Xin Gang Wang, Geyong Min, John E. Mellor. Mobile Computing and Networks Research Group. Department of Computing, School of Informatics, University of ...
Adaptive QoS Control in Cellular and WLAN Interworking Networks* Xin Gang Wang, Geyong Min, John E. Mellor Mobile Computing and Networks Research Group Department of Computing, School of Informatics, University of Bradford, Bradford, BD7 1DP, UK. E-mail: {X.G.Wang, G.Min, J.E.Mellor}@bradford.ac.uk
Abstract: To provide end-to-end Quality of Service (QoS) support for the integrated WLAN and cellular networks is a challenging task, due to the various constraints, such as the unbalanced capacity of two systems, handoff from users’ mobility and unreliable wireless media. In this paper, we propose a generic reservationbased QoS model for the integrated cellular and WLAN networks. It uses an adaptation mechanism to address the above issues and to support end-to-end QoS. The validity of the proposed scheme is demonstrated via simulation experiments. The performance results reveal that this new scheme can considerably improve the system resource utilization and reduce the call blocking probability and handoff dropping probability of the integrated networks while still maintaining acceptable QoS to the end users. Keywords: WLAN and Cellular network Integration, QoS framework, adaptive QoS, dynamic bandwidth adaptation.
1. INTRODUCTION Interconnecting WLAN radio access network with 3G or even 2G offers an efficient way to enhance the network operator service. It is an important part of the next generation mobile communication systems [1-3]. However, the design of a network architecture that provides end-to-end Quality of Service (QoS) support is a challenging task, particularly when the objective is to make the interoperation between the two technologies as seamless and as efficient as possible. Many difficulties emerged when providing the QoS solution, such as the unbalanced capacity of two systems, handoff from users’ mobility and unreliable wireless media. To enable efficient use of scarce resources provided by the cellular networks while also maintaining strong service guarantees, we propose a generic reservationbased QoS model for the integrated cellular and WLAN network. Under the proposed QoS framework, we develop adaptation mechanisms to address the various challenges generated by the design of an integrated WLAN and 3G networks. The validity of the model is demonstrated via simulation experiments. The performance results indicate that the proposed model can improve the system resource utilization and considerably reduce the call blocking probability and handoff dropping probability of the integrated networks while still maintaining acceptable QoS to the end users. This paper is organized as follows. Section 2 discusses the problems associated with the QoS over the integrated system. In Section 3, we introduce and analyze the proposed QoS framework. Section 4 describes our simulation setup and analyses the performance results based on the proposed framework. Finally, Section 5 concludes the paper. *
This work is partially supported by the EU Network of Excellence (NoE) Euro-NGI. P24/1
2. QOS ISSUES FOR INTEGRATED WLAN AND UMTS Unlike in the homogeneous wired networks, providing QoS for integrated WLAN and UMTS networks has some fundamental bottlenecks. Firstly, WLAN and UMTS have different transmission-rate capacity over the radio interfaces, therefore the handoff between the two systems makes the maintenance of QoS connection very hard. WLAN can provide a transmission speed from 11Mb/s up to 54Mb/s theoretically, while UMTS has only 144kb/s at vehicular speed, 384 kb/s at pedestrian speed and 2 Mb/s when used indoor. If we keep the QoS resource assigned by UMTS to a connection which is actually in a WLAN hotspot, the advantage of the high speed of WLAN is not fully taken. On the other hand, if we use a WLAN parameter for a station in the UMTS network, the connection may not be admitted at all. Therefore, to maintain a sensible QoS framework, one has to consider the significant difference transmission capacity between two systems especially when user handover takes place. The second constraint is that WLAN operates on a free ISM band and has a lot of uncontrollable interference (i.e. microwave), although some techniques are used to reduce the interference like spreading spectrum. Such kinds of problems are beyond engineering control and hard QoS guarantee is very difficult to achieve in certain conditions. To support QoS in packet switching networks, there has to be some mechanisms to control network loads under a threshold so that the system can provide a satisfied performance. The third bottleneck is that 3G cellular networks are very well designed with careful network planning and mature admission control algorithms. The achievable QoS level is relatively high, while 802.11e [4] WLAN works under a more robust environment and is difficult to achieve hard QoS.
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Figure 1. Proposed QoS architecture
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3. AN ADAPTIVE QOS ARCHETECTURE Increasing data service requirements and Internet applications is driving the cellular network evolving into an IP based packet switching network [2]. Our proposed QoS framework assumes a packet switching core network based on the UMTS network architecture. However, this holds the same relationship with the GPRS 2.5G networks or other packet switching cellular systems. The overall architecture is shown in Figure 1. It is well known that Internet has some fundamental scalability limitations [5] when it comes to manage individual traffic flows with the approach of resource reservation. Its successor, the prioritization approach addresses the scalability problem at the cost of coarser service granularity. To enable efficient use of scarce resources provided by the cellular networks while also maintaining strong service guarantees, we adopt the reservation based systems [6]. In WLAN the reservation is achieved by using the HCF of the 802.11e and in UMTS is achieved by the functionality of BS. We analyze each of the proposed components below. 3.1 Components Analysis • QoS Policy Provisioning Users’ context with QoS requirement is first issued to policy provisioning module (PPM), where the users’ subscribed information together with traffic classes is examined (Figure 2). Then a QoS signal with suggested degradation profile is made and sent to both the end user and connection admission control (CAC) module. PPM Degradation profile
Subscribe Information
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Figure 2. A PPM Structure • Degradation Profile It allows for negotiation of established QoS connection through the degradation profile. When the user requests to establish a QoS call, some network resources need to be admitted. The requested QoS has to be allocated when the connection is set up. If certain conditions change over the activation time, a negotiation procedure will be called. The degradation profile can include the following QoS attributes: • The minimum acceptable rate (bit/sec), • The Bit Error Rate (BER) or Frame Error Rate (FER), • The maximum loss ratio (the proportion of received packets to undelivered packets), • The maximum tolerated delay (ms), • The maximum tolerated jitter (ms) (the variation in delay). To describe how much the overall system is degraded, we define a new performance merit called system degradation degree. Some system parameters are described before we introduce the definition of system degradation. The traffic class of a connection is defined as Ci , where C i ∈ {C1 , C 2 , , Ci , , C K } and K is the number of service classes. The corresponding bandwidth requirement for each class is defined as Bi ∈ {B1 , B2 , , Bi , , BK } , for the sake of simplicity we assume that all the connections in the same class have the same requested bandwidth. Also let Di ∈ {D1, D2 , , Di , , DK } denote the minimum bandwidth request defined in the connection degradation profile. P24/3
Let p i (t ) denote the degradation probability of class i and n i (t ) the number of connections from class i at time t. Thus the degradable bandwidth at time t can be written as: K i =1
( Bi − Di ) p i (t )n i (t )
(1)
We define bandwidth degradation degree BR as the ratio between the bandwidth reduced and the requested bandwidth. K
BR =
i =1
( Bi − Di ) p i (t )n i (t ) K i =1
Bi ⋅ n i (t )
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The overall system degradation degree SD is the integration of BR over the period t: K i =1
SD =
( Bi − Di ) p i (t )n i (t ) K
t
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Bi ⋅ n i (t )
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• QoS Connection Admission Control (CAC) The CAC module receives a connection request from the PPM along with the QoS requirements and it consults with the MMM to get the users’ mobility status. Then CAC uses some reservation protocols, such as RSVP, to book the actual resource for users’ flow. Based on RSVP signaling feedback, the connection is finally granted, declined or renegotiated. Applying QoS means treating some traffic in preference to others, and this implies the ability to reject traffic. Especially in wireless mobile communication networks, uncontrolled error rate and users’ mobility make us have to look for adaptation solutions. The use of degradation profile provides us a gradation between different QoS merits; therefore, negotiation between different network flows is an effective way to improve the overall system performance. • QoS Mobility Management Module (MMM) Users’ mobility has a significant impact on the QoS of CAC and it plays an important role in the model. Within an integrated cellular and WLAN network, a handover can occur when a mobile node enters a Hot-Spot area or when it decides to leave the Hot-Spot area. Because Hot-Spots are usually within the coverage of cellular networks, the actual handover is not necessary to happen and a decision should be made on the users’ desire. We name this user triggered handover or Desirable Handover (DH). Note it is different from the general term of handover, because it is not time critical as the mobile nodes can be connected to WLAN and cellular networks simultaneously. A DH may occur when a mobile node roams into a WLAN. This implies that the route taken by data will change. Any QoS established for that flow before will be disrupted. A simple solution is to establish a new WLAN reservation before handing the mobile node over the WLAN, because DH’s time tolerance makes this approach realistic. Also notice that the wireless link bandwidth will have risen dramatically, so the new submitted QoS profile should consider users’ subscription status and give an appropriate request. A normal handover occurs when a mobile node roams from WLAN into a cellular network. A new reservation has to be made again. Moreover the actual handover time needs to be kept tightly in order to provide seamless service. Since the network resources that the user booked in WLAN is normally over the capacity of the UMTS, the actual probability of dropping handoff could be very high. An adaptation mechanism is needed to be embedded in CAC and this module, which can reduce the user’s QoS request by the definition of degradation profile. Therefore, the system performance can be improved without losing acceptable QoS level. • QoS monitoring
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Once the streaming data has been transmitted, traffic meters measure its temporal properties against the QoS contract. If the established end users’ QoS profile is not satisfied, this monitor may pass the state information to CAC or other components to trigger specific actions. This feedback approach enables the QoS merits to adapt with the dynamic changes in the networks. 3.2 QoS Class Mapping To provide a unified QoS traffic classes, the QoS traffic classes from UMTS and WLAN are mapped in a new set of QoS traffic classes namely: Broadband conversational (B-conversational), Broadband streaming (Bstreaming), Narrowband conversational (N-conversational), Narrowband streaming (N-streaming), interactive and background. Their mapping relationships are shown in Table 1. Class 1 2 3 4 5 6
Integrated Network B-conversational B-streaming N-conversational N-streaming Interactive Background
WLAN Voice Video Video Probe Best Effort
UMTS Conversational Streaming Interactive Background
Table 1. QoS Classes Mapping Table
4. PERFORMANCE ANALYSIS This section uses simulation experiments to investigate how the proposed approach can improve the overall QoS for the integrated cellular and WLAN networks. Following the assumptions widely used in previous studies [79], the call arrivals in our simulation follow an independent Poisson process and the session time of each connection is exponentially distributed. It is well known that dropping an established communication is worse than rejecting a new call. Therefore cellular systems reserve a guard bandwidth for the handoff calls in order to reduce the handoff dropping probability. The reserved guard bandwidth can be either static or dynamic [8, 10, 11]. The dynamic approach often outperforms the static one at the expense of generating more control overheads [2]. However, the static approach is often attractive in practice owing to its design simplicity. In our simulation, a static guard bandwidth (i.e., 5% of the system capacity) is employed to deal with handoff calls. Without loss of generality, the integrated network in the simulation consists of one cellular network and one WLAN hotspot. Since WLAN has higher capacity and cheaper than UMTS, we assume the handoff probability from UMTS to WLAN is 5 times as much as that from WLAN to UMTS. The system capacity for UMTS and WLAN is 2 mb/s and 11 mb/s respectively. The bandwidth requirement for each of four QoS classes {B1 , B2 , B3 , B4 } defined in section 3.3 and their acceptable degradation level defined in degradation profile are assumed to be a portion of the system capacity listed in Table 2. The reservation signaling cost before the establishment of each new or handoff connection is set to a fixed value. For the sake of clarity, all the relevant simulation parameters are summarized in Table 2. The simulation is carried out under various traffic loads. We compare the proposed approach with non adaptive multimedia services. Parameter UMTS Capacity (U) WLAN Capacity (W) UMTS to WLAN Handoff WLAN to UMTS Handoff Reservation signaling cost
Value Parameter 2 mb/s Session time 11 mb/s Guard Band {B1 , B2 , B3 , B4 } 0.05 0.01 {D1 , D2 , D3 , D4 } 1%*W Simulation Time Table 2. Simulation parameters
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Value Exp(50) 5% {5%*W,3%*W,5%*U,3%*U} {4%*W,2%*W,4%*U,2%*U} 1000s
In the experiments, we set the load to WLAN and UMTS identical in each single experiment and calculate the overall system performance merits. Figure 3 compares the bandwidth utilization supported by the proposed adaptive scheme in the integrated network to that without the adaptive scheme under various traffic loads. Clearly, the utilization for adaptive multimedia connection is better than that for non-adaptive multimedia. When the traffic load becomes higher, the advantage is more evident. The reason why adaptive multimedia can better utilize the system bandwidth is that the proposed scheme allows the network intelligently adjust each admitted QoS connection by its degradation profile and give sufficient amount of resources for the new or handoff calls. Figure 4 depicts the call blocking probability versus the traffic load for adaptive multimedia connections and non-adaptive multimedia connections. There is no call blocking probability for both methods with light traffic load. Clearly, with the increment of the traffic load the call blocking probability is increased. The adaptive approach reduced the call blocking probability compared with non-adaptive approach. Figure 5 further evaluates the handoff dropping probability in the integrated network. The handoff dropping probability for adaptive multimedia connection is less than that for non-adaptive multimedia. When the traffic load becomes higher, the trend is more evident. It reveals that the proposed approach reduces a great number of handoff dropping calls for the integrated WLAN and cellular system. Figure 6 shows the overall system degradation degree defined in Section 3. We can observe that the system degradation degree increases with the traffic load increment. In practical systems, this performance merit can be designed as threshold to control the system performance by the network operator.
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Figure 3. Utilization Over Traffic Load
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Figure 4. Call Blocking Probability Over Traffic Load
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Figure 5. Handoff Dropping Probability Over Traffic Load
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Figure 6. System degradation degree Over Traffic Load
5. CONCLUSIONS The rapidly deploying WLAN and the third generation cellular system represent the two major future wireless technologies. The design of a network architecture that efficiently integrates WLAN and cellular networks is a challenging task. To enable efficient use of scarce resources provided by the cellular networks while also maintaining strong service guarantees, we proposed a generic reservation-based QoS model for the integrated cellular and WLAN networks. Our proposed model supports the delivery of adaptive real-time flows for end users taking the advantage of high data rate WLAN systems as well as the wide coverage area of cellular networks. We specifically analyze the different components of the model and their interactions. The performance of the system is revealed via simulation. The results show that the proposed scheme effectively exploits system resources. Simulation experiments also indicate that the adaptive multimedia framework outperforms the non adaptive approach in terms of lower handoff dropping probability and call blocking probability while still maintain acceptable QoS to the end users.
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