348 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL

348 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 22, NO. 2, FEBRUARY 2004

TP-Planet: A Reliable Transport Protocolfor Interplanetary Internet

Özgür B. Akan, Member, IEEE, Jian Fang, and Ian F. Akyildiz, Fellow, IEEE

Abstract—Space exploration missions are crucial for acqui-sition of information about space and the universe. The entiresuccess of a mission is directly related to the satisfaction of itscommunications needs. For this goal, the challenges posed bythe InterPlaNetary (IPN) Internet need to be addressed. Currenttransmission control protocols (TCPs) have very poor perfor-mance in the IPN Internet, which is characterized by extremelyhigh propagation delays, link errors, asymmetrical bandwidth,and blackouts. The window-based congestion control, whichinjects a new packet into the network upon an ACK reception,is responsible for such performance degradation due to highpropagation delay. Slow start algorithms of the existing TCPsfurther contribute to the performance degradation by wastinglong time periods to reach the actual data rate. Moreover, wirelesslink errors amplify the problem by misleading the TCP sourceto unnecessarily throttle the congestion window. The recoveryfrom erroneous window decrease takes a certain amount of time,which is proportional to the round-trip time (RTT) and furtherdecreases the network performance.

In this paper, a reliable transport protocol (TP-Planet) is pre-sented for data traffic in the IPN Internet. It is intended to addressthe challenges and to achieve high throughput performance andreliable data transmission on deep-space links of the IPN Back-bone Network. TP-Planet deploys a rate-based additive-increasemultiplicative-decrease (AIMD) congestion control, whose AIMDparameters are tuned to help avoid throughput degradation.TP-Planet replaces the inefficient slow start algorithm with anovel Initial State algorithm, which allows the capture of linkresources in a very fast and controlled manner. A new congestiondetection and control mechanism is developed, which decouplescongestion decisions from single packet losses in order to avoid theerroneous congestion decisions due to high link errors. In orderto reduce the effects of blackout conditions on the throughputperformance, TP-Planet incorporates the blackout state procedureinto the protocol operation. The bandwidth asymmetry problemis addressed by the adoption of delayed selective acknowledgment(SACK). Simulation experiments show that the TP-Planet signif-icantly improves the throughput performance and addresses thechallenges posed by the IPN Backbone Network.

Index Terms—Bandwidth asymmetry, blackouts, high propaga-tion delay, InterPlaNetary (IPN) Internet, reliable transport pro-tocol.

I. INTRODUCTION

THE developments in space technologies in the last decadehave enabled the realization of deep-space scientific

missions such as Mars Exploration. These missions produce

Manuscript received December 3, 2002; revised July 1, 2003 and September20, 2003.

The authors are with the Broadband and Wireless Networking Laboratory,School of Electrical and Computer Engineering, Georgia Institute of Tech-nology, Atlanta, GA 30332 USA (e-mail: [email protected]; [email protected]; [email protected]).

Digital Object Identifier 10.1109/JSAC.2003.819985

NetworkProximity

Earth

Planetary Satellite Network

Mars

Earth Relay Orbit

Interplanetary Backbone Network(Deep Space Communication Link)

Fig. 1. Deep-space network architecture for the Mars Exploration mission.

a significant amount of scientific data to be delivered to theEarth. For successful transfer of scientific data and reliablenavigational communications, NASA enterprises have outlinedsignificant challenges for the development of next-generationspace network architectures. The next-generation deep-spacenetworks are expected to provide communication services forscientific data delivery and navigation services for the explorerspacecrafts and orbiters [6]. The next step in the design anddevelopment of deep-space networks is expected to be theInternet of the deep-space planetary networks, which is definedas the InterPlaNetary (IPN) Internet [25].

A typical deep-space network architecture shown in Fig. 1is proposed for the Mars Exploration mission [7]. The architec-tural elements of the proposed infrastructure can be summarizedas follows.

• IPN Backbone Network: It includes the direct link ormultihop paths between the outer-space planets and theEarth, as well as the Earth-based infrastructure elementssuch as a ground station for the deep-space network. Itmay also include relay stations placed at gravitationallystable Lagrangian points of outer-space planets such asJupiter and Pluto [7].

• Planetary Satellite Network: It consists of the satellitesorbiting the planets to provide communication relay andnavigation services to the surface elements.

• Planetary Proximity Network: It provides the commu-nication links between diverse range of surface elements,which are often spread out to form an ad hoc network.

Among the above architectural elements of the IPN Internet,we mainly focus on the IPN backbone network, where the sourceand sink end-points are basically the ground station at the Earthand the planetary gateways connected to the relay satellites or-biting around the outer-space planets as shown in Fig. 1. Thisis because the IPN backbone network plays a significant rolefor the performance of entire deep-space communication. The

0733-8716/04$20.00 © 2004 IEEE

AKAN et al.: TP-PLANET: RELIABLE TRANSPORT PROTOCOL FOR INTERPLANETARY INTERNET 349

most important characteristics and the challenges posed by theIPN backbone network are listed as follows.

• Very long propagation delays: The deep-space commu-nication links may have extremely long propagation de-lays. For example, the end-to-end round-trip time (RTT)for the Mars–Earth communication network varies from8.5 to 40 min, according to the orbital location of theplanets [12].

• High link error rates: The bit-error rates on the deep-space links are very high, usually on the order of 10[12].

• Blackouts: Periodic link outages may occur due to or-bital obscuration with the loss of line-of-sight because ofmoving planetary bodies, the interference of an asteroid,or a spacecraft [6].

• Bandwidth asymmetry: The asymmetry in the band-width capacity of forward and reverse channels istypically on the order of 1000:1 in space missions [12].

These challenges need to be addressed in order to meet thecommunication requirements of deep-space missions. How-ever, the existing TCP variants [3], [8], [9], [13], [16], [17],[20], [21] have been shown to achieve very poor performancein deep-space communication networks [1]. The dominantfactor in this performance degradation is the extremely highpropagation delay in deep-space links [1]. This is solely dueto the window-based mechanism used by the current TCP pro-tocols during slow start and congestion avoidance algorithms.In the slow start algorithm, the congestion window sizeis increased by one packet per received ACK until the slowstart threshold is reached, i.e., . However, thisapproach wastes the link resources for a very long duration,which is proportional to the propagation delay. Forand min, it is shown in [1] that the slow startalgorithm cannot utilize the link resources for approximately120 min in deep-space links.

The inefficiency in link utilization due to window-basedmechanisms also exists during the congestion avoidance phase,i.e., , where the TCP source increments the conges-tion window size by roughly one at each RTT. The performanceevaluation study in [1] shows that the window-based TCPprotocols achieve throughput of approximately 10 bytes/s forthe link capacity of 1 Mb/s, packet loss probability of 10and min. In other words, the entire deep-spacelink remains almost unutilized during the entire connectionperiod. Note that minutes is within the RTT rangefor communication links between Mars and Earth, i.e., 8.5 to40 min based on the orbital position [12].

Furthermore, the current TCP protocols are designed forwired links, which are reasonably assumed to have negligiblebit-error rates. Therefore, they invoke congestion controlmechanisms in case of a single packet loss. However, thisassumption does not hold in deep-space communication links.Consequently, the packet loss-based congestion detectionmechanism results in unnecessary rate throttle and leadsto severe throughput degradation. Much research has beenperformed in recent years in order to address the throughputdegradation due to wireless link errors [5]. However, thesesolutions cannot be directly applied to IPN backbone network

because of the amplifying effects of the extremely high prop-agation delay and the other abovementioned characteristics onthe problem.

The TCP performance on the links with high bandwidth-delay products and errors is analyzed in [18]. Many transportprotocols [2], [3], [15] are proposed for satellite links, whichare also characterized by high bandwidth-delay products andhigh bit-error rates. Nevertheless, these studies mostly referto geostationary earth orbit (GEO) satellite links with typicalRTT values around 550 ms, which are very low compared withRTTs in deep-space communication links. Moreover, packetlosses due the blackout conditions may also mislead the con-gestion control mechanisms based on packet losses. In [14],an enhancement for TCP is developed to address signal lossconditions due to mobility. However, the blackout situations indeep-space links are much more complicated due to extremelyhigh propagation delay and hence solutions as in [14] cannotbe applied directly.

There are more challenges which need to be addressed bythe new transport protocols in IPN backbone network. Thesechallenges are consequences of the characteristics of the deep-space links and can be summarized as follows.

• Delayed Feedback: TCP is expected to respond to net-work state. This expectation creates problems in long-delay environments, since TCP uses end-to-end signalingfor its control loops. When higher RTT is experienced,the older information about link conditions is received atthe source. Thus, the congestion control decision basedon such past information might not lead to proper action.Therefore, congestion control schemes, which react to in-stantaneous packet loss situations, do not yield proper re-sponse on the links with high propagation delay.

• Buffer Size: In order to assure 100% reliable transport,retransmission mechanism is inevitable. However, thisbrings considerable amount of memory requirement. Forexample, the transport protocol source should maintain1.2 GB buffer size for min and the averagedata transmission rate of 1 MB/s.

There already exists an active research on transport layer pro-tocols for space-based communication networks. Space com-munications protocol standards-transport protocol (SCPS-TP)[10], [11] is a set of TCP extensions developed by ConsultativeCommittee for Space Data Systems (CCSDS) for space commu-nications. SCPS-TP is designed to support current communica-tion environments and those of upcoming space missions [10].SCPS-TP is developed based on the existing TCP protocols withsome modifications and extensions to address the challengesposed by space-based systems such as link errors, bandwidthasymmetry, and link outages. It can provide full, best-effort andminimal reliability according to the mission specific communi-cation requirements. The capabilities of the SCPS-TP are basi-cally a combination of existing TCP protocols, which are shownto be inadequate in addressing the challenges in IPN backbonenetwork [1]. For example, SCPS-TP with Vegas congestion con-trol uses window-based scheme and adopts slow start algorithm.Although the rate-based version of SCPS-TP is under develop-ment, it disables congestion control mechanism and performstransmission with user-selected fixed rate [24]. On the other


ImmediateStart Blackout

RateDecrease

RateIncrease

Hold Rate

Φ>φ i

Φ<φd

φ <Φ<φ idΦ<φd

Follow-Up

Φ>φ i φ <Φ<φ id

t=RTT

t= 2*RTTINITIAL STATE

STEADY STATE

Fig. 2. TP-Planet protocol operation state diagram including substates and the state transitions based on congestion control decision mechanism.

hand, for example, SCPS-TP uses TCP-Vegas [8] congestion de-cision mechanism based on the RTT variation. However, sincethe window-based nature of TCP-Vegas cannot fully utilize thelink, it is not even possible for it to experience the conges-tion and hence variation in RTT. Therefore, congestion decisionbased on RTT variation does not provide proper congestion con-trol functionality. Furthermore, due to the extremely high prop-agation delay, the variation in RTT may not be measured accu-rately such that the resultant congestion control behavior mayalso not be accurate.

As pointed out in [1], there exists an urgent need for reli-able transport protocol for IPN Internet. In this paper, a reli-able transport protocol TP-Planet for IPN Internet is presented.TP-Planet is mainly implemented at the IPN backbone networknodes, i.e., the TP-Planet source and sink are the ground sta-tion at the Earth and the planetary gateway connected to therelay satellites orbiting around the outer-space planets. The ob-jective of TP- Planet is to achieve high throughput performanceand reliable data transmission by addressing the challenges inthe IPN backbone network. In order to address the challengesdue to extremely high propagation delay, TP-Planet deploys anewly developed end-to-end rate-based additive-increase mul-tiplicative-decrease (AIMD) congestion control, whose AIMDparameters are adjusted to compensate for the throughput degra-dation. Two novel algorithms, i.e., initial state and steady state,constitute the structure of the TP-Planet protocol. Initial statealgorithm replaces the inefficient slow start algorithms in orderto capture link resources in a very fast and controlled manner.In steady state, a new congestion detection and control mech-anism is deployed to minimize the erroneous congestion deci-sions due to high link errors. In order to reduce the effects ofblackout conditions on the throughput performance, TP-Planetincorporates the blackout state procedure into the protocol op-eration. The bandwidth asymmetry problem is addressed by theadoption of delayed selective acknowledgment (SACK) options.Performance evaluation via simulation experiments reveals thatTP-Planet significantly achieves high throughput performanceand addresses the challenges in the IPN backbone network.

The remainder of the paper is organized as follows. TheTP-Planet protocol overview along with the detailed operationof the Initial state algorithm is presented in Section II. InSection III, the steady state algorithm including the newrate-based AIMD congestion control and blackout state be-

havior are explained. The performance evaluation presentedin Section IV is followed by the concluding remarks stated inSection V.

II. TP-PLANET: INITIAL STATE

The TP-Planet source starts its connection in the initial stateat by calling the algorithm, as shownin Fig. 2. The TP-Planet source then goes to the steady state bycalling the algorithm at as shownin Fig. 2.

The slow start algorithm used in the existing TCP protocolsis shown to be inefficient on deep-space links of the IPN In-ternet [1]. In order to avoid performance degradation due to theslow start algorithm, the TP-Planet deploysalgorithm, which achieves capture of link resources in a veryfast and controlled manner. The algorithm is composed of twomain procedures, i.e., immediate start and follow-up.

A. Immediate Start

TP-Planet starts a connection in the immediate start state at, where the actual RTT is divided into time intervals of size

. TP-Planet then emulates slow start and congestion avoidancealgorithms of the conventional window-based TCP protocols bytreating time intervals of size as the RTT of the emulated con-nection. The objective of the immediate start phase is to probethe network in a fast and controlled manner so that the trans-mission rate can be increased quickly according to the feedbackfrom the sink. The determination of the interval size is pre-sented in Section II-C. Here, we present the protocol operationin the Immediate Start phase in two parts, i.e., emulated slowstart and emulated congestion avoidance.

1) Emulated Slow Start: As shown in Fig. 3, the first RTT isdivided into time intervals of size . This is done in order to havea more fine-grained time unit than the extremely high RTT ofthe deep-space link. The number of data packets transmitted ineach interval (cwnd), is increased geometrically in each intervalby emulating the window-based behavior of the slow start al-gorithm of the classical TCP protocols. This increase continuesuntil the emulated slow start threshold ( ) is reached,i.e., .

In addition to data packets, NIL segments [3] are also sentin each time interval during the immediate start phase. The


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0 T 2T 3T 4T 5T

. . . . . . . .

RTTRTTT

Emulated Congestion Avoidance

6T

IMMEDIATE START

Emulated Slow Start

Fig. 3. Example illustration of time framing mechanism in the immediate start phase for ssthresh = 8.

Fig. 4. NIL segment generating algorithm.

objective of NIL segment transmission is to probe actuallink resource status in the beginning of the connection. NILsegments are chosen from the unacknowledged outstandingdata packets to be used for packet loss recovery at the receiver.NIL segments are encapsulated by low-priority Internet pro-tocol (IP) packets. Note that the type of service (TOS) fieldof the IP packet header can be used for this purpose. Hence,assuming the relay satellites serving as routers along the IPNbackbone link, as shown in Fig. 1, have priority-queueingcapability, low-priority NIL segments are discarded first in caseof congestion. Therefore, NIL segment transmission does notaffect the throughput of the actual data packet transmission.If there is no congestion, they are received and ACKed backby the sink, which reveals that there are still unutilized linkresources along the path. NIL segments are created with the

algorithm shown in Fig. 4, whereis the length of the queue of unacknowledged outstanding

data packets, and is a counter. Further details of the NILsegment generation can be found in [3].

In the emulated slow start phase, the number ofNIL segments transmitted in each interval is determined such

that the total number of packets transmitted does not exceed theemulated slow start threshold, i.e., .

In Fig. 3, we assume that the emulated slow start thresholdis eight packets, i.e., . Therefore, the number ofdata and NIL segments transmitted at each time interval duringthe emulated slow start phase can be summarized as follows.

• 1st and .• 2nd and .• 3rd and .• 4th and .

In general, the number of data packets and NIL segmentstransmitted in the th time interval during the emulated slow startis given by

(1)

(2)

2) Emulated Congestion Avoidance: The emulated slowstart is left for emulated congestion avoidance phase when

. Since there is no feedback informationabout the link resources as yet, the TP-Planet source does notincrease the number cwnd of data packets transmitted in eachtime interval. Therefore, until the end ofthe emulated congestion avoidance phase. However, for furtherprobing the link status, the source increases additivelyby one NIL segment per interval emulating congestionavoidance algorithm of the classical TCP protocols. As inFig. 3, during this phase, cwnd is kept constant, andis increased by one segment in each until the end of theimmediate start, when becomes equal to cwnd. Thetransmission of NIL segments is terminated at the end of thefirst RTT, and the TP-Planet source goes to the follow-up stateof the initial state algorithm as shown in Fig. 2.

B. Follow-Up Phase

During this phase, the feedback for the packets transmittedin the immediate start phase are started to be received by theTP-Planet source. In order to save scarce reverse channel re-sources of the bandwidth asymmetrical deep-space link, the sinkdoes not ACK back all the packets it receives. Several data


packets are ACKed by a delayed SACK, whose details are ex-plained in Section III-D. Since NIL segments are transmittedto probe the link status, each received NIL segment indicatesthe existence of unutilized link resources. Hence, the TP-Planetsink counts the total number of packets received in everyperiod and sends this information back to the source. This infor-mation is carried in NIL ACKs. The TP-Planet source transmits

packets in until the first NILACK received at . Then, the TP-Planet sourceadjusts its transmission rate by using the information car-ried in NIL ACKs, i.e., , until the end of .Therefore, the transmission rate at the end of the initial statedepends on the number of ACKs received in the last time in-terval of the follow-up phase.

Let be the number of packets received by theTP-Planet sink during . Since the totalnumber of packets sent in the last interval of the immediatestart phase is , the data transmission rate at theend of the initial state is expressed by

(3)

In addition to the data packets, the TP-Planet source alsostarts sending NIX segments during the follow-up phase. TheNIX segments are much smaller than the data packets, i.e.,40 bytes. They are carried in both low- and high-priority IPpackets. During the follow-up phase, low- and high-priorityNIX segments are transmitted with the transmission rate ,which is equal to the transmission rate of the data packets, i.e.,

. The objective of the NIX segment transmission isto capture congestions and make decisions accordingly in thesteady state. The details of the congestion detection mechanismbased on NIX segments are explained in Section III. Thefollow-up phase is over at and the source leavesinitial state for the steady state as shown in Fig. 2.

Consequently, the TP-Planet source can capture the link re-sources as soon as based on the number of ACKsit receives from the sink. It can achieve this without leading tocongestion by controlling the number of probe packets injectedinto the network.

C. Determination of the Time Interval

In the initial state, the extremely high actual RTT is dividedinto time intervals of size and the conventional TCP behavioris emulated to capture the available link resources in a controlledmanner. The performance of the initial state depends on the sizeof the intervals. While the smaller increases link utiliza-tion, it also increases overhead incurred by NIL segment trans-mission.

The objective of the Initial State is to reach a certain datatransmission rate, , as soon as possible without leading to acongestion. During the follow-up phase, the TP-Planet sourceadjusts its transmission rate according to the feedbackreceived from the sink every period via NIL ACKs. Since

is the maximum number of packets transmitted inone interval during the emulated slow start phase, the trans-mission rate reached in the follow-up phase is dependent on

. Here, we assume that the TP-Planet source has a

given target minimum transmission rate requirement . Thisrequirement can be mostly due to two main reasons.

• Application: The minimum transmission rate is requiredin order to meet specific application needs such astransmission of scientific multimedia data which requires100% reliability and a minimum bound on the transmis-sion rate.

• Latency: The maximum allowed latency can be adver-tised by the specific space mission requirements as themaximum duration for the transmission of certain amountof data. This determines the minimum target transmissionrate requirement for a given connection.

Consequently, in order to achieve the target transmission rateof packets/s at , the correspondingis calculated as

(4)

The emulated slow start phase of the immediate start terminateswhen the number of data packets transmitted in one time in-terval reaches the emulated slow start threshold, i.e.,

. Therefore, from (2), it follows that the duration ofthe emulated slow start phase is given by

(5)

During the emulated congestion avoidance phase, the numberof data packets per interval (cwnd) is kept constant. Moreover,the number of NIL segments is increased by one per intervaluntil the end of the immediate start, i.e., . In orderto probe the network resources in the range of , thesource increases the number of NIL segments transmitted ineach interval until it is equal to the number of data packets, i.e.,

. The immediate start is over whenreaches at . Therefore, the duration of theemulated congestion avoidance phase can be expressedby

(6)

Since the total duration of the immediate start is, from (5) and (6), it follows that

(7)

As is negligible compared withitself, from (4) and (7), the size of the time interval

can be calculated by

(8)

where is the target transmission rate in packets/s. At the be-ginning of a connection, RTT is also not known to the TP-Planetsource. Therefore, the TP-Planet source also caches the RTT ofthe past connections and uses that value in order to calculate thetime interval size by (8) during the initial state.

Consequently, the emulated slow start threshold can be ex-pressed by using (4) and (8) as follows:

(9)


Fig. 5. Initial State() algorithm.

Thus, at the beginning of the connection, andare set by (8) and (9). The first RTT period is then divided intotime intervals of size . The emulated slow start and emulatedcongestion avoidance phases are performed in the first RTT inorder to perform resource probing. In the second RTT, the trans-mission rate is increased by sending a new data packet for eachreceived ACK until . By this way, TP-Planet sourcecan increase its transmission rate very quickly and efficientlyutilize the resources during the initial state without leading toany congestion.

D. Connection Establishment

The conventional TCP protocols perform three-way hand-shake for connection establishment. The existing protocols sendconnection request segment at the beginning of the connectionand do not transmit any data segments until the connection ACKis received from the receiver. This approach results in waste ofhuge bandwidth for at least a duration of one RTT in deep-spacelinks. In order to avoid this inefficiency, the TP-Planet sourcedoes not wait for the ACK for the connection request and startsdata transmission in the initial state as if the session request isgranted. If the request is rejected, then the connection is termi-nated after one RTT.

As a result, TP-Planet achieves better utilization of link re-sources in the early phases of the connection by the initial statealgorithm. The overall initial state algorithm of TP-Planet issummarized in Fig. 5.

III. TP-PLANET: STEADY STATE

The TP-Planet source leaves initial state for steady state atand remains in the steady state until the connec-

tion is terminated. During the steady state operation, TP-Planetsource can be in one of the four states, i.e., increase rate, de-crease rate, hold rate, and blackout, as shown in Fig. 2. Inthe beginning of the steady state, the source goes to hold ratestate, where no transmission rate change is performed. Duringsteady state operation, TP-Planet deploys a new congestion con-trol scheme. Hence, the transitions between these states in thesteady state is decided based on this congestion control scheme.Therefore, the data transmission rate can be increased, de-creased, or hold according to the current state.

A. Congestion Control

TP-Planet deploys a new congestion control scheme in orderto address the challenges due to high link error rates in the IPNbackbone network. The objective of this method is to decouplenetwork congestions and packet losses due to errors.

During the steady state, the TP-Planet source transmits low-and high-priority NIX segments continuously for congestion de-tection purposes. NIX segments are carried by IP packets, whichare marked as high and low priority using TOS field in the IPpacket header. NIX segments differ from NIL segments usedin initial state in terms of their size and functionality. UnlikeNIL, NIX segments are much smaller compared with data seg-ments, i.e., 40 bytes. They do not carry any information and,thus, cannot be used for error recovery purposes.

Low- and high-priority NIX segments are transmittedsimultaneously with the same rate equal to the datatransmission rate , i.e., . The objective of this isto obtain congestion decision support via comparison betweenthe reception statistics of both low- and high-priority NIXsegments. Since low- and high-priority NIX segments areequal in size and are transmitted with the same rate ,they experience the same packet loss rate due to space linkerrors. However, assuming the relay satellites serving as routersalong the IPN backbone link as shown in Fig. 1 have priority-queueing capability, low-priority NIX segments are discardedfirst in case of a congestion. The only reason for low- andhigh-priority NIX segments to have different packet loss ratesis the additional loss experienced by low-priority segmentsdue to congestion. This reasoning constitutes the basis for ourcongestion detection method via NIX segment transmission.

TP-Planet sink counts the number of received lowand high priority NIX segments in a sliding timewindow of . The received NIX segments are not ACKedback to the sender. Note that this also avoids an overhead in thereverse channel, which can also become a bottleneck due tobandwidth asymmetry in deep-space links. Only the receptionstatistics within a time window of are returned to the senderevery period. This information is carried by NIX ACKs.TP-Planet source receives NIX ACKs carryingat the end of each measurement period .

Let be the ratio of the number of received low- and high-priority NIX segments, i.e.,

(10)


Assuming that the low- and high-priority NIX packets expe-rience same packet loss rate due to the link errors within ameasurement period , since the only reason foris the congestion along the path, the TP-Planet source infers thata congestion exists if .

The entire congestion control of the TP-Planet source is de-termined according to the value of . The transitions betweenincrease, decrease, and hold rate states in Fig. 2 are performedbased on . In order to avoid unnecessary state transitions, deci-sion is made via comparison of with preset rate decrease ,and the rate increase thresholds , as shown in Fig. 2. Thesethresholds are protocol parameters, whose selection is an imple-mentation issue. The values of and , which yield highestprotocol performance, are determined during simulation exper-iments and are given in Section IV.

The summary of the congestion control mechanism is givenas follows.

1) : In this case, the TP-Planet source infers thatcongestion is experienced along the path. Thus, the sourcegoes to the decrease rate state, where the transmissionrate is decreased multiplicatively, i.e., .

2) : In this case, the data transmission rate iskept unchanged until next feedback is received from thesink.

3) : The TP-Planet source infers that no congestionis experienced. Consequently, it increases the data trans-mission rate additively, i.e., .

The additive-increase and multiplicative-decrease pa-rameters are used to perform AIMD rate control every pe-riod. The selection of these AIMD parameters are explainedin Section III-B. The steps of the steady-state algorithm of theTP-Planet protocol is summarized in Fig. 6.

B. New Rate-Based AIMD Scheme

As mentioned before, current TCP protocols achieve verypoor performance on the links with extremely high propagationdelay mostly due to their window-based operation [1]. Thethroughput of the window-based TCP protocols and rate-basedschemes are inversely proportional to the RTT [22] and thesquare-root of RTT (see the Appendix), respectively. Thus, therate-based congestion control schemes are more robust to exces-sive propagation delays than the window-based mechanisms.Hence, in order to address the adverse effects of extremely highpropagation delay on the throughput performance, TP-Planetdeploys rate-based AIMD congestion control. The steady statethroughput of the rate-based AIMD scheme is derived in theAppendix and is expressed by

(11)

where and are the additive-increase and the multiplicative-decrease factors, respectively, and is the packet loss proba-bility. It is observed from (11) that the throughput of the rate-based AIMD scheme depends on the values of and . There-fore, TP-Planet adapts its AIMD parameters to the link condi-tions, i.e., RTT and packet loss rate, such that their adverse ef-

Fig. 6. Steady State() algorithm.

fects on the performance are compensated and a given targetthroughput is achieved.

The additive-increase parameter of the rate-based AIMDscheme to achieve a target throughput of packets/s can beobtained from (11) as follows:

(12)

where is the multiplicative-decrease factor, and is the packetloss probability, respectively. Here, the target throughput canbe defined as the average data rate required to transmit cer-tain amount of information within the certain delay bound, asin Section II-C.

These AIMD parameters and in (12) are the rate changeparameters to be used to control the rate with period RTT.However, the TP-Planet source performs rate control withthe feedback received from the sink every period, where

. Thus, the rate control parameters and in


(12) cannot be directly used for TP-Planet. These parameters,instead, represent the upper bound for the rate change of theTP-Planet source within one RTT period. Hence, the AIMDparameters to be used by the source with period can bederived from and .

Let be the NIX segment reception statistics feedbackperiod. Thus, the TP-Planet source performs at mostnumber of rate changes within one RTT period.

Let and be additive-increase and multiplicative-decreaseparameters to be used by TP-Planet for rate control with period

. Then, can be calculated as

(13)

By the same reasoning, the additive increase factor to be usedby TP-Planet source can also be calculated as

(14)

The TP-Planet source uses AIMD scheme during the steadystate operation as shown in Fig. 2. At the end of the initial state,i.e., , it calculates the packet loss rate by usingthe number of transmitted and ACKed data packets and NILsegments. The TP-Planet source then uses (12)–(14) to calcu-late its AIMD parameters, i.e., . Consequently, accordingto the result of the congestion decision mechanism presentedin Section III-A, the transmission rate is multiplicatively de-creased or additively increased with and , respectively.

C. Blackout State Behavior

Link outages due to loss of line-of-sight by orbital obscura-tions lead to burst packet losses and decrease in the throughput.In order to provide reliable transport, SACK options [20] areadopted by TP-Planet to address burst losses. Due to the possibleinadequacy of the number of SACK blocks in the SACK op-tion field for very long blackout durations, timeout mechanismis also included in TP-Planet. In order to reduce the throughputloss due to blackouts, blackout state is developed and incorpo-rated into the steady state.

The TP-Planet source receives data ACKs for reliability con-trol purposes and NIX ACKs for NIX segment reception statis-tics. If the source does not receive any type of ACK for a certainperiod of time , it infers this condition as blackout and goesto the blackout state as shown in Fig. 2. The objective of theblackout state procedure is to reduce the throughput degrada-tion due to the blackout situation.

During blackout, the TP-Planet source keeps sending low-and high-priority NIX packets without changing its trans-mission rate. The same blackout event is also detected bythe TP-Planet sink if no packet is received within period.Although the sink does not receive any low- and high-priorityNIX packets during the blackout, it keeps sending NIX ACKswith as (0, 0). These ACK packets are calledZero NIX ACKs. The objective of Zero NIX ACKs is to helpthe TP-Planet source to capture accurate information regardingthe blackout situation and act accordingly.

Since RTT is very high, the effect of blackout on the perfor-mance changes with its relative location of blackout occurrence

t 0 t 1

(rtt-x)

t 2

Normal ACKsstate=(Increase,Decrease, Hold)

t 3

L


t 4

L

No ACKsstate=Blackout

Source Sink

Zero NIX ACKsstate=Hold

t

(2x-L)

Fig. 7. Blackout condition observed from TP-Planet source for L < 2x.

with respect to the sink. Let be the time when blackoutoccurs, and is the duration of the blackout. Assume that theblackout occurs at a position seconds away from the TP-Planetsink. For , there are two distinct cases accordingto the duration of the blackout and its relative distance to theTP-Planet sink in time.

1) : After from , i.e., at ,the TP-Planet source detects the period without ACKs. Ifthe duration of the period with no ACK takes more than

, then the source moves to blackout state at , asin Fig. 7.

In this case, the TP-Planet source does not send anynew data packets but sends low- and high-priority NIXsegments without changing their transmission rate. It alsodoes not invoke congestion control mechanism. It startsretransmission of the packets whose retransmission timeris expired. At , the TP-Planet source re-ceives normal ACKs for duration of . Therefore, theTP-Planet source infers that blackout is over and goes toany of the increase, decrease, and hold states according tothe information it receives in the first NIX ACK, as shownin Fig. 2. At , the source starts to re-ceive Zero NIX ACKs for duration of . These Zero NIXACKs, are in fact, transmitted by the receiver when it de-tects the same blackout condition. Therefore, TP-Planetdoes not go to blackout state; instead, it goes to hold state,where it keeps sending new data packets with the sametransmission rate again, as shown in Fig. 2. Consequently,the TP-Planet reduces the effect of blackout on the perfor-mance by not wasting the link resources for a duration of

.2) : In this case, the TP-Planet source detects no

ACK period and goes to blackout state atfor the blackout which occurred at . It again doesnot change its transmission rate and sends no new datapacket. At , the source starts to receive zeroNIX ACKs for duration , as shown in Fig. 8. This re-veals that the blackout is over for TP-Planet source andthen the source leaves blackout state for hold state asshown in Fig. 2. It stays in hold state and keeps sendingdata packets with the same transmission rate before theblackout was detected. At , the Zero NIXACKs period is over, and according to the informationreceived in the first NIX ACK, the source performs statetransition. Hence, the duration of is efficiently utilizedby the help of the Zero NIX ACK mechanism.

Consequently, the blackout state reduces the throughputdegradation due to blackout conditions and improves the linkutilization for duration of or in the cases and

, respectively.


Source Sink

t

2x

No ACKsstate=Blackout

L

t 0 t 1

(rtt-x)


t 3

Zero NIX ACKsstate=Hold

t 2

Fig. 8. Blackout condition observed from TP-Planet source for L � 2x.

D. Delayed SACK

TP-Planet uses selective acknowledgment (SACK) [20] op-tions for assurance of reliable data segment transmission.TheTP-Planet sink continuously sends SACK back to the source foreach data packet it receives. Given that the data packets are 1 KBand SACKs are 40 B, then the ratio of the traffic in the forwardand reverse channels is 25:1, i.e., 1 KB/40 B. Thus, the band-width asymmetry up to 25:1 causes no congestion in the reverselink. However, the bandwidth asymmetry in the space links isusually on the order of 1000:1 [12]. Thus, sending one SACKfor each data packet can cause reverse channel to be congested,resulting in packet losses in the reverse link.

In order to avoid this problem, TP-Planet deploys SACKcongestion control by delaying the SACKs. The TP-Planetsink maintains delayed-SACK factor and sends one SACKfor every data packets received. If there is no packet lossand, hence, no change in the SACK blocks, then the TP-Planetsink keeps delaying SACKs with a delayed-SACK factor of

. Otherwise, it sends a new SACK with an updated blockimmediately. Therefore, the amount of traffic on the reversechannel is controlled by adjusting the delayed-SACK factor

. Effects of the blackout on throughput and the improvementachieved by delayed-SACK is evaluated in Section IV-D.

IV. PERFORMANCE EVALUATION

In order to investigate the performance of TP-Planet, weconducted extensive simulation experiments. The improvementin the initial phase of the connection achieved by the initialstate algorithm is evaluated in Section IV-A. Throughputperformance of TP-Planet is analyzed in Section IV-B alongwith the overhead introduced. Effects of blackout conditions onthe performance and the performance of TP-Planet with the im-provement by blackout state are investigated in Section IV-D.TP-Planet performance on deep-space links with asymmetricalbandwidth and the improvement with delayed SACK areexplored in Section IV-E.

A. Initial State Performance

In order to avoid performance degradation due to inef-ficient connection starting behavior, TP-Planet deploys the

algorithm in Figs. 2 and 5. Here, wesimulate a topology where the source and sink are connectedthrough a deep-space link with s and 10 .We perform same experiment with TP-Planet, TCP-Peach [3]and TCP-NewReno. The reason is that most of the current TCPprotocols deploy initial phase behavior based on the slow startalgorithm, which is also used by TCP-NewReno.

In Fig. 9, the transmission rate change in the initial state ofthe TP-Planet source is plotted, where the target transmission

0

20

40

60

80

100

120

140

160

180

200

0 200 400 600 800 1000 1200

Dat

a R

ate

(pac

kets

/s)

Time (s)

Initial State

Fig. 9. Transmission rate change in the initial state of the TP-Planet source forRTT = 600 s.

rate of the TP-Planet source is assumed to be 100 packets/s. Be-cause of the very high propagation delay and 100% reliabilityrequirement, the amount of buffer required for retransmissionmechanism is proportional to the data transmission rate. There-fore, for very high data rates, this brings considerable amount ofmemory requirement. For example, the source needs to main-tain 0.6 GB of buffer for min and the average datatransmission rate of 1 MB/s. Thus, we set target data rate to 100packets/s in order to maintain practicality in terms of memoryrequirements.

As explained before, the TP-Planet source performs emulatedslow start phase, which ends once the number of data packetstransmitted in one time interval is equal to . As seenin Fig. 9, the emulated slow start phase lasts for approximately

s and the TP-Planet source reaches 100 packets/sdata rate by then. Emulated congestion avoidance phase thenstarts and lasts until the end of the first RTT of the connectionperiod. During this phase, the data transmission rate is not in-creased; instead, the NIL segment transmission rate is increasedlinearly to further probe the link resources. At , thesource goes to follow-up period and increases its transmissionrate based on the feedback received from the sink every pe-riod. At s, the transmission rate reaches 200 packets/s.

TCP-Peach [3] deploys jump start algorithm in order tocapture link resources very quickly. In jump start, the sendersets the congestion window (cwnd) to 1. After sending the firstdata segment, it transmits NIL segments every ,where rwnd is the receiver window size. As a result, after oneRTT, the congestion window size increases very quickly as theACKs for NIL segments arrive at the sender. The performanceof jump start is shown in Fig. 10. The congestion window ofTCP-Peach reaches 20 packets in at most duration.It is, however, still very low compared with the performance ofinitial state of TP-Planet, which reaches 200 packets/s data rateat the end of .

The slow start performance is, however, not even close towhat initial state of TP-Planet achieves in deep-space links ofthe IPN. In Fig. 10, the congestion window evolution dependenton time is shown during the slow start. The slow start periodlasts for approximately for threshold window size of 20


0

5

10

15

20

25

0 500 1000 1500 2000 2500 3000 3500

Con

gest

ion

Win

dow

(pa

cket

s)

Time (s)

Jump Start

0

2

4

6

8

10

12

14

16

18

20

22

0 500 1000 1500 2000 2500 3000 3500

Con

gest

ion

Win

dow

(pa

cket

s)

Time (s)

Slow Start

Fig. 10. Congestion window size change during jump start algorithm ofTCP-Peach+and slow start algorithm of TCP-NewReno and forRTT = 600 s.

packets. Therefore, the link is not efficiently utilized for 60 mindue to unsuitability of the slow start algorithm to extremely highpropagation delays in deep-space links. Recall that for higherthreshold window size values, the link is not efficiently utilizedfor a longer period of time [1].

B. Throughput Performance

In order to show the throughput performance of TP-Planetin deep-space links, we perform several experiments byvarying packet loss probability and the size of the data tobe transmitted. We assume 1 Mb/s as the capacity of the linkand s. The target transmission rate is set to be100 packets/s, i.e., 100 KB/s for data packets of size 1 KB. Theprotocol parameters given in Table I are used during simulationexperiments unless otherwise stated. The investigation of theeffects of these parameters on the protocol performance are leftfor future study.

In [1], existing TCP protocols including TCP-Vegas, whichis adopted by SCPS-TP protocol as a congestion controlscheme for space-based communications [10], have beenshown to achieve very poor performance in deep-space links.For s and 10 , the throughput achievedby TCP protocols is approximately around 30 B/s and, hence,almost the entire link remains almost unutilized [1]. Al-though TCP-Peach+ has significantly outperformed other TCP

TABLE IPROTOCOL PARAMETERS USED IN EXPERIMENTS

p=10^{-5} p=10^{-4} p=10^{-3}

0

10

20

30

40

50

60

70

80

90

0.1 1 10 100

Thr

ough

put (

pack

ets/

s)

File Size (MB)

Fig. 11. Throughput for changing p and file size with RTT = 600 s.

schemes for the same link, the performance degradation wasyet too serious that it could achieve throughput around 93 B/s.Thus, the same experiments are not repeated here, and thedetails can be found in [1].

TP-Planet simulation experiments are performed for trans-mission of varying file sizes between 100 KB and 200 MB andfor varying packet loss probability of 10 10 and 10 .As shown in Fig. 11, the throughput increases with increasingfile size. This is because the larger the file size is, the longerTP-Planet stays in the steady state. As a result, the link utiliza-tion is increased. Although throughput decreases for increasingpacket loss probability, this degradation also decreases forincreasing file size. This is mostly because throughput im-provement by new congestion control scheme is higher whenthe protocol stays in steady state longer. For transmissionof 200 MB and 10 , TP-Planet throughput increasesup to 82.2 packets/s approaching its target throughput value,i.e., packets/s. Hence, TP-Planet outperforms ex-isting TCP protocols by approximately 10 times in terms ofthroughput.

In Fig. 12, the effect of RTT on the throughput performance isshown. The experiments are performed for the transmission ofa 50-MB file. The increase in RTT leads to throughput degrada-tion as shown in Fig. 12. However, the throughput degradationdue to high propagation delay is not as severe as it is for conven-tional TCP protocols [1]. This is because of the new rate-basedcongestion control scheme used in steady state of the TP-Planet.At s, TP-Planet achieves 44.72 KB/s throughput.Note that this value can further increase with increasing file size,as in Fig. 11.


0

20

40

60

80

100

120

150 200 250 300 350 400 450 500 550 600

Thr

ough

put (

pack

ets/

s)

RTT(s)

Fig. 12. Throughput for changing RTT for file size of 50 MB and p = 10 .

0

10

20

30

40

50

60

70

80

90

100

0.1 1 10 100 1000

Ove

rhea

d (%

)

File Size (MB)

p=10^{-3} p=10^{-4} p=10^{-5}

Fig. 13. Overhead due to transmission of NIL and NIX segments for changingfile size and p with RTT = 600 s.

C. Overhead

During the connection period, the TP-Planet source bringsoverhead to the deep-space link. This overhead is due to NILsegment transmission to probe the link resource in the initialstate and low- and high-priority NIX packets transmission forcongestion decision support in the steady state. In this section,we investigate the overhead caused by the injection of NIL andNIX segments into the network.

In Fig. 13, the overhead incurred by TP-Planet is shown fortransmission of files with varying sizes and for different packetloss rates, i.e., 10 10 and 10 . For very small files,i.e., MB, the overhead is relatively high. Because, in thiscase, a significant portion of the connection period is spent inthe initial state, the number of NIL segments transmitted is highcompared with that of data packets. As the file size increases,the overhead decreases as in Fig. 13. This is because the over-head in the steady state is due to the transmission of small sized(40 bytes) NIX segments and hence is much lower comparedwith initial state. Therefore, as the file size increases, the timespent in steady state also increases, which in turn decreases theoverall overhead in the connection period. As a matter of fact,the scientific data delivered during space exploration missions

30

35

40

45

50

55

60

0 20 40 60 80 100 120 140

Thr

ough

put (

pack

ets/

s)

Blackout Duration (s)

w/ Blackout State w/o Blackout State

Fig. 14. Throughput for changing blackout duration with RTT = 120 s andp = 10 .

are significantly high [6], i.e., in the order of gigabytes, whichleads to very low overhead.

On the other hand, the overhead does not significantly varyfor different . As a matter of fact, the amount of NIL seg-ments transmitted in the initial state depends only on the targetthroughput and the RTT and, thus, it is not dependent on . Thisoverhead exists only in the beginning of the connection. Al-though the NIL segment transmission during initial state bringsoverhead, it also leads to significant performance improvement,as observed in Section IV-A. As the NIX transmission rate isequal to data rate and the congestion control is robust to linkerrors, the overhead due to NIX transmission is also indepen-dent of . The overhead due to NIX transmission exists after theinitial state until the end of the connection. However, the con-gestion control decision support provided by NIX segments leadto significant throughput performance improvement. For 1 GBfile size and 10 , the overhead becomes as low as 8.1%,which is quite low compared with throughput improvement witha factor more than 10 .

D. Blackout Conditions

When a blackout is detected, TP-Planet moves to blackoutstate as shown in Fig. 2 in order to reduce its effect onthe throughput performance, as explained in Section III-C.Throughput achieved by TP-Planet for different blackout dura-tions is given in Fig. 14. Here, s, 10 , andthe target data rate is assumed to be 50 KB/s. The simulationsare performed for a duration of 600 s, where the blackoutoccurs at s.

As in Fig. 14, throughput decreases with increasing blackoutduration as expected. This decrease is observed in both of thecurves representing the TP-Planet operation with and withoutblackout state procedure. However, blackout state procedure im-proves the performance for long blackout durations. For even ablackout of 150 s, which is one-forth of the entire simulationtime, TP-Planet achieves 36.41 packets/s throughput with thehelp of blackout state behavior. This corresponds to an approx-imately 14% performance improvement over the case withoutthe blackout procedure.


0

10

20

30

40

50

60

5 10 15 20 25 30 35 40

Thr

ough

put (

pack

ets/

s)

Delayed-SACK Factor (d)

100:1 1000:1

Fig. 15. Throughput for changing delayed SACK factor d withRTT = 120 s

and p = 10 .

E. Bandwidth Asymmetry

In order to address bandwidth asymmetry problems,TP-Planet delays SACKs with a certain delayed SACK factor.Simulation experiments are performed to show the effect ofbandwidth asymmetry on the performance and the improvementachieved by delayed SACK. Here, s, 10 ,and the simulation time is assumed to be 1200 s. The target rateis set to 50 KB/s. In Fig. 15, two cases with different bandwidthasymmetry ratios are investigated.

1) 100:1. In this case, forward and reverse link capacities are100 and 1 KB/s, respectively. Since the ratio of the size ofdata packets and SACK packets is 25:1, the actual asym-metry ratio is 4:1 in this case. Therefore, the throughputis not significantly degraded by asymmetrical channel ca-pacity of the deep-space link. As shown in Fig. 15, anincrease in the delayed SACK factor also leads to an in-crease in the throughput. However, throughput decreasesfor both cases with either too high or too small delayedSACK factor of . This is because if it is too small, reversechannel is congested. On the other hand, if is too large,the source receives less number of SACKs than it expectswhich leads to performance degradation. Thus, at ,throughput achieved increases up to 52.02 packets/s.

2) 1000:1. In this experiment, bandwidth asymmetry onthe order of 1000:1 is simulated by setting forwardand reverse link capacities to 100 KB/s and 0.1 KB/s,respectively. Thus, the throughput is dramatically af-fected by the bandwidth asymmetry in this case, wherethe actual bandwidth asymmetry is 40:1. Therefore,throughput improvement with delayed SACK method ismore significant for higher bandwidth asymmetry. Forthe case without delayed SACK, throughput decreasesto up to 1.99 KB/s. As in Fig. 15, throughput increaseswith increasing number of delayed SACKs. However,this pattern does not last long since very high delayedSACK factors cannot be reached. This is because a datapacket loss yields new SACK blocks, which requiresto be transmitted back to the source immediately. Fordelayed SACK factor of 15, throughput increases up to

21.37 KB/s, which corresponds to ten times increasein the throughput performance compared with the casewithout delayed SACK, i.e., in Fig. 15.

V. CONCLUSION

The inadequacy of the current TCP protocols in the IPN back-bone network has already been known and the need for newtransport protocol has been pointed out in [1]. In order to addressthis need, a new reliable transport protocol (TP-Planet) is devel-oped in this paper. The objective of TP-Planet is to address thechallenges posed by the IPN backbone network for reliable datadelivery and achieve high throughput performance. TP-Planetdeploys a rate-based AIMD congestion control scheme. It runson top of the IP layer and does not require any specific mod-ification to the lower layers in the current TCP/IP suite. Per-formance evaluation via simulation experiments revealed thatTP-Planet significantly improves the throughput performanceand addresses the challenges posed by deep-space communica-tion networks.

TP-Planet replaces the inefficient slow start algorithms witha novel initial state algorithm, which captures link resourcesin a very fast and controlled manner. Simulation experimentsshowed that TP-Planet can reach high initial data rates veryquickly. In order to address the challenges due to extremelyhigh propagation delay, TP-Planet deploys a rate-based AIMDcongestion control, whose AIMD parameters are tuned to helpavoid throughput degradation. A new congestion control mech-anism, which decouples congestion decision from single packetloss events, is developed to minimize the erroneous congestiondecisions due to high link errors. Consequently, TP-Planet im-proves throughput with a factor more than 10 compared withthe current TCP protocols. In order to reduce the effects ofblackout conditions on the throughput performance, TP-Planetincorporates blackout state behavior into the protocol operation.By this way, it achieves up to 14% performance improvement inblackout conditions. The bandwidth asymmetry problem is ad-dressed by the adoption of delayed SACK options. As a result,TP-Planet is a reliable transport protocol equipped with diverseset of algorithms and functionalities, which can address the re-quirements of the IPN backbone network.

Note that TP-Planet is mainly implemented at the IPN back-bone network nodes, i.e., the TP-Planet source and sink are theground station gateway at the Earth and the planetary gatewayconnected to the relay satellites orbiting around the outer-spaceplanets. The end-to-end transport control can be achieved byusing the existing transport protocols developed for terrestrialwireless networks on the PlaNetary surface networks in con-junction with TP-Planet on the IPN backbone network. How-ever, the detailed description of such cooperation and its perfor-mance evaluation are beyond the scope of this paper and left forfuture study.

APPENDIX

The time-dependency of the transmission rate is shownin Fig. 16. The rate-based generic AIMD scheme is assumed toincrease the rate , additively with at each RTT, i.e.,


Ni10 1 2 . . .

Ri.ξ

R i

LCi

α

Xi

R i+1D i

tσ σ σ σ. . .

R(t)

Fig. 16. Data rate change of rate-based generic AIMD congestion control.

. It throttles the transmission rate multiplicatively byif the congestion is detected, i.e., .

Let be the total number of packets transmitted in ,which can be calculated by

(15)

For being the throughput achieved in ,the steady-state throughput achieved by the rate-based genericAIMD scheme is given by

(16)

Let be the number of packets transmitted in the thloss cycle , which starts and ends with rate halving dueto congestion decision. If the duration of is , thenthe throughput achieved in is given by .Assuming the evolution of the transmission rate to beMarkov regenerative process with rewards [19], then thesteady-state throughput can be calculated by

(17)

where and are the means of and , respectively.The transmission rate change during , i.e., the value of thetransmission rate at th RTT of the , is given by

(18)

Given that is the total number of RTTs in , i.e., the rateis throttled in th RTT, then the transmission rate atthe end of the is expressed by .Hence, the expectation of the i.i.d random variable denotingthe transmission rate, i.e., , can be calculated as

(19)

If lasts for , where , then the totalnumber of packets transmitted during can be calculated by

(20)

For rate-based AIMD scheme, whose rate change is performedwith RTT granularity, this can be calculated by replacing the

integration with the discrete summation and substituting (18)into (21) as follows:

(21)

Thus, for mutually independent random variables of and ,the mean of can be calculated by taking expectation of bothsides of (21) and substituting (19) as follows:

(22)

On the other hand, the total number of packets transmitted inloss cycle can also be calculated by , where

and are the number of packets transmitted untilthe dropped packet and in the last RTT, respectively. Therefore,

can also be calculated by

(23)

where the expectation of the random variable is given by

(24)

where is the packet loss probability, if loss-based congestiondetection is used by the generic AIMD rate-based congestioncontrol algorithm. By substituting (19) and (24) into (23), andequating it to (22), we solve for and obtain it as follows:

(25)

Thus, it follows from (17), (22), and (25) that the steady-statethroughput of the rate-based generic AIMD congestion controlscheme as a function of rate-increase and decrease paratemeters,i.e., and , and RTT, , and the packet loss probability , canbe expressed as follows:

(26)

ACKNOWLEDGMENT

This paper is dedicated to the memory of the seven astronautson the Columbia space shuttle. One of their missions was toinvestigate the IPN Internet and to test a mobile Internet protocolin space.

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[24] D. T. Tran, F. J. Lawas-Grodek, R. P. Dimond, and W. D. Ivancic,“SCPS-TP, TCP and rate-based protocol evaluation for high-delay,error-prone links,” presented at the SpaceOps 2002, Houston, TX, Oct.2002.

[25] E. Travis, “The interplanetary Internet: Architecture and key technicalconcepts,” presented at the Internet Global Summit, INET 2001, June 5,2001.

Özgür B. Akan (M’00) received the B.Sc. and M.Sc.degrees in electrical and electronics engineeringfrom Bilkent University, and Middle East TechnicalUniversity, Ankara, Turkey, in 1999 and 2001,respectively. He is currently a Research Assistant inthe Broadband and Wireless Networking Laboratoryand working toward the Ph.D. degree at the Schoolof Electrical and Computer Engineering, GeorgiaInstitute of Technology, Atlanta.

His current research interests include adaptivetransport protocols for heterogeneous wireless archi-

tectures, next-generation wireless networks, and deep-space communicationnetworks.

Jian Fang received the B.S., M.S., and Ph.D. degreesfrom Shanghai Jiao Tong University, Shanghai,China, and is working toward the Ph.D. degree fromGeorgia Institute of Technology, Atlanta.

Currently, he is a Research Assistant in theBroadband and Wireless Networking Laboratory,Georgia Institute of Technology. He was a ResearchAssociate in the Department of Computer Scienceand Engineering, the Chinese University of HongKong, Shatin, for one year. After that, he becamea Lecturer with Shanghai Jiao Tong University. His

current research interests are in interplanetary Internet and satellite networks.

Ian F. Akyildiz (M’86–SM’89–F’96) received theB.S., M.S., and Ph.D. degrees in computer engi-neering from the University of Erlangen-Nuernberg,Erlangen, Germany, in 1978, 1981, and 1984,respectively.

Currently, he is the Ken Byers DistinguishedChair Professor with the School of Electricaland Computer Engineering, Georgia Institute ofTechnology, Atlanta, and Director of Broadbandand Wireless Networking Laboratory. He is anEditor-in-Chief of Computer Networks and for the

newly launched Ad Hoc Networks Journal and an Editor for ACM Journal ofWireless Networks. His current research interests are in sensor networks, IPNInternet, wireless networks, and satellite networks.

Dr. Akyildiz received the Don Federico Santa Maria Medal for his servicesto the Universidad of Federico Santa Maria, in 1986. From 1989 to 1998, heserved as a National Lecturer for ACM and received the ACM OutstandingDistinguished Lecturer Award in 1994. He received the 1997 IEEE LeonardG. Abraham Prize Award (IEEE Communications Society) for his paperentitled “Multimedia Group Synchronization Protocols for Integrated ServicesArchitectures” published in the IEEE JOURNAL OF SELECTED AREAS IN

COMMUNICATIONS (JSAC) in January 1996. He received the 2002 IEEE HarryM. Goode Memorial Award (IEEE Computer Society) with the citation “forsignificant and pioneering contributions to advanced architectures and protocolsfor wireless and satellite networking.” He received the 2003 IEEE Best TutorialAward (IEEE Communicaton Society) for his paper entitled “A Survey onSensor Networks,” published in the IEEE Communication Magazine, in August2002. He also received the 2003 ACM Sigmobile Outstanding ContributionAward with the citation “for pioneering contributions in the area of mobilityand resource management for wireless communication networks.” He has beena Fellow of the Association for Computing Machinery (ACM) since 1996.

Documents

348 IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL