An Analysis of VoIP Service Using 1 EV-DO
Revision A System
, Senior Member, IEEE
, Pi-Chun Chen, Yang Yang, and Qinqing Zhang
, Senior Member, IEEE
Abstract—While voice-over-Internet protocol (VoIP) on wireline network is maturing, VoIP on wireless mobile network is still in its infancy. This disparity is due to the fact that the wireline band-width is abundant and can be traded off for delay performance and overhead, whereas bandwidth in wireless mobile network is still a scarce resource. With the deployment of 1 EV-DO revision 0 (DOr0) worldwide, the spectrum efficiency has been significantly improved. However, DOr0 still lacks of features essential for VoIP. For this reason, 1 EV-DO revision A (DOrA) has been standard-ized in the 3GPP2 with many improvements favorable for VoIP implementation. In this paper, we identify challenges and explore the feasibility of implementing VoIP using DOrA. We develop both analytical and simulation models to evaluate the VoIP capacity and delay performance over the air interface.
Index Terms—Air interface capacity, CDMA2000, voice-over-Internet protocol (VoIP), wireless communication, 1 EV-DO.
VOICE-over-Internet protocol (VoIP) has made consider-able progress in wireline networks in the last decade , . VoIP in wireless has drawn much interest recently because of the convergence of all IP architecture in wireless and wireline networks. Initial VoIP efforts ,  were focused on the wire-less local area networks (LANs) since the achievable data rate is close to that of the wireline network. Call admission and band-width allocation are designed to support large voice capacity and to meet the delay requirements.
Despite the success of VoIP in wireline and wireless LAN net-works, little progress was made on wireless cellular networks. This is because VoIP implementation adds considerable IP and other overhead, which decreases spectral efficiency. In addi-tion, VoIP service requires stringent end-to-end quality-of-ser-vice (QoS) support that is not yet available to ensure the tight delay constraint. With the deployment of 1 EV-DO revision 0 (DOr0) ,  worldwide, the spectrum efficiency for wire-less mobile data applications has been significantly improved. However, DOr0 still lacks the capabilities essential to VoIP im-plementation. Recognizing this, 1 EV-DO revision A (DOrA)  has been standardized with many improvements favorable to VoIP implementation.
In this paper, we explore the possibility of implementing VoIP using DOrA. We identify the challenges that hinder the imple-mentation of VoIP on wireless network in general, and inves-tigate possible solutions to meet these challenges. We develop
Manuscript received October 7, 2004; revised June 3, 2005.
The authors are with Bell Laboratories, Lucent Technologies, Whippany, NJ 07981 USA (e-mail: email@example.com; firstname.lastname@example.org; yyang7@ lucent.com; email@example.com).
Digital Object Identifier 10.1109/JSAC.2005.858882
simulation and analytical models to evaluate the expected per-formance including voice capacity and resultant delays when VoIP is implemented using DOrA.
II. VOIP IMPLEMENTATIONUSING1 EV-DO DORA When VoIP is implemented using DOrA, the coverage, ca-pacity, and voice quality should be similar to that provided by the CDMA2000 system. These requirements present challenges to system designs. In this section, we examine some of the so-lutions and establish assumptions, design criteria, and the resul-tant system model to facilitate the analysis that follows.
A. Speech Coder and Silence Suppression
In this paper, we assume that an enhanced variable rate codec (EVRC) speech coder  is used for VoIP. The EVRC speech encoder generates frames with four variable data rates, i.e., full, 1/2, 1/4, and 1/8 rate, with the probabilities of 38%, 5%, 0%, and 57%, respectively. Further, silence suppression is assumed by not or rarely transmitting the 1/8 rate packets, resulting in a 43% voice activity factor.
B. IP and Other Packet Overhead
For VoIP applications, voice frames have to be placed into IP packets. The added protocol overhead represents an in-tolerable amount of spectral inefficiency for wireless mobile networks. For this reason, IP header compression is necessary. Header compression algorithms have been standardized for CDMA2000 network and DOrA. We assume that header com-pression is utilized for both forward link (FL) and reverse link (RL). To demonstrate the importance of header compression, consider a full rate EVRC frame that has 171 bits. Together with 24 cyclic redundancy bits and six coding tail bits, the total payload is 201. Without header compression, the overhead will be about 360 bits, which is more than the payload, reducing the spectral efficiency significantly. If the header compression is designed in such a way that all overhead including IP overhead can be fit into 55 bits, the encoder packet size will be 256 bits, resulting in an overhead of only 33% when compared with circuit-switched voice frame.
C. End-to-End QoS Support
To ensure end-to-end QoS in a packet-switched network, a series of features have been defined in DOrA to meet the need for different applications. Main QoS support can be categorized into two categories: signaling mechanisms  to support the call features similar to the current cellular voice calls, and trans-port mechanisms  to meet comparable quality of the cellular
voices. The end-to-end QoS framework for DOrA is currently under standardization by 3GPP2. In this paper, we assume that the QoS architecture and mechanism ,  are in place to differentiate the VoIP flow from other applications.
D. FL Multiple Packet Size Support
In DOr0 , each requested data rate is associated with only one packet size. When the sector schedules a packet, it must transmit the specific packet size corresponding to the mobile requested data rate regardless of the data backlog situation. As-sume that a mobile is at a good RF location and indicates that a high data rate corresponding to 4096-bit packet size can be used. Since VoIP is a low data rate service, only a few hun-dred bits will be in the buffer at each transmission. In this case, the scheduler will have to pad the rest frame till 4096 bits. This mismatch causes inefficient use of FL RF resource. DOrA has the improvement by defining multiple packet sizes associated with each requested data rate. The shorter packets have stronger channel coding structure and, hence, better performance. The sector has the flexibility to choose a packet size to match the data backlog and reduce the packet transmission delay.
E. FL Packets Multiplexing From Multiple Users
The DOr0 system is optimized to support a small number of users downloading large amounts of data from the network. For VoIP, this assumption is violated since there might be many users transmitting at a very low data rate. For example, an EVRC speech coder generates 50 packets/s, and there are a total of 600 time slots per second on the FL. Therefore, each sector can only support less than 12 VoIP users if each voice packet needs to be transmitted within a 20 ms delay.
To overcome the shortage of time slots, DOrA introduces a multiuser packet (MUP) feature that allows packets from up to eight users to be packed into a single physical-layer packet. The decision to transmit a MUP is dynamically made by the sector on a packet-by-packet basis. Considering a VoIP packet, a MUP packet of 1024 bits can practically accommodate up to four-user packets, while MUP of larger sizes can support a maximum of eight packets. Since certain data rates only support MUP packets up to 1024 bits, it presents a practical limit of four-user MUP packets capacity. In addition, a user’s data rate needs to be at least 153.6 kb/s to qualify for any MUP operation. At lower rate, only a single-user packet (SUP) can be transmitted.
F. RL HARQ OPERATION
One of the major improvements of DOrA over DOr0 is the adoption of the hybrid ARQ feature on the RL. Each subframe (SF) spans four time slots, and is associated with an interlace channel index of 1, 2, and 3. The maximum number of allowed SF transmissions is four SFs per packet. To minimize delay for the VoIP application however, each VoIP packet needs to com-plete its transmission within three SF transmissions. This is be-cause there may be three voice frames arriving every 60 ms from the EVRC source and there are nine SF in the same time period. If each packet requires no more than three SF to complete, these three voice frames can be completed in nine SF. Otherwise, the
voice frame following these three voice frames will incur addi-tional queuing delay.
G. RL Resource Management Control
The RL link of the DOrA system is designed to tolerate a given received interference power level. The interference power is measured by the ratio of total received power to thermal noise power, and is referred to as rise-over-thermal (RoT). Gener-ally, a system operated at a higher RoT will have higher RL capacity; however, the tradeoffs in operating at a high RoT are higher user transmit power and a greater chance for power con-trol instability.
III. VOIP PERFORMANCECRITERION AND
To study the delay and capacity performance of VoIP using DOrA, theoretical analysis and computer simulations were car-ried out. In this section, we summarize the assumptions and the system model used for the VoIP performance investigation.
A. VoIP Performance Criteria
For voice service, the acceptable delay guideline has been studied extensively and been summarized in the G.114 Stan-dard. At this point, it is not yet clear what guideline will be appropriate for the VoIP latency bound. The complication stems from the fact that VoIP packet delay varies on a packet basis, whereas the guideline from G.114 is obtained with fixed end-to-end delay. To proceed with this study, we assume that the frame erasure rate (FER) due to packet loss and packet delay exceeding the target latency bound be kept within 2%. Further, at least 98% of VoIP users in the network should meet the above criterion, and the interference level in terms of RoT should be kept below a given threshold.
B. Simulation Setup
A computer simulation was carried out to verify the theoret-ical analysis and provide more comprehensive and detailed in-formation regarding VoIP performance and system capacity. In each run of the simulation, a certain number of mobiles are ran-domly placed into the network with uniform area distribution. The number of users per sector is, thus, a Poisson random vari-able. Erlang capacity for VoIP is defined as the average number of users per sector. The values of the system parameters used in the simulations follow the standard recommendation .
IV. ANALYSIS OFVOIPONFORWARDLINK(FL)
A. Analytical Model
In this section, we utilized the field measurements of the ex-isting DOr0 system as reported in  to estimate VoIP capacity. A simplified first-in–first-out (FIFO) scheduling model with ex-tended bulk-service was proposed to analyze the delay and ca-pacity performance on the FL. The FIFO scheduler was selected to provide consistent delay and jitter performance for all VoIP users across the RF conditions. As shown in Fig. 1, a Poisson arrival with rate represents the aggregate traffic destined for all VoIP users in the sector.
Fig. 1. FIFO queueing model for FL scheduler.
The arrival consists of three types of users based on different RF conditions of the users.
• Type 1 represents traffic from users that do not qualify for MUP operation, but multiple VoIP packets from the same user can be transmitted in the same packet. To take this into account, the analysis makes a minor modification on the FIFO scheduler assumption. If there are multiple VoIP packets destined to the same mobile in the queue, the scheduler will transmit up to four packets together. Defining as the average number of VoIP packets con-tained in a Type 1 transmission, the effective traffic arrival rate of Type 1 traffic to the scheduler is reduced by times. The estimation of will be given later in (14). • Type 2 represents traffic from users that qualify for the
MUP operation with up to four users per packet.
• Type 3 represents traffic from users that qualify for the MUP operation with up to eight users per packet. Denoting as the probability of Type traffic with
, the arrival rate for Type traffic can be written as . As explained above, the effective traffic arrival rate
to the scheduler is . The normalized
probability for the effective traffic arrival to the scheduler is
The bulk server serves the users in FIFO fashion, and the service rate depends on the type and number of users in the queue. The rule is defined as follows:
• when serving a SUP;
• , , when serving a MUP containing
type 2 or type 3 users;
• , , when serving a MUP containing
type 3 users.
In the following, we discuss the estimation of parameters used in the model.
• The aggregate VoIP packet arrival rate
(packets/slot), where 43% is the voice packet activity factor with silence suppression, 50 is the number of voice frames per second from the EVRC vocoder, 600 is the number of slots per second in DOrA, as defined in the standards , and represents the average offered voice traffic load in the sector in Erlangs. Defining as the
probability for the data rate control DRC index , , it is easy to see
(2) according to [13, Table 220.127.116.11–2]. For the analysis we used the field measurements of provided in . A simplified assumption is made that user requested DRC rate does not change over time, so the user in poor RF condition never recovers. This assumption is likely to lead to conservative capacity projections, as the actual user’s RF condition is dynamic and DRC rates adapt to the RF conditions.
• The bulk-service rates , , can be written as , where represents the control channel overhead on the DOrA FL. 10% is assumed in the analysis. is the average packet transmission length in slots when users are contained in the packet and can be evaluated based on the service rule described above. • The scheduler transmits a SUP if any of the following
conditions are satisfied.
— There is only one user in the queue. In this case
for state (3)
where and are the average packet transmis-sion length when the packet contains Type 1 traffic and Type 2 or 3 traffic, respectively.
— There are multiple users in the queue, but the head-of-line (HoL) user is a Type 1 user, or the user next to the HoL is a Type 1 user. In this case
for state (4)
and can be evaluated from field measurement on packet transmission performance
(5) where is the probability that a SUP of DRC index is transmitted successfully in slots. is the number of nominal slots defined by the standard  for DRC index . Table I shows some DOr0 field measurements on that is used for this analysis. Note that some data rates are defined only in one slot and are not shown in the table.
• The average packet transmission performance for a MUP depends on both the number of users included in the trans-mission and the RF conditions of these users, since a MUP transmission can only be terminated when positive ac-knowledgment (ACK) for all users targeted in the
trans-Fig. 2. State transitions from and to statemof continuous time Markov chain. TABLE I
DOR0 FIELDMEASUREMENT ONfb (k)g
mission are received, or the full HARQ process has been exhausted. It can be shown that
is the cumulative distribution function (CDF) of the packet transmission length when the MUP contains user packets. is the nominal slot of the MUP.
is the modified probability mass function (PMF) of packet transmission length of DRC index , taking into account of the effect of the missed ACK for the MUP transmission
where is the ACK miss rate during the MUP transmis-sion. is assumed in the study.
B. VoIP Performance Analysis
We now evaluate the FL VoIP user performance. Denoting the number of users in the scheduler queue as , the behavior
TABLE II VALUES OFfh g
of is governed by the continuous-time Markov state transition shown in Fig. 2.
Several assumptions are made to construct the Markov model. • The discrete slot-based packet scheduling operation is
ap-proximated in the continuous time domain.
• The distribution of the type of users in the queue is depen-dent on scheduling decisions.
Defining the PMF of as , it satisfies the following equations:
where is the conditional probability that the scheduler serves users when there are users in the queue and takes its value from Table II. For example, if the scheduler serves five users when there are more than five users in the queue, it means the five HoL users are all Type 3 users, while the sixth user is
not. Hence, for .
Denoting the user waiting time as , its distribution can be evaluated from
where . and
are the Laplace transform of and ,
respectively. represents the Laplace
transform of bulk-service time distribution when users are served together. The system utilization is then the server busy probability
(11) Meanwhile, the average number of VoIP packets that are packed into a Type 1 packet need to be estimated to facilitate the analysis. As previously described, can be approximated as
where is the number of VoIP packets in the queue belonging to the same Type 1 mobile when that mobile is scheduled. As-suming the individual user packet arrival can be approximated in the Poisson process, the distribution of given a total of user packets in the queue is then
(13) Combining (11)–(13), can be evaluated as
C. System Delay Bound Evaluation
Based on the capacity criterion defined in Section III-A, we derive the system delay bound for a target FER covering a certain percentage of the VoIP users in the network. In the DOrA system, the target packet error rate (PER) is 10 on FL packet transmissions. Fast physical-layer retransmission mech-anisms can be employed to further reduce the net PER to below 10 . As a result, the contribution of packet loss to voice frame erasures is negligible. Instead, the primary contributor to voice frame erasures is the excessive packet delay beyond the system delay bound.
The FL packet latency consists of two parts.
• The queueing delay at the scheduler under a certain VoIP traffic load. With the FIFO scheduler, the packet waiting time experienced by different users is similar regardless of their RF conditions, and the distribution is given by (10). • The packet transmission latency over the air. It depends on the packet transmission rate, hence, the RF condition of the user. The users in the worst RF conditions experience the worst packet latency performance.
We will mark the user who has the worst performance out of -percent of the total users in the network as . With the sim-plified assumption that a user’s RF condition does not change, the packet transmission rate index for user can be identified as
The distribution of the packet transmission duration (in slot units) in the interlaced structure can be obtained as
where . Combining (10) and (15), the FL packet latency distribution for user can be obtained as
Fig. 3. FL delay bound for 98% of users achieving less than 2% FER.
Fig. 4. Average number of users packed in a physical packet.
where and is the Laplace transform of and , respectively. The system delay bound is identified as
D. Analysis and Simulation Results
Fig. 3 shows the VoIP capacity estimate from the analysis and simulation under the quality criterion of 2% and capacity criterion of 98%. It can be seen that VoIP capacity gain in voice loading diminishes with higher delay-bound due to the nonlinear increase of the queueing delay. From the curve, it is estimated that about 35 Erlangs can be supported with a 70 ms delay bound on the FL if the handsets are equipped with two receiving antennae.
Fig. 4 shows the MUP efficiency in terms of the average number of VoIP packets contained in one physical-layer packet. As the key technique of VoIP transmission in the DOrA air in-terface, it is clear from the figure that MUP operation plays a critical role in absorbing the loading with more VoIP traffic.
In Fig. 5, the lower curve shows the sector throughput when only voice bits are included. The middle curve shows the sector throughput when voice bits, the overhead bits and the padding bits are included. The upper curve shows the sector throughput
Fig. 5. Aggregate sector throughput performance.
when all bits are included and all time slots are fully utilized. From Fig. 4, the VoIP throughput efficiency is rather poor, since a significant gap exits between the lower and the middle curves. This implies that the packets are often filled with significant number of padding bits. This situation can be improved when the system supports both VoIP traffic and best effort data. In this case, padding bits can be replaced by the best effort data bits, thus increasing the RF packing efficiency and application throughput.
Further observation can be made that there is significant reduction in sector throughput with VoIP throughput when compared with the best effort packet data throughput. In , about 1.1 Mb/s sector throughput can be achieved when sup-porting best-effort data services. When supsup-porting VoIP, the sector throughput decreases to less than 500 kb/s. The loss is due to scheduling the delay-sensitive VoIP packets. For best effort data packets, the scheduler is able to optimize the sector throughput based on users RF condition. However, in the case of VoIP, the scheduler is forced to serve users in poor RF conditions under time constraint to meet the stringent delay requirement, hence, the reduction in the sector throughput.
V. ANALYSIS OFVOIPONREVERSELINK
A. Theoretical Analysis for VoIP Capacity
For VoIP services on the RL, each user is assigned its own high-speed data traffic channel for the entire call. Therefore, the traditional queueing analysis still applies to the evaluation of the Erlang capacity of the system. We expand the Erlang capacity analysis for the circuit-switched voice in a code-division mul-tiple-access (CDMA) system – and further enhance the analysis by considering the effect of HARQ on system capacity. The Erlang capacity is constraint of the system outage probability. The system outage probability is defined as the probability that the rise over thermal RoT exceeds a certain threshold. The RoT is often expressed as , where is the so-called loading of the sector. By further derivation and decomposition, the sector loading can be expressed as
In this expression, is the number of active users in the current sector, is the other cell interference factor, is the total bandwidth (in Hertz), is the required bit energy to interference-plus-noise ratio for user , is the bit rate, is the percentage of chip energy from the overhead channels relative to the chip energy from the traffic channel, is the percentage of the chip energy from the control channel relative to the chip energy from the traffic channel, and is a binary random variable indicating whether user is transmitting data traffic or not. The system outage probability is, thus
(20) where is typically between 0.1–0.25, which corresponds to a RoT of 6–10 dB. In this expression, the number of users is a Poisson random variable with a mean of based on the lost call held (LCH) model , where is the average call arrival rate and is the average call holding time. is a bi-nary random variable depending on the traffic activity factor. The required bit energy to interference-plus-ratio is a random variable, depending on the coding and modulation of the encoder packet, the target PER, and the number of trans-missions introduced by the HARQ operation. The bit rate is also a random variable depending on the rate control algorithm. The objective is to estimate the Erlang capacity that the system can provide under the outage probability.
B. Calculation of the System Outage Probability
From (20), the system outage probability can be written as
Let denote the maximum number of transmissions for an encoder packet. The required to meet a target packet error rate (PER) depends on the coding and modulation scheme, and the number of transmissions. For a particular encoder packet with corresponding rate , the will take a finite number of discrete values with probability
and , where , , is the
prob-ability of an encoder packet transmitted in subpacket, and is the required for the encoder packet after transmissions to meet the target PER.
The transmission rate is also a random variable. Let de-note the size of the transmission rate set. The probability of is (23)
and , where , is the proba-bility of a user choosing rate .
Therefore, the probability distribution function (PDF) of will take an number of discrete values as
(24) Since the required for an encoder packet is usually represented by the aggregated value over the total number of transmissions, the average per subpacket can be com-puted as the aggregated divided by the total number of transmissions. Thus, (24) becomes
The Activity Factor: The activity of the channel depends on two factors: the on–off activity of the data traffic source, and the scheduling of the packet transmission over the packet data channel. Let denote the probability that the source is gen-erating a traffic encoder packet, and denote the probability that a subpacket of the encoder packet is being transmitted over the channel. depends on the number of subpacket transmis-sions for the encoder packet. It equals the ratio of the number of transmissions to the maximum number of transmissions per encoder packet . The conditional probability of given the number of transmissions is
The Outage Probability: When the number of users is large, the left side of the inequality (21) or the loading can be approximated as a Gaussian random variable with mean and variance . Assuming , , and are independent random variables, the mean is
(27) The variance is derived as
PARAMETERS FORVOIP ERLANGCAPACITYANALYSIS
PROBABILITIES OFNUMBER OFTRANSMISSIONS
where is the mean of the number of users per sector. For simplicity we consider the average other cell interference level, which is modeled as a ratio of the average number of users in the current sector. In other words, the other cell interference is assumed to be in the variance calculation.
The outage probability is given as
(29) where is the error function; and are given by (27) and (28), respectively.
C. Erlang Capacity for VoIP in DOrA
We apply the analytical model as described in the previous section and evaluate the Erlang capacity for VoIP in DOrA. The parameters used for VoIP Erlang capacity analysis are summa-rized in Table III.
From the link level simulations with 1% target PER after a maximum of three transmissions, the probabilities of the number of transmissions and corresponding are shown in Table IV.
Pilot, DRC, RRI, and DSC are overhead channels, which are always active when the user is admitted to the system. With the traffic to pilot ratio values in Table III, the overhead percentage of the traffic channel is, thus, 0%, 36.45% for the 256-bit EP size.
D. Comparison of Analytical Results and Simulations
Using the above parameters, we compute the RoT distribution under different Erlang capacity situations as shown in Fig. 6. The simulation results are also shown in the figure. We can see that the analytical results match the simulation results very well in terms of RoT distribution with different loadings. To meet the outage probability of 1% at a RoT threshold of 7 dB, 35 Erlang voice capacity can be supported.
E. Delay Analysis
Depending on the RF conditions, each packet may require one to four SF to complete using HARQ. The number of SF or equivalently, the transmission time for each packet, can be described by a PMF as
Fig. 6. Comparison of analysis and simulation results of RL RoT.
where is the time needed for SF transmissions. They are 6.7, 26.7, 46.7, and 66.7 ms, respectively. Further, is the proba-bility for each SF due to HARQ.
In addition to the delays due to multiple SF transmissions using HARQ, delays may occur when the voice frame arrives at a time not necessarily at the boundary between two SFs, and when the next SF following the packet’s arrival is not avail-able. Let us define the time interval between the voice frame arrival and the boundary of the available SF as the time align-ment delay . In this paper, we assume that the time alignment delay within a SF follows a uniform distribution.
Without losing generality, let us assume that a voice frame arrives in a middle of a SF. We denote the next three SFs after the voice frame arrival as a SF with interlace index 1, 2, and 3. To reduce transmission delay, we adopt the greedy algorithm by which each voice frame is placed into the next available SF. Now, let us define four mutually disjointed events and probabil-ities by considering the three SFs following the arrival of a voice frame. Let be the probability that the voice frame is placed into SF 1. The subscript indicates the fact that the delay due to the unavailability of the SF is zero. Let , , 2, and 3 be the probability that the voice frame is placed into the SF . In this case, we denote as the probability that all the SF’s with index 1, 2, and 3 are not available. Precise analysis of the above probabilities is very involved and is beyond the scope of this paper. For this reason, we simplify the analysis by assuming that that the probability of transmitting the fourth SF using HARQ is negligible . Under this condition, . To obtain we further assume that the system is in an equilibrium state in which the probability that the SF 1 is available is approxi-mately identical for every three SF periods, although there may be some small variations in reality. We obtain
(31) To obtain , we observe that the voice frame will be placed into SF3 only when SF1 and SF2 are both not available. This probability is given by
Also, since , we have
(33) In the above, we have provided the delay probability due to unavailability of the SF. The probability density function of the alignment delay is clearly the combination of this delay and the delay due to arrival in the middle of a SF, which can be written as
(34) where is defined as the gate function that is equal to 1 in the interval of 0 and 20 ms, and 0 elsewhere.
The VoIP packet transmission delay distribution can be ap-proximated by the summation of the time alignment delay and the delay due to the multiple SF transmissions of HARQ as given in (30). Assuming that these two delays are statistically independent, the PDF for the packet transmission delay for the RL can be obtained from
F. Computer Simulations
As discussed in previous sections, the EP size of 256 bits is mostly used to accommodate the small voice frame. To mini-mize the transmission delay, power control and traffic channel power to the pilot (T2P) power ratio is set in such a way that al-most all packets complete successful transmission in three SFs. By aggregating packets from all users in the system, it is ob-served that the probabilities that a packet completes transmis-sion in 1, 2, and 3 SF are 0.41, 0.48, and 0.11, respectively. The probability that a packet needs four SFs is negligible. Sub-stituting these values in (30) and computing (35), the PDF for packet transmission delay of a typical user is plotted in Fig. 7. Shown in Fig. 7 are also the simulation results for delays of all packets in the system when the Erlang capacity in each sector is 26 and 35, respectively. As can be seen from the figure, the the-oretical analysis for the delay matches that from the simulations very well. As long as the T2P values can be controlled so that the probability of the fourth SF transmission is negligible, al-most all packets in the system can be limited to within 66.7 ms, which includes the 60 ms HARQ delay and the 6.7 ms voice frame alignment delay.
For commercial services, however, it is usually important to ensure the voice quality of each user, not the PER of all packets in the system. Consider users whose PER is below a given threshold, say 1%. One useful criterion is to require percent of users in the system to meet the above criterion. A typical value for , for instance is 98%. Through simulations, the PDF of users that can meet 1% FER is shown in Fig. 8 for two system loadings. From the figure with a delay limit of 70 ms, 98% of users can meet the performance criteria under the two Erlang capacity values.
By combining Figs. 7 and 8 using the 35 Erlang curves, we obtain Fig. 9 in which the axis represents the percentage of
Fig. 7. Packet delay distribution comparisons of simulation and analysis.
Fig. 9. Percentage of users not meeting 2% FER versus average FER.
Fig. 8. User delay distribution with different Erlang loading.
total frame erasures in the system and axis represents the per-centage of users not meeting the 2% FER. From Fig. 9, it can be seen that the percentage of users not meeting the 2% FER can be approximated by a linear function of the system packet delay given in (35). The slope of the linear function is approxi-mately 12 using the best-fit technique and the intercept is about 0. For example, about 60% users will not be able to meeting the
2% FER in the system if the average FER of the system is about 5%.
In this paper, we evaluated the feasibility of supporting VoIP service using the 1 EV-DO Revision A system. We proposed an analytical model and carried out computer simulations to study the possible capacity and delay performance. Based on the analysis and simulations, the support of VoIP using 1 EV-DO Rev. A appears technically attractive. The expected Erlang ca-pacity is estimated to be comparable to that of a circuit switched CDMA2000 system.
The authors would like to thank S. Vitebsky, A. Stoylar, X. Wang, and the reviewers for their helpful discussions and comments.
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Qi Bi(SM’92) received the B.S. and M.S. degrees from Shanghai Jiao Tong University, Shanghai, China, and the Ph.D. from Pennsylvania State University, University Park.
He is a Bell Laboratories Fellow in the Mobility Solutions Unit, Lucent Technologies, Whippany, NJ. He currently heads a team with responsibilities of analyzing and designing the third-generation wireless digital communication systems. He served as the Guest Editor ofWireless Communications and Mobile Computing(Wiley). He is also a recognized leader outside of Lucent Technologies and has served as technical chair in many international conferences. He holds more than 40 U.S. patents. His present focus is in the areas of high-speed wireless data network delivering VoIP, broadcast and multicast services, push to talk, and broadband wireless communications.
Dr. Bi was the recipient of numerous honors including the Advanced Tech-nology Laboratory Award in 1995 and 1996, the Bell Laboratories President’s Gold Award in 2000 and 2002, The Bell Laboratories Innovation Team Award in 2003, the Speaker of the Year Award from the IEEE New Jersey Coast Sec-tion in 2004, and the Asian American Engineer of the Year Award in 2005. He has served as the Technical Vice-Chair of the IEEE Wireless Communica-tions and Network Conference 2003, Technical Chair for Wireless Symposium of the IEEE GLOBECOM 2000–2002, and organizer of the First and Second Lucent IS-95 and UMTS Technical Conference in 1999 and 2000. He served as Feature Editor of theIEEE Communications Magazine(2001), Editor of the IEEE JOURNAL ONSELECTEDAREAS INCOMMUNICATIONSand the IEEE TRANSACTION ONWIRELESSCOMMUNICATIONS.
Pi-Chun Chenreceived the M.S. and Ph.D. degrees in electrical engineering from the Wireless Informa-tion Laboratory (WINLAB), Rutgers University, Pis-cataway, NJ, in 1996 and 1999, respectively.
Upon her graduation, she joined Applied Re-search, Telcordia Technologies, as a Research Scientist conducting research in the area of advanced wireless technology analysis. Her work included the packet data protocol design to provide the evolution path of the Personal Access Communi-cations Systems (PACS) to 3G system and network deployment tools’ algorithm. Since November 2000, she has been Member of Technical Staff at Bell Laboratories, Lucent Technologies, Whippany, NJ. She is responsible for system level performance analysis and algorithm design for 3G communication systems. Her research interests include radio resource management for QoS services, power control, handoff, random access performance, admission, and congestion control.
Yang Yangreceived the B.S. degree from the Uni-versity of Science and Technology of China, Hefei, China, in 1994, and the M.E. and Ph.D. degrees from Stevens Institute of Technology, Hoboken, NJ, in 1997 and 1999, respectively, all in electrical engineering.
Since 1999, she has been with Lucent Tech-nologies, Whippany, NJ, as a System Engineer. Her current research interests and activities are focused on the performance analysis of CDMA2000 and 12EV-DO systems, algorithm designs for air interface control and optimization, traffic modeling, and engineering of the wireless networks.
Qinqing Zhang(S’95–M’98–SM’03) received the B.S. and M.S.E. degrees in electronics engineering from Tsinghua University, Beijing, China, and the M.S. and Ph.D. degrees in electrical engineering from the University of Pennsylvania, Philadelphia.
She is Member of Technical Staff at Bell Labo-ratories, Lucent Technologies, Whippany, NJ. Since joining Bell Laboratories in 1998, she has been working on the design and performance analysis of wireline and wireless communication systems and networks, radio resource management, algorithms and protocol designs, and traffic engineering. She is also an Adjunct Assistant Professor in the Department of Electrical and System Engineering, University of Pennsylvania. She is the coauthor ofDesign and Performance of 3G Wireless Networks and Wireless LANs(Springer, 2005). She has published numerous papers in IEEE journals and conferences. She has been awarded 6 patents and has 14 patent applications pending.
Dr. Zhang serves on the Editorial Board of the IEEE TRANSACTIONS ON
WIRELESSCOMMUNICATIONS and on the technical program committees of various IEEE conferences.