arX

iv:1

508.

0501

7v2

[cs.

NI]

21

Aug

201

5

On the Packet Allocation of Multi-BandAggregation Wireless Networks

Sanjay GoyalNYU Polytechnic School of Engineering

Brooklyn, NY, USAsanjay.goyal@nyu.edu

Tan Le, Amith Chincholi, Tariq Elkourdi{lebatan; amith.vikram}@gmail.com, tariq.elkaurdi@njit.edu

Alpaslan DemirInterDigital Communications, Inc.

Meville, NY, USAalpaslan.demir@interdigital.com

Abstract—The use of heterogeneous networks with multipleradio access technologies (RATs) is a system concept that bothacademia and industry are studying and exploring for nextgeneration wireless networks. A multi-RAT cross-layer techniqueto minimize average packet end-to-end delay for a system with asingle user terminal and a single QoS class is proposed in [1]. Inthis paper, we present a generic theoretical framework to obtainthe optimal packet distribution over multiple RATs when multipleuser terminals are present in the system and also when the systemsupports different QoS classes simultaneously. We also proposea packet scheduling algorithm, called OMMA Leaky Bucket, toachieve the optimal packet distribution scheme. A descriptionof the Opportunistic Multi-MAC Aggregation (OMMA) systemarchitecture is presented which includes functional description,discovery and association processes between multi-RAT devicesand dynamic RAT update management. We finally present sim-ulation results which show performance gains with the proposedOMMA Leaky Bucket scheme in comparison to other existingmechanisms.

I. I NTRODUCTION

The widespread use of multi radio access technology(multi-RAT) capable devices has attracted many researchersfrom academia and industry towards the concept of multi-RAT aggregation. Simultaneous use of multiple RATs is aviable solution to improve throughput. Typical multi-RATwireless devices support IEEE 802.11 based Wi-Fi RATs likeIEEE 802.11n, cellular technologies like UMTS/WCDMA,HSPA, CDMA20001x-EVDO, WIMAX, LTE, GSM, andshort range wireless technologies such as Bluetooth. Band-width aggregation solutions across multiple RATs could beimplemented at different layers such as at the applicationlayer, transport layer, or between IP and MAC layers. Theaggregation solutions at transport or application layers maynot be very efficient in terms of performance since it is dif-ficult for these aggregation schemes to effectively work in anenvironment with varying channel conditions due to the lackofinstantaneous channel information at these layers. Aggregationschemes at a layer between IP and MAC is more promis-ing when feedback information from the MAC about theinstantaneous channel conditions of the RAT is available. Thebandwidth aggregation solutions described by Koudouridiset al. [2] and Dimouet al. [3] showed aggregation at a layerbetween IP and MAC called Generic Link Layer (GLL). GLL

is responsible for multi-radio cooperation, which integratesdifferent RATs at the link layer and efficiently maps userservice demands to multiple radio access networks. Thesepapers on GLL investigated multi-radio transmission diversityand multi-radio multi-hop schemes. Trinhet al. [4] proposeda mechanism to switch radio resources between differentavailable RATs in a multi-RAT device. A controller (DigitalUnit Controller) monitors the entire resource utilizationforall available RATs. If in any RAT, average packet loss rateand average channel utilization are not sufficient to fulfillthe QoS requirement, it borrows resources from another RATwhich has unused resources. If no such RAT is available thentraffic will be dropped. On the standardization side, IEEE802.1 OmniRAN task group is working on a Open MobileNetwork Interface (OMNI), a common module below the IPlayer enabling simultaneous operation of any IEEE 802 accesstechnology [5].

In addition, there are many papers in the literature onthe topic of data allocation over multi-RAT systems. Kr-ishnaswamyet al. [6] and Ramaboliet al. [7] provided ananalysis and survey on aggregation schemes over multi-RAT systems. Aggregation at the transport layer described in[8][9][10][11] aggregated multipath TCP traffic of multi IPflows over non-contiguous and contiguous frequency bands.Zhang et al. [12] presented a load balancing scheme basedon queuing theory when aggregating traffic between RATs.A resource allocation scheme over multiple RATs for voiceand video communication services is proposed by Wuet al.[13], which maximizes network capacity while maintainingthe requirements for individual users’ quality of service.Konet al. [14] proposed an autonomous parameter optimizationscheme using a machine learning algorithm to maximize thethroughput of the heterogeneous RAN aggregation system.An analytical framework to minimize the end to end delayon general wireless multi-path aggregation systems for re-altime multimedia traffic was proposed by Maoet al. [15].This delay includes the delay along the paths and the re-sequencing delay at the receiver. Koudouridiset al. [16]evaluated the effects of different Multi-Radio TransmissionDiversity (MRTD) schemes for TCP flows over heterogeneousradio links. Results indicate that MRTD schemes provide

substantial gains in terms of goodput and show a significantreduction in file download times. These gains were shown tobe due to the diversity obtained and the suppression of theunwanted duplicate acknowledgements that frequently causedegradation in TCP performance over a radio channel. In[17] a quantitative analysis in terms of throughput and energyefficiency was presented for different multi-radio transmissiondiversity schemes. They analyzed different combinations ofradio access selection and user scheduling. In this paper, ascompared to other works, we provide a general solution tothe multi-user, multi-RAT, multi-QoS scenario. Furthermore,it provides an insightful analysis of the performance of thesystem under different packet distribution techniques.

In our previous work [1], we presented an analyticalframework for data allocation at the opportunistic multi-MACaggregation (OMMA) layer for a single access point (AP), asingle station (STA) with a single type of QoS traffic. TheOMMA layer is common to all RATs and resides just belowthe IP layer but above the radio protocol stacks as shownin Figure 1. We investigated the problem of minimizing theaverage packet latency (the sum of queuing delay and servingdelay) under certain constraints. We proposed an algorithmto compute the ratio of optimal packet distribution acrossRATs to be implemented at the OMMA layer. Moreover, westatistically characterized the reordering delay for thissystemand proposed an algorithm to compute a modified optimalpacket distribution ratio which takes reordering delay intoaccount.

Fig. 1. Multi-RAT aggregation using OMMA layer

In this paper, we propose an analytical framework for thegeneral case where there are multiple STAs in the system andIP traffic corresponds to more than one QoS category. Themain contributions of this paper are as follows:

• An analytical framework for multi-RAT systems to derivethe optimal packet allocation ratio over multiple RATs.

• A smart packet allocation algorithm for multi-RAT sys-tems which maintains the above optimal packet allocationratio and also minimizes the re-sequencing delay at thereceiver due to packet coming out of order.

• The architecture and functional description of the OMMAsystem which includes a discovery and association pro-cess between multi-RAT devices, and dynamic RAT up-date management.

• Simulation results showing the performance of theOMMA system with the proposed algorithms, and com-parison to other schemes.

The rest of the paper is organized as follows. Section IIdescribes the system model, and the problem statement. Theanalytical framework for optimal packet allocation over mul-tiple RATs is described in Section III. Section IV presents apacket scheduling algorithm for minimizing the re-sequencingdelay. The architecture with functional design of the OMMAsystem, and the flow management at both OMMA senderand receiver for IP packets are presented in Section V,and Section VI, respectively. Section VII presents simulationresults and analysis. Section VIII concludes the paper.

II. SYSTEM DESCRIPTION

A. System Model

The wireless system under consideration is a Wi-Fi system.The system consists of an AP, and a number of Wi-Fi STAs.Both AP and STAs have the capability of supporting multipleRATs (sayM RATs), where all RATs operate on differentspectral bands. The RATs belong to the IEEE 802.11 protocolsuite, i.e., 802.11n [18], 802.11ac [19], etc. As shown inFigure 1, a common layer called OMMA resides below theIP layer but above the protocol stacks of all RATs. At theAP, a stream of incoming IP packets arrive at the OMMAlayer. This incoming IP packet stream is then split by theOMMA layer into M sub-streams each of which is assignedto a corresponding transmit buffer in each RAT.

The incoming IP packets at the AP may belong to oneof many different IP QoS classes. Each RAT which supportsenhanced distributed channel access (EDCA) [20], where thehigh-priority traffic has a higher chance of being sent than low-priority traffic, independently performs mapping of IP QoSclasses to 802.11 QoS classes, called access categories (ACs).The packets sent from the OMMA layer to different RATs, i.e.,sub-streams mentioned above, will be stored in one of fourdifferent queues corresponding to four ACs in the MAC [20].Inside each AC queue, there are multiple virtual sub-queuescorresponding to each STA that the AP needs to send datato. This queuing structure is modeled as a two dimensionalqueuing system as shown in the Figure 2.

Let Qi,k denote the queue at the AP which stores packetscorresponding to ACk to be sent to STAi. Each queueQi,k

is modeled as M/G/1 queue with the following assumptions:• Arriving IP packets follow the Poisson process.• Serving time of IP packets follows the general distri-

bution. This is defined as the contention time of theCSMA/CA process plus the transmission time (includingretransmissions if required) for the packet confirmed tobe sent out successfully.

• Packets are served in the order they arrive in the queue,i.e., first-in-first-out (FIFO).

Fig. 2. MAC queueing mechanism in EDCA mode

• Serving time of an IP packet is assumed to be identicallydistributed, mutually independent and independent of theinter-arrival time.

When data from different ACs need to be transmitted bythe MAC, the CSMA/CA process of each AC works inparallel to contend for the channel. All ACs have prioritiesassigned to them for sending out data so as to support theQoS requirements of different types of traffic. The priorityorder of ACs is enabled by setting different parameters ofthe CSMA/CA processes. The AC which wins the contentionprocess will get the channel to send out the data from itsown virtual queues. Packets in the other ACs will remain intheir respective queues for this duration, and then resume theirown CSMA/CA processes after the current transmission iscompleted. We assume that ACk is the winner and currentlycontends the channel. Inside this AC, there areN independentvirtual queues, which store data packets forN different STAs.The AP has different mechanisms to select the STA for servingat this time. If the virtual queue to be scheduled isQi,k, the APwill only send out data corresponding to STAi and ACk. Thischannel access duration may be used to transmit one packetor multiple packets for the queue. After this transmissionis completed, the CSMA/CA processes of all the ACs areresumed. The next AC, which wins the channel access nextsends out the data. When the channel access turn returns to ACk again, the next STAi+ 1 will be served if they follow theRound-Robin Scheme. In other words, packets of all virtualqueues corresponding to different STAs need to be served atleast once before the first STA is served again. The detail ofthis process in the EDCA mode could be found in [21].

Because of this queueing system in the EDCA mode, eachqueueQi,k can be modeled as M/G/1 queue with vacations.

The vacation time with the queueQi,k is the duration that theAP serves other STAs or when the AP is sending data of otherACs.

B. Problem Statement

In this paper, the problem of packet scheduling of IP trafficover multiple RATs in a multi-RAT device is considered. Asdescribed in section II-A, the main IP stream is split intoM sub-streams each of which is assigned to a correspondingtransmit buffer in each RAT.

When a system with a single STA, single AP and singleIP flow from AP to STA is considered, the challenge is todetermine how to optimally distribute packets correspondingto the IP flow across RATs such that the average end-to-end delay per packet is minimized. Placing all packets inthe transmit buffer of the RAT with the lowest latency mayincrease the average packet queuing delay. On the other hand,dispersing them across all RATs randomly may decrease theaverage packet queuing delay and serving delay, however itmay result in out-of-order reception of packets at the receiverdue to differences in link latencies. This can cause longerqueuing delays at the receiver to rearrange packets beforesending them up to the IP layer. A smart packet assignmentstrategy to minimize both average end-to-end packet latencyand out-of-order packet reception delay, and thus maximizethroughput is essential.

In a system with multiple STAs, and a single AP withmultiple IP flows, the MAC layer at the AP sorts the IPflows according to their corresponding access categories andSTA addresses. The main IP stream for each STA is splitinto multiple QoS streams that are queued in separate buffersin the sender MAC layer. Each buffer corresponds to anaccess category that has a certain priority. However, this setup complicates the traffic shaping because, for any RAT, theaverage packet delay not only depends on the queuing delaydue to unserved packets in the same buffer, but also on thedelay due to buffering and channel access by packets of theother buffers. This is because all packets queued in the MAC ofa particular RAT share the same MAC scheduler and physicallayer. Thus a modified packet assignment strategy (comparedto the single STA single IP flow case) to minimize averageend-to-end packet latency is essential.

III. O PTIMAL SCHEDULING SCHEME AT OMMA LAYER

A. Terminology and Assumptions

We use the following terminology shown in Table I. Notethat the analytical framework developed in this section is forthe general case of scheduling data corresponding to ACk

from AP to STA i. To keep the notation simple, we omit thesubscriptsi and k. The terms defined below correspond toRAT index j ∈ [1,M ].

Note that:

• Vj is defined as the average vacation time of the queueQi,k corresponding to RATj, i.e., the average timeduration that RATj stops serving queueQi,k and serves

λj Average arrival rate of IP packets

µj Average serving rate

ρj Fraction of arrival rate and service rate, i.e,λj

µj

Xj Average packet serving time

Tj Total average delay per packet

Wj Average packet queuing delay

Vj Average vacation time

TABLE INOTATIONS FORACCESSCATEGORYk FOR STA i (Qi,k)

CORRESPONDING TORAT j AT AP

queues belonging to other STAs (6= i) or other ACs (6= k).

• V 2j is the second moment of the average vacation timeVj .

• X2j is the second moment of average serving timeXj .

B. M/G/1 Queing Model with vacations

We first look at the queuing model of queueQi,k. Theaverage service time of one packet sent over RATj is theinverse of service rate,

Xj = E{Xj} =1

µj

(1)

The second moment of the average service time of packetscould be written as:

X2j = E{X2

j } (2)

We model queueQi,k as an M/G/1 queue with vacations.Using the derivation and proof of Pollaczek-Khinchin (P-K)formula for this model as shown in [22], the average per packetdelay at queueQi,k corresponding to RATj can be writtenas:

Wj =λX2

j

2(1− ρj)+

V 2j

2Vj

(3)

The total delay experienced by one packet in a system isdefined as the sum of queuing delay and serving delay. Thetotal average delay for each packet at AP is given by

Tj = Wj +Xj =λjX

2j

2(1− λj

µj)+

V 2j

2Vj

+Xj (4)

Note that, for each queueQi,k, the parametersXj , X

2j , µj , Vj , V

2j could be measured and fed back by

RAT j to OMMA layer. So, if the arrival rateλj is known,the average packet delayTj of the Access Categoryk towardSTA i could be calculated by (4).

C. Optimization Problem Statement

In this section, we formulate the optimization problem tofind the optimal scheme at the OMMA layer to distribute theincoming IP traffic corresponding to ACk toward STAi acrossmultiple RATs. Assuming there areM RATs at the AP to senddata toN different STAs. The data sent from queueQi,k of theAP needs to be scheduled to be sent out onM separate RATs.We continue to use subscriptj in the following equations toindicate RAT indexj.

The summation of all arrival ratesλj of IP packets, corre-sponding to ACk toward STAi, from OMMA layer into eachof the RATs is equal to the IP packet arrival rate into OMMA,λ, i.e.,

λ =M∑

j=1

λj (5)

To ensure that the queues do not overflow, we impose thefollowing constraint.

λj < µj for 1 6 j 6 M (6)

SinceTj, shown in (4), is the average packet delay at MAClayer of RATj at the AP, the average packet delay over allM

RATs is the weighted average delay of allTj for 1 6 j 6 M .The weighting factor for each RATj is the ratio of the packetarrival rate on RATj to the total packet arrival rate into theOMMA layer, i.e., λj

λ. Thus the average packet delay over all

M RATs is:

F =

∑Mj=1

(

(λjX

2

j

2(1−λjµj

)+

V 2

j

2Vj+ 1

µj) ∗ λj

)

λ(7)

This expression for the average packet delay over allM

RATs is the objective function that need to be minimized.The optimization problem can now be stated as follow:

Minimize F =

∑Mj=1

((

λjX2

j

2(1−λjµj

)+

V 2

j

2Vj+ 1

µj

)

∗ λj

)

λ(8)

Subject to:

∑Mj=1 λj = λ

λj > 0 for 1 6 j 6 M

−λj > −µj for 1 6 j 6 M

(9)

D. The convexity of the objective function

In this section, we prove thatF (λ1, λ2, ..., λM ) is a convexfunction. We can rewriteF asF =

∑Mj=1 f(λj), where:

f(λj) =

(

λjX2

j

2(1−λjµj

)+

V 2

j

2Vj+ 1

µj

)

∗ λj

λ(10)

To prove thatF is convex, it is sufficient to prove thatf(λj)is convex. The second derivative off(λj) is:

∂2f

∂λ2j

= −µ3jX

2j

(λj − µj)3 ∗ λ(11)

In the above equation, all of the variables are positive. Sinceλj < µj for 1 6 j 6 M based on constraint (9) of the opti-mization problem, the second derivatives∂2f

∂λ2

j

for 1 6 j 6 M

are always positive for any value ofλj . Since f(λj) hasa positive second derivative, it is strictly convex and so isF (λ1, λ2, ..., λM ).

E. The Lagrangian optimization method

SinceF (λ1, λ2, ..., λM ) is a convex function, we can usethe Lagrangian optimization method to solve the optimizationproblem in equations (8) and (9). The Lagrangian function canbe written as

L(λ, γ, β, δ) = F (λ1, λ2, ..., λM )− γ ∗ (M∑

j=1

λj − λ)−

M∑

j=1

βj ∗ (λj)−M∑

j=1

δj ∗ (−λj + µj) for 1 6 j 6 M (12)

In this Lagrangian function,λj for 1 6 j 6 M are the un-known variables.γ, βj , δj for 1 6 j 6 M are the Lagrangianmultipliers. Since the objective functionF is a convex func-tion, there is an optimal solution set(λ∗

j , γ∗, β∗

j , δ∗j ), for 1 6

j 6 M for the optimization problem (using the result of theKarush-Kuhn-Tucker conditions [23]).

The optimal solution has to satisfy the following set ofequations:

∂F∂λj

− γ − βj + δj = 0 for 1 6 j 6 M (a)

γ ∗ (∑M

j=1 λj − λ) = 0 (b)

βj ∗ λj = 0 for 1 6 j 6 M (c)

δj ∗ (µj − λj) = 0 for 1 6 j 6 M (d)(13)

In the equation set (13), there are total3M + 1 formu-lations. We also have3M + 1 variables in the equationset which includes the solution setΛ∗ = (λ∗

1, λ∗2, ..., λ

∗M ),

the Lagrangian variablesγ∗, β∗ = (β∗1 , β

∗2 , ..., β

∗M ), and

δ∗ = (δ∗1 , δ∗2 , ..., δ

∗M ). It is feasible to solve the equations and

find a unique solution setΛ∗ = (λ∗1, λ

∗2, ..., λ

∗M ).

From (13)(c), sinceλj > 0, we haveβj = 0 for 1 6 j 6

M .From (13)(d), sinceµj > λj , we haveδj = 0 for 1 6 j 6

M .Also λj < µj for 1 6 j 6 M , implies that the arrival rate is

smaller than the equivalent service rate. So the total incomingtraffic will always be served optimally such that

∑Mj=1 λj = λ.

Substituting these values into (13), we get:

∂F∂λj

− γ = 0 for 1 6 j 6 M (a)

∑Mj=1 λj − λ = 0 (b)

βj = 0 for 1 6 j 6 M (c)

δj = 0 for 1 6 j 6 M (d)

(14)

Equation (14)(a) equals to:

(µjV2j + 2Vj − µ2

jVjX2j − 2µjVjλγ) ∗ λ

2j+

(−2µ2jV

2j + 2µ3

jVjX2j − 4µjVj + 4µ2

jVjλγ) ∗ λj+

(µ3jV

2j + 2µ2

jVj − 2µ3jVjλγ) = 0 (15)

The solutions for equation (15) are:

λ∗

j =

µj ±µ2j

√

VjX2j

√

µ2jVjX

2j − µjV

2j + (2λγµj − 2)Vj

(16)

Since the objective function is strictly convex, it has aunique non-negative globally optimal solutionλj , 1 6 j 6 M .Note that in (16), the solutionλj still depends on the unknownvariableγ.

Substituting these values ofλj into (13)(b), we get:

M∑

j=1

µj ±µ2j

√

VjX2j

√

µ2jVjX

2j − µjV

2j + (2λγµj − 2)Vj

= λ

(17)whereγ is the only unknown variable. This is equivalent

to:

M∑

j=1

∓1

√

2λµ3

j

γ +

(

1µ2

j

−V 2

j

µ3

jVjX

2

j

− 2

µ4

jX2

j

)

=

M∑

j=1

µj − λ

(18)Assume 2λ ≫ µj for 1 6 j 6 M , then

(

1µ2

j

−V 2

j

µ3

jVjX

2

j

− 2

µ4

jX2

j

)

≪(

2λµ3

j

)

. The term(

1µ2

j

−V 2

j

µ3

jVjX2

j

− 2

µ4

jX2

j

)

in (18) becomes negligible.

Equation (18) can be approximated as:

M∑

j=1

∓1

√

2λµ3

j

γ

≈M∑

j=1

µj − λ (19)

So finally we get:

γ ≈

(

∑Mj=1 ∓µ

3

2

j

)2

2λ(

∑Mj=1 µj − λ

)2 (20)

Substituting the value ofγ from equation (20) into equation(16), we get the values ofλ∗

i for 1 6 j 6 M as

λ∗

j = µj±µ2j

√

VjX2j

√

√

√

√

õ2jVjX

2j − µjV

2j +

(

∑

Mj=1

∓µ3

2

j

)

2

(∑

Mj=1

µj−λ)2∗ µj − 2

∗ Vj

(21)Note that we need to try up to2M different combinations

of the sign∓ in (21) to find the solutionsλ∗j . Each candidate

solution setΛ = (λ1, λ2, ..., λM ) should be checked with theconstraint set (9). Once we find a local minimum solution,we could stop the trial process since the objective functionis strictly convex, as proved in section III-D, and so thediscovered local minimum is also the global minimum solutionΛ

∗.

IV. PACKET FLOW CONTROL

In a multi-RAT system such as OMMA, which performaggregation on a packet basis, re-sequencing delay is a criticalfactor that needs to be addressed. Re-sequencing delay foreach packeti is defined as the time, packeti has to wait atthe OMMA layer of the receiver node for all of the earlierpackets until they are received successfully. It happens whendata packets are received out of order due to packets traversingover multiple links, each with different packet latency. Atthetransmitter side, packets of the main stream are split into mul-tiple sub streams for transmission over different links whichmay possibly have different latencies. The OMMA layer mayincur re-sequencing delays while reordering the packets beforesending them to the IP layer. The re-sequencing problem hasa severe impact on both UDP and TCP applications. Forexample, the QoS of real-time UDP applications like voiceover IP or live video streaming could suffer because out oforder packets would be counted as lost packets and be ignoredat the receiver side. For TCP, it is even more serious becauseout of order packets could generate the duplicate ACK issue,which triggers an unnecessary congestion control mechanismthat reduces the effective throughput.

An optimal packet assignment strategy at the OMMA trans-mitter is necessary to minimize the packet reordering delayatthe receiver. In order to minimize per packet reordering delayat the receiver we have proposed a method that is describedin Algorithm 1.

In this algorithm we maintainM token variablesTj for1 6 j 6 M , one for each RAT. Initially, the token foreach RAT is assigned to be 0. Token for each RATj isincremented iteratively by

λ∗

j

µj, whereλ∗

j is the optimal rate forRAT i calculated by the minimum delay algorithm presentedin Section III, until at least one of the tokens exceeds 1. The

Algorithm 1 OMMA Leaky Bucket Algorithm1: for j = 1 to M do2: Tj ⇐ 03: end for4: while Unscheduled packets set6= ∅ do5: for j = 1 to M do6: Updateµj from Meta Data Feedback of MAC layer7: Updateλ∗

j by equation 21

8: Rj ⇐λ∗

j

µj

9: end for10: while ∀Tj < 1, 1 6 j 6 M do11: for j = 1 to M do12: Tj ⇐ Tj +Rj

13: end for14: end while15: find i with Ti =max T {T1, T2, ..., TM}16: send current packet on RATi17: Ti ⇐ Ti − 118: end while

RAT corresponding to the token which exceeds 1 is chosen tosend the next incoming unscheduled packet at OMMA. Thenthis token is decremented by 1 and the process of incrementingtokens is continued as before. This algorithm is run “ahead”ofevery packet arriving at OMMA, i.e., OMMA always knowswhich packet ID will be scheduled on which RAT. Since theλ∗j values are chosen so as to minimize average delay per

packet, this algorithm ensures that the RAT chosen to sendeach packet is such that the packet experiences the minimumdelay and also arrives in the correct order with respect to itspreceding and succeeding packets at the receiver.

V. OMMA A RCHITECTURE

This section describes the architecture of the OMMA layerand its main functional modules. This section also providesthedetails of some key operations performed at OMMA which arerequired to support multi-RAT aggregation.

A. Functional Description

A high level architectural view of OMMA is shown inFigure 3. It includes all functional blocks of the OMMA layerincluding the main interfaces for control signaling and datasignaling. The OMMA layer consists of the following mainfunctional blocks.

1) STA RAT Capability Database:At the AP, STA RATcapability database is used to store RAT capability information(i.e., list of all common RATs) for each of its associated STAs.Moreover, because of poor link quality due to interference ormobility, a subset of RATs may be unavailable for a STA. Thisdatabase also stores a list of available RATs at a given timefor each associated STA. This information of RAT capabilityand available RATs is updated by the OMMA Controller asdescribed later.

Fig. 3. Block Diagram of the OMMA Layer

2) OMMA Controller: The OMMA controller is responsi-ble for updating the STA RAT Capability Database either incase of newly associated STAs or change in availability ofRATs for already associated STAs. The OMMA Controlleralso receives feedback metricsµj , Vj , V 2

j andX2j from each

RAT j (1 6 j 6 M ) corresponding to ACk from AP toSTA i for that RAT. It then classifies those metrics based onthe STA ID i and QoS classk, and sends them to OMMASchedulers of the corresponding STAs. It also calculates thearrival rate λj (corresponding to ACk from AP to STAi) of incoming IP packets and provides this information tothe OMMA Scheduler as one of the parameters required tocalculate the optimal split of IP packets across multiple RATs.Moreover, the OMMA controller provides system parameters(e.g. number of RATs, type of RATs, matched set of RATswith STAs to be associated) during discovery and associationprocess as described later.

3) OMMA Scheduler:The OMMA layer maintains a sep-arate OMMA Scheduler module corresponding to each as-sociated STA and each QoS class supported by the system.The OMMA layer also maintains anIP Packet STA Classifiermodule and also aSTA QoS Classifiermodule to read theIP packet header and send it to the corresponding OMMAScheduler module for further processing. The OMMA Sched-uler communicates with the STA RAT Capability Database toextract the list of available RATs for a STA. It also selectsRATs based on the feedback metrics provided by the OMMAController and the list of available RATs for that STA providedby STA RAT capability database. On the transmitter side, itdistributes packets across selected RATs based on a given

packet assignment scheme. On the receiver side, the OMMAscheduler is responsible for aggregating packets receivedfromRATs and sending them to the IP layer.

B. Key operations at OMMA

This section describes some key operations that are impor-tant to enable communication between multi-RAT devices.

1) RAT Capability Discovery:Each multi-RAT device canhave a different RAT capability (i.e., set of supported RATs).This generates the need for a discovery and association processin which a device (STA/AP) can advertise its RAT capabilityparameters (i.e., number of RATs, type of RATs, etc.) to otherdevices. This way, a STA and an AP can associate with eachother on the set of RATs common to them.

The AP can advertise its RAT capabilities either in the bea-con (in the passive scanning mode) or in the Probe Response(in the active scanning mode) which is generated in responseto a Probe Request from the STA. The beacon is sent on allavailable RATs at the AP while the Probe Response is senton the same RAT on which the Probe Request was received.The STA, which receives AP’s RAT capabilities, selects theset of RATs common to itself and the AP with the help ofthe OMMA Controller. The STA signals the set of commonRATs in the Association Request message sent to the AP onevery common RAT. The AP stores the information of RATcapabilities of the STA in its STA RAT Capability Database.

2) OMMA Mode Selection:This procedure is requiredto decide the mode of operation of the OMMA Schedulerat both sender and receiver. The modes of operation couldeither be based on a pre-defined set of policies for every IPflow, or could be based on feedback parameters received byOMMA from each RAT. Some examples of OMMA modesare described in Section VII (referred to as packet schedul-ing schemes). At an AP, the OMMA Controller makes themode selection decision and signals this decision to OMMAScheduler. The OMMA Scheduler enables/disables packettransmission on certain RATs based on the mode decision.Furthermore, the OMMA Controller at an AP sends modedecisions to the OMMA receiver at a STA using one of theavailable RATs for that STA. At a STA, mode informationreceived in the beacon is signaled to the OMMA Controller,which in turn configures the OMMA Scheduler accordingly.

3) RAT Availability Update Management:Since thewireless link on each RAT may have variable link qualityparameters such as packet loss rate, jitter due to factors suchas interference, mobility, some of the RATs common betweenthe STAs may be usable while the others may not be usable.Thus the AP transmitting data to a STA may not be aware ofwhich RATs are usable at any given time.

A procedure for dynamic management of RAT availabilityfor every STA-AP pair addresses this problem. The AP sendsbeacons on all its RATs periodically. The STA reads thebeacon information on all RATs common to itself and the AP.If the STA is able to read the beacon information successfullyon any RAT, it identifies that RAT as being available andassigns a value ’1’ to that RAT. If the STA is unable to read the

beacon information successfully on any RAT, it identifies thatRAT as being unavailable and assigns a value ’0’ to that RAT.Thus the OMMA Controller at the STA generates a binaryvector of length equal to the number of RATs common to itselfand the AP. Each bit in the binary vector indicates whether aRAT is available or not.

The STA periodically sends this RAT availability binaryvector to the AP using one of the common RATs. Thisinformation is sent from the RAT to the OMMA Controllerwhich in turn stores it in the STA RAT capability database.In this way, the RAT Availability information of any STA-APpair is periodically refreshed.

VI. IP FLOW MANAGEMENT AT OMMA

This section describes the flow management at both theOMMA sender and receiver for incoming IP packets. Theprocedure of RAT selection for incoming IP packets at theOMMA sender is described. We also describe the operationsat OMMA receiver required to send IP packets (i.e., receivedfrom multiple RATs) to IP layer. In this work all IP packetsare taken ofsingle QoS class.

A. OMMA Sender Operation

Fig. 4. OMMA Sender

A high level view of the OMMA sender is shown in Figure4. The OMMA sender takes the decision of RAT selectionto send the incoming IP packets. Procedure for routing ofincoming packets to a subset of RATs is described below.

1) At OMMA, the incoming packet is delivered to theIPPacket STA Classifierand OMMA Controller both,

2) IP Packet STA Classifiersends packet to OMMA Sched-uler corresponding to its destined STA,

3) Scheduler makes the decision on RAT/RATs selectionby using RAT availability provided by the STA RAT

Capability Database, and feedback metrics (arrival rate,serving rate and average packet delay) provided fromthe OMMA Controller,

• It selects all the RATs which fulfill the minimumrequirement of QoS class of incoming packet

• In case of starting phase, when there is no feedbackavailable, it chooses all the available RATs for thatSTA

4) The Scheduler distributes all the packets on the selectedRATs based on the algorithm described in next section,

5) RAT switch happens when one of the RATs of multipleselected RATs is not able to fulfill the requirement ofgiven QoS class. In this situation, it chooses randomlya RAT from the set of other available RATs that are notcurrently chosen for that STA.

B. OMMA Receiver Operation

Fig. 5. OMMA Receiver

A high level view of OMMA receiver is shown in Figure5. At the OMMA receiver, OMMA maintains a separateIPPacket STA Classifierfor each RAT. EachIP Packet STAClassifierreads the packet header and sends it to the OMMAScheduler corresponding to that STA. The OMMA scheduleraggregates the data packets received from multiple RATs andsends them to IP layer.

VII. PERFORMANCEEVALUATION

A. Single AP - Single Station

In this section, we present a simulation scenario with singleserver sending data to a single STA over an IP backhaulnetwork and through the AP as in Figure 6 using OPNETV16.0 simulator. Downlink data sent from the server is set asbest effort traffic (all data belongs to a single QoS class). BothAP and STA are capable of supporting two RATs, i.e., IEEE802.11n RAT operating on 2.4 GHz ISM band over single 20

Fig. 6. Simulation setup for single AP - single Station scenario

MHz channel, and, a proprietary RAT which is a modifiedIEEE 802.11n RAT operating on the television whitespace(TVWS) band. The latter is capable of aggregating four TVWSchannels (5 MHz per channel) at the MAC layer. The OMMAlayer resides on top of the two RAT protocol stacks and belowthe IP layer at the AP and the client. It is responsible fordistributing across RATs at the sender side and collecting IPpackets from the two RATs at the receiver. When downlinkIP traffic reaches the AP, the OMMA layer at the AP eithersends all traffic over one RAT or distributes the traffic acrosstwo RATs. The server sends successive multiple files to theSTA, by setting up a TCP connection per file transfer, duringthe simulation time. When a file download is completed,the corresponding TCP connection is terminated and a newTCP connection is setup for the next file. We evaluate theperformance of several packet allocation schemes at OMMAby analyzing the simulation results with several parameters.The packet scheduling schemes used at the OMMA layer are:

• ISM Band Only: AP sends all the data to STA over theRAT operating on ISM band only. RAT operating onTVWS band is disabled.

• TVWS Band Only: AP sends all the data to STA over theRAT operating on TVWS band only. RAT on ISM bandis disabled.

• 50% - 50% Traffic Split: 50% of the incoming IP packetsat the AP are sent over the RAT operating on ISM bandwhile the other 50% of the packets are sent over RAToperating on the TVWS band. Packets are assigned toRATs sequentially as they arrive at the AP (regardless ofthe packet ID).

• Load Balancing: Incoming IP packets are assigned tothe two RATs in the ratio of the serving rates of RATs.Packets are assigned to RATs sequentially as they arriveat the AP (regardless of the packet ID).

• RAT Selection Per TCP Flow: Packets of a single TCPflow assigned to the same RAT. One RAT is selected atany given time using the Round-Robin scheme.

• Minimum Delay: Incoming IP packets are assigned to thetwo RATs in the ratio of the optimal packet distributionscheme determined based on minimization of the averagedelay per packet as presented in Section III. Packets areassigned to RATs sequentially as they arrive at the AP(regardless of the packet ID) but still maintain the optimalpacket distribution ratio.

• Leaky Bucket: Incoming IP packets are assigned to thetwo RATs in the ratio of the optimal packet distributionscheme determined based on minimization of the average

delay per packet as presented in Section III. However,packets are smartly assigned to RATs using the OMMALeaky Bucket technique to minimize both per-packetdelay and out-of-order packet reception as presented inSection IV.

We set the thermal noise of the radio front-end of each RATsuch that average SNR at the client equals to 10 dB on theISM band and 20 dB on the TVWS band, where the TVWSband consists of four TVWS channels of 5 MHz each and theISM band consists of a single 20 MHz channel. Every 30 s,the server sends a new file to the client, which creates a newTCP connection. Since the file size determines the offered loadfrom application to transport layer, in our simulation setting weadjust the file size such that the maximum sustainable offeredload would be achieved for each scheduling scheme. The valueof the offered load for each scheme that we simulated is shownin Table II.

No Scheme Offered Load (Mbps)1 ISM Band Only 102 TVWS Band Only 17.53 50% - 50% Traffic Split 184 RAT Selection Per TCP Flow 165 Load Balancing 276 Minimum Delay 277 Leaky Bucket 27

TABLE IIOFFEREDLOAD FOR EACH SCHEME INSMALL RTT SCENARIO

Small RTT

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Fig. 7. Average TCP throughput with small RTT

We set the packet latency within the IP cloud module shownin Figure 6 to be negligible, which leads to an average end toend round trip time (RTT) as small as 50 ms with aggregationschemes and 100 ms with single RAT schemes. In addition,the average MAC latency difference between two RATs isaround 15 ms, which is equivalent to around 15% of the RTT

in the single RAT schemes and 30% of the RTT in the caseof aggregation schemes with multiple RATs.

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Fig. 8. Instantaneous packet latency with small RTT

The performance comparison for scheduling schemes withsmall RTT scenario is captured in Figures 7, 8 and 9. Theaverage throughput comparison between the packet allocationschemes is shown in Figure 7. Throughput withTVWS BandOnly scheme outperforms throughput withISM Band onlybyaround 170%. This is due to the assumption that noise levelon the TVWS RAT is 20 dB while it is 10 dB on the ISMband. Based on the packet latency graph depicted in Figure 8,the RATs suffer high packet latency at the beginning of thesimulation but later converge. In theISM Band Onlyscheme,packets experience higher packet latency as well as it takeslonger time to converge. The TCP flow control needs moretime to absorb the network load on the low SNR at ISM bandbranch.

It is also interesting to see in Figure 7 that50% - 50% TrafficSplit and theTVWS Band Onlyschemes have almost the samethroughput. TheRAT Switching Per TCP Flowscheme haseven worse performance compared to theTVWS Band Onlyscheme. This due to the fact that both50% - 50% Traffic Splitand RAT Switching Per TCP Flowschemes do not adapt tothe unequal channel qualities on RATs. TheRAT SwitchingPer TCP Flowscheme sends each packet that belongs to thesame TCP flow on the same RAT; therefore, it does not sufferout of order packet delay at the receiver. That leads to afewer number of packet retransmissions as shown in Figure9. However, since this scheduling scheme uses the same RATfor each TCP flow, and simply splits the throughput equallyon each RAT, it suffers higher average packet delay as shownin Figure 8.

The aggregation schemes such asLoad Balancing,Minimum Delay, and Leaky Buckethave higher performancecompared to single RAT,50% - 50% Traffic Split, and RAT

0 100 200 300 400 500 6000

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Fig. 9. Average instantaneous number of retransmissions with small RTT

Switching Per TCP Flowschemes. The higher performingschemes have the adaptive scheduling with channel metricfeedback on each RAT, and thus have lower packet latencycompared to the schemes not leveraging channel metricfeedback. Among all the schemes defined in this paper,the Leaky Bucketscheme has a slightly lower number ofretransmissions than theMinimum DelayandLoad Balancingschemes when the curves converge to steady state. TheLeakyBucketscheme implements smart scheduling per packet, thathelps to reduce the reordering delay, and thus less number ofduplicate ACKs and retransmissions are observed.

High RTTIn this section, we present a simulation scenario in which

we model higher packet delay in the IP cloud module withthe values uniformly distributed between 80 to 120 ms. Thisleads to an end to end round trip time of 250 ms, which isnear to the end-to-end RTT recommended by ITU standardfor delay sensitive services like voice or live streaming video[24]. The average MAC latency difference between two RATsis reduced to 3 ms. MAC latency difference between RATs isequivalent to 1.2% of the RTT.

The simulation results in this scenario are shown in Figure10. The conclusions of the previous case still hold althoughthe performances values are all reduced because of longerend to end RTT. TheTVWS Band Onlyscheme alwaysoutperforms theISM Band Onlyscheme. Both schemes sufferhigh packet latency at the beginning of the simulation butconverge later. However, theISM Band Onlyscheme suffersinitial performance degradation for a longer duration beforeit converges due to the lower SNR condition. The TCP flowcontrol scheme needs more time to absorb the network load.

The 50% - 50% Traffic Splitand RAT Switching Per TCPFlow schemes still have worse performance compared to the

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(a) Average TCP Throughput

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Fig. 10. Performance Comparisons with long RTT scenario

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Fig. 11. Instantaneous Number of Retransmissions with longRTT

TVWS Band Onlyscheme since they are unable to adapt tothe unequal channel SNR levels. The aggregation schemeshave better performance while theLeaky Bucketperforms thebest because of its smart scheduling scheme resulting in lowernumber of retransmissions as shown in Figure 11.

B. Single AP - Multi Stations

In this section, we present the simulation scenario with asingle AP communicating with two STAs simultaneously asshown in Figure 12. The configurations of AP and STAs arekept similar as theSingle STAscenario. We set the interferencelevel of the radio front-end of each RAT such that the effectiveaverage SNR levels at each STA are equal to 10 dB on the ISM

Fig. 12. Simulation setup for single AP - multi STAs scenario

band and 20 dB on the TVWS band. We configure STA 1 tooperate on both the ISM and TVWS bands, while we configureSTA 2 to operate on the ISM band only as a legacy device.STA 1 and STA 2 access the same channels on ISM band,while STA 1 aggregates data on both the ISM and TVWSbands. The IP traffic belongs to the best effort AC (ACBE)in this experiment. We implement four scheduling schemes atthe OMMA layer,50% - 50% Traffic Split, Load Balancing,Minimum Delay, andLeaky Bucket. Similar to theSingle STAcase, the server sends a new file to each STA in every 30 s.Similarly the value of the offered load sent to each STA isadjusted for scheme such that the maximum sustainable loadwould be achieved. We set the same offered load for eachSTA and set the packet latency at the IP cloud module to benegligible so that the RTT is small. The offered load value perSTA for each scheduling scheme is shown in Table III.

Figure 13 presents the comparison of total throughput sentfrom the server to both STAs with four different schedul-ing schemes mentioned above. TheLoad Balancingschemeachieves better throughput than the50% - 50% Traffic Splitsince it is a scheduling scheme that adapts with channelquality at the PHY layer. TheMinimum Delayscheme dueto the optimal traffic split scheme at the OMMA layer

No Scheme Offered Load (Mbps)1 50% - 50% Traffic Split 11.62 Load Balancing 123 Minimum Delay 12.44 Leaky Bucket 12.8

TABLE IIIOFFEREDLOAD FOR EACH SCHEME INSingle AP - Multi STAsSCENARIO

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Fig. 13. Total TCP Throughput of both STAs

achieves higher performance than theLoad Balancingandthe 50% - 50% Traffic Splitschemes. TheLeaky Bucketmethod further improves the performance. The deploymentof the smart packet scheduling to reduce the reordering delayat receiver side gives the edge to theLeaky Bucketschemeto achieve the best throughput among the tested schemes.In this experiment, theLeaky Bucketmethod’s throughputoutperformsMinimum Delay, Load Balancing, and50% - 50%Traffic Splitscheduling schemes by 0.82 Mbps (3.36%), 1.64Mbps (6.7%), and 2.49 Mbps (10.53%), respectively.

Figure 14 demonstrates the average end to end packetlatency of the four scheduling schemes. All four schedulingschemes show high packet latency at the beginning of sim-ulation due to the TCP ramp-up procedure before they allconverge. Although packet latency performance of theLeakyBucketis similar to the other schemes, it gives a better averageTCP throughput.

The same conclusion can be made about the average numberof TCP retransmissions as depicted in Figure 15. All fourschemes have approximately the same percentage of TCPpacket retransmissions of around 10% given different offerednetwork load as captured in table III. TheLeaky Bucketschememanages the highest offered load with the same level of packetretransmission as other schemes. These results show that theLeaky Bucketscheme outperforms all the other schedulingschemes.

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Fig. 15. Average instantaneous number of retransmissions per STA

VIII. C ONCLUSION

We presented an analytical framework to derive the optimalpacket scheduling strategy over multiple RATs when a singleAP communicates with multiple STAs with IP traffic ofvarious QoS classes. It gives an optimal packet distributionminimizing the average end-to-end packet latency. We pro-posed a per-packet scheduling algorithm, called OMMA LeakyBucket, which not only distributes the packets over multiRATs using the derived optimal distribution but also minimizesre-sequencing delay at the receiver. We also described theOMMA system architecture which includes a functional de-sign, a solution for discovery and association process betweenmulti-RAT devices, and a dynamic RAT update management.Finally, we provided a set of simulations including both singleAP - single STA and single AP - multi STAs scenarioswhile comparing the proposed OMMA Leaky Bucket approachwith various alternatives. The proposed OMMA Leaky Bucket

scheme outperforms all other schemes in terms of throughput,packet latency, and number of retransmission in almost all thecases. For future work, we will be looking at the problem ofOMMA operation in a wireless multi-cast scenario by lookingat traffic management schemes and packet assignment methodsat the OMMA layer to optimize multi-cast throughput and tominimize packet latency.

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