation (LWA) and LTE Wi-Fi Radio Level Integrationwith IPSec tunnel (LWIP). LWA realizes the aggregationat PDCP layer while LWIP realizes it at IP layer. LWIParchitecture includes Small cell eNB (SeNB) and Wi-FiAP integrated at their radio protocol stacks as shownin Figure 1 [3]. It includes a Link aggregation layer(LAL) over LTE and Wi-Fi radio protocol stacks forsupporting flow steering across LTE and Wi-Fi linkswithout any additional headers to packets. LWIP can berealized by co-located and non co-located deployments.In this paper, we investigate issues related to Co-locatedLWIP (C-LWIP) deployments.

S1 U

Fig. 1: Protocol stack of Co-located LWIP.

The challenges with small cell deployments includeco-tier interference due to densification of small cellsand QoS provisioning. Usage of orthogonal RATs (LTEand Wi-Fi) emerges as a solution to address this problem.The users located at the high interference zone of LTEcan connect to Wi-Fi thereby reducing the effect of co-tier interference. Currently, LTE small cells and Wi-FiAPs are independently deployed, which makes an LTEuser who is facing high interference difficult to find asuitable Wi-Fi AP to associate with for obtaining better978-1-5386-3531-5/17/$31.00 c© 2017 IEEE

service. The C-LWIP architecture ensures availabilityof Wi-Fi link for users even in the interference region,but C-LWIP deployment suffers from co-tier interference(for both LTE and Wi-Fi) when density of their deploy-ment increases, and leads to coverage holes when thedensity decreases.

Another challenge is to ensure QoS for LWIP users,which corresponds to allocating sufficient radio re-sources for their guaranteed bit rate (GBR) flows. Ina typical indoor scenario, the SINR received by aUser Equipment (UE) is constrained by the number ofobstacles and number of interfering LWIP nodes thatexist in its vicinity. LTE scheduler such as ”Priority SetScheduler” allocates more resources to the UEs withpoor SINR in order to meet their GBR requirements.Such a QoS centric allocation adversely affects theoverall system throughput.

II. RELATED WORK

The existing solutions for interference mitigation in-clude Inter-Cell Interference Coordination (ICIC) andeICIC [4]. These solutions employ frequency reuse andsubframe muting to reduce interference in LTE networks.The offloading algorithm presented in [5] prioritizes thetraffic with specific QoS requirement. The voice andvideo flows are sent through the cellular network whilethe elastic flows are sent through Wi-Fi. This approachensures QoS of GBR flows, but it does not intend tomaximize the utilization of network resources. In [6],to overcome poor availability and performance of thecellular network, the authors have used two key ideasviz., leveraging delay tolerance and fast switching fromcellular to Wi-Fi to reduce the load on the cellularnetwork. Initially, all flows are sent through Wi-Fi, ifWi-Fi is unable to transmit packets of a flow in a smalltime window (delay tolerance limit), then that flow isquickly switched to the cellular network. This solutionfocuses on maximizing the utilization of Wi-Fi, but itfails to maximize the overall system throughput.

In [7], the authors proposed an α-optimal scheduler inwhich scheduling across multiple RATs is formulated asan optimization problem. Steering the incoming trafficacross different RATs viz., LTE and WLAN is donebased on the value of α. For different values of α,the scheduler morphs its purpose as a proportional fairscheduler, maximum throughput scheduler, or maximizeminimum rate scheduler. In [8], the authors proposed a”water-filling” based interpretation for resource alloca-tion across multiple RATs. The fraction of traffic sentover a RAT is proportional to the ratio of users peakcapacity on that RAT and its throughput on the otherRAT. The above mentioned schedulers enable efficientscheduling across both LTE and Wi-Fi simultaneously,but they do not aim to reduce interference in densedeployment scenarios.

To address the problems which exist with aforemen-tioned works in the literature and to efficiently use theC-LWIP architecture, we propose a Power Aware Dy-namic Traffic Steering (PRECISE) algorithm. The pro-posed PRECISE algorithm does efficient flow steeringwith intelligent power control to minimize interferenceand to ensure GBR requirements with improved systemthroughput.

III. PROPOSED WORK

The PRECISE algorithm is designed with the follow-ing objectives: (i) Mitigation of co-tier interference indense deployment of LWIP system, (ii) Meeting GBRrequirements of the users including those experiencingpoor SINR, and (iii) Dynamic steering of the flowsacross LTE and Wi-Fi links to maximize the systemthroughput. Algorithm 1 details the components andworking of the PRECISE algorithm. Initially, the flowstate information of all downlink flows is collected bya centralized decision making entity (for e.g., LWIP

Algorithm 1 Power awaRE dynamiC traffIc StEeringInput: Set of all flows in the system (Fi), i ∈ {flows1,. . . ,k}, SINR of UEs associated with C-LWIPnode

1: for Every N ms do2: if Gs ≥ 90% then . Trigger IM Phase3: ΘIM (PL

j , PWj )

4: Set Tx power obtained through optimization5: else . Trigger GI Phase6: ΘGI(PL

j , PWj )

7: Set Tx power obtained through optimization8: end if9: AI ←TOPSIS(Fi) . Flow Steering

10: if LL > LW then11: if GBR flows unsatisfied then12: Steer set of unmet GBR flows (ΦG) to

Wi-Fi13: else14: Steer set of NGBR flows (ΦNG) with

highaffinity index to Wi-Fi

15: end if16: else17: if GBR flows unsatisfied then18: Steer set of unmet GBR flows (ΦG) to

LTE19: else20: Steer set of NGBR flows (ΦNG) with

highaffinity index to LTE

21: end if22: end if23: end for

gateway). Note that this LWIP gateway assists only inregulating the transmit power of LWIP nodes. If require-ments of GBR flows are met, then the algorithm aimsto improve the system throughput by mitigating the co-interference by triggering Interference Mitigation (IM)phase. In this phase, optimal transmit power values arecomputed and set for LTE and Wi-Fi radios to reduce co-tier interference across interfering C-LWIP nodes. TheIM phase is continued as long as QoS requirements ofGBR flows are met. If GBR requirements of some of theflows are not met, then GBR Improvement (GI) phase istriggered in which the transmit powers of LTE and Wi-Fiinterfaces of C-LWIP nodes are adjusted in order to meetthe GBR requirements. Gs corresponds to percentageof GBR flows satisfied. Both IM and GI phases arefollowed by flow steering across LTE and Wi-Fi linksin order to achieve their corresponding objectives. Flowsteering involves ordering different flows based on theiraffinity to an interface. An ith flow’s affinity to aninterface is given by its affinity index (AIi). If thereexists unsatisfied GBR flows then a set of unmet GBRflows (ΦG) are moved first to the lightly loaded interfaceof C-LWIP node. If GBR requirements are met, then a setof NGBR flows (ΦNG) with high affinity are moved tothe corresponding interface to maximize the throughputof the system. ΦG is obtained by iteratively picking setof unmet GBR flows served in a heavily loaded linkthat can be accommodatable on the other link. Howto accommodate a flow on a new link is discussedlater in Section III-C. The power control function ofPRECISE algorithm runs at LWIP gateway whereas theflow steering runs at C-LWIP node so that it can takeindependent and fast decision on steering of flows acrossLTE and Wi-Fi links.

IM Phase GI Phase

LTE Coverage in

IM Phase

Wi-Fi Coverage

in IM Phase

Coverage expanded

by GI Phase

Coverage reduced

by GI Phase

A

CB

D

Fig. 2: Variation in coverage observed in different phasesof PRECISE algorithm.

Figure 2 depicts the coverage pattern during IM andGI phases. It includes LTE operating on one licensedfrequency band across all the cells and Wi-Fi using oneunlicensed channel across all the cells. During IM phase,

LTE and Wi-Fi coverages appear to cover the alternatecell edge regions in order to reduce co-tier interference(note that this figure is a closer approximation and itmay vary based on user density and their positions inthe network). In Figure 2, points ’A’ and ’D’ denotelocations of two C-LWIP nodes where as ’B’ and ’C’denote the regions of interest. When C-LWIP nodesat ’A’ and ’D’ transmit with same power, then theregions ’B’ and ’C’ suffer from high co-tier interference.IM phase is then triggered which reduces LTE co-tierinterference in the regions ’B’ and ’C’ by reducing thetransmit power of LTE in C-LWIP node ’A’. Similarly,Wi-Fi interference at regions ’B’ and ’C’ is reducedby reducing the Wi-Fi transmit power of C-LWIP node’D’. During GI phase the edge of coverage region iseither expanded or shrank based on the number ofUEs with unmet GBR flows. Expansion or increase intransmit power on a link corresponds to improving bitrate for a UE with unmet GBR requirements. Reductionin coverage region corresponds to reducing interferencefor UEs associated with other C-LWIP nodes withoutdegrading GBR guarantees of the current C-LWIP node.

A. Interference mitigation using orthogonal RATs (IMPhase)

We have formulated the interference mitigation sub-problem as a mixed integer non-linear programming(MINLP) problem with an objective of minimizing co-tier interference within a RAT. IM phase sets the optimaltransmit powers to LTE and Wi-Fi interfaces of C-LWIPnode. The SINR maximization of C-LWIP is formulatedas follows.

Maxi. ΘIM =

U,B∑i=1,j=1

αLi,j×SINRL

i +αWi,j×SINRW

i

s.t.B∑

j=1

αLi,j ≤ 1 ∀i and

B∑j=1

αWi,j ≤ 1 ∀i

αLi,j =

{1, if SINRL

i ≥ ThLTE

0, otherwise

αWi,j =

{1, if SINRW

i ≥ ThWi−Fi

0, otherwise

αLi,j =

{0 or 1, if αW

i,j = 1

0, otherwise

αWi,j =

{0 or 1, if αL

i,j = 1

0, otherwise

PLmin ≤ PL

j ≤ PLmax; PW

min ≤ PWj ≤ PW

max

ΘIM is the sum over LTE and Wi-Fi SINRs of usersassociated with LTE and Wi-Fi links of C-LWIP nodes.Here, PL

j and PWj are the transmit powers of LTE and

Wi-Fi interfaces of jth LWIP node, respectively. αLi,j is

a binary variable which corresponds to association ofith user with jth C-LWIP node over LTE interface andαWi,j denotes the association of ith user with jth C-LWIP

node over Wi-Fi interface. B and U denote the numberof C-LWIP nodes and number of users in the system,respectively. This optimization problem (ΘIM ) can besolved using an MINLP solver.

B. GBR improvement using dynamic power control (GIPhase)

The objective of this sub-problem is to maximize thethroughput of GBR flows. GI is formulated as an MINLPproblem with an objective to improve the throughput forthose UEs whose GBR requirements are not met. Thiscan be achieved by maximizing the sum of weightedSINRs of those UEs.

MaximizeΘGI =

U,B∑i=1,j=1

rLi,j×SINRLi +rWi,j×SINRW

i

s.t.{SINRL

i − (γ ×Θ(SINRLi )) ≥ 0; if Θ(SINRL

i ) ≥ SM

SINRLi −Θ(SINRL

i ) ≥ 0; otherwise{SINRW

i − γ ×Θ(SINRWi ) ≥ 0; if Θ(SINRW

i ) ≥ SM

SINRWi −Θ(SINRW

i ) ≥ 0; otherwise

PLmin ≤ PL

j ≤ PLmax; PW

min ≤ PWj ≤ PL

max

Weight of UE depends on number of unmet GBR flowswith that UE. The weight rLi,j corresponds to fractionof unmet GBR flows of ith UE associated with jth C-LWIP node through LTE interface. rLi,j = ϑi,j∑

i ϑi,j, here

ϑi,j corresponds to the number of unmet GBR flows ofith UE associated with jth C-LWIP node. Θ(SINRL

i )and Θ(SINRW

i ) correspond to SINRs of LTE and Wi-Fi observed during the IM phase, respectively. γ denotesthe tolerable fraction in reduction of SINR for the userswho operate with the highest Modulation and CodingScheme (MCS). SM corresponds to the minimum SINRat which a UE gets the highest MCS.

C. Flow steering across LTE and Wi-Fi links

The PRECISE algorithm selects a flow to be steeredfrom one interface to other using a multi-attribute de-cision making (MADM) technique called as Techniquefor Order Performance by Similarity to Ideal Solution(TOPSIS) [9]. TOPSIS makes use of various decisionmaking parameters (DMPs) from UEs such as LTESINR, Wi-Fi SINR, and available bandwidths in LTE andWi-Fi links. Link Aggregation Layer (LAL) of C-LWIPnode gathers all these DMPs (refer Figure 1). TOPSISchooses best suitable flow to be moved to an appropriatelink based on these DMPs. The flow steering algorithmis executed once in every N ms.

Subroutine: TOPSIS for Ranking Flow AffinityInput: Set of all flow (Fi) parameters, Link to whichflow affinity has to be obtained

1: Vector Normalization of all flow parameters Fi,j

where i ∈ {flows 1,. . . ,k}, j ∈ {network param-eters}

2: Apply given set of weights wT = {w1, . . . , wn}3: Fi,j ← Fi,j × wj

4: Find A+ (Positive ideal solution) and A− (Negativeideal solution)

5: Find Positive ideal separation (S+) and NegativeIdeal separation (S−)

6: Calculate Ci for each flow: Ci ← Si−

Si++Si−

7: AI ← sort {Ci} in descending order8: Return the flow affinity index AIi for every flow Fi

TOPSIS: Subroutine TOPSIS [9] shows the procedureinvolved in prioritizing the flows. For every flow i, DMPsare obtained from the LAL of C-LWIP node. All theseDMPs are normalized and appropriate decision makingweights (w) are given to them. Following processing ofDMPs, Positive Ideal Solution (PIS) and Negative IdealSolution (NIS) are computed. PIS is a set of best valuesamong all flows for each parameter while NIS is a setof worst values among all flows for each parameter. Forexample, PIS will contain the largest value in SINR butthe smallest value in packet error rate (PER). RelativeCloseness (RC) is a metric which emphasizes how closea flow is to PIS or NIS. Affinity Index (AIi) of an ith

flow towards a specified interface is obtained by rankingthem based on its RC. Flow with a large difference fromNIS and less difference from PIS has high affinity forsteering.

D. Obtaining Decision Making Parameters for TOPSIS

TOPSIS uses the following DMPs for decision mak-ing: load of LTE (LL), load of Wi-Fi (LW ) and GBRi

requirements of ith flow. Loads on both LTE and Wi-Filinks are calculated as follows:

LL =

∑Ni=1 uPRBi

tPRB ×N(1)

LW =BT

BT + IT(2)

Here, tPRB is the total number of physical resourceblocks available in a Transmission Time Interval (TTI=1ms in LTE). uPRB is the number of PRBs used for datatransmission in a TTI. In Eqn (1), LL is found as theratio of total number of uPRB to the total number ofavailable PRBs over N TTIs (we have set N=200 whichcorresponds to a Decision Making Interval - DMI).Eqn (2) is used to obtain load on Wi-Fi by estimatingchannel busy time over a total time of N ms. BT andIT correspond to busy and idle times of Wi-Fi channel,respectively.

In order to find that LTE can accommodate moreflows, the cumulative throughput of all GBR flows issubtracted from the maximum achievable throughput.Available bandwidth in LTE (AL) can be obtained asfollows.

AL =ML −N∑i=1

OLi (3)

Here, ML denotes the maximum throughput that canbe achieved by LTE under the given network conditions,given by ML = BW × log(1 + ΨL), where ΨL corre-sponds to average SINR of UE in LTE. OL

i correspondsto the throughput observed by ith user in LTE. Similarly,to estimate that Wi-Fi can accommodate more flows ornot, current channel utilization by all GBR and NGBRflows (i.e., LW ) is subtracted from the maximum channelutilization under the given network conditions.

AW = (MCW − LW )× PD (4)

Here MCW denotes the maximum channel utilization(network is fully loaded), LW denotes the load on Wi-Fi and PD denotes the average physical layer data rateof Wi-Fi, which is given by,

PD = BW × log(1 + ΨW )

where ΨW corresponds to SINR of UE on Wi-Fi link.

IV. SIMULATION SETUP AND PERFORMANCERESULTS

The performance of PRECISE algorithm is evaluatedin a dense deployment scenario. We have compared thePRECISE algorithm with an existing Wi-Fi offload [6],[10], 3GPP Rel. 12 [11] and a state-of-the-art α-optimalscheduler [7] to observe its performance benefits. Wi-Fi offload algorithm prefers to use only Wi-Fi linkwhenever Wi-Fi is available and switches to LTE linkon observing poor SINR in Wi-Fi. In 3GPP Rel. 12solution, a UE associates with either LTE or Wi-Filink of C-LWIP node. The UE prefers to associate withthe link having higher SINR. The α-optimal schedulerassociates a set of flows through LTE and Wi-Fi linksbased on throughput achieved by that UE in differentRATs. When α=1, the scheduler does a proportionallyfair split among the flows through LTE and Wi-Fi links.Figure 3 shows the simulation scenario where we haveconsidered a building of dimensions 50 m × 50 m ×10 m having four C-LWIP nodes placed with a meanEuclidean distance between LWIP nodes as 20 meters.The positions of C-LWIP nodes in the building areshown in Figure 3. The building has two floors and a wallper every 10 meters. Path loss model includes wall andfloor losses. For creating more challenging environment,we have considered LTE operating with reuse factor oneand Wi-Fi operating in the same channel across all APs.The other important simulation parameters are shown in

Table I. We have used Matlab based solver (fmincon) tosolve the proposed MINLP problems.

0

50

40

300

1020

20

3010

40

0 50

5

10

-40

-20

0

20

40

60

X: 26

Y: 25

Z: 7

X: 35

Y: 41

Z: 1

X: 42

Y: 17

Z: 7

X: 11

Y: 23

Z: 4

Fig. 3: SINR distribution 3D-map of the building chosenfor conducting experiment with 4 C-LWIP nodes. Thex,y, and z coordinates of C-LWIP nodes are also givenin the map.

TABLE I: Simulation Parameters

Parameter Value# of UEs, LWIP Nodes 100, 4

Max Tx power of LTE & Wi-Fi 23, 20 dBmLTE path loss model 3GPP indoor path loss model

Wi-Fi path loss model ITU path loss modelLTE MAC Scheduler Priority Set Scheduler (PSS)

UE position RandomUE mobility model Constant Position Mobility Model

Wi-Fi Standard IEEE 802.11nWi-Fi frequency and bandwidth 2.4 GHz, 20 MHzLTE frequency and bandwidth 2.6 GHz, 10 MHz

A. SINR Distribution

The SINR distributions of UEs are observed in twocases (i) LWIP with fixed power (set to the maximumpower) and (ii) LWIP power obtained from the PRECISEalgorithm. Figure 4 shows CDF of SINR of UEs. Itcan be observed that PRECISE algorithm has improvedSINR of UEs by 4 dB in both LTE and Wi-Fi links ascompared to fixed power allocation. This improvementis achieved because PRECISE algorithm optimizes thetransmit power of LTE and Wi-Fi links in order to reducethe co-tier interference across multiple LWIP nodes. Thisoperation of PRECISE algorithm resembles fractionalfrequency reuse across different RATs.

B. Ensure GBR in the Network

PRECISE algorithm ensures data rate requirements ofGBR flows and maximizes the throughput for NGBRflows thereby maximizing the entire network throughput.In this experiment UE traffic includes GBR and NGBRflows. GBR flows comprise of conversational voice(G.711) at 87.2 Kbps GBR, Video call (HD) at 1.2Mbpsand Streaming Video at 1.2Mbps [12]. NGBR flows

0

0.2

0.4

0.6

0.8

1

-20 -10 0 10 20 30 40 50

CD

F

SINR (dB)

Fixed Power (LTE)PRECISE (LTE)

Fixed Power (Wi-Fi)PRECISE (Wi-Fi)

Fig. 4: CDF of UEs SINR.

include Sync apps and file downloads. Total number ofdownlink flows in the network at any instance followsPoisson distribution with mean varying from 300 to8000 flows. Figure 5 is an instantaneous capture ofthroughput and number of unmet GBR flows. Data pointsare plotted for every 200 ms which corresponds to a DMIof PRECISE algorithm. The experiment is conducted forlow, medium and high load conditions (load= 100, 300and 600 flows), triggers are observed in all the cases.In case of load=100 flows, the number of GBR flowsare low and they are satisfied. As the load increases,the number of unmet GBR flows increases. This triggersGI phase, which regulates the transmit power of LWIPnodes in order to reduce the number of unmet GBRflows. During GI phase the transmit power of LTE andWi-Fi links are obtained by solving the optimizationproblem ΘGI . In case of high load (load=600 flows), GIphase is triggered more than IM phase as the number ofunmet GBR flows is very high.

0

20

40

60

80

100

120

140

# o

f G

BR

flo

ws n

ot

me

t

GI Phase TriggerLoad=100Load=300Load=600

0

20

40

60

80

100

120

140

0 2 4 6 8 10

Thro

ughput (M

bps)

Time (Seconds)

Load = 100Load = 300Load = 600

Fig. 5: Events triggered for varying load.

C. Different Phases of PRECISE Algorithm

To study different phases of PRECISE algorithm, wehave conducted four different experiments. Across eachexperiment, the mean number of flows in LWIP network

0

10

20

30

40

50

100 200 300 400 500 600

# o

f T

rigg

ers

Load (# of flows in the network)

IM-Phase TriggersGI-Phase Triggers

Fig. 6: Triggers for different threshold.

0

0.2

0.4

0.6

0.8

1

0 0.5 1 1.5 2 2.5 3 3.5

CD

F

User Throughput (Mbps)

Wi-Fi PreferredRel-12

α-OptimalPRECISE

Fig. 7: CDF of UEs throughput.is varied from 100 to 600. We have counted the numberof IM triggers and GI triggers observed for differentloads. Figure 6 shows the number of times IM and GIphases are triggered. As the load in the network (thenumber of flows) increases from 100 to 600, the numberof GBR flows unsatisfied increases. In order to reducethe number of unmet GBR flows, GI phase is triggersaccordingly.

0

20

40

60

80

100

120

140

160

300 350 400 450 500 550 600

Th

rou

gh

pu

t (M

bp

s)

Load (# of flows in the network)

Wi-Fi PreferredRel-12

α-OptimalPRECISE

Fig. 8: Throughput for different load.

D. Throughput Analysis

Performances of different algorithms are comparedwith PRECISE algorithm. Figure 7 shows CDF of UEs

throughput observed for a fixed load (load = 600 flows)when different algorithms are employed. In case of Wi-Fipreferred algorithm, UE throughput is low because a UEthat is associated with LWIP node strictly confines to useWi-Fi resource even when both LTE and Wi-Fi SINRsare high. The UE prefers to use LTE only when Wi-FiSINR is lesser than a threshold which leads to inefficientresource utilization. Rel-12 allows the UE to choose andassociate the flow to the link with better SINR. Hencethe UE throughput has improved significantly comparedto Wi-Fi preferred algorithm. α-optimal scheduler dis-tributes the flows of a UE proportionally across LTE andWi-Fi links based on the throughput achieved by that UEon each RAT (observed over each DMI). In the case ofPRECISE algorithm not only the flow steering acrossLTE and Wi-Fi links are done, but also the efficientpower regulation has lead to improved user throughput.Figure 8 shows the throughput achieved by varying theload, for different algorithms. As the load increasesthroughput of the network increases significantly in caseof PRECISE compared to other algorithms because ofefficient flow routing and power regulation. PRECISEalgorithm has improved the network throughput by 48%as compared to the α-optimal scheduler. PRECISE al-gorithm has outperformed 3GPP Rel-12 based LTE Wi-Fi interworking by 84%. α-optimal thrives to maximizethe throughput of all the UEs in the network hence itindulges in steering the flows with high data require-ment on to the best interface. PRECISE algorithm alsoconsiders the type of traffic (GBR, NGBR) involved inorder to maximize the GBR satisfaction in high loadcase. Figure 9 captures the fraction of unmet GBR flowsin the network when different algorithms are employed.PRECISE algorithm minimizes the unmet GBR flowscompared to other algorithms because of its ability topick GBR flows first and steer to the best interface inorder to satisfy their requirements. PRECISE algorithmhas reduced the number of GBR flows unmet by 35%as compared to α-optimal scheduler.

0

50

100

150

200

300 350 400 450 500 550 600

Nu

mb

er

of U

nsa

tisfie

d G

BR

Load (# of flows in the network)

Wi-Fi PreferredRel-12

α-OptimalPRECISE

Fig. 9: Unsatisfied GBR.

V. CONCLUSIONS

The standardization of co-located LWIP has enabledsophisticated control over LTE and Wi-Fi RATs. Wehave proposed a downlink steering algorithm, PRECISE,with an objective to ensure QoS and to maximizingthroughput in co-located LWIP deployment scenario.The PRECISE algorithm employs power control toreduce interference in dense deployment scenario andimproves the performance of GBR flows. It also dynamicsteers the flows across LTE and Wi-Fi links usingMADM technique, which associates a flow through themost affine interface in order to improve the networkthroughput. The PRECISE algorithm has outperformedthe throughput of state-of-the-art α-optimal schedulerby 48% and 3GPP Rel-12 LTE Wi-Fi interworking by84%. PRECISE algorithm has reduced the number ofunmet GBRs compared to other existing algorithms;notably it has reduced unmet GBR flows by 35% ascompared to α-optimal scheduler. As a part of futurework, the PRECISE algorithm can be further improvedby considering traffic steering based on battery level ofUEs.

VI. ACKNOWLEDGMENT

This work was supported by the project ”ConvergedCloud Communication Technologies”, MeitY, Govt. ofIndia. We thank Mukesh Kumar Giluka for his contribu-tion in this work.

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