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Congestion Control Policies for IP-BasedCDMA Radio Access Networks

Sneha K. Kasera, Member, IEEE, Ramachandran Ramjee, Senior Member, IEEE,

Sandra Thuel, and Xin Wang, Member, IEEE

Abstract—As CDMA-based cellular networks mature, the current point-to-point links used in connecting base stations to network

controllers will evolve to an IP-based Radio Access Network (RAN) for reasons of lower cost due to statistical multiplexing gains, better

scalability and reliability, and the projected growth in data applications. In this paper, we study the impact of congestion in a best-effort

IP RAN on CDMA cellular voice networks. We propose and evaluate three congestion control mechanisms, admission control,

diversity control, and router control, to maximize network capacity while maintaining good voice quality. We first propose two new

enhancements to CDMA call admission control that consider a unified view of both IP RAN and air interface resources. Next, we

introduce a novel technique called diversity control that exploits the soft-handoff feature of CDMA networks and drops selected frames

belonging to multiple soft-handoff legs to gracefully degrade voice quality during congestion. Finally, we study the impact of router

control where an active queue management technique is used to reduce delay and minimize correlated losses. Using simulations of a

large mobile network, we show that the three different control mechanisms can help gracefully manage 10-40 percent congestion

overload in the IP RAN.

Index Terms—IP radio access networks, call admission control, diversity control, router control, active queue management.

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1 INTRODUCTION

CELLULAR wireless networks have become an indispen-sable part of the communication infrastructure. CDMA

is an important air-interface technology for cellular wirelessnetworks. It has been selected for implementation in boththe North American and European 3G standards. Tradi-tionally, in these wireless access networks, the base stationsare connected to radio network controllers or base stationcontrollers by point-to-point (usually T1/E1) links. Theselinks, also called backhaul links, are expensive and their useimposes an ongoing cost on the service providers. In suchnetworks, reliability comes at a high price: by replication oflinks or controllers. As CDMA-based cellular networksmature, the current point-to-point links will evolve to an IP-based Radio Access Network (RAN). Several telecommu-nication equipment vendors are pursuing IP-based RANs(e.g., [1], [2]). IP RAN is also being provided as an option inthe 3G standards [3]. Replacing the point-to-point linksbetween the base stations and the radio network controllersby an IP RAN has the following advantages:

. Cost: Point-to-point links, including T1 links, areexpensive and cannot be shared. An IP networkwill benefit from statistical multiplexing gains andcould be shared with other wireless and wirelineapplications.

. Scalability and Reliability: Replacing point-to-pointlinks by a distributed IP network will providealternate paths to more than one network controller,thereby improving reliability and scalability. Forexample, it is shown in [4] that adding a selected fewpaths between base stations and network controllersresults in the majority of the gains in resiliency tofailures.

. Data Applications: Increasingly, a large number of IP-based “data applications,” including Web browsing,email, streaming, and packetized voice (voice overIP) are being offered in wireless networks. Hence,wireless access networks must support IP traffic. AnIP RAN efficiently addresses this eventuality.

While the use of an IP RAN results in the aboveadvantages, mechanisms must be designed to controlIP RAN congestion. Congestion occurs when the offeredtraffic exceeds the engineered IP RAN capacity. There areessentially three approaches to control and avoid conges-tion: First, the network can be over-provisioned or peak-provisioned so that congestion never occurs. Althoughsimple, this is not a practical solution because accessnetwork bandwidth is still very expensive compared to corenetwork bandwidth. According to the financial firm Fried-man Billings Ramsey, it costs 14,000 times more to carry a bitof information one mile on a local network than on a longdistance network [5]. Second, one can reserve resources inthe access network. While several research efforts havefocused on this problem (e.g., [6], [7]), inaccurate resourceestimation due to dynamic load patterns and/or mobility,variations in the wireless environment, and the wide rangeof application characteristics makes it a very hard problem

IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 4, NO. 4, JULY/AUGUST 2005 1

. S.K. Kasera is with the School of Computing, University of Utah, 50 S.Central Campus Drive, Rm 3190, Salt Lake City, UT 84112.E-mail: [email protected].

. R. Ramjee is with Bell Laboratories, Lucent Technologies, 600 MountainAvenue, Rm 2B-307, Murray Hill, NJ 07974.E-mail: [email protected].

. S. Thuel is with Bell Laboratories, Lucent Technologies, 101 CrawfordsCorner Road, Rm 4F-515, Holmdel, NJ 07733.E-mail: [email protected].

. X. Wang is with the Department of Computer Science and Engineering,University of Buffalo, 201 Bell Hall, Buffalo, NY 14260-2000.E-mail: [email protected].

Manuscript received 18 Nov. 2003; revised 30 Apr. 2004; accepted 27 May2004.For information on obtaining reprints of this article, please send e-mail to:[email protected], and reference IEEECS Log Number TMC-0194-1103.

1536-1233/05/$20.00 � 2005 IEEE Published by the IEEE CS, CASS, ComSoc, IES, & SPS

to solve. Besides, even though various reservation schemeshave been proposed and implemented in routers, theseapproaches are yet to be widely deployed in current IPnetworks. The third approach is to assume a best-effortIP RAN and use properly designed policies to control andavoid congestion. This is the focus of our paper.

We study the impact of congestion in a best-effortIP RAN on CDMA cellular voice networks. Congestionintroduces loss and delay jitter in the user traffic. Uncon-trolled loss and delay jitter could drastically reduce thevoice quality. Therefore, congestion control techniques areessential in maintaining good voice quality. We focus on thevoice application for two reasons: 1) current cellularnetworks are predominantly used for voice transmissionand 2) voice has tighter delay and loss requirements thandata (where retransmission is an option).

We propose and evaluate three congestion controlmechanisms to maximize network capacity while maintain-ing good voice quality: admission control, diversity control,and router control. Call admission control in current CDMAcellular voice networks is restricted to controlling the usageof air interface resources. We first propose two newenhancements to CDMA call admission control that con-sider a unified view of both IP RAN and air interfaceresources to adequately match the number of voice users tothe engineered capacity. The principle underlying bothschemes is regulation of the IP RAN load by adjusting theadmission control criterion at the air-interface. The key ideais to reduce the number of calls accepted in the CDMAsystem by reducing the admissible power threshold as afunction of the IP RAN loss during IP RAN congestion.Reduction in the number of calls will result in alleviation ofthe IP RAN congestion and enhance the voice quality aswell. Next, we introduce a novel technique called diversitycontrol that exploits one of the unique features of CDMA,namely, macrodiversity or soft-handoff. A cellular user insoft-handoff transmits and listens to multiple base stationssimultaneously. During IP RAN congestion, our diversitycontrol technique allows dropping of selected framesbelonging to potentially redundant soft-handoff legs, there-by reducing congestion gracefully while maintaining ade-quate voice quality. Last, we study the impact of routercontrol in the form of active queue management [8]. IProuters using a drop tail mechanism during congestioncould produce high delays and bursty losses resulting inpoor voice quality. Use of active queue management at therouters reduces delays and loss correlation, thereby im-proving voice quality during congestion. Using simulationsof a large mobile network, we evaluate the behavior of thethree different control mechanisms and show how thesetechniques help manage congestion in the IP RAN grace-fully. To our knowledge, this is the first paper to considerthe impact of congestion in IP RAN on CDMA networkperformance. Although derived and studied in the contextof a CDMA system and voice traffic, our schemes could alsobe adapted to other systems and traffic types as well.

The rest of the paper is structured as follows: In Section 2,we present an overview of the problem. In Section 3, wepresent two call admission control algorithms that take intoaccount both the air interface and IP RAN resources toregulate incoming traffic. In Section 4, we present ourdiversity control techniques that selectively drop soft-handoff legs to control congestion. In Section 5, we presentour router control technique using active queue manage-ment. In Section 6, we present our simulation resultsdemonstrating the benefits of all three congestion control

techniques. Section 7 contains the impact of our policies onnetwork provisioning. Section 8 contains future directionsfor enhancing our research and, finally in Section 9, wepresent our conclusions.

2 PROBLEM SETTING

In this section, we describe the components of a CDMAwireless access network that uses an IP RAN and identifyour problem space. Fig. 1 shows a wireless access networkwith mobile devices communicating with base stations overwireless links. The base stations communicate with the restof the voice or data network through the access networkcontrollers (ANCs) (also called Radio Network Controller,RNC, in 3G UMTS [9] and Base Station Controller, BSC, inCDMA2000 [10]). Note that this part of the network iscommon to both wireless voice and data traffic. Thenetwork separates only beyond the ANC where voiceframes are forwarded to the MSC (PSTN) and data framesare forwarded to the Data Service Nodes (Internet). Eachbase station typically communicates with one hundred ormore mobiles and each ANC typically controls several tensof base stations. An ANC performs two main wirelessfunctions, frame selection and reverse outer loop power control.Frame selection exploits one of the key properties of aCDMA network, namely, soft-handoff. In soft-handoff, amobile communicates with more than one base stationsimultaneously. Soft-handoff helps reduce interference onthe wireless link, thereby increasing CDMA capacity. Whenin soft-handoff, a mobile receives multiple frames in thedownlink direction (also called forward link) and combinesthem to construct a single voice frame. In the uplinkdirection (also called the reverse link), the ANC receivesmultiple frames from the mobile. It performs the frameselection function which involves selecting the frame withthe best quality among the ones it receives. If the framesfrom all the different legs of a call in soft-handoff call do notarrive within a preset time interval (20 ms in the case ofCDMA2000), the ANC forwards the current best frame tothe network. In other words, a late frame is treated as if itwere a dropped frame and, thus, controlling delay in theaccess network is extremely important.

In addition to frame selection, the ANC also performsreverse outer loop power control, in which it sets targetsignal to noise ratio (Eb=It) for each mobile at each basestation. The target (Eb=It) is set such that the target frameerror rate for the flow after frame selection is maintainedbelow a preset limit (such as 1 percent). Each base stationreceives the target Eb=It for each mobile device from the

2 IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 4, NO. 4, JULY/AUGUST 2005

Fig. 1. CDMA Wireless Access Network with IP RAN.

ANC and instructs the devices to increase or decrease theirtransmission power. The mechanism for implementingouter loop power control also provides a control channelbetween the base stations and the ANC and could be usedfor piggybacking control information between the basestations and the ANC.

We now describe the operation of the IP networkbetween the base station and the ANC. On receiving voiceframes from different mobiles, a base station aggregatesseveral voice frames into an IP packet and sends them outtowards the ANC. Voice frames are typically only a fewtens of bytes in length. Their aggregation helps in reducingIP header overhead. Voice frames belonging to the differentlegs of soft-handoff are transmitted by different basestations and, hence, arrive at the ANC on different IPpackets. On receiving IP packets from the base stations, anANC demultiplexes the voice frames and performs frameselection and outer loop power control functions andforwards the best voice frame uplink. Voice frames alsocontain power information that is used by the ANC forouter loop power control. Therefore, packet loss and, hence,loss of voice frames due to RAN congestion could result inimperfect outer loop power control. This could cause thepower consumption in a cell to be higher than its expectedvalue and thereby reduce the overall capacity. Thus,controlling loss in the access network is very important.1

The link leading to the ANC is likely to become abottleneck during congestion because this link carries theaggregate traffic of several tens of base stations. While thelink will be engineered to take into account the statisticalmultiplexing gains of this aggregation, offered traffic couldtemporarily exceed the engineered capacity of the link dueto hot spots or other reasons. We require mechanisms torespond to these temporary congestion events in a gracefulmanner. This congestion response is the focus of the rest ofthe paper. While we focus only on the reverse path frombase stations toward the ANC, most of the techniquesdescribed here can be applied to the forward path as well.

In summary, in this paper we will study the effect of asingle bottleneck link in the common path from the basestations towards their ANC for aggregated CDMA voicetraffic arriving as IP packets and propose solutions tocontrol congestion gracefully.

3 CALL ADMISSION CONTROL

CDMA systems are typically interference limited and relyon the processing gain2 to be able to operate at a lowsignal-to-interference ratio. In order to minimize theinterference, the mechanisms adopted by the CDMAsystems control the power emitted on each channel (ineither direction) to keep the signal-to-interference ratio(SIR) at a receiver at a target value. When the power limitof a base station is reached, the SIR can no longer bemaintained at the target level and calls serviced by the basestation are blocked or dropped. Hence, call admission isclosely tied to power control. The call admission control incurrent CDMA systems is restricted to controlling theusage of air interface resources. The point-to-point accesslinks between the base stations and the ANC are expectedto be loss-free. The presence of an IP RAN adds a new

dimension to this process since a lossy or congested best-effort IP RAN could result in high losses and delays,thereby reducing the voice quality. In this section, wedescribe two schemes to enhance the air interface calladmission control algorithms that also take the IP RAN lossinto consideration when deciding on admitting new calls.We first present the call admission control mechanism thatconsiders the air interface resources only, and then describeour two enhancements to admission control that consider aunified view of both the IP RAN and the air interfaceresources.

3.1 Admission Control at Air Interface (CAC)

We consider a single base station that is serving ageographical area called a cell. The relationship betweenreceived power at the base station and cell load and anassociated admission control threshold, in terms of theallowed received power, is described as follows: The totalinterference at a mobile includes the interference from themobiles of the same cell and the neighboring cells and fromthermal noise. For a CDMA cell with M signals, one foreach mobile, there are M � 1 interferers from the same cell.If we assume perfect power control on the reverse link andthat the signals transmitted from all the mobiles arrive atthe base station with the same received power S, the ratio ofsignal bit energy Eb to total interference and thermal noisepower spectral density It can be expressed as [11]

Eb

It¼ Bw

S=R

ðM � 1ÞvfSð1þ fIÞ þN0Bw; ð1Þ

where vf is the channel activity factor, fI represents theinterference due to other cells, R represents the mobiletransmission rate, Bw is the spreading bandwidth, and N0 isthe thermal noise power spectral density. Given a targetEb=It with the processing gain represented as Gp ¼ Bw=R,the number of mobiles M a cell can admit is a function ofthe received power S:

M ¼ 1þGp1

ðEb=ItÞvfð1þ fIÞ� N0Bw

Svfð1þ fIÞ: ð2Þ

The total received power can be represented asPtotal ¼ vfð1þ fIÞSM þNoBw. Using this expression toobtain S as a function of the total received power andinserting it into (2), we can determine the number ofmobiles M a cell can admit when the total received signalpower is Ptotal by the following equation:

M ¼1þGp

1ðEb=ItÞvf ð1þfI Þ

1þ NoBw

Ptotal�NoBw

: ð3Þ

When the total received power is restricted to Pmax, themaximum number of connections that can be admitted by acell is obtained from (3) by replacing Ptotal with Pmax. Thisforms the basis of the air interface admission controlalgorithm. Note that this algorithm does not take thequality of the IP RAN into account. In the next section, weenhance this algorithm to also consider access network load.

We end this section by describing the relationshipbetween the total received power and the cell load. Thecell load, represented by �, is simply determined by theratio M=Mpole, where Mpole is the maximum cell capacity orthe pole capacity. The maximum cell capacity, Mpole, can beobtained by setting Ptotal ! 1. Therefore, Mpole ¼ 1þ

KASERA ET AL.: CONGESTION CONTROL POLICIES FOR IP-BASED CDMA RADIO ACCESS NETWORKS 3

1. Although we do not study the imperfections in outer loop powercontrol due to loss of power control information, our congestion controlmechanisms do control IP RAN loss.

2. Processing gain is defined as the ratio of transmitted signal bandwidthto the data signal bandwidth (information rate).

Gp1

ðEb=ItÞvf ð1þfI Þ . Thus, the total received power and cell load

are related by the following equation:

Ptotal

NoBw¼ 1

1� �: ð4Þ

Equation (4) is used for call admission control enhance-ments in the next section.

3.2 Enhanced Admission Control

In this section, we propose two enhanced admission controlalgorithms which respond to changes in the access network loadby regulating the admission control criteria at the air interface,thereby indirectly adjusting the load entering the access network.Instead of using the maximum allowable received power,Pmax, to perform admission control, each base station uses avariable received power threshold, P adm, that is periodicallycalculated by the ANC. The calculations are based on the airinterface capacity and cell load, as well as IP RAN loss rate.

Our enhanced admission control schemes are calledMaximum-Power-based call admission control (CAC-MPC)and Usage-based call admission control (CAC-UC). Bothschemes share the same basic approach: The measuredpacket loss rate is used to calculate a power scaling factor �,which is then used to scale the admission control threshold,P adm, for the air interface, thereby throttling incomingtraffic and reducing congestion. We first present the generalmethodology that we use to adaptively quantify the loss-rate and then present our two admission control schemes.

3.2.1 General Methodology

The purpose of admission control in the IP RAN is to keepthe packet loss rate within a target level so that the qualityof voice transmission can be maintained. We use a feedbackcontrol strategy based on a constrained integral control law[12]. In this scheme, the packet transmission between basestations and the ANC is monitored by the ANC. Since allthe uplink traffic will go through the ANC, the ANC candetect the packet loss in the IP RAN. The power scalingfactor � is calculated periodically by comparing themonitored loss rate with a target loss rate. With themeasured loss rate of the IP RAN represented as L andthe target loss rate as L�, the scaling factor � for a period n iscalculated as:

�½n� ¼ min max �min; �n�1 � �L� L�

L�

� �; �max

� �; ð5Þ

where the parameter � controls the adaptation speed of thescaling factor and �min and �max are the minimum andmaximum values allowed of the scaling factor.

For an integral controller such as ours, higher � leads to afaster response. However, higher values of � can cause

larger oscillation and even instabilities. Also, if j� L�L�

L� j istoo large, � will be set to a value that could scale up P adm

causing undesired air interface control. In order to obtaintighter control, we constrain �within the range �min; �max½ �.3

3.3 Maximum-Power-Based Control (CAC-MPC)

In our Maximum-Power-based control, the admissioncontrol threshold power P adm is obtained by scaling down

the maximum allowed receiving power Pmax by �, when themeasured loss rate exceeds the target value:

P adm ¼ �Pmax: ð6Þ

Combining (3), (4), and (6), the total number of mobiles acell can admit is given by:

Madm ¼ Mpole

1þ NoBw

�Pmax�N0Bw

¼ Mpoleð1� 1� �max

�Þ; ð7Þ

where �max is the maximum load allowed corresponding tothe maximum allowed received power Pmax. Hence, the

total load allowed in a cell is �adm ¼ 1� 1��max

� . Since theload cannot be negative, the range ½�min; �max� for the scalingfactor is ½1� �max; 1�.

Note that (7) can set the allowed cell load to zero. We usea safeguard mechanism to set a lower limit on the thresholdpower according to a predetermined planned load. Thisalso ensures fairness since only cells with higher thanplanned load will be throttled back when the IP RAN iscongested. In this case, we set

P adm ¼ maxfP plan; �Pmaxg; ð8Þ

where P adm is the planned limit on threshold power.

3.3.1 Usage-based Control (CAC-UC)

In usage-based control, the admission control thresholdpower P adm at a cell is calculated based on the current loadof the cell, as well as the IP RAN loss rate. The controlprinciple is that the cells with higher load should bear alarger share of the total load reduction. When the loss ratein the IP RAN is higher than the target value, P adm isobtained by scaling down the current received power P cur

of a cell, with the scaling factor � obtained using (5). Sincethis can result in a much smaller power threshold relative tothe maximum allowable power Pmax, the power thresholdshould not be set back to Pmax immediately after the loss isrestored to the target level. P adm is scaled up progressively,using a scaling factor � greater than one. The usage-basedpower control algorithm is described below:

P adm ¼ maxfP plan; � �plan

�cur Pcurg if � < 1 or L > L�:

minf�P adm; Pmaxg; otherwise:

(

In the above equation, �plan and �cur represent separatelythe planned cell load and current cell load. In order toensure fairness to a lightly loaded cell when power is beingscaled down and also to avoid an excessively slow responsefollowing a period of low loss (when the initial value of � isgreater than one), the load ratio of a cell (relative to theplanned load) is used as an additional scaling factor. Theuse of the two factors together, instead of using either theload ratio or the scaling factor � separately, results in fasterresponse to congestion and also keeps the loss close to thetarget level. Furthermore, as with the maximum powerbased scheme, we add a safeguard mechanism to place alower limit on the threshold power and prevent penalizingcells operating below the planned load.

Detailed performance evaluations of these two admis-sion control algorithms are presented in Section 6.

The use of current load measurement in the design of calladmission control in CDMA systems has also beenproposed in [13]. However, the focus of the scheme


3. With the range constraints, care must be taken that � does not getabsorbed into the extreme states. Assume that � is the largest error thatoccurs once the system is in closed-loop operation. The parameter � can beprevented from being absorbed into the extreme states if � < �max��min

� .

proposed in [13] is on air interface resources. Our schemeenhances the air interface call admission control to alsoconsider the IP RAN resources. We believe that the calladmission control proposed in [13] is complementary to ourresearch and could be integrated with our scheme.

4 DIVERSITY CONTROL

Recall that CDMA supports macrodiversity or soft-handoff(SHO) in which a mobile user transmits and listens tomultiple base stations simultaneously. The uplink packetsreceived at the multiple base stations are forwarded to theANC for frame selection and the best frame is forwardedinto the wired network. Diversity allows mobile users tosmoothly transition their connections from one cell to thenext without loosing connectivity or suffering servicedegradations as is typical in hard-handoff scenarios.

In typical cellular networks, mobiles can be in SHO withup to six base stations at once with one primary leg and upto five secondary legs. These multiple legs constitute whatis known as the mobile’s active set. Field measurements(also corroborated by [14] and [15]) indicate that CDMAvoice users tend to operate in SHO mode almost half of thetime, with an average of about 1.5 legs per call. However,field experience also suggests that the time interval inwhich a user actually needs more than one leg tends to berelatively short and that the primary leg is typically the bestquality leg. This indicates that there may be extendedperiods of time where a user has more than one leg but theprimary leg might have been sufficient, resulting in someunnecessary redundant traffic in the wireless access net-work. Clearly, how often this occurs depends heavily on theaggressiveness or conservativeness of the active set man-agement policy and relative differences in voice qualityacross the diverse legs due to the air link quality. Forwireless access networks where the above field observa-tions hold true, one could expect nonprimary legs to beredundant often. This redundancy can be exploited using atechnique we call diversity control.

Diversity control selectively discards uplink voice radio framesfrom potentially redundant secondary legs for some mobile usersat the base stations in such a way that the voice quality is notnoticeably degraded while reducing traffic in the IP RAN tomanage congestion. Key challenges in diversity control arethe service degradation and restoration policies, whichdictate how users are chosen for and freed from diversitycontrol, respectively.

We propose and evaluate two diversity control policies,referred to as the service-degradation policy (SDP) and theframe-discard policy (FDP). SDP implements a binaryservice-level model for each mobile user, wherein the cellstag SHO users as being in a degraded or nondegraded state;mobiles with degraded service must discard all theirsecondary legs. FDP, on the other hand, drops secondarylegs for SHO users randomly, each time a packet is sent bythe cell.

In both policies, each cell periodically sets a diversitycontrol target based on the estimated packet loss rate in theIP RAN.4 The diversity control target for the nth controlperiod is a drop probability denoted by Pn, computed by afirst-order autoregressive model given as:

Pn ¼ min max 0; Pn�1 þ �L� L�

L�

� �; 1

� �; ð9Þ

where �, L, and L� represent the adaptation speed factor,measured loss rate, and target loss rate, respectively, asdefined for the admission control scaling factor in (5).Although this equation is very similar to (5), the dropprobability is limited to a value range between 0 and 1 andthe variance term to adjust for the measured loss rate erroruses a positive value for the adaptation speed parameter �.This ensures that the drop probability increases as themeasured loss rate increases above the target and that itdecreases otherwise. � controls how smoothly dropprobabilities change, leading to higher Pn variances as �increases. While higher values of � lead to faster reaction tochanges in rate loss, the higher Pn variances may sometimeslead to a relatively high mean drop probability. It is possibleto obtain fast reactivity and a low mean drop probabilitywithout introducing high variance by adjusting the dropprobability in a nonlinear (e.g., exponential) fashion.However, a discussion of this topic is beyond the scope ofthis paper.

Once the drop probability is computed, the SDP and FDPpolicies differ on how it is used to discard the traffic fornonprimary legs. In SDP, the drop probability represents aservice degradation threshold for each cell. This thresholdindicates what percentage of SHO users need to be in adegraded service state for the duration of the control periodand users are randomly selected for degradation. In FDP,the drop probability represents the frame drop probabilityfor each cell. Every time a cell assembles an aggregated IPpacket to send to the ANC, it randomly drops secondaryvoice frames according to the frame drop probability. Notethat the key difference between SDP and FDP is that SDPdoes not consider the number of SHO legs each user has inits selection process and it forces the discard of all of adegraded user’s nonprimary legs.

Restoration is the counterpart to the degradationpolicies. In SDP, a degraded user is restored at a controlperiod arrival when its service-level status is toggled tonondegraded. As a result, a diversity control periodconstitutes the minimum amount of time a degraded usermust wait prior to being restored. In FDP, on the otherhand, restorations occur on a per-leg, per-packet basis, aspreviously discarded legs are reenabled to transmit.Consequently, FDP exerts a finer grain control by rando-mizing the selection of frames to be discarded on everysingle packet transmission, which occurs in the millisecondtimescale rather than on the second timescale where theSDP diversity control period operates.

Since SDP explicitly affects per-user state, diversitycontrol comes into play on call handoff events, where achange in a user’s active set needs to be managed. Forinstance, if a degraded user looses all its nonprimary legson a handoff, should this user be restored? If, on the otherhand, the handoff would add more legs, should the newlegs be degraded? For simplicity, we assume that degradedusers are restored if they loose all their secondary legs andthat degraded users are prohibited from adding any morelegs due to handoff. FDP would be affected by call handoffsif they were to trigger the recomputation of cell framedropping probabilities but for simplicity, we make no suchassumption.

Diversity control, by itself, might not be sufficient torecover from congestion, particularly in situations wherethe instantaneous amount of diversity to be exploited is lowor the congestion is very high. As a result, it is advanta-


4. Packet loss rate estimate is conveyed to the base stations by the ANC.

geous to apply diversity control in conjunction with loadcontrol, to ensure that they jointly achieve the goal ofmaintaining good call quality (by minimizing the averageuser frame error rate, FER).

In Section 6, we evaluate the potential benefits ofdiversity control and discuss the impact of a joint diversityand load-control on the CDMA network.

5 ROUTER CONTROL USING ACTIVE QUEUE

MANAGEMENT

When congested, a router typically follows a drop tail policywhere packets arriving at the router are queued as long asthere is space to buffer them and dropped otherwise. Eventhough it is simple to implement, the drop tail policy posestwo important problems:

. If the buffer size is large, the queuing delays can bevery high. Given the frame selection deadline of 20ms for CDMA2000 networks at the ANC, thesedelays would cause the transmitted packets to arriveat the ANC too late to be of any use. However, if therouters could provide the required delay bound,then this problem would not exist.

. The packet drops tend to be bursty. In a CDMAsystem, frames from calls in soft-handoff arrive indifferent IP packets at the IP bottleneck link at aboutthe same instant. If the loss is bursty, the multipleframes associated with different legs of a soft-handoff call could be dropped at the same time.When the number of calls in soft-handoff is largeand multiple frames associated with different legs ofthe soft-handoff are good (meaning that it willsuffice even if any one of these frames is received atthe ANC), the bursty loss would increase theprobability of frame error.

Active queue management (AQM) [8] is a form of routercontrol that attempts to provide congestion control bymonitoring the congestion state of a router queue and pro-actively dropping packets before the buffers become fulland queuing delays become too high. Some of the AQMpolicies (e.g., [16]) drop packets with a certain probability toavoid bursty loss.

It appears that the random dropping and tight delay featuresof AQM policies are an excellent fit for the unique delay deadlineand soft-handoff requirements of a CDMA access network. Forexample, an AQM policy could reduce the queuing delaysin the routers so that the voice frames could be received atthe ANC by the deadline. An AQM policy could also helpprevent all the frames associated with different legs of asoft-handoff call from being dropped. It should be notedthat proposing or designing an efficient AQM policy is notwithin the scope of this paper. Rather, we want to study theimpact of router control using any reasonable AQM policy.

In order to examine the benefits of AQM, we study theuse a variant of the RED AQM policy called SRED, firstproposed in the context of signaling overload control in[17]. This policy uses a timer-based approach in which thebottleneck queue length is measured and an exponentiallyweighted moving average, Qn, is computed every T timeunits. Qn is compared to a minimum and a maximumqueue length, denoted Qmin and Qmax, respectively, and afraction of packets that could be allowed into the queue iscomputed. The function for determining the fractionallowed in the ðnþ 1Þth interval, fnþ1, is described below:

fnþ1 ¼fmin; Qn � Qmax

1; Qn � Qmin

max fmin;Qmax�Qn

Qmax�Qmin

� �; otherwise:

8><>:

fmin is the minimum fraction allowed in a given timeinterval. It is set to a very small value. Once the fractionallowed is computed, a deterministic algorithm, firstproposed by Hajek [18] and shown to perform well in[17], is used to drop incoming packets. Hajek’s determinis-tic algorithm is described below. A variable r is firstinitialized to 0, then the forward/drop decision proceduredescribed below is used.

r :¼ rþ fn:

If r � 1

r :¼ r� 1

forward packet

else drop packet:

In the next section, we evaluate the performance impactof all three congestion control techniques on the IP RAN.

6 PERFORMANCE STUDY

In this section, we study the performance of our congestioncontrol policies in a CDMA IP RAN with a single bottle-neck. We use a custom designed simulator. The simulatorconsists of two parts. The first part simulates user mobility,call generation, call termination, and soft-handoffs. Thesecond part of the simulator uses the traces generated fromthe first part and simulates the generation and aggregationof voice frames, IP packet transmission through a singlebottleneck link, and frame selection at the ANC.

6.1 Simulation Model

We now describe our model and the various parametersthat we use in our simulation study.

6.1.1 Mobility Model

In order to simulate a very large PCS network, we use awrap around technology as advocated by the authors in[19]. This approach eliminates the boundary effects in anunwrapped topology. We simulate our PCS network using awrapped mesh topology with the number of cells rangingfrom 25 to 64. In order to simulate soft-handoffs, we assumethat cells are rectangular and all neighboring cells (top, left,bottom, right) overlap. This results in regions in the networkwith one, two, and four soft-handoff legs, as shown in Fig. 2.A similar approach using rectangular cells, but with overlapbetween two neighboring cells only (resulting in regionswith one and two soft-handoff legs), has been used in [20]and [21]. We assume that the mobile user spends anexponential amount of time in each region with averageresidence times in regions with one, two, and four legsdistributed with the ratio 10:4:2.5. The movement of themobile users is based on the two-dimensional random walkmodel used in [22]. In this model, the mobile users move toone of their neighboring cells with equal probability. In oursimulation, while the user always moves from a one-leg to atwo-leg region and from a four-leg to a two-leg region, webias the movements from two leg regions such that there isan 80 percent chance of going to a one leg region and only a20 percent chance of going to a four-leg region. Thismovement behavior, coupled with the average residence


times in the different regions, gives us an average activesoft-handoff leg value of 1.44. This is between the values of1.2 and 1.7 average legs, as presented in [14], and within therange evaluated for a nominal propagation environment in[15]. Mobiles are generated randomly and uniformly acrossthe cells. In order to load a cell, we generate call arrivals foreach mobile user. The call rate varies with the requiredsimulation load of the cell. The arrival rate per user isdetermined as follows:

We start with the assumption that call arrivals at eachuser are based on a Poisson process with a mean arrival rate� and the call holding times are exponentially distributedwith a mean value 1=�. Now, if the call holding times werealways smaller than the call interarrival times, then � couldbe simply determined as a function of the required cell loadand the number of users. Interestingly, even if we pick themean arrival rate � to be less than the mean call holding rate�, interarrival times can be smaller than call holding times.In such situations, a call arrival event does not result in anactual call. In other words, a mobile user initiating a call attime to cannot make another call sooner than to plus the callholding time. In order to take the call holding time intoaccount in generating the required call load, we use thefollowing analysis:

Let X be a random variable that represents the callholding time. Let Y be another random variable thatrepresents the time between when a call is over until thetime the next call is made. Therefore, the interval betweentwo calls (T ) is given by

T ¼ X þ Y :

As noted earlier, E½X� ¼ 1=�. Also, since the call arrivalevents are generated from a Poisson distribution, theinterarrival time is exponentially distributed. Now, due tothe memoryless property of exponential distributions, thetime of a call arrival starting at an instance to is independentof the time of a call arrival starting at an instance to þX.This implies that E½Y � ¼ 1=�. Therefore,

E½T � ¼ E½X� þ E½Y �¼ 1=�þ 1=�:

The effective mean call arrival rate, �eff , is 1=E½T �.For determining the mean call arrival rate, we first find

the �eff as a function of the simulation load of a cell and thenumber of mobile users. Next, we use the above relation-ship to obtain � as a function of �eff . In summary, althoughcall arrival events are generated at every user with a meanarrival rate �, not all arrivals result in an actual call.Effectively, calls are generated at a rate �eff , which is lessthan �.

6.1.2 Air Interface Parameters

The simulation parameters adopted for the air interface areas follows: We use three sectors per cell in our simulationand the sectorization gain is set to 2.55 [23]. This amountsto a sectorization efficiency of 0.85 for a three sectorantenna. We set the ratio of bit energy to noise densityEb=It to 7 dB, the spreading bandwidth Bw to 1.23 MHz,thermal noise of a cell NoBw to -108 dBm, voice rate to9.6 kb/s, activity factor to 0.5, and the cell interferenceparameter fI to 0.67. The maximum allowable cell load andengineered load for each cell are 0.9 and 0.7, respectively.The RAN is provisioned to support the total engineeredload of all the cells. While the modeling of the dynamicquality behavior of soft-handoff user legs remains an openresearch issue, we assumed a simple model where weightsare assigned to each user leg, adding up to 1.0 across alllegs and biasing the weight of the primary leg to behighest. More specifically, for a single leg call, the primaryleg is assigned a weight 1.0; for a call with two soft-handofflegs, the primary leg is assigned a weight 0.75 and thesecondary leg a weight 0.25; for a call with four soft-handoff legs, we assign a weight 0.25 to the primary legand 0.25 to each secondary leg.

In order to isolate the effects of congestion in the RAN,we assume that the bottleneck is only in the IP RAN andthere is enough capacity at the air interface. Based on thecall arrival and handoff events in the trace, we generatevoice frames every 20 ms for each active call. These framesare sent to the appropriate base stations. The base stationsthen aggregate these frames into IP packets, which are thensent to the ANC through the bottleneck link. Theprovisioning in the RAN is such that when the load of allthe cells is 0.7, the planned load and the bandwidthutilization of the bottleneck link in the RAN is 0.90. Themaximum drop tail router delay is set to 20 ms. To measurethe IP RAN loss, a sequence number field is added in everyIP packet containing aggregated voice frames from the basestations. The base station identities and the gaps insequence numbers are used to determine the IP RAN loss.This simple approach also distinguishes IP RAN loss fromthe frame loss in the wireless links. Our congestion controlpolicies kick in only in the presence of IP RAN loss.Otherwise, the standard CDMA power control and calladmission control are followed by the ANC to handlewireless link errors. Although we do not address themultiple bottleneck scenarios in this paper, IP packet lossinformation could also be correlated across multiple basestations to determine the congested links and to subse-quently control traffic in only those cells that contribute tothe congestion on those links.

The scaling factor for load control in the RAN iscalculated using (5). For both maximum-power-based loadcontrol and usage-based load control, the typical para-meters are 0.01 for the targeted packet loss rate at the RAN,0.01 for the scaling factor adjustment parameters �, and 0.1for the minimum value of �. The maximum value of � is setto one for maximum-power-based load control and 1.3 forusage-based load control. The typical control interval timeperiod when parameters are updated is 5 seconds for bothadmission control schemes.

The parameters used in our simulation are summarizedin Table 1.


Fig. 2. Soft-handoff regions for a 5� 5 cellular network.

6.2 Performance Metrics

In order to study the impact of our control policies on theCDMA network with an IP RAN, we use the followingthree performance measures:

. Call blocking rate: Call blocking rate is defined to bethe number of calls blocked over the number ofcalls received. This measure shows the directimpact of IP RAN capacity limitations on CDMAnetwork capacity.

. Frame Error Rate (FER): The frame error rate isdefined to be the number of frames not received atthe ANC over the number of frames sent by themobile users. We study both average and instanta-neous FER. Normally, the FER refers to the frameerrors in the wireless link. However, in this paper,the FER represents the loss in voice quality due toboth the wireless link errors and the lost frames dueto IP RAN congestion.

. Burst Size: Burst size is defined to be the number ofconsecutive frames with errors.

The frame error rate and burst size together represent agood measure of user voice quality, while the call blockingrate is a good measure of network efficiency. We now lookat our three congestion control techniques and study theimpact of congestion on these performance measures.

6.3 Performance of Call Admission ControlAlgorithms

We compare the performance of three different admissioncontrol schemes—the basic scheme that controls admissionin the air interface (CAC) by using a received powerthreshold and the two enhancements (CAC-MPC and CAC-UC) that measure packet loss rate to regulate the admissioncontrol power threshold. In addition to the call blocking rateand the frame error rate performance measures, we alsoevaluate the performance of the three schemes with respectto IP packet (aggregated frames) loss rate and average

admission control power threshold values since theseparameters impact the admission control algorithmsdirectly. The average power threshold value is normalizedwith respect to the maximum allowable received power ineach cell and averaged over all cells. We also study theimpact of load control parameters including the target lossrate, L�, the load control interval, and scaling factor �.

We compare the performance of the three schemes usingaverage values of the performance measures over the entireduration of the simulation. This is followed by a compar-ison of the temporal behavior of the three schemes. Last, weexamine the sensitivity of our enhanced call admissioncontrol schemes to different system parameter values.

6.3.1 Performance Comparison of CAC, CAC-MPC, and

CAC-UC

Fig. 3a and Fig. 3c show the variation of the average packetloss rate and received voice frame error rate as a function ofthe offered load. The loss rate is almost zero until theoffered load exceeds 0.7, the planned load. Both maximum-power-based control and usage-based control are seen tomaintain the average loss rate at the targeted level,1 percent, which is up to eight times lower than theperformance of basic CAC. Fig. 3a and Fig. 3c also showthat the average frame error rate and the average packetloss rate behave almost identically with changing load. Wefind similar behavior in other simulations as well.

When the access network is congested, the two enhancedadmission control schemes reduce the threshold power foradmission control. As a result, new calls from heavilyloaded cells are blocked. Fig. 3d shows that the blockingrate of all the schemes increases almost linearly as the loadincreases beyond 0.8. As expected, the blocking rates of theenhanced admission control schemes are higher than that ofthe basic CAC scheme. In practice, due to the self-regulatingbehavior of reverse outer loop power control, the ANCwould instruct the mobiles to increase their power levelssince it typically assumes that losses only occur in the airinterface. This would result in a higher blocking rate thanthe blocking rate shown in the figure for the basic CACscheme.

Fig. 3b plots the average admission power levels for theschemes. It can be seen that CAC-UC allows up to 20 percenthigher average admission power than CAC-MPC. This isbecause CAC-UC responds faster to the changing powerlevel in each overloaded cell. However, due to the nonlinearrelationship between the admission control power thresh-old and the air interface capacity, the relative decrease inblocking rate is much smaller than the relative increase inthe admission control power. The call blocking probabilityfor CAC-MPC and CAC-UC is seen almost identical inFig. 3d.

In order to study the size of frame error bursts under thethree call admission control schemes, we plot the fraction ofthe number of frames in error for a given frame error burstsize over the total number of frames versus the frame errorburst size in Fig. 4. Fig. 4 shows that both the load controlschemes keep the burst size low with the CAC-UC schemeperforming slightly better than the CAC-MPC scheme. Asexpected, the basic CAC scheme does not perform as well,showing higher burst losses.

6.3.2 System Dynamics

In order to understand and compare the temporal behaviorof the three schemes, we use a snapshot of the simulation


TABLE 1Simulation Parameters

between 7,500 seconds and 8,000 seconds. Fig. 5a and Fig. 5bdepict the variation with time of offered cell load, the powerthreshold for admission control, and packet loss rate at twovalues of average offered cell load, 0.8 and 0.9.

As before, maximum-power-based load control results ina lower (more conservative) power threshold; consequently,the threshold needs to be adjusted less often and lessdrastically as compared to usage-based load control. At thehigher offered load, both load control schemes apply alower power threshold, while CAC-UC also adjusts thethreshold more frequently than CAC-MPC. Both the loadcontrol schemes effectively control the packet loss ratekeeping it stable and close to the target. Fig. 5a and Fig. 5balso show that CAC-UC and CAC-MPC have similarbehavior of packet loss variation with time.

The instantaneous frame error rate, which directlyimpacts voice quality, is shown in Fig. 6a and Fig. 6b,again in the time window between 7,500 and 8,000 seconds.Similar to the instantaneous loss rate, the frame error rate iswell controlled under both load control schemes. At light tomedium loads, the frame error rate is reduced faster byusage-based control than by maximum-power-based con-trol, but the former scheme also results in larger and morefrequent oscillations. The frame error rate variation issmaller at higher loads, since the admission control powerthreshold is better controlled.

6.3.3 Sensitivity to Target Loss Rate, Control Interval,

and Load Adaptation Speed

We study the system performance at three different valuesof the target loss rate: 0.008, 0.01, and 0.03. Fig. 7a showsthat the proposed load control schemes can effectivelyguarantee a range of voice frame error rates (and similar IPpacket loss rates). Fig. 7b shows that blocking increases asthe target loss rate is lowered (thereby increasing voicequality). As before, CAC-UC allows a higher powerthreshold for admission control than CAC-MPC and,correspondingly, has a slightly lower call blocking rate.

In order to study the impact of the control interval atwhich packet loss is estimated and power thresholds arecomputed by the ANC, we perform simulations for threevalues of the control interval: 1 second, 5 seconds, and

10 seconds. Fig. 8a shows that more frequent load control

leads to lower average loss rate for both control schemes at

moderately high loads. At very high loads, this effect

becomes less important because the large difference be-

tween the current and target values of the packet loss rate


Fig. 3. Performance metrics of CAC, CAC-MPC, and CAC-UC as afunction of offered load: (a) Average packet loss rate. (b) Average powerthreshold. (c) Average frame error rate. (d) Average call blocking rate.

Fig. 4. Fraction of errored frames over total number of frames versusburst size, offered load = 0.85 for CAC, CAC-MPC and CAC-UC.

Fig. 5. Time variation of offered load, power threshold, and packet lossrate for CAC, CAC-MPC, and CAC-UC: (a) at offered load 0.8 and (b) atoffered load 0.9.

results in a very aggressive load reduction within a singlecontrol interval. Fig. 8b shows that the blocking rate does notchange appreciably with the tested control intervals.

The control factor � in (5) controls the adjustment rate ofthe load control parameter � with respect to change in loss.Hence, it controls the rate at which the RAN load isadjusted toward the target value. We examine the systemperformance with different values of �. Fig. 9 shows howthe performance of the call admission control algorithms asa function of �. A higher value of � is seen to result in loweraverage loss rate at moderately high loads only. The effectof � on the overall blocking rate is small. Our simulationsalso indicate that a higher value of � reduces the frame errorrate faster, but results in more oscillatory behavior. In oursimulations, a value of 0.01 for � achieves good response tochanging load conditions with minimal oscillations.

6.4 Impact of Diversity Control

We conduct a set of simulations to evaluate the perfor-mance of the service-degradation and frame-discard poli-cies (SDP and FDP) for diversity control introduced inSection 4.

Fig. 10a shows frame error rates (FER) while Fig. 10bdepicts call blocking results. As the mean offered cell loadvaries from 70 percent to 95 percent, both figures compare:1) the performance of call admission control (CAC) only,against 2) the performance gains of adding usage-based

admission control (CAC + UC = CAC - UC), and c) theadditional gains of adding diversity control (CAC + UC +DIVC = CAC - DIVC). The diversity control period is set to1 second, as preliminary sensitivity studies suggest this tobe a reasonable balance between control overhead andreactivity. The admission control period is set to 5 seconds,as done in all previous admission control experiments. Thedrop probability is computed by (9).

The DIVC curves shown are for the frame-discard policy,FDP, as performance for the service-degradation policy,SDP, is virtually indistinguishable from FDP. Interestingly,we observe that although both policies differ on how theydiscard frames for certain mobile users, the diversityselection effect at the ANC masks these differences,resulting in an uplink traffic stream with the same FER inboth cases. This suggests that both streams performcomparably well in discarding the legs of least quality.

Fig. 10b shows that the diversity and call admissioncontrols exhibit comparable call blocking rate at all loads(note that the CAC-DIVC and CAC curves are almostidentical). This demonstrates that the enhanced admissioncontrol is rarely triggered, if at all, so any call blockingobserved is due to the basic CAC scheme and not due toIP RAN capacity constraints. Diversity control operates atthe relatively low call blocking rates of CAC whilemaintaining the average FER at or below the target levelof 1 percent, as shown in Fig. 10a. Thus, diversity controldelivers both low blocking and the desirable FER resultingin a graceful adaptation to congestion.

Intuitively, by discarding frames at the cells that wouldhave likely been redundant in the diversity selectionprocess at the ANC, the packet loss rate in the RAN isreduced. This keeps the FER below the trigger point whereload control would have been needed to increase callblocking in order to reduce packet loss. For instance, at aload of 95 percent, using load control only to reducecongestion requires about a 6 percent increase in the call


Fig. 6. Time variation of the received voice frame error rate: (a) atoffered load 0.8 and (b) at offered load 0.9.

Fig. 7. Performance metrics of CAC-MPC and CAC-UC as a function of

offered load at target loss rate 0.008, 0.01, and 0.03: (a) average frame

error rate and (b) average call blocking rate.

Fig. 8. Performance metrics of CAC-MPC and CAC-UC as a function of

offered load with control interval 1s, 5s, and 10s: (a) average frame error

rate and (b) average call blocking rate.

Fig. 9. Performance metrics of CAC, CAC-MPC, and CAC-UC as a

function of offered load with control factor � at 0.005, 0.01, and 0.05:

(a) average packet loss rate and (b) average call blocking rate.

blocking rate to maintain a target FER below 1 percent.CAC, on the other hand, operates at low blocking rates butdoes not react to any fluctuations in RAN congestion state,resulting in large increases in FER as loss in the RANincreases at higher loads. This illustrates an importantpoint: provided that soft-handoff legs have a high prob-ability of being redundant most of the time, diversitycontrol results in a win-win by reducing loss in the RANand also maintaining low call blocking rates.

In obtaining the results shown in Fig. 10, we bias thequality of the frames from the primary leg to be the highestin the two leg soft-handoff, as described in Section 6.1.2. Inthe four leg soft-handoff case, the frames from the primaryand secondary legs are assumed to be of the same quality.This implies that dropping secondary frames at the basestations for diversity control does not increase the overallframe error rate as long as the primary frames are receivedat the ANC. This is likely to be true when a call is in asteady state, but at times during transition from oneprimary leg to another, the primary leg might producepoor quality frames in comparison to the secondary legs. Inthese transition periods, the use of diversity control couldpotentially increase the frame error rate temporarily.However, these transition periods are small and are notlikely to significantly reduce the voice quality. Oursimulation models do not capture this effect. A carefulanalysis of these transient effects will be an interestingfuture work.

Recall that the aggressiveness with which diversitycontrol reacts to changes in measured rate loss is controlledby the adaptation speed parameter �. We observe that avalue of 0.10 for �, used for the simulation results shown inFig. 10, provides a good balance between fast responsive-ness and mean drop probability. Though the variance wasreasonable, efforts to further reduce it by lowering the valueof � would make the control’s response sluggish, causingthe load control to be triggered and, subsequently, increasethe call blocking rate. For instance, at a load of 95 percent,values of 0.05 and 0.01 for � yielded call blocking rates of11.7 percent and 13.2 percent, respectively (contrasting withthe 9.5 percent observed for � ¼ 0:10). This clearlyillustrates that while the joint operation of diversity andload control is effective at maintaining the target FER, thetrigger point for load control depends on the aggressivenesswith which the diversity control is tuned to react tocongestion episodes.

It is desirable to use a fast-acting diversity controlwhenever possible in order to avoid or minimize theincrease in call blocking rates that will ensue when the loadcontrol is triggered. However, regardless of how aggressivethe diversity control policy is, its capabilities are limited bythe average amount of redundant data available for discard

at the cells. When the average load increases beyond thelimits of what diversity control can manage, load controlshould be applied to increase call blocking rates in order toensure acceptable quality levels are maintained. Theapplication of both load and diversity controls, in fact,achieves the joint goal of maximizing network call capacitywhile minimizing voice quality degradations during con-gestion episodes in the IP RAN. Note that the benefits of ajoint diversity and load-controlled RAN are not visible inFig. 10 because the offered load range depicted is wellwithin the limits of what diversity control can indepen-dently manage. Simulation experiments with both controlsoperative confirm that diversity control has a dominanteffect in this load range.

In conclusion, since diversity control exploits trafficredundancy, it can be a very effective congestion controltechnique by adapting to congestion without either increas-ing the frame error rate or blocking. However, itsperformance depends crucially on the average amount ofredundancy per user (i.e., number of SHO legs) as well ason the ability to select low-quality legs to be discarded.

6.5 Impact of Router Control Using AQM

We now study the impact of router control using activequeue management (AQM) by turning AQM on and off inthe router. The AQM policy used in these simulations isdescribed in Section 5.

We first consider the scenario where a router guaranteesthat packets do not experience any delay more than aprespecified delay threshold. The router queue is set to amaximum length such that the queuing delay never exceedsthe delay threshold. Any packets arriving when the queue isfull are dropped in a drop tail manner. If this delaythreshold is set to the time by which the packets areexpected to arrive at the ANC, then all packets arrive in timeat the ANC. We find that turning on AQM in this scenariodoes not help to improve the CDMA network performance.In fact, both the blocking rate and average frame error rateare slightly worse than the scenario when AQM is turnedoff. There are two reasons for this behavior: First, the routeris able to bound the delays of packets within the requiredthreshold. An attempt to reduce the delay causes unneces-sary loss. Proactively dropping packets does not speed upthe load control because load control operates at muchlarger time scales (5 seconds). Second, in our experiments,the probability of multiple frames associated with differentlegs of a soft-handoff being good is low. This implies thatrandom packet dropping does not necessarily ensure that agood frame is received at the ANC. Hence, there is no drasticimprovement in the frame error rate.

When the router is not able to provide the required delayguarantee (for example, to accommodate data bursts), wefind that turning on AQM helps improve performanceconsiderably, especially when no additional controls, suchas load control or diversity control, are applied at the ANC.We conduct an experiment in which the required delaythreshold at the ANC is 20 ms, but the router queues uppackets with a maximum delay of 30 ms and uses SRED asthe AQM policy. Fig. 11a shows the frame error rate as afunction of frame number. Here, only the basic CAC schemeis being used. The average cell load offered to the IP RAN is85 percent of the maximum cell load. This is much morethan the load (70 percent) that the line capacity isprovisioned to handle. We see that without AQM, theframe error rate is very high and close to 100 percent, butwith AQM, the frame error rate is brought down to less


Fig. 10. Performance of CAC and the incremental gains of diversity and

load control with respect to: (a) mean user FER and (b) call blocking

rate.

than 10 percent. This is because the bottleneck queuebecomes full very fast and packets do not get processed fastenough. Every packet arriving after the queue is full ornearly full suffers a delay greater than 20 ms and arrives atANC too late. We also find that the average frame error rateover the entire simulation time is reduced from 98 percentto 4 percent when using AQM with the basic CAC scheme.

Fig. 11b shows the frame error rate as a function of framenumber when maximum power-based load control is usedwith AQM turned on and AQM turned off. We find thatwithout AQM, high delays in router queue due to drop tailpacket dropping cause large spikes in frame error rate. Themagnitude of these spikes is brought down when AQM isapplied. We observe that the spikes are several hundredframes wide. This is because our load control policycontrols admission of new calls only and does not dropexisting calls. The existing calls could continue to contributeto the congestion for several seconds. In our experimentsusing AQM and CAC-MPC, we also find that the callblocking rate is brought down from 15.7 percent to13.4 percent and the average frame error rate during theentire duration of the simulation is reduced from about2 percent to 1 percent. As expected, our experiments withother maximum queuing delay values show that as themaximum router queuing delay value is increased (i.e., thedrop tail buffer size is increased), the improvement due toAQM becomes even higher.

In summary, router control using an AQM policy thatensures smaller delays will result in better CDMA networkperformance.

7 IMPACT ON NETWORK PROVISIONING

Voice transmissions generally have high quality require-ments and peak provisioning could be used for supportingvoice calls in the IP radio access network. However, thedynamic variations in call traffic patterns may result inunder-utilization of a peak provisioned IP RAN. Addition-ally, the rapid growth of wireless traffic and the evolutionof radio technology may quickly render the resources in anexisting peak-provisioned IP RAN in-sufficient.

In previous sections, we have shown that our proposedcongestion control algorithms could effectively control thenetwork load and maintain the expected voice qualityunder various network conditions. In this section, we studythe effect of efficient load control on network provisioning.We consider cells that have three sectors and each sector hasthree carriers. The air interface parameters are the same asin Section 4 and the planned air interface load to support isalso set at 0.7. We first vary the number of cells connectingto one ANC and compare the number of OC-35 links neededto support the required voice quality. Next we vary thenumber of OC-3 links in the IP RAN and compare thenumber of cells that can be supported.

Fig. 12a depicts, respectively, the number of OC-3 linksneeded to support the traffic from various number of cells tothe ANC using peak provisioning and efficient provisioningenhanced with our load control algorithms. With the sametarget voice quality, the provisioning with our algorithms isseen to require up to 20 percent less OC-3 links. Thisreduces the IP RAN bandwidth cost significantly.

With an IP RAN provisioned in advance, Fig. 12b showsthe number of cells that can be supported with and withoutload control algorithms enabled. With load control, about30 percent more cells can be supported by an existingIP RAN provisioning. In other words, if the number of cellsconnecting to an ANC is fixed, up to 30 percent more trafficis allowed for each cell without the need to upgrade theIP RAN.

8 FUTURE DIRECTIONS

In this section, we present and discuss some futuredirections for enhancing our research.

8.1 Congestion Control for Data

One interesting difference between voice and data, withrespect to the frame selection function of the ANC, is thatwhile the best voice frame is always forwarded, the bestdata frame is only forwarded if the cyclic redundancy check(CRC) is successful. Thus, one can further improve diversitycontrol techniques by dropping data frames that fail theCRC at the base station. Currently, we are studying theimpact of these techniques for IP RAN congestion control inthe presence of wireless data traffic.

8.2 Congestion in the Downlink Direction

The focus of this paper has been congestion in the uplinkdirection. While call admission control and router controltechniques are applicable equally in both directions, the useof diversity control in the downlink direction is not as


Fig. 11. FER versus frame number, offered load = 85 percent, maximumrouter queuing delay = 30 ms, for (a) CAC and (b) CAC-MPC.

Fig. 12. Peak provisioning versus load control with respect to: (a) meanuser FER and (b) call blocking rate.

5. OC-3 stands for Optical Carrier Level 3 and corresponds to 155.52Mb/sec [24].

straightforward. This is because, in CDMA systems, whileframe selection from different legs is performed in the uplinkdirection, frame combining (using maximal rate combining) isperformed in the downlink direction at the mobile device.This requires that all base stations transmit the same frameat the same time in the downlink direction. Thus, thedropping of frames from selected legs during soft-handoffusing diversity control could result in higher error rates fora given mobile device, but this also reduces the overallinterference level on the wireless downlink and could resultin better reception for other mobile devices. A detailedstudy of diversity control in the downlink direction isnecessary to determine its efficacy. It is important to notethat in the downlink direction, IP multicast (e.g., sourcespecific multicast [25]) could be used to transmit the framesfrom the ANC to multiple base stations, thereby reducingthe usage of the link connecting the ANC and the router.

8.3 Wireless RAN

So far, we have assumed that the radio access network iscomprised of wired links with fixed capacities. However, itmight be advantageous in certain locations to connect basestations to a network controller through fixed wirelessbackhaul links (possibly, multihop for range extension). Theemerging IEEE 802.16 standard [26] with range up to30 miles is one suitable wireless RAN technology. In thiscase, capacity of the RAN is no longer fixed and congestionin the RAN could also be caused by variations in thewireless link capacity, potentially due to interference oratmospheric effects. An adaptive solution, as proposed inthis paper, is essential in combating these dynamiccongestion events in the wireless backhaul.

9 CONCLUSIONS

In this paper, we studied the problem of congestion controlin the IP RAN of a CDMA wireless access network andexamined three control techniques, called admission con-trol, diversity control, and router control.

In the case of call admission control, we presented twoenhanced admission control mechanisms. These schemesmake use of a variable power-based admission controlthreshold at the air interface to regulate load entering theRAN. Simulations show that both algorithms are able tocontrol packet loss rate and frame error rate in the RAN at adesired level. The usage-based control scheme achieves ahigher power-threshold and, hence, slightly lower call-blocking than the maximum-power-based scheme, but thelatter scheme results in a smoother, less oscillatory control.Both algorithms were also shown to be robust to the controlparameters. In the case of diversity control, we proposedtwo novel policies called Service Degradation Policy (SDP)and Frame Discard Policy (FDP) for gracefully adapting tocongestion. The results were very promising. Both policieswere shown to be able to adapt to significant congestionwithout either increasing the frame error rate or blocking. Inthe case of router control, we evaluated an active queuemanagement policy called SRED and found that routercontrol that ensures low delays is essential for achievinglow frame error rate during congestion.

In the future, issues related to data traffic, downlinkcongestion, and wireless RANs, as discussed in Section 8,need careful examination.

ACKNOWLEDGMENTS

The authors thank Tom LaPorta, Srinivas Kadaba, andGanesh Sundaram for useful discussions on the topic.

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Sneha Kumar Kasera received the MS degreein electrical communication engineering from theIndian Institute of Science, Bangalore, and thePhD degree in computer science from theUniversity of Massachusetts, Amherst. He iscurrently an assistant professor in the School ofComputing at the University of Utah, Salt LakeCity. From 1999-2003, he was a member of thetechnical staff in the Mobile Networking Re-search Department of Bell Laboratories. He has

held research and development positions at Wipro Infotech and theCenter for Development of Advanced Computing at Bangalore, India.Dr. Kasera was one of the recipients of the 2002 Bell Labs President’sGold Award for his contribution to wireless data solutions. He has servedin many technical program committees, including those of ACMSigmetrics, NOSSDAV, IEEE Infocom, IEEE ICNP, IEEE SECON,ACM Mobicom, and Mobiquitous conferences. His research interestsinclude computer networks and systems encompassing mobile andpervasive systems and wireless networks, network security andreliability, overload and congestion control, multicast communication,Internet pricing, Internet measurements, and inferencing.

Ram Ramjee received the BTech degree incomputer science and engineering from theIndian Institute of Technology, Madras, and theMS and PhD degrees in computer science fromthe University of Massachusetts, Amherst. Hehas been at Bell Labs, Lucent Technologies,since 1996, where he is currently a distinguishedmember of the technical staff. He is also anadjunct faculty at the Electrical EngineeringDepartment of Columbia University, where he

teaches graduate courses in wireless networks. Dr. Ramjee is an areaeditor of ACM Mobile Communications Review, an associate editor ofIEEE Transactions on Mobile Computing, and a technical editor of IEEEWireless Communications Magazine. He has published more than 30papers in premier conferences and journals and holds 10 US patents.His research interests include protocols, architecture, and performanceissues in wireless and high speed networks.

Sandra R. Thuel received the BS degree incomputer engineering from the University ofPuerto Rico, Mayaguez, in 1986, and the MSand PhD degrees in electrical and computerengineering from Carnegie Mellon University,Pittsburgh, in 1988 and 1993, respectively.Since 1993, she has been a member of thetechnical Staff at Bell Labs, Lucent Technologies(formerly AT&T). Her research interests includemobility management, wireless voice and data

networking, quality of service, scheduling, and resource management.

Xin Wang received the BS and MS degrees intelecommunications engineering and wirelesscommunications engineering from Beijing Uni-versity of Posts and Telecommunications, Beij-ing, China, in 1990 and 1993, respectively, andthe PhD degree in electrical engineering fromColumbia University, New York, in 2001. From2001 to 2003, she was a member of thetechnical staff in the area of mobile and wirelessnetworking at Bell Labs Research, Lucent

Technologies, New Jersey. She is currently an assistant professor inthe Department of Computer Science and Engineering at the StateUniversity of New York at Buffalo, Buffalo, New York. Her researchinterests include modeling and analysis of mobile and wireless networks,integrated network infrastructure design and performance enhancementacross network layers, applications and heterogeneous networks,network and mobility management, QoS, signaling and control, as wellas adaptive network services and applications.

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