Such services until now have been optimized for voice tele- phony which requires .... helps in reducing the overhead for re-establishment every time there is data ...
Optimal MAC State Switching for cdma2000 Networks Mainak Chatterjee and Sajal K. Das Center for Research in Wireless Mobility and Networking (CReWMaN) Department of Computer Science and Engineering University of Texas at Arlington Arlington, TX 76019-0015 E-mail: chat,das @cse.uta.edu
Abstract— This paper deals with the performance modeling of the various MAC states as defined by the cdma2000 protocol. Our method uses a composite performance metric which has the capability of proportionally combining three basic parameters: channel utilization, waiting time and the saving in the signalling overhead. The scheduler at the base station is not only responsible for admitting new services into the system but also for switching the MAC states of a service depending on its activity. Since the true nature of the wireless data traffic is yet unknown, we use a mix of Poisson-distributed voice packets and Pareto-distributed data packets. We derive analytical expressions and also conduct simulation experiments to study the nature of the performance curve and thus compute the optimal values of expiration timers at which the MAC states should be switched such that the system performance is maximized. We show how our model can be made suitable for different systems by tuning the scaling functions (or weights) for each of the three performance parameters considered.
I. I NTRODUCTION The dominant revenue earner in wireless communications today are still voice services. However, there has been an ever growing demand for multimedia services such as data, fax, video and so on, thus making data service a tremendous potential for revenue for the operators in near future. Computing devices are no longer limited to desktop computers and laptop devices. Palm-top computers and mobile phones with data compatibilities are becoming more and more pervasive. There is also a great deal of technology “pull” fueling the explosion around wireless data. Two types of services, voice and data, are associated with wireless data. However, there are some fundamental differences between voice and data services. Voice services are delaysensitive and aim to provide equal service to all the users, regardless of their location in the cell for a cellular architecture. These features result in power-sharing schemes, where mobiles receiving weaker signal are allocated more power than those receiving stronger signal to yield an optimal solution for voice. Such services until now have been optimized for voice telephony which requires continuous bit-stream type service with no delays. Data, on the other, hand works well with discontinuous packetized transmission and can tolerate more delay. A relatively modest data rate is sufficient for high-quality voice This work is supported by Texas Advanced Research Program grant TARP003594-013, Texas Telecommunications Engineering Consortium (TxTEC) and by Nokia Research Center, Irving, Texas.
service and voice users cannot substantially benefit from higher data rates. Packet data systems, on the other hand, are aimed at maximizing the throughput. Given that different data users have different data rate requirements, the goal is no longer to serve everyone with equal power and equal grade of service. Rather, the goal is to allocate users the maximum data rate that each can accept based on the application needs and wireless channel conditions. Efficient frequency reuse-ability among neighboring cells has made CDMA one of the best suited technologies for mobile communication networks [9]. The requirements for future multimedia services include higher capacities, increased spectral efficiency, higher speeds and differentiated services. Flexibility for cdma2000 extends to the spectrum bands that can be used for its deployment. It is also compatible with the IMT2000 spectrum bands [3], so operators acquiring new spectrum will be able to experience the benefits of cdma2000 as well. In this paper, we model the performance of layer-2 of the protocol stack in a cdma2000 standard based on a composite performance metric. The MAC states and their transitions are studied along with the activity timers for each state. Based on the activity of a service, its MAC state is made to switch from one to another. Three basic system parameters are considered viz, channel utilization, waiting time and the saving in the overhead due to non-switching of states. A composite metric for the system performance is then formalized by combining these basic parameters with a goal to derive the optimal expiration time for the active state. We study the performance of cdma2000 networks serving both voice and data applications. For modeling voice services, we use Poisson distribution and hence the inter-arrival time for the packets is exponentially distributed. The session duration as well as the inter-arrival time for packets of data services are modeled using Pareto distribution [13]. We conduct simulation experiments in which a scheduler caters to the need of multiple users. While admitting a service, the scheduler not only considers the promised load of all the ongoing services but also the actual load being offered at that time by all the on-going services. We derive the nature of the composite performance curve with respect to the expiration timer and also obtain the optimal expiration timer which ultimately helps in maximizing the system performance. We show how our model can be made suitable for different systems by tuning the scaling functions (i.e., weights) for each of the three basic system parameters considered.
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The rest of the paper is organized as follows. Section II discusses the capabilities of cdma2000 for multimedia services. Section III discusses the transitions between the MAC states. Section IV deals with the performance metric along with the wireless traffic model. The analytical expressions for the three system parameters are derived. Section V presents the simulation results to validate our model. Conclusions are offered in the last section. II. C APABILITIES OF
cdma2000 has emerged as one of the strongest contenders for wireless data networking [12]. IS-95 based CDMA systems can support data rates up to 14.4 Kbps. IS-95B supports data rates kbps across seven supplemental code chanof nels on the downlink. cdma2000 is an evolution from IS-95B provides next-generation capacity while maintaining backward compatibility [14]. cdma2000 includes sophisticated MAC features which can concurrently support multiple data and voice services, thus effectively supporting very high data rates (up to 614kbps). cdma2000 is much more enhanced than IS-95-B and allows 3G deployment while maintaining the current 2G support for IS-95 in the spectrum an operator has today. It supports multiple simultaneous services (voice/packet data/circuit data) by providing different data rates and a sophisticated multimedia QoS control.
the design of the MAC layer so that a large number of users can be supported with the same amount of resources. Due to resource constraints, channels are only allocated on demand and released during periods of inactivity. The negotiations for reestablishment of the dedicated channels incur some latency and increases signalling overhead. The base station keeps the state information (which is done at the service initialization) of the on-going services using the dedicated MAC channel and this helps in reducing the overhead for re-establishment every time there is data exchange.
A. The MAC Layer The medium access control (MAC) layer of cdma2000 provides extensive enhancements to negotiate and support concurrent services [8]. It also manages QoS trade-offs between services already admitted into the system. The design is centered on optimizing packet data services. It decouples data services from voice service, based on the recognition that the two services have fundamentally different requirements. Layer 1 (physical layer) of cdma2000 provides the radio interface which delivers the data streams of the multiple concurrent services. It supports multiple supplementary channels which can specifically cater to the individual service requirements. For example, one channel can carry packet data which can tolerate more delay and channels errors, whereas another channel can carry circuit data which is more sensitive to delay and channel errors. The MAC layer manages the resources that are available at the physical layer and coordinates the usage of the channels by allocating, reallocating or deallocating the codes. It also deals with the contention issues amongst the contending nodes.
III. MAC S TATE T RANSITIONS The MAC states as defined in cdma2000 [7] are shown in Figure 1. It can be noted that some of the states in the current standard have been renamed since they were overlapping with functionality of the data services standard IS-707, and possibly going beyond the scope of a standards document by prescribing implementation. However, the basic philosophy for the multiple states remains the same. The four MAC states can be categorized into two groups depending on the data service, which can be active or inactive. A data service is connected to the base station in the active, control hold and suspended states but not in the null state. Transitions between these MAC states are controlled by the expiration timers. By setting these timers to certain values, cdma2000 MAC can adapt to multiple data services with various resource requirement profiles. A node before the start of any service is in the null state and thus in no way connected to the base station. It only monitors the power level of some control signals through some control channels. When a node wants to initiate a session for a particular service, it sends a request to the scheduler at the base station for the allocation of a dedicated MAC channel to switch to the control-hold state. This request contains all the information about the service to be initiated. After acquiring the MAC channel, the node goes into the control-hold state, the service is established, and the MAC channel is retained till the end of the service. It also maintains the session status and registers the data activity. However, being in control-hold state, it cannot transmit any data since the MAC channel is just a control channel, though sometimes short messaging can be done on the control channel. ACTIVE
Dedicated Traffic Channels
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B. Support for Data Services cdma2000 offers two types of data services – packet data and circuit data. In packet data services, channels are multiplexed among different users whereas in circuit data channels are dedicated to a particular service to support high speed data exchange. Since most of the data services exhibit very bursty traffic characteristics, in this paper we concentrate on the packet data option. The bursty traffic is also accompanied by periods of inactivity. These properties are taken into consideration in
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Fig. 1. Various MAC states of cdma2000
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Transitions between the MAC states are based on some timer functions and the activity. By choosing proper timer values, the scheduler can switch the MAC states to accommodate more data services. These timer values can be made dynamic which will depend on the specific service and the system load. When a data burst arrives it must acquire some traffic channels, the number of which depends on the bit rate requirement of that session. The scheduler finds out whether it can allocate the required number of traffic channels. If so, the service goes into the active state where it starts transmitting data. The service remains in the active state till it has transmitted the data burst. Now the question arises whether the service should release the dedicated traffic channels immediately or hold them for a certain amount of time even after the burst has been transmitted. But the question arises, how do we decide on the time, rep, for which the state should be active for resented by , then the that service? If there is a data burst within same traffic channels are allocated. If there is no activity within then the traffic channels are released and the service goes back to the control-hold state. Note that it still holds the MAC channel. This situation is illustrated in the example shown in Figure 2. In case 1, the second burst does not arrive after the first burst departs. Whereas in case 2, within and does not contends the second burst arrives within for the traffic channels.
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the suspended state then the node has to first get a MAC channel and follow the same procedure. From the suspended state, the node can go into the null state if there is no activity for the . We do not consider the option where a node duration can directly go into the active state from suspended state. In our modeling, we are not interested in the suspended state because and thus we assume that a service will not be idle for time will not go into the suspended state. There can be two reasons for a service being denied admission into the system. First, there might not be any MAC channel available which can be dedicated for that service. Second, the controller at the base station finds that the system load is already high and the inclusion of another service might result in the SNR (signal to noise ration) dropping below a certain threshold. CDMA systems do not give a hard threshold but gracefully degrade the performance of all on-going services if the SNR decreases [16].
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IV. P ERFORMANCE M ODELING The performance of a MAC protocol is usually measured in terms of throughput and waiting time. Throughput can be defined in various ways. We consider the channel utilization ( ) as the measure of throughput of the system. The waiting time ( ) is defined as the average waiting time of the bursts before they acquire the traffic channels. Another parameter which needs to be taken into consideration is the saving in signalling overhead ( ) prior to possibly every data exchange. Particularly for cdma2000, the overhead of re-establishing the dedicated traffic channels can be minimized by holding the dedicated traffic channels even after a burst has been transmitted. The dedicated traffic channels are held for an extra period of . If a burst arrives within that time, the same traftime fic channels are allocated and the switching overhead is saved. , then there is If a burst arrives after the expiration of would result in the usual signalling overhead. A large savings in the signalling overhead but at the same time underutilize the channel capacity and hence increase the waiting time. needs to be found for which the So an optimal value of overall system performance will be maximized. , and are All the above three system parameters , . Our objective is to maximize the chanfunctions of nel utilization, maximize the saving in signalling overhead and minimize the waiting time. We define the overall system performance as a composite metric which is an additive function . of the three basic parameters. We observe that The signalling overhead can be defined in such a way that . So, we define S as the fraction of the bursts arriving within . It also represents the probability that the arriving burst finds that traffic channels are being held. Thus, U and S can be bounded but W can be unbounded in general. To deal with the unbounded waiting time, we observe that the expected waiting time is at least half the frame time because any arriving burst has to wait at least till the beginning of the next frame. We therefore define a function which is the reciprocal of the waiting time and is bounded within the same interval as the other two parameters. This function could be written as such that . =
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The performance optimization problem deals with the switching between the control-hold state and active states. The cost factor associated with it is the overhead due to this switching. If the cost is low then it is affordable to switch frequently between states, otherwise it might be desirable to remain in the active state in anticipation of new bursts and hold the traffic channels even if there is no data to be transmitted at this time. However, this results in wastage of resources that could have been used by other services. On the other hand, if the traffic channels are released immediately after the transmission of the burst, then it has to contend for the traffic channels every time there is a burst which might incur additional delay. Similar to traffic channels, the MAC channel can also be re, leased if there is no activity for a long period of time, say and the node can go into the suspended state as shown in Figure 1. The released MAC channel can be used to admit another service if the system load permits. In the suspended state the requirement profile is still maintained. If there is a burst from
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Optimizing these parameters individually would result in three different (perhaps conflicting) operating points. A better optimization function can be derived by combining all these three parameters. A first approximation of our objective is to L
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which is to be maximized. For deriving analytical expressions for , , and and the optimal , one must consider the type of traffic which is supported by the system, i.e., both voice and data traffic. =
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interval to see the effect of any long range dependencies. It might even be difficult to find any kind of correlation among the data pattern within that interval of time. B. Composite Nature It is our belief that wireless data would not strictly follow Poisson or Pareto distribution, but will have components from both. Now the question arises about the percentage distribution of voice and data traffic. It has been observed that the number of data users has increased steadily in the last couple of years. It might then be appropriate to evaluate a system which considers all possible ratios of voice and data components for a given load. In this paper, we will consider a mix of voice and data in a fixed ratio which can vary. As mentioned earlier, wireless data will have packets from the IP domain as well as the telephony domain. Therefore, the base station would receive both forms multiplexed. For any resource allocation scheme, the base station has to consider the multiplexed stream. Let us define the voice fraction as g
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A. Network Traffic Model Modeling network traffic using a Poisson or Markovian arrival process is common because of its theoretical simplicity. It also has some favorable properties like the smoothening of the total traffic by statistical aggregation of multiple Markovian arrival processes, which are individually bursty in nature. Careful statistical analysis of data collected from experiments on the Ethernet LAN traffic [10] for long durations has shown that the data exhibit properties of self-similarity [2], [13] and that there is long-range dependencies among the data. It has been observed that such traffic is bursty over a wide range of time scales, and can usually be generated by heavy-tailed distributions with infinite variance [4]. Pareto distribution is one such distribution with heavy tail and large burstiness. This selfsimilar nature of Ethernet traffic is different both from the conventional traffic models and also from the currently considered formal methods of packet traffic [10]. However, there is still a considerable debate [6] over the actual modeling of network traffic because it has serious implications on the design and analysis of networks. Modeling based on self-similar traffic generally ignores the time-scale in which the experiments are performed. The finite range of the time periods of our observations makes it necessary to study and model network traffic as not strictly self-similar. The amount of correlation that we should consider should not only depend on the correlation nature of the source traffic but also on the time scale which is specific to the system under consideration. In our opinion, simply considering either of the two (Poisson or Pareto distribution) would not truly characterize the nature of wireless data. Till date, there has been no single model which represents the true nature of wireless data. Services like file transfer, e-mail and store-and-forward fax – usually known as short-messages services (SMS) – are relatively short and can be represented as a Poisson Model [15]. Interactive data services can be modeled as a queue of packets at each source with a random arrival process into the queue. The expected session length of these services is 1-2 minutes, which is really a short
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is the fraction for data load. Clearly Then implies no voice component whereas implies no data component. Let us now consider both the models. 1) Voice Model: We assume that active users produce and transmit voice packets at a certain rate while inactive users do not transmit at all. A voice call shows periods of activity and inactivity. We model the duration of both talk spurts (activity) and gaps (inactivity) as exponentially distributed. If the mean duration for the talk spurts is and the mean duration for the gaps is , then activity is defined as ]
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where the channel utilization is uniformly distributed between 0 and 1. It is also assumed that the duration of a voice session is exponentially distributed with a certain mean. 2) Data Model: Real Internet traffic has been shown to be self-similar, which means that such traffic is bursty over a wide range of time scales. As Ethernet traffic was shown to be different from conventional traffic models, we use heavy-tailed Pareto distribution for modeling data traffic. We assume that the active data spurts are independent and identically distributed according to the Pareto distribution with shape parameter and scale parameter . The cumulative distribution function for Pareto distribution is given by =
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It is also assumed that the duration of a data session is Pareto distributed with a certain mean.
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C. Expressions for , and In the following, we derive expressions for the three parameters – channel utilization, waiting time, and savings in sig. We consider that the total nalling cost – as functions of load offered to the system is due to both voice traffic and data traffic. The data is not stream-oriented but appear in bursts and shows periods of activity and inactivity. Activity is defined as a ratio of the total length of burst over total time. The notations used in the analysis are given below. ?
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3) To find : The waiting time of a burst will depend on whether channels are being held for that burst or it has to go through the contention process. Note that is the probability that the burst finds reserved channels. The expected waiting because the burst has to wait till the time in this case is beginning of the next frame. We also need to find the waiting time for the bursts which go through the contention process to get the desired number of channels. The probability, , that a burst does not get the required number of channels can be obtained by summing all the probabilities that there are fewer number of channels available than the required number. Therefore, is given by >
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1) To find : The saving in the state switching overhead . From is the fraction of the bursts arriving within time queuing theory results of exponential distributed burst lengths [5], we know that the probability of a burst arriving within is given by , where is the mean inactivity length and is given by
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where can take the values 1,3,6,9 or 12. We regard the probability of a burst leaving the queue after waiting for one epoch and think of it as the probability of success (one frame) as in Bernoulli trials. Note that the probability of waiting for the second epoch is independent of the fact that it waited for the first epoch, therefore we can regard the various epochs as separate but similar experiments in the spirit of Bernoulli triis the exals. A known result in Bernoulli trials states that pected number of experiments to be performed before success is reached. Therefore, it follows that the number of epochs a request waits before leaving the queue is given by . Combining the two cases, we get
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we can obtain the optimal value of for which the performance of the system is maximized. But the above inequality because the does not yield a closed form expression for inequality results in a transcendental equation. Therefore, we choose to evaluate the optimal timer by simulation which is described below.
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But with non-zero , some bursts might not get traffic channels due to channel holding by other services and the expected channel utilization will decrease, the amount of which
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TABLE I S IMULATION
Number of MAC Channels Number of Traffic Channels Service Arrival Rate ( ) Activity ( ) Mean Service Length ( ) Frame Duration ( ) Mean Burst Length ( ) Shape Parameter ( ) Voice Fraction ( )
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ever, the enhancements proposed to IS-2000, called 1XTREME [11], has this feature implemented and we assume that our current system can dynamically reallocate codes at the beginning of each frame. The number of dedicated channels that can carry data simultaneously is limited by the number of dedicated channels available and not due to any noise interference. This can be justified by the fact that the interference amongst the users is proportional to the additive signal strength, so the noise interference can be approximated as a linear function of the number of traffic channels.
PARAMETERS
30 120 0.005/frame 0.5 - 0.7 2 mins 20 ms 30 frames 1.5 0.7
1 Channel Utilization 0.5/Waiting Time Saving in Overhead
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To evaluate the performance of cdma2000 networks, we simulated an environment where a scheduler at a base station handles multiple service requests simultaneously. The requests are made for service connection establishment at a certain service arrival rate . A service can be accepted or rejected based on two criteria. First, a MAC channel must be available. Second, the actual load of the system must be below a certain threshold. This threshold is really a representation of the quality of service being offered by the system and can be varied as desired. If a service is admitted into the system, then the service specifies its requirement profile, i.e., the type (voice/data) and the bit rate requirement. The generation of bursts (a collection of packets) for a voice service is Poisson distributed and that of data service is Pareto distributed with shape parameter . We use = 1.5, which is a typical practical value [4]. The session lengths for voice services and data services are also Poisson and Pareto distributed, respectively. We restrict the duration of a data service to 10 times the average duration. The number of channels requested can be 1,3,6,9 or 12 with equal probability. The parameters used for simulation are given in the Table I. Although the number of MAC and traffic channels are kept constant, in actual deployment of CDMA systems they are more likely to be statistical. We do not consider the possibility of a node going into the suspended state. This might not be realistic since a packet data session may prolong for several minutes. For example, viewing the contents of a web page may take a long time. In that case, it might be a good idea to transit to the suspended state and relinquish the control channel. In this paper, we do not consider such a scenario though the optimal timer for the suspended state can be found in a similar manner. Interested readers may refer to [1] for traffic model on web browsing. If a service is admitted then the service goes from the null state to the control-hold state by acquiring a MAC channel. The session retains the MAC channel till the termination of the service. Once a service is admitted, it generates bursts, the length of which is geometrically distributed with a certain mean. For every burst, the scheduler tries to allocate the required number of traffic channels and holds those channels for some amount of time even after the transmission of the burst. The allocation of the codes is done at the beginning of each frame. This is in contradiction to the basic cdma2000 system which cannot continuously reallocate codes at the beginning of each frame. How-
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Fig. 4. System performance for activity = 0.5
To obtain the optimal amount of time the dedicated traffic channels need to be held even after the transmission of the burst, . The we conduct extensive simulation over a range of behavior of the three system parameters are studied separately for three different activities. The average activities chosen were 0.3, 0.5 and 0.7. In practice, these values correspond to low activity, average activity and high activity. Figures 3-5 show and respectively. It can the performance for
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Activity = 0.2 Activity = 0.3 Activity = 0.4 Activity = 0.5 Activity = 0.6 Activity = 0.7
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be noted that, the -axis is not labeled. This is because, it represents three different parameters as given in the legend of the figures. From the plots we can see the that there is a trade-off between waiting time and the savings in overhead due to nonswitching of dedicated channels. We took the additive sum of the three parameters with , and equal weights, i.e, . We observe that as increases from zero, the overall performance increases and attains a maxima. , the performance falls. This is exWith the increase in pected because Figures 3-5 conclude that channel utilization and the saving in switchand waiting time decay with . The overall (composite) ing overhead increases with system performance as obtained from analysis and simulation is shown in Figure 6 for activity = 0.3, 0.4, 0.5, 0.6 and 0.7. for which the overall performance maxiThe region of mizes depends on the values of the scaling functions used. But whatever scaling functions are used, the nature of the overall will be the same. It is obperformance with respect to served that the sensitivity of the optimal region increases with the increase in activity. It is assumed that none of the service is inactive for such a long time that it has to be suspended. Thus, and . we do not consider |
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Fig. 5. System performance for activity = 0.7
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VI. C ONCLUSIONS In this paper we proposed a model to evaluate the performance of cdma2000 MAC protocol with a view to maximize the overall system performance in terms of channel utilization, waiting time and signalling cost saving in the overhead due to non-switching of traffic channels. The composite performance metric takes into consideration the various MAC states as offered by cdma2000. We used a mix of voice packets which were Poisson distributed and data packets which were Pareto distributed, and generated services of different types with different requirements. The performance model has provisions for scaling functions that can be tuned for different system parameters for different data networks. Analytical formulations are
presented to compute the optimal operating point. By simulation experiments we found the optimal value of the expiration timer for the active state and thus validated our analytical model. R EFERENCES [1] H.K. Choi and J.O. Limb, “A Behavioral Model of Web Traffic”, International Conference of Network Protocols (ICNP) 1999, pp. 327-334. [2] M.E. Corvella and A. Bestavros, Self-Similarity in World Wide Web Traffic: Evidence and Possible Causes, IEEE Transaction on Networking, Vol 5, No. 6, Dec 1997, pp 835-846. [3] E. Dahlman, B. Gudmundson, M. Nilsson, and A. Skold, UMTS/IMT2000 based on wideband CDMA, IEEE Communications Magazine, Vol. 36 Issue 9, Sept 1998, pp 70-80. [4] Z. Hadzi-Velkov and L. Gavrilovska, Performance of the IEEE 802.11 Wireless LANs under Influence of Hidden Terminals and Pareto Distributed Packet Traffic, IEEE International Conference on Personal Wireless Communications, 1999, pp 221-225. [5] A. Papoulis, Probability, Random Variables, and Stochastic Processes, Third Edition, McGraw Hill, 1991. [6] M. Grossglauser and J.C. Bolot, On the Relevance of Long-Range Dependence in Network Traffic, IEEE Transaction on Networking, Vol 7, No. 5, Oct 1999, pp 629-640. [7] D.N. Knisely, S. Kumar, S. Laha, and S. Nanda, Evolution of wireless data services: IS-95 to cdma2000, IEEE Communications Magazine, Vol. 36 Issue 10, Oct 1998, pp 140-149. [8] D.N. Knisely Q. Li, and N.S. Ramesh, cdma2000: A Third-Generation Radio Transmission Technology, Bell Labs Technical Journal, Jul-Sep 1998, pp 63-78. [9] W.C.Y. Lee, Overview of cellular CDMA, IEEE Trans. Veh. Technol., vol 40, May 1991, pp 291-302. [10] W.E. Leland, M.S. Taqqu, W. Willinger, and D.V. Wilson, On the SelfSimilar Nature of Ethernet Traffic (Extended Version) , IEEE Transaction on Networking, Vol 2, No. 1, Feb 1994, pp 1-15. [11] G.D. Mandyam and G . Fry, 1XTREME: A Step Beyond 3G, IEEE DSP Workshop, Oct 2000. [12] R. Prasad and T. Ojanpera, A survey on CDMA: evolution towards wideband CDMA, Proceedings of IEEE 5th International Symposium on Spread Spectrum Techniques and Applications, Vol. 1, 1998 , pp 323331. [13] V. Paxon and S. Floyd, Wide Area Traffic: The Failure of Poisson Modeling, IEEE Transaction on Networking, Vol 3, No. 3, Jun 1995, pp 226244. [14] Y.S. Rao and A. Kripalani, cdma2000 mobile radio access for IMT 2000, IEEE International Conference on Personal Wireless Communication, 1999, pp 6-15. [15] A. Sampath and J.M. Holtzman, Access control of data in integrated voice/data CDMA systems: Benefits and Tradeoffs, IEEE Journal on Selected Areas in Communication, Vol. 15, No. 8, Oct 1997, pp 1511-1526. [16] A.J. Viterbi, CDMA: Principles of Spread Spectrum Communication, Assison-Wesley Publishing Company, 1995.
0-7803-7476-2/02/$17.00 (c) 2002 IEEE.
