EFFICIENT RADIO RESOURCE MANAGEMENT SCHEME FOR MULTIMEDIA SERVICES IN BROADBAND MOBILE NETWORKS J. Luo1 , M. Dillinger1 , E. Schulz1 , E. Mohyeldin1 , M. Weckerle1 , and B. Walke2 1
SIEMENS AG, Otto-Hahn-Ring 6, D- 81730, Munich, Germany,
[email protected] 2 Aachen University of Technology, D-52074, Aachen, Germany
Abstract - This paper defines a radio resource management concept for multimedia services in future broadband mobile radio networks. Based on the Hiperlan/2 system we design an adaptive radio resource scheduler which autonomously adapts to current traffic types and volume. We assume IP traffic models for voice, HTTP and video users and optimize the scheduling functions in the access point (base station) of broadband mobile network in order to maintain QoS for the services. The interworking among admission control, load control and the adaptive scheduler is shown. The principles discussed here are not restricted to H/2 but can be exploited for any radio access technique (RAT) using adaptive schedulers, e.g. dynamic scheduling for reconfigurable terminals. Keywords - Radio Resource Management, Broadband Radio Network I. INTRODUCTION Radio system beyond 3G will take into account the interworking of heterogeneous networks co-existing in the same operating area. The radio resource management (RRM) strategy is based on the estimated traffic types and their volume to allow for an adaptive scheduling algorithm, which is useful in reconfigurable systems embedded with heterogeneous traffic types. The load information and traffic information are required to be shared by co-operating networks. The interworking among sub-systems and different control layers of the future system is illustrated in our work [1]. In this paper, we only include single system part. Each RAN needs an efficient interworking among the resource scheduler, load control unit and admission control function based on the measured and predicted traffic volume. We will show how to design the scheduling function in an IP-based network and how admission control takes into account the scheduling information based on priority vector information (PVI) to make intelligent decisions. HIPERLAN/2 (H/2) is a good representative for broadband wireless radio network and is mainly designed for slow vehicular speed and short-range coverage area with a high data rate up to 54Mbits/s. We choose H/2 as an experimental network that has high tendency to emerge as a sub-network co-operated with cellular networks. It offe rs IP packet based convergence layer to guarantee the QoS to different type of networks [2]. Furthermore, it has a high tendency that the evolution of the 3rd generation mobile wireless networks will
0-7803-7589-0/02/$17.00 ©2002 IEEE
be embedded by mobile wireless Internet. With our ongoing activities in mobile network architecture for 3rd generation mobile system [3], we envisage that the broadband networks are based on IP transport. Based the review of the similarities between Hiperlan/2 and IEEE 802.11a, it is concluded that same basic principle of mode selection can be applied in both systems. The paper is organized as following: the related H/2 RRM functions and system restriction are introduced in section II. Three type of traffic models are depicted in section III. In section IV, the scheduling algorithms and the affiliated admission control function are revealed; Simulation results are in section VI. In the end, some conclusions and an outlook are drawn. II. SYSTEM OVERVIEW The call admission control (CAC) function assigns committed priority values along with finite classification to the admitted user traffic to the scheduler. As depicted in [1], the scheduler is required to consider all the incoming sessions, ongoing sessions with different QoS requirements based on the priority vectors and apply right scheduling algorithm in the same priority queue. The performance of scheduling algorithm also informs the load control function about the delay constrains in order to help the later upgrade the prioritization configuration during the connection. The physical limitation of H/2 system should be analyzed first before the scheduling algorithm is analyzed. It is defined in [4][5] that in DLC PDU trains, only LCH channel are assigned for users, i.e. 54bytes PDU packets are generated for each unit of traffic transmission. According to the structure of the MAC frame, the payload of the traffic is combined with the control channels (SCH, FCH, BCH, ACH and RCH) to be scheduled in the 2ms frame. As already been calculated in [6], the maximum time for the scheduler to utilize is 1887.6us, denoted by TM . The duration time of the LCH channel is TLCH , which varies according to the user bandwidth, i.e. the coding scheme and modulation scheme. E.g. in the basic mode, the LCH channel covers TLCH = DLCH / RBAS = 72us , where D LCH denotes the amount of bits carried by one LCH channel, RBAS is the basic mode transmission rate of 6Mbits/s. The highest mode defines the theoretical upper bound of system throughput and maximum number of users. Suppose NLNK
PIMRC 2002
denotes the number of links supported in one MAC fame, the following restriction applies: TM ≥
NLNK
∑ (Q ⋅ T i =1
i
LCH
+ TSCH ) + max( 0, ( N LNK − 3) ) ⋅ TFCH (1) 3
Where TFCH shows the time for each Information Element (IE) in FCH channel with one IE indicating 3 connections [4]; TSCH is the time duration for SCH channel and constant over different link types, i.e. the control channel is kept as basic mode transmission; Qi is the number of LCH channels, i.e. quantum, assigned to the ith link. In the round robin algorithm introduced in section IV, Qi is constant for different connections. III. TRAFFIC MODEL Video, voice, and data traffic are three major typical traffic classes that we considered in this paper. A. Voice over IP For IP based voice communication, considering the activity, the on-off model is adopted as [7]. In a conversation, the term talkspurt is defined as the active phase for each party. Producing these talkspurts is an on-off model with activity and silent periods defined by a negative exponentially distributed random variable with the mean values equal to 3s. The peak data rate of the AMR coded during on period is 12.2 Kbps, the packet size generated during active period is 32 Bytes (payload), while the header composed from RTP, UDP and IP has 40 Bytes using IPv4 and even 60 Bytes using IPv6. An ideal header compression scheme is assumed in framing protocol overhead of 8 Bytes. The transmission time interval of these (32+8) Bytes packets is assumed to be 20 ms. B. Video over IP The video traffic presents two key characteristics. One is the variable data rate due to scalable video ability of H.26* from ITU and MPEG standards. The current ‘state of art’ scalable video coding algorithms are often based on those two groups o f standards; the next characteristic is real time service requirement. The video is captured, encoded in a scalable bit stream, packetized, most appropriately using RTP/UDP and then put into IP packets. The IP packets can contain information in the differentiated service field corresponding to the priority of the packet. The video IP packets are defined in the second priority class. We assume video sources generating 30 frames/s. Each frame consists of 250 000 pixels that are digital coded. The AR Markov model presents the video traffic with continuous-state, discrete-time stochastic process behavior.
Let λ (n) represent the bit rate of the single source during the nth frame. A first order Markov process λ (n) is generated by the recursive relation:
λ ( n) = aλ ( n − 1) + bw(n)
(2)
where w (n ) is a sequence of independent Gaussian random variables and a and b are constants. The key parameters of the model are: a = 0.8781 , b = 0.1108 and E ( w) = 0.572 . From the measurement, E (λ ) = 0 .52 bits/pixel give fairly accurate approximation of the bit rate as detailed in [8]. Due to the high throughput nature of video traffic, the compressed IP header can be neglected in the simulation campaign [9]. C. Data Service and HTTP Traffic The data service refers to the applications like WWW, Email or FTP. As HTTP traffic is the dominating data service [6], it is modelled and the corresponding scheduling algorithm is investigated. ETSI suggested a data traffic model which is defined in [10]. However, it does not consider the more strictly required traffic models, which need at least the objects level. Alternatively, we choose [11] as a reference to model the HTTP traffic. We model the web session consisting of a main object and many in-line objects. The viewing time is concatenated after the previous in-line object. The number of in-line objects is best approximated by a gamma distribution. The size of both the main object and each inline object follows approximately a Lognormal dis tribution, though with different mean and variance, where the detailed parameters can be found in [6]. IV. PROPOSED SCHEDULING STRTEGY Radio resource scheduler controls the traffic allocation in the MAC frame according to the traffic QoS classification. It should measure the available radio resource and inform admission control function to issue the admission commands. The control structure is shown in Fig. 1. The scheduler measures incoming traffic type, reserves the bandwidth for individual traffic class according to priority vector introduced in sub-section A; in addition it allocates the traffic based on dedicated algorithms for traffic types (zdimension) introduced in sub-section B. A single AP coverage area is investigated in this paper. The x axis in Fig. 1 shows the direction of traffic flow, the y axis is the QoS axis, which defines how much weight the QoS level gets and z axis is the user axis, which describes how to put the user data into the QoS queue. In this paper, we assume that the voice users have highest priority, i.e. we allocate the resources to different traffic types with assigning different traffic weight. Three different scheduling algorithms are shown in z dimension.
B. Scheduling Algorithms in Z-dimension
QoS Class 1 Queue
(Classes) Y
To Resource Estimator
We adopt the first come first serve (FCFS) scheduling algorithm for voice over IP traffic according to the real time nature. The earlier coming talkspurt is put in the queue with higher priority.
PW 1 QoS Class 2 Queue
Admitted Sessions PW 2
Scheduler QoS Class 3 Queue
PW 3 Z
X
(Queue Length)
(Traffic in Same Class)
Fig. 1, Radio resource scheduler structure A. Y-Dimension Scheduling and FSP Algorithm The CAC is based on required QoS and current load of the system to assigns priority weight (PW) for each traffic type. Let φ j denote the PW value for the jth class, with j = 1,2,3 . In this case, the idea of GPS [13] can be applied. For the jth traffic stream as shown in Fig. 1, the amount of resource in the interval (τ , t ] will fulfill the following requirement: S m (τ , t ) φm ≥ S n (τ , t ) φn
(3)
, where S m (τ , t ) represents the amount of session m traffic served in an internal (τ , t ] ; whereas the positive real number φ m is its weight. A session is backlogged at time t if a positive amount of that session’s traffic is queued at time t. In the case that there only exist the same class traffic services, by assigning different weights for each user can realize the scheduling algorithm described in the following sub-sections. Therefore, by integrating all the types of services together in both side of the equation, a minimum service rate can be obtained as the ratio between the committed weight to the sum of all. In addition, the PW can also applied into the calculation of flexible switching point (FSP), as shown in the following equation: 3
TS =
∑φ j= 1
3
∑φ j =1
j
⋅N
jD
3
with
T A = T M − max( 0,
∑N
jD
j
⋅N
+
(4)
jD
j =1
j
⋅N
+ N jU − 3
j =1
3
In the high throughput requirement case, i.e., the video traffic, the method of poling users in the same QoS class with successive transmission requires recalculation of the virtual finishing time for each in-service traffic. The high bit rate transmission reduces the impact of the quantisation effect resulted from the packetising. It can be proven that LFFS scheduling algorithm works optimally for high throughput required for real time service. Lemma1: Scheduling resource over finite number of services is a mathematical programming problem with convex property. Lemma2: For a convex mathematical programming problem, the local optimum tells the same truth as global optimum. Theorem: Last finish first serve (LFFS) scheduling algorithm gives the lowest average waiting time for high throughput, real time service. TABLE 1, Comparison between LFFS and round robin algorithm a Users
LFFS
RR
1Users
2 ms
2 ms
2Users
4s
6s
a
⋅ T A + o (T A )
3
∑φ
All arrived packets of HTTP traffic, i.e., data traffic in the second class are kept in a circular queue. A small unit of resource, called quantum, is defined. The resource scheduler goes around this queue, allocating the resource to each process for a time interval of one quantum. Newly arrived traffic is added to the tail of the queue. The performance of the RR algorithm depends heavily on the size of the quantum. If the quantum is very large, RR algorithm is similar to the FCFS algorithm. The optimal quantum size for HTTP traffic is shown in [6].
)
jU
. Where N jD and N jU
gives the number of user expected to appear in the MAC with jth weight in downlink and uplink respectively, TS is the time dedicated to downlink phase. The following part of the section discusses the z-dimension scheduling algorithms.
Result from 100s’ System Simulation
Brief Proof: Suppose U i defines the allocated rate for session i. The homogeneity of allocated resource contains monotonous relationship with mean value of waiting time. The product of allocated rate gives the measurement of homogeneity. And LFFS algorithm guarantees the N maximum U . TABLE 1 shows the waiting time LNK
∏
i
i
comparison between LFFS and round robin algorithm.
A. Overview The packet error ratio (PER) resulted from the fading and interference in the wireless network is most severe to the system. The investigation in this paper considers that ARQ algorithm is based on selective repeat (SR) algorithm [14], i.e. the reception of a packet is acknowledged by the receiver by sending either an acknowledgment (ACK) or negative acknowledgment (NCK) to the transmitter. Only the erroneous packets are retransmitted. Due to the reason that only the performance of scheduling algorithm is the emphasized aspect, the retransmission because of time out is not considered. B. Scheduling for Voice over IP Traffic
Where the system utilization factor ρ is a random variable based on the bit rate as defined in the VoIP sub-section. We consider video frame, i.e. 1 / 30 s as one unit. The comparison of simulation results and theoretical analysis is shown in Fig. 2. The main reason of the discrepancy is due to the short simulation time (100s). If 20 ms is chosen as the criteria to judge the video user satisfaction, the maximum number user can be admitted can also be found in Fig. 2. 2
10
1
10
0
Waiting Time
V. SIMULATION RESULTS
10
-1
10
1
For voice over IP traffic, one instance tW of the waiting time
C. Result for Scheduling over HTTP Traffic Class It is shown that quantum size with 2 LCH channel is optimal for HTTP user in such system. The detailed PDF curve of user throughput in [6] shows a sudden degradation of user throughput will happen if the number of HTTP exceeds 400. It means the maximum number of data users is not restricted by the radio resource but the system limitation, i.e., maximum connections one AP can handle [5]. The coexisting scenario of voice and HTTP traffic is detailed in [6]. D. Scheduling Algorithm for Video Traffic Similar like the analysis for voice over IP traffic, the waiting time can also be analyzed by Little’s theory [12], as the following equation: 1
T=
ρ
0
11
5 10 Video Users in a Single AP Coverage (100s Simulation Time)
15
Fig. 2, Video traffic scheduling for different bandwidth E. Mixed Traffic Scheduling As described in previous chapter, the GPS scheduling algorithm is applied over different service classes. A low loaded system case with 80 HTTP users, 5 video users and varying number of voice users is studied. In Fig. 3 and Fig. 4, the PER is assumed to be 0, PW vector [voice, video, HTTP] as [3, 2,1] , [5,2,1] , [10, 2,1] and [∞,2,1] is compared, where the last case [∞,2,1] means voice user has supreme priority, video and HTTP traffic utilize the rest resource based on the PW value. It can be seen that if the PW is designed in the safe region, i.e., the PW works in the region that guarantees certain number of first priority users without sudden degradation for lower priority users’ performance; therefore, the resource management has more freedom, e.g. load control via assigning time varying weights to different services. -1
10
[3,2,1] [5,2,1] [10, 2,1] [inf, 2,1]
Safe Region -2
10
-3
10
0
5
10
15
20
25
30
35
40
Number of Users in a Single AP Area w.r.t Weighting Vector
1
∫ 1− ρ ⋅ λ ⋅ f
6
54M Link 36M Link 27M Link 18M Link 12M Link 9M Link 0
Average Waiting Time for Voice Service Wv[s]
For the H/2 system, the quantisation of transmission resulted from fixed length of PDUs in MAC frame and real time characteristic of the voice traffic give the complexity of the analysis problem. The arrival rate depends on the number of users; whereas, the processing time varying according to the maximum rate. The low utility of the PDUs gives big discrepancy away from the ideal utilization. Two solutions have been investigated, i.e. alternative radio frame utilization and dynamically sized PDU usage, which are detailed in reference [15].
5
3
10
variable TW is the discrepancy between the time when one talkspurt is served and the time when this talkspurt is terminated. The waiting time gives the upper bound for maximum delay during the talkspurt time, which can help to judge the performance of the scheduling algorithm.
2
-2
ρ
⋅ df ρ
(5) Fig. 3, Waiting time of voice user w.r.t. voice users and weighting vector
8
10
[3,2,1] [5, 2,1] [10, 2,1] [inf, 2,1]
System Throughput [bits/s]
Safe Region 7
10
6
10
5
10
4
10
0
5
10
15
20
25
30
35
40
system condition when the system is highly loaded. The optimal value for the key parameters, e.g. PW vector should be in look-up table for different traffic composition. Based on the PW vector introduced in this paper, system admission control and load control policy can be designed. E.g. weighting vector can be configured according to the billing system, system load, interference behavior, etc. The interworking of varying PW vector, load controller and CAC are under investigation and is important for joint radio resource management schemes where heterogeneous networks are coupled.
Number of Users in a Single AP Area w.r.t Weighting Vector
Fig. 4, System throughput w.r.t. voice users and weighting vector An investigation of adjusting PW vector and system load is also made. The scenario that only one video user with varying number of voice users and 200 data users is studied. To save space, only the waiting time result of the video user is shown in the following figure. The big difference in medium voice density case is due to the significant ARQ impact in severe system condition and the low utilization factor of voice traffic. Whereas, in the same PER case, the upper curve shows much worse performance than the lowest one, where they resulted from only 1 element difference in the PW vector.
[1]
[2]
[3]
[4]
[5]
0
10
[6]
Video Traffic Waiting Time [s]
PER=0.1, [3,2,1] PER=0, [3,2,1] PER=0.1, [3,3,1]
-1
10
[7] [8] -2
10
[9] -3
10
0
10
20 30 40 50 60 Number of Users in a Single AP Area
70
80
Fig. 5, Waiting time of video traffic w.r.t. voice users in single AP coverage area
[10] [11]
VI. CONCLUSIONS AND OUTLOOK
[12]
A maximum number of users in a single AP coverage area can be obtained according to the proposed scheduling algorithm and PW vector, as it is an important value for load and admission control. The interworking among scheduler, load control and admission control functions has significant impact to the scheduling algorithm. Based on observed waiting time and throughput, the CAC can effectively make admission decisions. The adaptive priority vector can significantly improve the system performance in worse
[13]
[14] [15]
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