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Priority Based Scheduling to Provide Differentiated QoS for Delay Sensitive Services in HSDPA Joseph S. Gomes1 , Hyeong-Ah Choi1 , Jae-Hoon Kim2 , JungKyo Sohn3 , Hyeong In Choi3 1 Department of Computer Science, George Washington University, Washington, DC 2 Access Network & Mobile Terminal R&D Center, SK Telecom, Seoul, Korea 3 Department of Mathematics, Seoul National University, Seoul, Korea {joegomes,hchoi}@gwu.edu,
[email protected], {jgsohn,hichoi}@snu.ac.kr
Abstract— In wireless cellular networks, normally synchronous dedicated channels are used for real time services due to their delay sensitivity. However the increasing demand for high data-rate multimedia real time services has led to the use of asynchronous time shared channels in the forward link for realtime services in 3G wireless networks, such as HSDPA, EV-DO and EV-DV. Strict and different QoS requirements for realtime services, along with time varying channel conditions and constraints on the total forward link transmit power imposes a great deal of challenge on the user scheduler. In this paper we consider the problem of scheduling users on the forward link in a multiuser system where multiple users can be scheduled during each interval. We introduce a delay aware priority based scheduling algorithm SPS-Delay (Strict Policy Scheduling for Delay) to support differentiated QoS requirements in the form of tolerable latency specified by the user applications. We developed an HSDPA system in OPNET, and implemented our scheduling algorithm along with other well-known algorithms. Our simulations show that SPS-Delay provides improved performance over other schedulers in meeting the delay constraints if allowed by radio conditions and cell capacity.
I. I NTRODUCTION In traditional cellular networks such as UMTS and Cdma1x, real-time services are usually transported using synchronous dedicated channels due to the delay constraints imposed by these services. On the other hand high data rate (HDR) systems such as HSDPA, EV-DO and EV-DV were used for best effort data services only. With increasing demand of high data rate real time services such as video streaming and video conferencing, HDR systems are currently being considered for supporting high speed real-time services. However in all the HDR systems, multiple users in the same cell share the same wireless channel. Moreover the channel conditions experienced by different users are different due to their distances from the base stations and the different interference levels observed. The same user’s channel condition can also vary over time due to fast fading of the received signal. All these together make it difficult for the scheduler to distribute the resources in a manner that ensures the delay requirements for all users to be met. In addition to this, different user services can have different delay constraints, that makes the scheduler’s job even harder. This work was in part supported by the Access Network & Mobile Terminal R&D Center, SK Telecom, Seoul, Korea.
In multiuser cellular systems, opportunistic scheduling and its variations can be used to increase system throughput and provide fairness to some extent by utilizing multiuser diversity and favoring the users with peaks in their channel conditions [1], [2]. This can be further enhanced by using MIMO techniques to exploit multiplexing gains to dramatically increase the data rate without increasing required bandwidth [3], [4]. However they do not provide differentiated QoS support. Fairness issues in scheduling have been studied in depth in [5], [2]. P roportionalF air (PF), the most popular packet scheduling algorithm considering fairness, is thoroughly investigated in [6], [7]. [8] proposes an algorithm using barrier functions that provides delay guarantees for VoIP traffic but does not take into consideration different delay constraints from separate services. A marginal utility based scheduling algorithm for policy driven Qos support and its integration with access control algorithms were proposed in [9] and [10]. Reference [11] first proposes the notion of QoS for a realtime user in the following way. The QoS requirement of user i is P rob {Wi > Ti } ≤ δi , where Wi is a packet delay for user i, and parameters Ti and δi are the delay threshold and the maximum probability of exceeding it, respectively. It proposes a throughput optimal scheduling algorithm MLWDF that supports QoS definitions of the above form. A scheduling algorithm is throughput optimal if it is able to keep all user queues at the base station stable if this is at all feasible to do with any algorithm. We introduce the notion of priority based scheduling where a priority value is used to differentiate the QoS requirements imposed by the separate user services. We classify each user into a different priority-class of QoS, based on their delay constraints. E.g. users with 80 ms and 160 ms delay thresholds can belong to priority classes c1 and c2 respectively. A service operator may have policies on how to prioritize QoS classes during times of overload and how to distribute surplus capacity when the total system load do not exceed the system capacity. In this paper we consider such priority based QoS support. We experimentally show that our SPS-Delay algorithm very effectively satisfies high priority users by guaranteeing their delay constraints if it is at all feasible, whereas MLWDF, PF and AA-RRS treat users with different delay requirements equally and thus fail to make such guarantees for any QoS class, specially under stressful conditions.
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II. S YSTEM M ODEL We particularly focus on High Speed Downlink Packet Access (HSDPA) that was introduced in the Release 5 of the 3GPP UTRAN specifications to increase downlink packet data throughput. HSDPA offers high data rates using a fast and complex channel control mechanism based upon short physical layer frames (2 ms), Adaptive Modulation and Coding (AMC), fast Hybrid-ARQ and fast scheduling. In the uplink direction, HS-SCCH channels are used by the UEs to notify the NodeB of the Channel Quality Indicator (CQI) and a positive or negative acknowledgement pertaining to the received frame. CQI indicates the instantaneous channel quality experienced by the user, so that the Node-B can adjust its transmission parameters (modulation type, coding rate, number of codes) to cope with variations in channel conditions. We make the following notations and assumptions. The system consists of a single Node-B and n users u1 , · · · , un . Node-B is allocated M HS-PDSCH codes and total transmission power λ by the RNC for downlink transmission. ri (t) is the instantaneous data rate for ui that can be used at tti t. It is chosen from a discrete set of data rates {rmin , · · · , rmax } according to the requested CQI cqii reported by the user ui . codes(cqii ) is the required number of parallel codes for transmitting using cqii . Note that NodeB can use any CQI less than or equal to cqii to transmit to ui . Wi (t) is the head-of-line (HOL) latency, i.e. the amount of time the HOL packet of user ui has spent at the base station. A packet is removed from the user’s queue at the base station only after the last fragment of the packet is sent to the user. We differentiate the users using priority-classes based on delay constraints where πi denotes the priority-class for ui such that πi ∈ Π = {1, .., k}. Each πi is associated to a delay threshold Tπi , i.e. if ui belongs to priority-class πi then its delay threshold Ti = Tπi . In general we assume higher classes (with higher πi ) have lower delay thresholds. We also assume that the transmitter can transmit packets to multiple users during the same tti, i.e. we use code multiplexing. III. Q O S BASED S CHEDULING Here we briefly describe three different scheduling schemes that we compare our algorithm with. PF and M-LWDF algorithms were originally designed for single user transmission per tti. However to be fair in comparison we implemented code multiplexed versions of these algorithms. Proportional Fairness: The PF scheduler orders the reri (t) , where ri (t) is the instantaneous ceivers using the ratio th i (t) data rate and thi (t) is the current throughput over a sliding window of w0 ttis. In our implementation it schedules the users with the highest ratios as long as it does not run out of HS-PDSCH codes. PF does not make any QoS guarantees. It considers all users to be equally important and adds a fairness property with respect to user throughput. AA-RRS: The Round Robin Scheduler (RRS) schedules one user per tti in a round robin fashion and thus does not exploit multiuser diversity at all. The Antena Assisted Round Robin Scheduler (AA-RRS) [3] scheme exploits multiple code channels by scheduling multiple users in each tti. During
each tti after scheduling the next user, if there are more code channels left, it continues to schedule more users in a round robin fashion. M-LWDF: At each tti the M-LWDF scheduler chooses the user j = argmaxi (γi ri (t)Wi (t)). The scheduling decision tries to factor in both the channel conditions and the states of the user queues. Moreover it can shape the delay distribution of the users using the paramater γi . According to [11], the suggested value for γi is γi = ai /r¯i , where ai = −(logδi )/Ti , r¯i is the average channel rate (not the throughput experienced by the user) with respect to user i and δi is the maximum probability of exceeding the delay threshold Ti . IV. PRIORITY- CLASS BASED S CHEDULING USING S TRICT P OLICIES We try to incorporate delay constraints into a strict set of policy requirements. Although the problem can be generalized for any number of priority-classes, for simplicity let us first consider only classes 1 and 2 with corresponding delay thresholds T1 and T2 . We will call these two classes Class1 and Class2 respectively. The goal of our policies is to define fair rules for governing resource allocation under all circumstances, i.e. to guarantee each user its delay requirement and fairly distribute the surplus capacity when there are enough resources, and to satisfy users from higher classes before the lower classes when there is not enough resources. We refer to a user whose HOL packet wait-time is more than its delay threshold as an unsatisfied user. We use SP to denote the scheduling priority of a user. Our policies are as follows: (P1) Let ug, us, sg and ss represent unsatisfied Class2, unsatisfied Class1, satisfied Class2 and satisfied Class1 users respectively. Then the scheduling priority relationship among the users from the two classes can be expressed as follows: SPug > SPus > SPsg > SPss
(1)
(P2) Among multiple unsatisfied users within a class the user with the best current data rate has highest priority. (P3) Among multiple satisfied users within a class that are experiencing similar channel conditions the user with the longest HOL wait-time has highest priority. (P4) When all users are satisfied, surplus capacity must be proportionally distributed among the Class1 and Class2 users under similar conditions (with same data rate) according to their delay thresholds. V. M ARGINAL U TILITY F UNCTION Let U be the set of users in the system who are elligible to receive at current tti. A user is elligible if there are packets to be sent to that user. We will define marginal utility functions Mπ (i) for each class π ∈ Π = {Class1, Class2}, whose purpose is to assign a utility value to each user ui ∈ U , following the policy rules described earlier. The utility value will depend on the user’s class, its current data rate and current HOL wait time. Eventually the utility values will be used to determine which user(s) will be scheduled next, where users with higher utility will have higher scheduling priority. Let
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Mπ (i) = Pπ (i)·di (t) where Pπ denotes the preliminary utility function. For any Class2 user g and Class1 user s, functions PClass2 and PClass1 have to follow the following conditions: (C1) PClass2 (g)rmin > PClass1 (s)rmax , for Wg (t) > T2 and Ws (t) > T1 . This condition ensures that SPug > SPus . (C2) PClass1 (s)rmin > PClass2 (g)rmax , when Ws (t) > T1 and Wg (t) ≤ T2 . It ensures that SPus > SPsg . (C3) PClass1 (s) = PClass2 (g), when Ws (t) < T1 , Wg (t) ≤ T2 and Wg (t) = TT21 · Ws (t). This condition fulfills the requirement SPsg > SPss and policy (P4). (C4) For any two users i and i0 from the same class π, Pπ (i0 ) = Pπ (i), if Wi0 (t) > Tπ and Wi (t) > Tπ . This will make sure that the user with higher data rate has a higher marginal utility and thus satisfy policy (P2) (C5) Policy (P3) will be followed by making the preliminary utility function dependent on the HOL wait time for satisfied users. We define functions PClass2 and PClass1 satisfying the conditions above as follows. Let b = T2 , a = rrmax and w be the min HOL wait of the user. ( ab + 1 if w ≥ T1 PClass1 (w) = T2 (2) if w < T1 T1 w ( a2 b + a + 1 if w ≥ T2 PClass2 (w) = (3) w if w < T2 Functions PClass2 and PClass1 are shown in Fig. 1. From equations 2 and 3 it can be easily seen that, a2 b + a + 1 > a(ab + 1) 2
V (a b + a + 1)rmin
> (ab + 1)rmax
This satisfies condition C1. C2 is also satisfied since (ab + 1) rmin > rmax Wg (t) when Wg (t) < T2 . It can be noticed from figure 1 that when HOL wait is less that delay threshold, PClass1 ’s slope is TT21 times the slope of PClass2 , which fulfills C3. Since two unsatisfied users from the same class have the same P value, the one with the higher data rate will have a higher marginal utility, which conforms to C4. Also for both the classes, satisfied users have P value proportional to their HOL wait, which guarantees P3. Suppose there are more than two classes, i.e. |Π| > 2. Let Tmin denote the delay threshold associated to the highest priority-class. Then Pπ can be generalized for any priorityclass π ∈ Π as follows: ( aπ b + aπ−1 + · · · + a0 , if w ≥ Tπ Pπ (w) = Tmin Tπ w, if w < Tπ Let M be the number of codes allocated for the HS-DSCH channels and codes lef t be the number of codes left to be assigned. During each tti t, our SPS-Delay (Strict Policy Scheduling algorithm for Delay) algorithm uses the following steps to produce the list of users that are scheduled to receive data. (1) let i be the user such that i = arg maxj {Mπj (w)} where user j belongs to class πj and its current HOL wait is w.
P Class1 P Class2
P
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ab+1
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(i) if (codes(cqii ) < codes lef t) send to user i using the data rate for cqii else send to user i at the maximum rate possible using codes lef t number of codes. (ii) update codes lef t and remove i from U . (2) if (codes lef t > 0 and U 6= φ) goto Step 1. VI. S IMULATIONS We developed an HSDPA system in the OPNET simulator. Our system extensively simulates all the key entities of the network such as UEs, Node-B, RNC, SGSN etc. with most of the functionalities at all the protocol layers. In the following section we briefly describe the relevant portions of our simulator implementation. A. OPNET Simulator At each TTI Node-B schedules one or more users from the scheduling candidate set (SCS) to receive packets based on the scheduling algorithm being used. SCS includes (i) users with packets waiting in the transmission queue at Node-B, and (ii) users with pending packets in the retransmission queues. Retransmission packets have higher priority over first time transmissions. A UE measures the SINR for each received packet and reports the corresponding CQI back to the NodeB. For this, actual value interface (AVI) [12] is used to map a given modulation and coding set (MCS) to a received perpacket average SINR for obtaining a specific BLER (we use 0.1). For each UE category we use a separate AVI table that maps an MCS to a corresponding threshold SINR value. A UE reports a CQI for its current channel condition in each tti based on the average received SINR over the two previous ttis. Error decision for a received packet is also made based on the threshold SINR for the MCS associated to the transmitted packet. If the received SINR is less than the threshold SINR the packet is in error, otherwise a uniformly distributed random number y ∈ [0; 1] is generated. If y ≥ 0.1 the packet is successfully received. A packet can be retransmitted upto 4 times when in error. We also model a maximum of 6 parallel
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H-ARQ processes per user and a delay of 12 ms for the NodeB to get the associated ACK/NACK response back, from the time of transmission. We assume Chase combining for the HARQ process and use the following model[13]: µ ¶ ¶ n µ X Es Es n−1 =² · (4) N0 C,n N0 k k=1
where (Es /N0 )C,n represents the combined Es /N0 after n transmissions and (Es /N0 )k corresponds to the kth transmission. ² is the Chase combining efficiency which is set to 0.93. B. Traffic Model and Simulation Results We have a total of 30 HSDPA receivers that are arbitrarily placed in the network. We assume a heterogeneous environment where the received SINR for all users are different. In fact the received SINR can change over time even for a single user. We use ITU Pedestrian A as the pathloss model. Shadow fading standard deviation is 10 dB. The sliding window size for throughput measurement is set to 1000 tti. Half of the users in the cell belong to Class1 and the other half to Class2. The delay threshold for the two classes are 160 and 80 milliseconds respectively. All users belong to category 7. The offered traffic for each user is 72 kbps, where one 720 byte packet is transmitted every 80 ms. We fix the number of HS-PDSCH transmission codes used by NodeB to 10. We vary the stress level on the network by changing the HSDPA transmission power. The receiver drops a packet when the delay for the packet exceeds its delay threshold. We call a user unsatisfied if it drops more than 10% of its received packets. Figures 2(a), 2(b) and 2(c) show the average drop rates for the users in Class1, Class2 and the combined set of all users
respectively. Figures 3(a), 3(b) and 3(c) show the percentage of satisfied users in Class1, Class2 and the combined set of all users. We have noticed that M-LWDF performs the best when the network is under less stress (transmission power is high). However due to higher SINR variability under stressful situations, when the number of users with data in their buffer or user diversity order (UDO) increases, the gain in ratio (ri (t)/r¯i outweighs the loss in ratio Wi (t)/Ti . This results in M-LWDF favoring the users with (temporarily) better channel instead of users with longer HOL wait time and thus making wrong decisions. Additionally M-LWDF wastes a lot of resources for users in bad channel conditions in an effort to improve their delay performance. During stressful conditions this takes away resources from the other users who could have been satisfied otherwise. This is why we see a sudden rise in packet drop rates for M-LWDF in figures 2(a), 2(b) and 2(c) and a sudden decline in the percentage of satisfied users in figures 3(a),3(b) and 3(c) when transmission power is below 13 watts. AA-RRS has high drop rates even in low stress scenarios, with packet drop rate and number of unsatisfied users increasing with decreasing power level. Notice that AARRS and PF have higher drop rates and lower percentage of satisfied users for Class2 users than Class1 users in general, as it is easier to maintain the Class1 delay threshold. However since SPS-Delay puts a higher priority on Class2 users, it behaves in an opposite manner. On the other hand M-LWDF has similar drop rates and satisfied user percentage between the two classes, since it tries to find a balance among all users. Overall SPS-Delay performs the best for Class2 users and the combined case. For Class1 users PF performs better than SPS-Delay under stressful conditions as SPS-Delay sacrifices
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Class1 users in order to support more Class2 users. In our experiments, SPS-Delay reduces packet drops by as much as 80% over PF for all users and as much as 90% for Class2 users. SPS-Delay also increases the number of satisfied users among all users by as much as 32% when compared to PF. Among only the Class2 users, SPS-Delay increases satisfied users by upto 50% over PF. Figures 4 and 5 show the cummulative distributions for delays experienced by the best and worst users (in terms of average SINR) respectively from both classes, in particular for the 12.5 watt transmission power scenario. We plot the user from Class1 and Class2 using grey and black colors respectively. We should note that the delay distributions depend on many variables, especially on the experienced SINR of the users and the Cell load with respect to the HSDPA transmission power. Consequently the values in the plots only have relative significance. The delays for the worst user for some algorithms can explode. Therefore the complete distribution for these algorithms is not shown in the figures for illustration purposes. Notice that PF provides the best delay performance for the best user from both classes as it favors users with better channel condition. SPS-Delay and M-LWDF have different delay distributions for the best users from 2 classes with the user with lower delay threshold having a better distribution. However M-LWDF fails to meet the QoS guarantee for the Class1 user, whereas SPS-Delay satisfies the best users from both classes. The delays for the worst user in both classes explode due to their poor channel conditions when PF or AA-RRS is used. SPS-Delay and M-LWDF, unlike AA-RRS and PF, manage to provide good delay performance for the Class2 worst user but fail in Class1 user’s case. Also notice that M-LWDF has the best delay distribution for the Class1 worst user and spends more resources, as noted before, on the Class1 worst user and on users in bad conditions in general. On the other hand SPSDelay can sacrifice lower priority users in bad conditions to provide better services for the higher priority users and users in good channel conditions. M-LWDF (Class 2 User)
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VII. C ONCLUSION We developed and investigated a priority-class based scheduling algorithm for High Speed multiuser systems under
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time varying channel conditions, that can provide Differentiated QoS for delay sensitive services. We compared our algorithm against well known schedulers using the HSDPA simulator we developed in OPNET. We observed that SPSDelay performs significantly better than other well-known algorithms in terms of guaranteeing delay constraints, reducing packet drop rate, and increasing satisfied user percentage, especially for higher priority users. R EFERENCES [1] R. Knopp and P. A. Humblet, “Information capacity and power control in single-cell multiuser communications,” in Proc. IEEE International Conference on Communications 1995, ICC ’95, June 1995, pp. 331–335. [2] X. Liu, E. Chong, and N. Shroff, “A framework for opportunistic scheduling in wireless networks,” Computer Networks Journal, vol. 41, no. 4, pp. 451–474, 2003. [3] O. S. Shin and K. B. Lee, “Antenna-assisted round robin scheduling for mimo cellular systems,” IEEE Communication Letters, pp. 109–111, March 2003. [4] R. W. Heath and A. J. Paulraj, “Multiuser diversity for mimo wireless systems with linear receivers,” in Asilomal Conference on Signals, Systems & Computers 2003, Nov. 2003, pp. 982–986. [5] T. Kolding, F. Frederiksen, and P. Mogensen, “Performance aspects of wcdma systems with high speed downlink packet access (hsdpa),” in Vehicular Technology Conference, 2002, pp. 477– 481. [6] F. Kelly, “Charging and rate control for elastic traffic,” European Transactions on Telecommunications, vol. 8, pp. 33–37, January 1997. [7] T. Kolding, “Link and system performance aspects of proportional fair scheduling in wcdma/hsdpa,” in Vehicular Technology Conference, 2003. VTC 2003-Fall. 2003 IEEE 58th, vol. 3, pp. 1717– 1722. [8] P. Hosein, “Scheduling of voip traffic over a time-shared wireless packet data channel,” in IEEE International Conference on Personal Wireless Communications, 2005. ICPWC 2005., Jan. 2005, pp. 38 – 41. [9] J. S. Gomes, M. Yun, H.-A. Choi, J.-H. Kim, J. Sohn, and H. I. Choi, “Scheduling algorithms for policy driven qos support in hsdpa networks,” in To appear in Vehicular Technology Conference, 2007. [10] J. S. Gomes, H.-A. Choi, J.-H. Kim, J. Sohn, and H. I. Choi, “Integrating admission control and packet scheduling for quality controlled streaming services in hsdpa networks,” Submitted for publication to IEEE Broadnets, 2007. [Online]. Available: http://student.seas.gwu.edu/∼joegomes/publications/IntegratingPaper.pdf [11] M. Andrews, K. Kumaran, K. Ramanan, A. Stolyar, P. Whiting, and R. Vijayakumar, “Providing quality of service over a shared wireless link,” IEEE Communications Magazine, vol. 39, no. 2, pp. 150–154, Feb. 2001. appetelinen, H. Holma, [12] S. Hmlinen, P. Slanina, M. Hartman, A. L¨ and O. Salonaho, “A novel interface between link and system level simulations,” in ACTS Mobile Telecommun, Oct. 1997, pp. 599–604. [13] F. Frederiksen and T. Kolding, “Performance and modeling of wcdma/hsdpa transmission/h-arq schemes,” in Vehicular Technology Conference, 2002, pp. 472– 476.
