New load-based resource allocation algorithms for packet scheduling ...

New Load-based Resource Allocation Algorithms for Packet Scheduling in CDMA Uplink Wern-Ho Sheen, I-Kang Fu and Kao Yi Lin Department of Communication Engineering National Chiao Tung University, Hsinchu, Taiwan 300 Fax: +886 3 5710116 Tel: +886 3 5712121 ext 54578 e-mail: [email protected] , [email protected] Abstract- Packet scheduling plays a key role in radio resource management of a wireless access network to best utilize the available radio resource. A radio-link packet scheduling consists of two steps: prioritization and resource allocation. Prioritization is a procedure that prioritizes users’ order of service so as to guarantee QoS (quality of services), user’s fairness and achieve as high as possible system throughput. Resource allocation, on the other hand, executes the ``real” resource allocation under the constraints of physical limitations imposed by the radio segment of the access system. This paper proposes two new load-based resource allocation methods for packet scheduling in CDMA (code division multiple access) uplink with multiple services. One is called maximum-load based power allocation (MLBPA), and the other real-load based power allocation (RLBPA). MLBPA is easier to implement, but RLBPA has the better performance. By taking into consideration of the realistic channel conditions such as imperfect power control and transmit power variation, both of our methods outperform the current methods in terms of system throughput, frame loss rate and transmission delay. In addition, by monitoring the system load at all times, the planned coverage of the system can always be guaranteed.

I. Introduction Multiple services provisioning is an important characteristic in 3G and future wireless access systems. For example, four service classes have been defined in the W-CDMA (wideband code division multiple access) system, including conversational, streaming, interactive and background services [1]. Different services require different levels of QoS (quality of services) that includes the requirements for data rate, delay and packet error rate. In a multiple services scenario, radio resource management (RRM) becomes much more complicated than the single service in achieving guaranteed QoS and maximum system throughput.

WCNC 2004 / IEEE Communications Society

Packet scheduling plays a key role and interacts closely with other RRM algorithms such as load and admission controls so as to best utilize the available radio resource [2]. It enables resource sharing among packet users with the objective of achieving guaranteed QoS and maximum system throughput. Packet scheduling is accomplished in two steps: prioritization and resource allocation. Prioritization is a procedure that prioritizes users’ order of service, and resource allocation actually allocates radio resource to users under the constraints of physical limitations imposed by the radio segment of the access system. Prioritization methods have been extensively investigated in the literatures [3]-[5]. Each method addresses a unique tradeoff between user’s fairness and maximum system throughput. At one extreme, the method of round robin, where users are scheduled one after another is most fair but with the lowest system throughput because users may be scheduled to transmit under a bad channel condition. At the other, the method of maximum C/I (carrier to interference ratio) gives a higher priority to users with better signal quality, and that results in a higher system throughput. Nevertheless, users who are nearer to the base stations would be favored due to the less propagation loss, and this apparently treats the users close to cell boundary unfairly [3]. The method of proportional fair (PROP-FAIR) was proposed to improve fair allocation among users while retain as high system throughput as possible [4]. Lastly, the exponential rule was proposed in [5] to further improve PROP-FAIR by taking the packet delay into consideration. After prioritization, radio resource needs to be actually allocated to users. The allocation, however, is limited by physical constraints such as maximum transmission power and coverage requirement imposed by the radio segment of the access system. In [6], the optimization of power and rate assignment for a multimedia CDMA system was addressed. It

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aims to minimize transmit power and maximize data rate under an AWGN (additive white Gaussian noise) channel. In [7], a combined rate and power adaptation over fading channel was considered. Power/rate adaptation for voice users and truncated rate adaptation for delay tolerable data users were proposed, respectively to improve system capacity of a CDMA system. Power and rate allocation for CDMA systems was analyzed in [8] with a retransmission mechanism being taken into account. Finally, a minimum transmit power allocation was proposed in [9] for the WCDMA system without considering the channel variation due to fast fading and imperfect power control. This paper proposes two new load-based resource allocation methods for packet scheduling in CDMA (code division multiple access) uplink. By monitoring the system load at all times, the planned coverage of the system can be guaranteed. In addition, by taking into consideration of the realistic channel conditions such as imperfect power control and transmit power variation, both of our methods outperform the current methods in terms of system throughput, frame loss rate and transmission delay. The rest of this paper is organized as follows. In Section Ⅱ, system load with multiple services is analyzed. Section Ⅲ presents the proposed power allocation algorithms, and simulation results are shown in Section Ⅳ. Finally, the conclusions are given in Section Ⅴ.

(2)

where sm,l , ( Eb / I 0 ) m ,l and SFl are the associated signal power, bit energy to interference density ratio and spreading factor, respectively. From (1) and (2), it is easy to show that

k m ,l

sm ,l  ρ m ,l  1 + ρ m ,l  = s0 [ ρ0 ] [1 + ρ0 ]

(3)

Using (3), and defining f be the other cell to home cell power ratio (2) becomes

ρm,l =

km,l ⋅ s0  2    σ + (1+ f ) ∑∑Nt,q ⋅ kt,q ⋅ s0  − km,l ⋅ s0  t q   

(4)

where N t , q is the number of users using t-th service type and q-th data rate. Define M = ∑∑ N p,t ,q ⋅ kt ,q be the total number of BPUs received at the base station. Then, (4) becomes

ρ m ,l =

M=

In a multiple services environment, the number of users is no longer meaningful in defining the system load. Instead, a basic power unit (BPU) that is required to serve a basic service will be defined, and the total number of BPUs will be used as the measure of the system load for the uplink of CDMA systems. Denote s0 , SF0 and ( Eb / I 0 )0 be the BPU, spreading factor, and bit energy to interference density ratio of the basic service, respectively. Then, the minimum signal to interference and noise ratio (SINR) required at base station to provide this service is given by

km,l ⋅ s0

σ + (1 + f ) ⋅ s0 ⋅ M − km ,l ⋅ s0 2

(5)

=

k m , l ⋅ s + ρ m ,l ⋅ k m ,l ⋅ s − ρ m ,l ⋅ σ 2

ρm,l ⋅ (1 + f ) ⋅ s 1 1+ f

(6)

 1 σ2  ⋅ 1 + −  .  ρ0 s 

As a result, the maximum achievable obtained from (7) as

M can be

1  1  (7) 1 +  1 + f  ρ0  or s →∞ Therefore, the system load L for multiple services can be obtained as M max M |σ →0 =

L (1)

where I total is the total signal power received at 2 base station, σ is the thermal noise power, and SF0 = W / R0 with W and R0 denoting the spread


sm ,l  Eb  1 = 2  ⋅ + σ I SF I l total − sm ,l  0 m ,l

ρ m ,l 

and

Ⅱ. Up-link Load Analysis with Multiple Services

E  1 s0 = 2 ρ0  b   I 0 0 SF0 σ + I total − s0

bandwidth and data rate, respectively. Likewise, the minimum SINR for the m-th service with l-th data rate is given by

M I total = M max I total + σ 2

(8)

which has the same form as in the single service case [10]. Furthermore, the load contributed by the users with m-th type service and l-th data rate is given by

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Lm ,l = (1 + f )

km,l ⋅ s0 I total + σ

2

=

1 1 + 1/ ρ m ,l

(9)

In other words, the power for j-th user is allocated as

sj =

and

L = ∑ Lm ,l .

(10)

m ,l

As in CDMA systems, the noise rise ( NR ) of the system is defined as

NR =

I total + σ 2

σ

2

=

1 1− L

(11)

and it can be used as a measure of system load in the link budget calculation for planning the system coverage [2]. Therefore, as one of the radio resource management algorithms, packet scheduling has to make sure that the system noise rise is below a maximum a value, denoted as NRmax in order to ensure the planned system coverage. When NR = NRmax , the system is said to be fully loaded.

Ⅲ. Proposed Resource Allocation Algorithms As mentioned, packet scheduling is accomplished in two steps: prioritization and resource allocation. In this paper, we are more concerned with the resource allocation part of algorithm. Prioritization used in this paper is the one proposed in [9] which gives the users’ priority (in the same service class) according to the following rule.

Dj  τj >0  τj φj =  n  Dj ⋅ (2 − τ j ) τ j ≤ 0

(12)

where φ j is the user’s priority, D j is the packet length remained to be transmitted, τ j is the time left until the transmission delay bound, and n is an positive integer. n = 2 in this study. In the following, j is used as the user’s index in a service class for notational simplicity. In addition, different prioritized service classes are possible. Each of them has different set of data rates and transmission delay bounds. Users within a higher service class are scheduled first. A. Maximum Load Based Power Allocation (MLBPA) The first proposed resource allocation algorithm is called maximum load based power allocation (MLBPA). The basic idea of this method is that the resource is allocated to users under the fully-loaded assumption, that is, the maximum noise rise NRmax is always presumed in the power allocation.


ρ j ( I max + σ 2 ) 1+ ρ j

(13)

in order to satisfy QoS. If this power allocation is feasible, namely the corresponding transmit power of the user is less than the maximum transmit power, then the user is allowed to transmit in the next frame, otherwise the user’s resource will be reduced and reallocated. I max in (13) can be evaluated according to (11), if NRmax is given. Fig.1 illustrates the operation flow of this algorithm, and Table.1 is a list of the associated parameters. After prioritization by using (12), the allocated power of each user is evaluated by (13), beginning with the minimum allowable spreading factor SFmin . Since the spreading factors are designed in the order of two, S F j will be doubled if the evaluated s j is unfeasible at transmitter side or the scheduled system load is over the maximum one, that is NRtotal ≥ NRmax . If the evaluated s j is still unfeasible when SF j = SFmax , then such a user will not be scheduled this time. The algorithm will be terminated when all users have been processed. In this study, data rate can only be changed through using different spreading factor, although the methods of multicodes can also be used. As is clear, the algorithm is very easy to implement, and because the system load is monitored at all times, the planned coverage can always be maintained. B. Real Load Based Power Allocation (RLBPA) In MLBPA, power allocation is based on the fully-loaded assumption. As a consequence, a larger power than necessary may be allocated to users and that results in excessive interference. To overcome this drawback, a real load based power allocation (RLBPA) is proposed. The basic idea of this algorithm is as follows. Power is allocated to users by assuming a system load, NRnext . After allocation, the resulting system load NRtotal is evaluated and compared to the presumed one. If the difference of the two is to within a threshold, denoted as ∆ th , then the allocation is done. Otherwise, the assumed load will be lowered by a value of ∆NR , and a reallocation will be initiated. This process will be repeated again and again until the condition is satisfied or the maximum number of iterations K is reached. At the beginning of the algorithm, NRnext = NRmax . Fig.2 is the operation flow of RLBPA, and parameters are listed in Table.1. Since the final

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allocation in RLBPA is based on the real load, the transmit power of a user will be lower and the system performance will be improved. In addition, through the proper selection of ∆ th , a load margin can be allocated within the system in order to counteract the effect of load variation due to time varying channel conditions and imperfect power control. The algorithm becomes more robust.

Ⅳ. Simulation results A. Simulation model The uplink of the WCDMA system is used to show the results of our proposed algorithms. Table 2 is the detailed simulation parameters. A center cell surrounded by two tiers co-channel cells with wrap around technique is simulated [10]. The cell radius is 1.8 km. Poisson arrival with equal arrival rate in each cell is applied with uniform initial location distribution. For simplicity, only the interactive class is simulated with a portion of system load is set aside for voice users. New users are admitted to the system through a load based admission control policy with data being generated by interactive data traffic model in [11]. The data packet is scheduled on a frame by frame basis. The transmitted frame is considered to be lost and be asked to re-transmit, if the received signal quality is lower than the requirement. The retransmission follows the stop-and-wait automatic repeat request (ARQ) scheme in [12]. Users with vehicular mobility model in [11] are considered with maximum Doppler frequency shift of 185Hz. Rayleigh fading channel is simulated by filtering AWGN technique in [13] and log-normal shadow fading with 20m correlation distance is considered [11]. Closed loop power control is executed slot-by-slot, and hard-handoff is provided for packet data users [14].

imperfect power control has been taken into account by properly selected the NR value used in the power allocation. In the algorithm proposed in [9], however, the minimum power is always allocated, and no room is left for accommodating the load variation. Fig. 4 shows the performance of average packet delay. 20% improvement over the reference algorithm is observed with RLBPA for λ < 0.5 . However, the improvement becomes smaller for λ > 1.0 , where the system is close to the fully-loaded condition. MLBPA also performs better than the reference algorithm, although the improvement margin is much smaller. The improvement of the average packet delay can be attributed to the reduction in the frame loss rate as shown in Fig. 3. Fig.5 is the performance of overall system throughput. RLBPA provides 7-15 % improvement over the reference algorithm for λ < 0.5 , and about 3% for λ > 1.0 . On the other hand, MLBPA provides 3-7 % and about 1.5 % improvement over the reference algorithm for λ < 0.5 and λ > 1.0 , respectively.

Ⅴ. Conclusions The issue of power allocation in packet scheduling is investigated for CDMA uplink. Two new load-based power allocation algorithms are proposed and simulated. By taking into consideration of the load variation due to time-varying channel conditions and imperfect power control in practical systems, both of our methods significantly outperform the current methods in terms of system throughput, frame loss rate and transmission delay. In addition, by monitoring the system load at all times, the planned coverage can always be guaranteed.

B. Simulation results Frame loss rate, average packet delay and overall system throughput are used as performance measures for comparing different power allocation algorithms. In addition to MLBPA and RLBPA, the algorithm proposed in [9], denoted as reference algorithm, is also simulated for benchmarking. Fig. 3 shows the results of frame loss rate. As is seen, all the algorithms provide significant improvement over the no packet scheduling case. However, MLBPA and RLBPA provide 25-30% and 30-40% improvement over the reference algorithm, respectively. The main reason for this improvement is that in our algorithms, the load variations due to propagation conditions and


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Table I

References

PARAMETERS IN MLBPA AND RLBRA ALGORITHMS [1] 3rd Generation Partnership Project (3GPP) TS 23.107 v5.5.0, “Quality of Service (QoS) concept and architecture”. [2] Harri Holma and Antti Toskala “WCDMA for UMTS – Radio Access For Third Generation Mobile Communications”, ISBN 0-471-72051-8 , John Wiley & Sons, pp.226-229, pp.157-159, pp.160-162, 2000. [3] J. M. Holtzman, “Asymptotic Analysis of Proportional Fair Algorithm”, IEEE International Symposium on Personal, Indoor and Mobile Communications, Vol. 2, F33-37, October, 2001. [4] A. Jalali, R. Padovani, R. Pankaj, “Data throughput of CDMA-HDR a high efficiency-high data rate personal communication wireless system”, IEEE Vehicular Technology Conference Proceedings, Vol. 3, pp1854-1858, Tokyo, May 2000. [5] J-H. Rhee, J. M. Holtzman, D-K Kim, “Scheduling of Real/Non-real Time Services: Adaptive EXP/PF Algorithm”, IEEE Vehicular Technology Conference, Vol. 1, pp462-466, April, 2003. [6] A. Sampath, P. S. Kumar, J. M. Holtzman, “Power Control and Resource Management for a Multimedia CDMA Wireless System”, IEEE International Symposium on Personal Indoor and Mobile Radio Communications, Vol. 1, pp21-25, September, 1995. [7] S. W. Kim, Y. H. Lee, “Combined Rate and Power Adaptation in DS/CDMA Communications over Nakagami Fading Channels”, IEEE Transaction on Communications, Vol. 48, Issue: 1, pp162-168, January 2000. [8] J. B. Kim, M. L. Honig, “Resource Allocation for Multiple Classes of DS-CDMA Traffic”, IEEE Transaction on Vehicular Technology, Vol. 49, Issue: 2, pp70-76, April 2001. [9] O. Sallent, J. Perez-Romero, F. Casadevall, R. Agusti, ”An Emulator Framework for a New Radio Resource Management for QoS Guaranteed Service in W-CDMA Systems”, IEEE Journal on Selected Areas in Communications, Vol.19, No.10, pp.1893-1904, October 2001. [10] Jhong Sam Lee and Leonard E. Miller “CDMA Systems Engineering Handbook”, Artech House, pp1020 –1022, pp1012-1016, 1998. [11] ETSI TR 101.112 v3.2.0, “Selection procedures for the choice of radio transmission technologies of the UMTS”. [12] 3rd Generation Partnership Project (3GPP) TS 25.231 v5.0.0, “MAC protocol specification”. [13] Gordon L. Stuber, “Principle of Mobile Communication”, Kluwer Academic Publisher, Fourth Printing, pp132-134, 2000. [14] K-Y. Lin, “New Packet Scheduling Algorithms with QoS Provisioning in WCDMA Systems”, MS Thesis, Department of Communication Engineering, National Chiao Tung University, Chinchu, Taiwan, R.O.C., July 2003.


Itotal

Total received signal power at base station

Inext

Presumed Interference power level in next frame

Imax

Maximum allowable interference power

Iother

Currently received interference from other cells

NRnext

Presumed noise rise level in next time duration

NRtotal

Total scheduled noise rise at base station

NRmax

Maximum allowable noise rise

SFj

Spreading factor used by user j

SFmin SFmax

Minimum usable spreading factor Maximum usable spreading factor

ρj

Required SINR for user j

Sj

Received signal power at base station from user j

Gj

Channel gain to user j

STj

Transmit signal power of user j

STmax

∆NR

∆ th K

Maximum transmit power Adaptation step of the presumed load Threshold between the real and presumed load Maximum number of iterations

TableⅡ SIMULATION PARAMETERS System Environment

WCDMA / Uplink channel

Cell Layout

19 cells with wrap around

Cell Radius

1.8 km

Interference Margin

3dB

User Initial Location

Uniform distribution

Path Loss Model

175.7+35.2Log10(R)

Standard Deviation of Shadowing

8dB

Correlation Distance of Shadowing

20m

Rayleigh Fading User Arrival Type Packet Data Traffic Model User Mobility ARQ Mechanism Type Packet Delay Bound

∆NR

∆ th

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Filtered AWGN Poisson process Interactive data traffic Vehicular mobility model Stop and wait 1 sec 0.01dB 0.1dB

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Fig.3 Frame Loss Rate

Fig.1 MLBPA Algorithm

Fig.4 Average Packet Transmission Delay

Fig.5 Average Throughput of Each Cell Fig.2 RLBPA Algorithm


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