Access Control for QoS Provisioning in Integrated Voice/Data CDMA Systems ∗ Cristina Comaniciu and Narayan B. Mandayam Wireless Information Network Laboratory (WINLAB), Rutgers University 73 Brett Road, Piscataway, NJ 08854-8060 EMail: ccris,
[email protected]
Abstract
Third generation (3G) wireless systems stipulate simultaneous support of diverse services ranging from voice to several forms of data. The quality of service (QoS) guarantees needed by the spectrum of services offered in a wireless integrated system can be provided by employing access control protocols. Access control for efficient radio resource management requires studying its interaction with power control, which may be different for voice and data services. This paper provides a general framework for access protocol design in integrated voice/data CDMA systems, including interactions with power control. Specifically, a summary of access control methods proposed at WINLAB is presented.
1
Introduction Third generation (3G) wireless systems introduce the new paradigm of “anytime, any-
where, anytype” wireless services. Consequently, a variety of multimedia services are to be accommodated over wireless links. This implies coexistence of various types of traffic with very different QoS requirements, such as different target bit error rates (BER), different ∗
This work is supported in part by the NSF under Grant No. NCR 97-06036.
1
sensitivity to delay and different information rates. The air interfaces for the 3G wireless systems [1, 2] for both Europe and Japan (WCDMA), and also for North America (cdma2000) are building upon the framework provided by the second generation wireless network CDMA technology. Therefore, it is of great interest to analyze integrated voice/data CDMA systems from the point of view of QoS provisioning and system capacity maximization. An adequate method to insure QoS in a wireless integrated system is to employ an access control protocol. The role of the access control protocol is to balance the system load such that real time services are given priority, while some bounded delay specification is met for delay tolerant applications. Further, in addition to the above two constraints, the access control should ensure that the BER target can be met for all users 99% of the time. The access control should be combined with an admission control strategy which allows a user to access the system only if after admission, the resulting number of users for each class of traffic is feasible, in that they achieve their QoS specifications. Thus, the design of access control has interactions with power control and has a direct impact on QoS guarantees for voice and data. This paper provides a general framework for designing access control protocols for integrated voice/data CDMA systems. Specifically, a summary of two different design approaches proposed at WINLAB is presented, that take into account the interaction of access control and power control.
2
Access Control Protocol Design Overview The access control problem arises naturally from the complementary QoS requirements
for voice and data traffic, and also from the interference limited nature of CDMA systems. Efficient design of access control in integrated voice/data CDMA systems depends on traffic and QoS characterization, implementation of power control and overall performance requirements. 2.1
Traffic and QoS characterization
Voice user streams constitute the real time traffic and are very sensitive to delays, can tolerate occasional errors (e.g. a BER of 10−3 ), and require relatively low transmission rates (e.g. 9.6Kb/s). Speech is characterized by periods of silence and activity and can be modeled
2
as an ON/OFF Markov process. We should note that in reality, multiple rates are used to indicate the activity/inactivity of voice and that all the users are required to transmit at all times at least a minimum rate for synchronization purposes, as is done in the IS-95 system. The ON/OFF model simplifying assumption is often made in the literature to simplify the analysis since it is sufficient to provide insight into the system functioning. The cumulative voice process can be approximated as a discrete-time Markov chain (see Figure 1). The λ and µ parameters represent the rate of flow from the OFF to ON and from ON to OFF states, respectively. µ P(1/0)
ΟΝ
OFF
P(0/0)
P(1/1)
P(Kv/Kv)
P(2/2)
0
λ (a)
P(3/2)
P(2/1) 1
P(0/1)
2
P(1/2)
............
Kv
P(Kv-1/Kv)
(b)
Figure 1: (a) ON/OFF Markov model for the voice activity (b) Discrete time Markov chain for the cumulative voice process The data users’ exact traffic characterization depends on the specific type of service. However, in general, data is error intolerant (e.g. BER of 10−6 ), therefore it will require a higher target signal-to-interference ratio (SIR) and the use of forward error correcting codes, combined with an ARQ protocol. In terms of delay, data is tolerant to moderate delays. Nevertheless, we will need to provide some statistical guarantees for the data service as well, such as minimum and average throughput. These measures are strongly dependent on the access control employed through the access delay incurred, correlated with the ability of maintaining a high target SIR. Another critical requirement for data is a high information rate (3G data requirements range from 144 Kbps to 2 Mbps). In designing the access control protocols, we have considered two different types of data traffic: non-transparent (store and forward) [5], suitable to characterize services such as email, file transfer, store-and forward facsimile, etc., and short message data service (SMS) [3]. For the non-transparent model, the number of data users in the system is deterministic and each user offers exactly one data packet/slot. For the SMS, the number of data users in 3
the system is modeled as a Poisson random variable. 2.2
Performance and Power Control Characterization
Since CDMA systems are interference limited, in order to achieve the target SIRs for both voice and data users (maintain the required BER performance), a maximum load for the system can be defined. When the actual system load is higher than this threshold we call this an outage (Figure 2 (c)). A simplified model for the error probability is to consider that all users lose their packets when an outage occurs, and all users decode their packets successfully if the load is maintained within the prescribed limit. It is important to mention that a critical performance characterization for the system is the probability of outage, which represents the percentage of time the outage condition is not met. Given the fact that the voice users are delay intolerant, they should have priority in utilizing the system resources. The remaining available load can be used by the data users, provided that the delays incurred are within specified limits. The system load definition and the value for the limiting threshold are different for specific considered cases. In a system with perfect power control, the outage condition is the power control feasibility condition [4]. For the imperfect power control case, the QoS for both voice and data is ensured by maintaining the ratio of the total received interference to the background noise level to a prescribed value
1 η
(0 < η < 1). The system load will depend
on the number of active voice and data users, on the rate requirements for both voice and data, and on the received SIRs for both voice and data. The SIRs are modeled as lognormal random variables when both open and closed loop power control can be employed (e.g. voice and non-transparent data services - the assumption is that the messages are long enough to perform close loop power control [8]), and as exponential random variables, for the case of short message data service (SMS) [6]. 2.3
General Framework for access protocol design in integrated CDMA systems
We consider the uplink of a DS-CDMA system, that is assumed to be slotted. In designing the access control protocol we take advantage of the voice users’ activity by scheduling more data users in the time slots when the voice load is low and reducing data transmission when 4
the voice load is high. We define the residual capacity as the remaining system capacity after the voice contribution is subtracted. The residual capacity represents the actual capacity available for data. Depending on the time scale used for determining the residual capacity, we can consider that all the voice users in the system are using the system resources (call arrival scale) or we can take into account the voice activity and determine the residual capacity at a packet transmission scale. In Figure 2 (a) and (b) the normalized load for the CDMA system is represented. The shadowed area represents the residual capacity at a call arrival time scale in Figure 2 (a), and at a packet arrival time scale in Figure 2 (b). The selected area in (a) is represented in (b) at a packet transmission scale and with the voice activity accounted for. It can be seen that (b) gives a much larger residual capacity for the same number of voice calls. The function which determines the residual capacity depends on the number of active voice users and is a sample path of a random process. Ideally, we would like to use the entire shaded area for the data users but we cannot know in advance what will be the number of active voice users in the next time slot. In practice, various access control protocols will determine the number of data users that are allowed to transmit in the next time slot. Sometimes the system resources will be used inefficiently; sometimes more users than the allowable number will transmit resulting in an outage for that particular time slot (see Figure 2 (c)). An important design criterion for the access control is to keep the outage probability less than or equal to 1%. This will ensure that 99% of the time the target SIR for both voice and data can be met. To summarize, the general design criteria for an access control protocol are: • Outage probability ≤ 1% • Prescribed minimum data throughput for the data users • Prescribed average data throughput for the data users • System capacity maximization The second criterion implies that a stability constraint needs to be imposed: the number of voice users in the system has to be limited such that, even if all voice users are active simultaneously, there will still be some room for the data users. 5
The general design steps for an access control protocol are: (1) Define the outage condition (2) Define a feedback dependence between a measurable quantity (e.g. system load at a given time) and the number of data users that can be allowed to transmit in the next time slot (3) Employ an access procedure to allow the predetermined number of data users to transmit in the next time slot Step (2) above, represents an implicit or explicit estimation of the residual capacity available for data at the next time slot, based on a measurable QoS parameterization available at a base station. To keep the outage probability low, one must first understand what generates an outage. We have distinguished three main causes for the outage: (A) Imperfections in estimating the residual capacity (B) Imperfections in scheduling the desired number of data users (C) Imperfections in power control The access control design strongly affects (A), whereas (B) is a characteristic of the access procedure employed for the data users. (C) arises as an effect of the power control algorithm used in the system. This reflects the strong interaction between the power control and the access control. Better power control results in more efficient access control. At WINLAB, two different approaches have been proposed to implement the general design steps outlined above. In the first approach, a broadcasted access probability is updated using a persistence state value which reflects the measured system load fluctuations. The second approach uses a delta modulation based prediction scheme to estimate the available capacity for data at the next time slot. Based on this prediction, two access procedures can be employed: a contention based access procedure using an access probability broadcast and a round-robin type of scheduling for the data users.
6
The first admission protocol based on a persistence state value was proposed by Viterbi in [3] for a data only system. The work was extended at WINLAB [6] for an integrated voice/data CDMA system and an access control for short message data service was designed, exploiting the delay tolerance of the data users and the voice activity. The protocol analysis was further extended in [7], where a stability analysis was also provided. A complete analysis of a persistence state based access control protocol for non-transparent data services has been presented in [8], together with comparisons with optimum and minimum mean square error (MMSE) prediction based access controls. It was shown that the prediction schemes represent benchmarks for performance and are complex to implement in practice, whereas the persistence state approach gives suboptimum performance with a very efficient implementation.
3
Persistence state based access control protocol We describe the persistence based access control for integrated voice/data systems ac-
cording to the design steps outlined in the previous section. We define the outage condition as in section 2.2, such that it reflects the interaction between the CDMA interference limited system and the power control. For the simplest case of perfect power control, the outage condition is given by the power control feasibility condition. For an integrated voice/data system, having v(n) active voice users, and d(n) active data users in the nth time slot, the power control feasibility condition can be written as: S(n) =
v(n) d(n) + < 1. av ad
(1)
S(n) represents the normalized load for the system at time slot n and av and ad are normalizing constants which depend on the bandwidth of the system, the rates for voice and data, respectively, and on the target SIR for voice/data [8]. Since v(n) is a random variable, it can be seen that the access control arises naturally as a problem of dynamically controlling d(n) such as to keep the normalized system load less than 1. The access control is based on broadcasting an access probability p(n) to all mobile stations (MS). Each data user will then flip a biased coin and will transmit a packet in the next time slot with probability p(n) and will refrain from transmitting with probability
7
1 − p(n). This access probability is computed at each time slot n as: p(n) = π j(n) ,
(2)
where j(n) is the persistence state value, dynamically updated at each time slot by +K or −1. π represents the initial access probability value which is a parameter of the protocol. At the end of each time slot, the base station (BS) measures the system load and compares it with some threshold Ω, which also represents a parameter of the access control protocol. The persistence state value is increased if the system load is above the threshold, and decreased otherwise. π, Ω and K are sensitivity parameters which are heuristically chosen. A discussion on how to choose these parameters can be found in [8]. The access probability update is based on a 1-bit feedback information from the BS at each time slot. At the call setup stage, each data user will receive initial values for π, j and K. A flowchart of the access protocol is presented in Figure 3. The parameter K allows to tradeoff system capacity for data access delay, for the case of non-transparent services. Large values of K yield good outage performance (or equivalently higher system capacity for a fixed outage probability) but large delays. The situation is reversed for small K values. The disadvantage of the scheme is that there are no analytical means to determine the optimum value for K for given system requirements (fixed outage probability and delay constraints for data), and consequently the bidimensional capacity for prescribed specifications. It was heuristically determined that K = 5, Ω = 0.7 and π = 0.95 are good choices for a wide range of voice and data loads [8]. The persistence state based access control was shown to work well also for the SMS case. The SMS data model is represented in figure 4. In each time slot, there are new arrivals modeled as Poisson random variables, with an average arrival rate ρnew . The packets blocked by the access control are allowed to retry until they gain access to the system. Further, the errored packets are also allowed to retransmit until they are correctly received. The admitted traffic is diminished or increased according to the system load fluctuations. Using an analogy with a queueing system, a critical load for data (for a given voice load) can be determined such that the system will be stable. This critical load represents the data capacity for the SMS for a given voice load. 8
4
Delta Modulation based prediction for access control The persistence state based access control protocol does not explicitly estimate the resid-
ual capacity available for data. As discussed in section 2.3, the impact of an access control protocol on the outage probability is determined primarily by the accuracy of the residual capacity estimation. The second family of access control protocols proposed at WINLAB [10], [11], dynamically predicts the residual capacity at the next time slot using a modified delta modulation technique. The main design goal is to completely eliminate the first cause for outage (section 2.3). Following the design steps from section 2.3, we further refine Step (2) into: • Determine the ideal residual capacity as a function of the number of active voice users for a particular time slot • Estimate the residual capacity for each time slot, based on voice activity measurements in the previous time slot. The ideal residual capacity is determined from the outage condition and has different expressions for the perfect power control and imperfect power control cases (section 2.2). The ideal residual capacity derivation for the perfect power control case is very straight forward. The maximum allowable number of data users in the next time slot can be determined by rearranging equation (1). To obtain the maximum value for the ideal residual capacity, a small positive value δ can be subtracted from the threshold, such that the strict inequality becomes an upper bound. Thus, the maximum number of data users that can use the system resources is given by: d(n) = ad (1 − δ) −
ad v(n) . av
(3)
Equation (3) represents the ideal residual capacity. It depends on the number of active voice users v(n) which is a random variable and it is not known apriori. Therefore only d as a function of v(n − 1) can be derived. The quality of the estimate an estimate d(n), d we gives the access control performance. To determine the residual capacity estimate d(n),
have proposed a simple, practical, prediction scheme, based on a modified delta modulation algorithm. 9
d Delta modulation is a technique for converting analog signals s(t) into digital ones s(t)
(Figure 5 (a)). The modulated signal is a staircase representation of the analog signal. The accuracy of the approximation is given by two parameters: the sampling frequency, fs , and the step of the modulation, ∆. There are two types of modulation distortions: (i) granular noise (too coarse quantizer for the flat regions) and (ii) slope overload distortion (if the signal d cannot follow it). s(t) changes too fast, s(t)
Our approach is to use this tracking technique to approximate the ideal residual capacity d is subject to both types of distortions as accurately as possible. The obtained estimate, d(n),
characterizing the delta modulation technique. The slope overload distortion can be avoided if the following condition holds: |d(n + 1) − d(n)| ≤ ∆.
(4)
d is greater than d(n) we have an outage, we would like to eliminate also Since each time d(n)
the second type of distortion. Therefore, we introduce a guard margin equal to the step of modulation (∆), such that the function to be approximated becomes dt (n) = d(n) − ∆. Secondly, we observe that using the assumption that the slot duration d is chosen to be sufficiently small such that at most ±1 variation in the number of active voice users can occur from one time slot to another, the ideal residual capacity has a staircase aspect. Consequently, it is more suitable to use a tristate modulation instead of a simple delta modulation. This assumption also insures that the no slope overload condition will hold for suitable chosen values for the slot duration, and the step of the modulation ∆. The proposed prediction technique, guarantees zero outage probability for the case of perfect power control and perfect scheduling. In Figure 5 (b) simulations results are plotted for the case of imperfect power control. The upper graph illustrates the relationship between the ideal residual capacity and the estimated one. It can be seen that the estimated function never exceeds the ideal residual capacity. The lower graph in 5 (b) illustrates the prediction d is the approximation. technique: dt (n) is the original function and d(n)
This scheme also allows for a very efficient implementation. At the end of each time slot, the base station (BS) determines the number of active voice users. In the first time slot it initializes the estimate for the next slot with the computed residual capacity value, which is 10
d obtained a function of v(1). Then, at each time slot, it compares the estimated value d(n),
at n − 1, with the actual computed value at nth time slot and based on the estimation error, it adjusts the estimate for the next time slot by ±∆. Once the residual capacity estimate is determined, an access procedure should be employed such that the desired number of data users will transmit at the next time slot. Two different access controls were proposed: Modified Delta Modulation with Scheduled access (MDM–S) and Modified Delta Modulation with Random access (MDM–R). For both cases, the BS provides a 2 bit feedback representing {−1, 0, +1}. The feedback information can be coded very efficiently by sharing the 2 bits with some other system information. Due to the fact that the necessary update frequency is very low, the method gives a very efficient downlink utilization for feedback information, e.g. for 10 voice calls and a voice activity of 0.4, the average number of feedback bits/slot is less than 0.3. For MDM-S, each user has to keep track of its position in a circular list whose length is given by the number of data calls in the system (Kd ). Based on the received feedback information, it computes the number of data users that can transmit in the next time slot and updates the list accordingly. When its position is at the top of the list, the MS transmits a packet. An alternate implementation will be for the BS to maintain a circular fair list with all data users admitted in the system and to signal users to transmit or not, using an 1 bit access flag: “1” if the user is allowed to transmit in the next time slot, “0” if not. A contention access procedure is used for MDM-R. Each user gains access to the system at time slot n with probability p(n), and refrains to transmit with 1−p(n). At data call setup the MS learns the initial value for the access probability and also the current access control parameters Kd and ∆. At each time slot, the access probability is updated by ±∆/Kd , or it is leaved unchanged if the 2−bit feedback does not represent update information. The flowchart for prediction based access control is presented in Figure 6. A prediction based access control has been also proposed for the SMS case, based on the same traffic shaping concept used by the persistence state protocol. It was shown that no gain in the Erlang capacity can be obtained by enforcing an access delay for data. However, the prediction based access control uses more efficiently the system resources and therefore it gains in capacity compared with the persistence state based one. In Figure 7 (a) the bidi-
11
mensional system capacity for both protocols is represented for 2% data blocking probability, and for the case of zero data access delay and infinite number of retransmissions for errored packets. For the non-transparent data service, a gain in system capacity can be obtained by enforcing an access delay. The bidimensional system capacity is defined as the maximum number of voice and data users that can be supported for a given throughput for data users (fixed value of data mean transmission delay) while complying with the outage condition 99% of the time. The 3D representation of the bidimensional capacity as a function of the access delay is given in Figure 7 (b) for the MDM-S case and imperfect power control. Due to the fact that the number of users admitted in the system is an integer number, the surface has a staircase aspect. The optimal operating points for the system are chosen such that the allowable number of voice and data users in the system is maximum whereas the access delay is minimum. The graph in 7 (c) represents a section through the Figure 7 (b) surface, for a mean access delay of 0.6. In practice, the number of voice users admitted in the system is determined by first imposing an average throughput requirement for the data users. This translates to a specified value for the access delay, and a graph similar with the one in Figure 7 (c) can be obtained. Then, the maximum admissible number of voice users is determined such that a minimum throughput for data users can be guaranteed. The most efficient resource utilization is achieved if the number of voice and data users admitted into the system represents an optimal point in the bidimensional capacity graph.
5
Conclusions and Future Work Access control for integrated voice/data CDMA systems represents a powerful mech-
anism for QoS provisioning in 3G wireless systems. Efficient radio resource management can be obtained by designing the access control protocol according to its interaction with power control for integrated systems. This paper represents a survey of recent work done at WINLAB in designing access control protocols for integrated voice/data CDMA systems. A general design framework is presented and two different strategies for implementing it are described. As future work, the interactions of access control with other resource management proto12
cols, such as handoffs, should be considered. Related work at WINLAB has already considered the interaction data access control with end-to-end error recovery mechanisms such as TCP [9]. Another interesting research direction is to integrate the proposed access control protocols with the new promising technology of software radios for interference cancelation [12]. The use of reconfigurable radio architectures in conjunction with access control will provide further flexibility in QoS guarantees.
References [1] F. Adachi, M. Sawahashi, and H. Suda, Wideband DS-CDMA for Next-Generation Mobile Communication Systems, IEEE Communications Magazine, vol. 36, no. 9, pp. 56–68, September 1998. [2] T. Ojanpera and R. Prasad, An Overview of Third-Generation Wireless Personal Communications: A European Perspective, IEEE Personal Communications, vol. 5, no. 6, pp. 59–65, December 1998. [3] A.J. Viterbi, Capacity of a Simple Stable Protocol for Short Message Service over a CDMA Network. Blahut, editor, Communications & Criptography, KAP, 1994, pp. 423-429. [4] A. Sampath, P.S. Kumar, J.M. Holtzman. Power Control and Resource Management for a Multimedia CDMA Wireless System, PIMRC’95, September 1995, vol 1., pp 21-25. [5] N.B. Mandayam, J.M. Holtzman and S. Barberis. Erlang Capacity for an Integrated Voice/Data CDMA System with Variable Bit-Rate Sources, PIMRC’95, September 1995, vol. 3, pp. 1078–1083. [6] N.B. Mandayam, J.M. Holtzman. Analysis of a Simple Protocol for Short Message Service Data Service in an Integrated Voice/Data CDMA System, MILCOM’95, San Diego, CA, November 1995, pp. 1160–1164. [7] A. Sampath, N.B. Mandayam, J.M. Holtzman. Analysis of an Access Control Mechanism for Data Traffic in an Integrated Voice/Data Wireless CDMA System, VTC’96, vol. 3, pp. 1448-1452. 13
[8] A. Sampath, J.M. Holtzman. Access Control of Data in Integrated Voice/Data CDMA Systems: Benefits and Tradeoffs, IEEE Journal on Selected Areas in Communications, vol 15, No 8, October 1997, pp. 1511-1526. [9] S. Ramakrishna, J. Holtzman, Interaction of a Wireless Data Access Control Scheme with TCP, Proceedings of the 3rd Mobile Multimedia Communications Workshop (MoMuC-3), September 1996. [10] C. Comaniciu, N. Mandayam, Delta Modulation Based Prediction for Access Control in Integrated Voice/Data CDMA Systems, to appear in the Special Issue on Spread Spectrum for Global Communications of the IEEE Journal on Selected Areas in Communication (JSAC), 4-th quarter 1999. [11] C. Comaniciu, N. Mandayam, Analysis of a Prediction Based Access Control Mechanism for Short Message Data Service in an Integrated Voice/Data CDMA System, to appear in the Proceedings of 49th IEEE Vehicular Technology Conference, May ’99. [12] I. Seskar, N. Mandayam, Software Defined Radio Architectures for Interference Cancellation in DS-CDMA Systems, To Appear in IEEE Personal Communications Magazine, September 1999.
14
(a)
(b)
(c) Figure 2: Interference limited residual capacity for CDMA Systems: (a) call arrival scale (b) packet transmission scale (c) outage versus inefficiency in utilizing system resources for an access control protocol
15
BS
Send initial values for the access control parameters: π , j, K Measure System Load at the end of the n-th time slot S(n)
yes
S(n)
Figure 4: Short message data service model
s(t) step of modulation
∆
granular noise
5
Residual capacity signal Estimated capacity signal
4 3 20
40
60 80 100 n −time slots −>
120
140
60 80 100 n −time slots −>
120
140
s(t)
dt(n), approx.−>
s(t), s(t)
6
5
dt(n) approximation
4
Ts= 1/ fs
3
slope overload distorsion
1 1 1 1 -1 -1 1 -1 1 1 1 1 1 1 1 1 -1 -1 -1 -1 -1
t
20
(a)
40
(b)
Figure 5: Delta Modulation: (a) Staircase approximation of an analog signal (b) Proposed Algorithm for Imperfect Power Control (Simulations)
17
BS
Measure v(1) and compute d(1)
For MDM-R: Send p(2), Kd , ∆
Initialize d(2)= d(1), compute p(2)
For MDM-S:
K d , ∆ , d(2)
Measure v(n) and compute d(n)
for n > 1 at the end of the n-th time slot
yes
no
d(n) = d(n)
Feedback information: 0
yes
d(n+1) = d(n)
d(n) < d(n)
Feedback information: + 1 d(n+1) = d(n) + ∆
Feedback information: - 1 d(n+1) = d(n) - ∆ (0)
(- 1)
feedback information on forward link
d(n+1) = d(n) MDM-R Mobile updates access prob. p(n+1) = p(n)
(+ 1)
MDM-S
MDM-S
MDM-S
no
d(n+1) = d(n) - ∆ MDM-R Mobile updates access prob. p(n+1) = p(n) - ∆ / K d
d(n+1) = d(n) + ∆ MDM-R Mobile updates access prob. p(n+1) = p(n) + ∆ / K d MDM-R: p(2), K d ∆
MS
MDM-R Bernoulli trial with p(n+1) If "1" send packet If "0" do not send
MDM-S round - robin scheduling
Figure 6: MDM-R and MDM-S
18
MDM-S: K d , ∆, d(2)
1.6
prediction, Pb
