On Access Selection Techniques in Always Best ... - Semantic Scholar

On Access Selection Techniques in Always Best Connected Networks G´abor Fodor, Anders Furusk¨ar, Johan Lundsj¨o Ericsson Research, Sweden, Torshamnsgatan 23, SE-164 80 Stockholm, Sweden Tel: +46 8 4043084, Fax: +46 8 7575720 Gabor.Fodor|Anders.Furuskar|Johan.Lundsjo @ericsson.com


Abstract — Always Best Connected (ABC) networks are expected to allow users to get connected to services using the access technology that is most suitable in terms of some user configured set of criteria. In this paper we focus on the case when this set is based on the user (application) defined QoS and develop algorithms that attempt to maximize the overall system capacity. In order to do this, we first identify two key requirements in the ABC environment. First, the necessary input parameters need to be made available for the access selection algorithm. These include status information about the candidate access networks and information on the user desired QoS. Second, in order to properly configure QoS in the selected access network, parameters need to be provided to the access QoS manager entities. Both requirements are nontrivial because user terminals and applications may be completely access technology unaware and remote (being several hops away) from the access network. We assume an end-to-end QoS and status information distribution mechanism proposed in previous work and study two selection algorithms that helps users and access providers being ”best connected” - in terms of QoS and capacity. 1 I. I NTRODUCTION After years of standardization and research activities, there is a growing consensus within the industry and the academia that future wireless systems will be based on access technologies that may be different in terms of QoS/mobility capabilities, availability (coverage), capacity, price and other features [1]. At the network layer, on the other hand, there is no foresee-able alternative to the Internet Protocol (IP) as an integration technology that allows application developers and service providers to offer an increasing variety of services [2]. In this heterogeneous environment, the Always Best Connected concept promises a seamless communications experience, in which users specify their personal preferences and connectivity is provided through the particular access system that is ”best” in terms of these preferences [3]. Recent advancements within research and standardization provide the key enablers of such seamless heterogeneous communication systems including advanced hand-over mechanisms [4], [5], wireless QoS solutions [6], context transfer [7] and Layer-2 (L2) trigger support [8], [9]. In previous works we focused on the important QoS related requirement that IP based applications need to be able to specify their QoS requirements in a simple and access agnostic manner such that a variety of (wireless) QoS technologies can receive their necessary input parameters (e.g. delay or throughput requirement). Specifically, in [10] we proposed a small set of (wireless) QoS qualitative parameters (hints) that are applicable to various access technologies. In [11] we examined the performance of these QoS hints - assuming an access specific translation function - over Universal Mobile Telecommunications System (UMTS). In order for ABC to become a reality, two key require-

ments that are not addressed by these works have to be met: 1. An architecture including suitable transportation mechanisms must be designed such that an access selection entity (which may be physically distributed) can receive terminal, network and operation-and-maintenance inputs for the access selection process. 2. The identification of (a) the necessary input parameters and (b) suitable access selection algorithms must ensure that users are indeed best connected in terms of QoS, price, and possibly other preferences. Such algorithms should optimize the combined capacity of the multi-access network. With respect to the first requirement, we realize that this is a challenging requirement, because it implies that input parameters from both the user terminal (QoS requirements and user preferences) and the candidate access technologies (status information) are made available to the access selection entity. With respect to the second requirement, we note that most of the existing and proposed access selection methods formulate the objective such that the user be best connected with respect to the QoS requirements. While this requirement is non-trivial on its own, we recognize that from the access provider’s point of view it is important that the overall capacity be maximized. In previous works we indicated that in multi-access networking, for a given set of QoS requirements, there is significant potential for capacity gain if overall resource management is coordinated [12], [13], [14]. The focus of this paper is on both aspects of the second requirement. Specifically, we first identify the necessary access selection input parameters (QoS requirement and access status). Second, we argue that the endto-end bearer service concept and associated IP level in-

formation distribution mechanism, as proposed in [10], can indeed make these parameters available to the selection entity. We study two simple algorithms that, for the respective input parameters can achieve near-optimum bearer service allocations and thereby near-optimum access selection. We compare the obtained overall capacity when using these algorithms with that of a reference algorithm which selects access randomly. We organize the paper as follows. In the next section we review related work and identify the contribution of our work. In Section III we introduce a high level model of the multi-access network that we consider in this paper. We briefly discuss alternatives for access selection. Next, we revisit the concept of the end-to-end bearer service of [10] and argue that its associated endto-end distribution mechanism can easily be extended such that not only QoS but also access technology specific status information can be transported to whatever entity along the end-to-end path requires such information. The problem of overall capacity maximization is formulated in Section V and exemplified in VI. The important conclusion from these two sections is the list of the most important input parameters that are necessary in order to select access. The subsequent section (Section VII) proposes user assignment algorithms that aim to realize the objective formulated in Sections V and VI in the setting of Sections III and IV. Numerical results are presented and discussed in Section VIII. Finally, Section IX highlights our main conclusions and outlines future research work.

al. identified the ”QoS based multi-radio integration” as an important future research topic and lists possible inputs for ”candidate cell prioritization algorithms” including terminal inputs, cell inputs and O&M inputs, but the authors do not propose specific algorithms [16] (see Table 13.8 of this reference). Seamless hand-over of a mobile node (MN) from one subnetwork to another is recognized as a key requirement within several working groups of the Internet Engineering Task Force (IETF). These works focus on allowing detection and transportation of L2 events (see for instance [8] and [9]) to MNs and access routers (AR), the transfer of context between ARs ([7]) and fast execution of the L3 hand-over. Since these works focus on the development of various (L3 and above) protocols, the access selection algorithm is generally considered out of scope. The contribution of this current work is the joint formulation of the QoS management and capacity nearoptimum access selection problem in the ABC context. Specifically, we believe that the problem formulation in which the input to the access selection algorithm includes both the user required QoS and the status of the candidate accesses under the constraint that the parameters need to be made available in an IP centric multiaccess architecture is new. Also, the access selection algorithms that operate on the previously proposed wireless hints and aim to maximize overall capacity are new. Thus, we believe that the results of this paper contribute to the design of QoS aware and high capacity ABC networks.

II. R ELATED W ORKS An early paper about ABC is [1] in which the ABC capability was identified as an important concept in the context of future multi-access systems. Recently, Gustafsson et al. considered a fairly general ABC scenario, identified ABC actors, proposed an ABC (business) reference model and pointed at key issues including access discovery, access selection, Authorization, Authentication and Accounting (AAA) support, mobility management, and profile handling [3]. In previous work we proposed a small set of QoS parameters (”wireless hints”) with associated distribution mechanism that allowed the configuration of an arbitrary domain along the end-to-end communication path in an ABC scenario [10]. In that work, the problem of access selection and capacity maximization was not considered. Along another line, we discussed principles for allocating multiple services onto different ’sub-systems’ (i.e. access networks) and proposed near-optimum service allocations that maximize combined multi-service capacity [12], [13] and [14]. In those papers, the authors assumed a-priory knowledge of the services that need to be allocated, rather than modelling users arriving oneby-one. The capacity of an integrated Code Division Multiple Access (CDMA) network supporting voice and data service is modelled and analyzed in [15] where similar voice-data capacity region results are presented as some of the numerical results of this paper. However, [15] did not consider multi-access aspects. Halonen et

III. A LWAYS B EST C ONNECTED : A RCHITECTURE A SSUMPTIONS The core of the next generation infrastructure is expected to be the IP based multi-service network (IP Backbone) that provides connectivity and transport via any access technology, including legacy generation systems (2G), evolving generation systems (3G), wireless local area networks (WLAN) and future access technologies. Such a multi-access infrastructure supports services and users having a wide variety of multiaccess capable (multi-mode) terminals. Thus, from a QoS and access selection point of view, the key elements of an ABC system include the multi-mode terminal (MMT), the (candidate) access networks, the access technology selector (ATS) entity and the IP backbone network, as depicted in Figure 1. Clearly, in this type of scenarios there is a need for a terminal model that allows for the separation of the entities that are independent of the underlying access networks (and associated QoS technologies) from the ones that manage the various L2 interfaces of the terminal. Accordingly, the MMT model of Figure 1 includes two main types of entities. The first one is the user terminal, (UT), where the applications and the operating system reside. Applications are expected to be access agnostic in the sense that whatever QoS control mechanism they use, it will be independent of both the L2 interface(s) that the MMT supports and the access gateway(s) that










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the user terminal can have access to. The user terminal also contains the QoS controller entity that actually controls the QoS on the application’s behalf. Since we expect the IP layer to continue to be the ”glue”, it is natural that the QoS control entity is at the IP layer. The second entity in the model is the access gateway (AG) that communicates with user terminals and manages QoS in the specific access network. For instance, this entity in Figure 1 would correspond to a mobile terminal (MT) in the UMTS reference model [6] or a WLAN PCMCIA card in a WLAN environment. Similar to the user terminal, the gateway node must (logically) also contain the IP controller which supplies information to the L2 QoS manager via a L2 specific translation function. This model allows the gateway node to be physically separated from the user terminal, which is the case, for instance, in a moving network [17]. We use the term ”access network” in a logical sense, referring to the set of network functions that are access technology specific. Note that with this definition several logical access networks may be implemented as one common physical network, even including nodes that are shared between logical access networks. The ATS entity can reside in the terminal and/or in the multi-access network, as shown in Figure 1. The ATS can logically reside between the user terminal and the access gateways. Alternatively, the ATS may be part of the network and can have direct access to important network status parameters, or it can communicate with access technology specific entities via the IP backbone network. As long as the sources of information to be taken into account in access technology selection are spread in different locations within the multi-access network and the terminal structure, the optimum location of the ATS is non-trivial. It is in fact an exciting research issue to identify the advantages and disadvantages of these options in different multi-access scenarios, left outside the scope of this paper. In this paper we assume that both the user terminal, the involved access networks and ATS can exchange information with one another. We note that information exchange between entities spread in the network and terminal in practice always will impose certain protocol delays. In the next

Although not explicitly shown in Figure 1 it is clear that the end-to-end path may consist of several domains including the access network, the IP backbone and the remote access network. In general, each of these domains may have its specific QoS technology and thus there is a need to distribute QoS information to whatever segments that need to be properly configured. This segment typically includes the wireless access networks. Likewise, access network status information may include technology specific elements that need to be transported to the selection entity (such as the ATS in Figure 1). Consequently, the requirements on the (common) end-to-end QoS/status information distribution mechanism include: It has to be applicable over various wireless and wireline QoS technologies. This implies that the mechanism must support the distribution of information elements that are sufficient and useful for various technologies and that is suitable to characterize the status of various access technologies. This requirement also implies that it must be independent from the local QoS mechanisms applied in the various domains. It must be independent from the application. It has to allow for exercising admission control in the access network. That is, QoS information may have to be provided to the wireless access network prior to user data transmission. It should be ”IP QoS” friendly in terms of interworking with the IETF standardized QoS mechanisms such as [18] and [19]. f

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B. The End-to-end Bearer Concept A schematic view of such an end-to-end QoS and access status information distribution mechanism is shown in Figure 2. The end-to-end signaling mechanism and associated information elements together with the domain specific QoS translation function constitute an endto-end bearer service, that serves as the basic ”carrier” of QoS and network status information. In Figure 2, the access gateways and access networks as well as the IP multi-domain backbone are modelled as a series of L2/3 domains each with associated QoS mechanisms. The end-to-end bearer provides a generic means to transport QoS and status information elements between the user terminal (UT) and any of the domains, including the access networks. Since the ATS is either part of one of the network domains or of the user terminal, information is supplied to it via the end-to-end information distribution mechanism. In Sections V and VI we will consider

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bit-rate corresponds to the signaled bit-rate (TSpec) parameter, see Table I. The capacity region is hence the set of possible user combinations for which acceptable system-level quality can be sustained for all service types. An example of a capacity region for two services is depicted in Figure 3. The function delimiting the capacity region is thus  may also be ex. This capacity region limit   function pressed as a non-parameterized  "! , i.e. the maximum number of service-\ users as a function of the number of users of the services \$# . The Appendix provides a simple example on the application of the above parameters in a simple 3-bearer 3-sub-system case.

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clear, that in general the overall (combined) capacity of the multi-access system depends on the traffic mixes in the sub-systems , which is determined by the service allocations . We call the rule set that assigns the service allocations for each service policy % . Note that for any feasible policy must hold for each . For instance, a simple service allocation policy is to distribute all services equally over the available sub-systems, that is to )[ let . A more attractive service allocation policy is the one that is feasible and maximizes for all input traffic mixes. We will refer to this particular policy as the optimum service based policy %* :








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Similarly to Equation (4), the combined capacity region associated with policy % is the set of possible user combinations that can be allocated in the multi-access system for all possible input traffic mixes:

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Figure 4 shows different combined capacity regions in a multi-access system supporting two types of services employing policies % and %* .

consumption of bearer service (and thereby the relative efficiency of supporting different bearer service types) typically differs between access technologies. That is, denoting the total amount of available resources in an access point (covering a cell or a sector, for instance) of sub-system by , the relative resource consumption of each user belonging to bearer service is when is set to

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In this section we first exemplify that the capacity regions in different access networks (sub-systems) in the case of a simple mixed data/voice service are indeed different. In the case of a Global System for Mobile Telecommunications/Enhanced Data Rates for Global Evolution (GSM/EDGE) and Wide-band CDMA (WCDMA) multi-access system we show that the combined capacity region depends on the service allocation.

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Consider a multi-access network with three subsystems and three services which we here call voice (1), streaming (2) and data (3). Denote . Assume that the capacity regions for a prescribed QoS for all subsystems are known (by using for instance measurements, technology specific modelling or simulation) in the form of the funcand . tions Problem: Determine the optimum service allocation policy % * such that the combined capacity of the multiaccess system is maximized for all overall traffic mix . Solution: The solution for this problem is provided in the Appendix.

    

                          



VI. A M ULTI -ACCESS B EARER S ERVICE A LLOCATION E XAMPLE : GSM/EDGE AND WCDMA Although not explicitly pointed out in the previous section, a key observation is that the sub-system capacity regions are in general technology specific and typically quite different. The underlying reason for this is that for a given QoS level, the relative radio resource

Capacity regions for voice and WWW bearer services for a multi-access system comprising GSM/EDGE and WCDMA access technologies are depicted in Figure 5. The QoS requirements assumed are a bit error rate that yields acceptable voice quality for the voice bearers, and a perceived throughput of 150kbps for the WWW bearers. 10MHz of spectrum is assumed to be available for both GSM/EDGE and WCDMA. The combined capacity region for these two systems depends on how bearer services are allocated onto the radio access technologies. As seen in Figure 5, for GSM/EDGE, the singleservice capacities are 125 users per sector for voice, and 30 users per sector for WWW. In a realistic scenario, the relative radio resource consumption for voice ) and WWW are thus and ) . Supporting a WWW user thus consumes about four times more radio resources than supporting a voice user [13]. (For a detailed discussion on the performance of voice and data (best effort) services including radio resource consumption aspects in various access technologies see also Section 13.4 of [16].) For WCDMA, the expected relative radio resource consumptions for voice and WWW are ) ) respecand tively. Noticeably, in WCDMA a WWW user consumes only about twice as much radio resources as a voice user. In terms of expected relative radio consumption, the service allocation principles corresponds to assigning users where the expected relative radio resource consumption for the bearer service in question compared to other bearer services is the smallest.

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B. The impact of service allocation on the combined capacity regions The service allocation principle may be illustrated by some simple examples involving GSM/EDGE and

VII. U SER A SSIGNMENT A LGORITHMS

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Fig. 6. The combined (overall) capacity region of a system consisting of two sub-systems, i.e. assuming 3 different service allocation policies: ”combined best ( )”, ”combined equal mix” and ”combined worst”



WCDMA access technologies. Three examples are given in Figure 6. In the ”combined best case”, WWW bearers are as far as possible allocated to WCDMA, and voice bearers as far as possible to GSM/EDGE. The shape of the combined capacity region may in this case intuitively be explained by starting from the combined single-service capacity for WWW, which is approximated as the sum of the sub-system WWW capacities. Then, when voice users are added, the slope of the combined capacity region follows the flatter slope of the GSM/EDGE capacity region. This continues until GSM/EDGE is full of voice bearers. Additional voice bearers must be allocated to WCDMA, and the steeper slope of the WCDMA capacity region is followed. For the best case, the selection of radio access technology for the two bearer services used to construct the combined capacity region is also depicted. Starting from a point on the combined capacity region, vectors corresponding to the allocation of services onto the different radio access technologies can be followed towards the origin. In the ”combined worst case” the allocation of bearer services is the opposite, WWW bearers are as far as possible allocated to GSM/EDGE, and voice bearers as far as possible to WCDMA. This yields a reversed order of the capacity region slopes, and a smaller combined capacity region. Allocating bearers with an equal mix in both access technologies (”combined equal mix”) yields a capacity region in between the two extremes. Comparing the different combined capacity regions, it is seen that using the best case allocation a capacity gain of up to 25% is achievable compared to equal service-mix allocation, whereas the gain over the worst case allocation reaches up to 35%. As seen above, with linear capacity regions, such as those in Figure 5, finding favorable bearer service allocations is straightforward.










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Recall that the allocation policy % determines for each service. For instance, a practical allocation policy in a given multi-access system can take the simple form ”For a total service voice-data service mix of 50%-50%, allocate 30% of voice users to access technology 1 and 70% to access technology 2 and allocate 80% of data users to access technology 1 and 20% to access technology 2”. As users arrive at and depart from the multiaccess system one-by-one, this corresponds to ”walking” along the lines of Figure 4. Thus, strictly keepservice allocation ratios would require the ing the maintenance of state information in the ATS, since to de# at the instant of the arrival of termine the actual user- up-to-date information on the number of already allocated voice and data users in the multi-access system (i.e. and ) is necessary. That is, the number of states in the ATS is \ which, depending on the actual multi-access scenario, may or may not be prohibitive. Therefore, to eliminate the need for state maintenance and also to avoid possible re-allocation of users, we seek simple algorithms that try to approximate the optimum allocation policy by assigning the proper access network
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A. Algorithm 1: Random Assignment Our first reference user assignment algorithm does not take into account bearer service type or estimates on the actual radio resource consumptions. User is simply assigned to sub-system with probability:



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This assignment method requires no input information, and results in equal expected service mixes in all sub-systems. B. Algorithm 2: Stateful Bearer Service Based Assignment This assignment algorithm assigns users according to the principles reviewed in Section VI. This is realized \ , through assigning user , using bearer service to sub-system with probability:

    

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where is the near-optimum relative sub-system bearer service allocation according to policy % * and is its component. Once the user has been assigned to a sub-system, that characterizes the current service mix needs to be updated. Note that this algorithm requires the signaled QoS parameters of Table I and assumes that the sub-system capacity regions (as depicted in Figure 3 and exemplified by the functions of Equation (14) in the Appendix) are known by the ATS.





 

    

C. Algorithm 3: Actual (Measured) Radio Resource Consumption Based Assignment Recall from the discussion of Section VI and specifically from Equation (7) that the individual user’s relative

 

resource consumption ) depends on the access technology. In real-life scenarios this relative resource consumption is in fact a random variable and its actual realization for a specific user depends on various factors (e.g. path loss, interference levels). Cellular user terminals and networks often employ measurements to ) assess the actual value of rather than consider it determined by Equation (7). In such a situation, the actual radio resource consumption of each individual user may also be used as a basis for radio access technology selection. Since this algorithm takes more information into consideration than the optimum service based algorithm, it may yield higher combined capacity. In this case the definition of
becomes:

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Fig. 7. The combined (overall) capacity region of GPRS and WCDMA sub-systems, i.e. assuming 2 different service allocation policies: ”combined best ( )”, ”equal mix”

D. Discussion on Algorithm 3 The performance achievable with the above user assignment algorithm is determined by the statistical properties of the actual radio resource consumptions in the sub-systems . Both the distribution of the radio resource consumptions within each sub-system, and its correlation between sub-systems affect the capacity. Measurements and simulations of GSM/EDGE and WCDMA systems indicate that within both these subsystems, the distribution of the down-link power, which may be used as an actual radio resource consumption measure, around its mean value, may be roughly approximated with a truncated lognormal distribution with a standard deviation of 6 dB. Further, with co-sited access points, a correlation coefficient between the resource consumptions in the two subsystems in the range of 0.5 to 0.7 depending on propagation characteristics is reported [13]. To fully exploit the capacity potential of actual radio resource consumption-based user assignment, accurate and ‘up-to-date’ estimates of the actual radio resource consumptions are required. For initial access selection, these estimates must further be available prior to the start of a session, and hence be estimated in the so called ’idle mode’. As opposed to the bearer service type of a user, which typically remains constant during a session, we observe that its actual radio resource consumption may vary rapidly with time. In systems like WCDMA employing fast power control, the down-link power follows the multipath fading and is updated +/-1dB every 0.67ms, and may hence change 15dB in 20ms. This calls for very frequent measurement and reporting of the actual radio resource consumptions, which in turn also results in short measurement periods. Protocol delays and measurement errors together thus may reduce the capacity achievable with actual radio resource consumption-based user assignment.




E. Summary In this section we outlined three sub-system (access technology) assignment algorithms that aim to assign the arriving user to subsystem
[ . The first algorithm serves as a reference case and needs no input parameters. Algorithm 2 requires that the


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ATS maintains a counter for each bearer service such that when the user arrives requesting bearer service \ , the values (and consequently the  :s) are available. These pieces of information allow the ATS to apply the near-optimum %* policy and determine * . Algorithm 3 determines
such that the selected sub-system is the one that appears to be the most (relative) resource efficient in terms of the actual (measured) ) value at the time arrival. Since this value can change rapidly in many radio access technologies, it imposes delay, measurement and other requirements on the multi-access system design. We will use this algorithm as a second reference case for Algorithm 2 in Section VIII. The required input parameters are summarized in Table III.

  
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VIII. N UMERICAL R ESULTS In this section we first present a set of combined capacity regions for GSM and WCDMA-based multiaccess networks, indicating the capacity gains achievable with service- based user assignment for different system scenarios, including standard General Packet Radio Service (GPRS) and High Speed Down-link Packet Access (HSDPA). In one specific scenario, we also investigate the capacity achievable with actual radio resource consumption-based user assignment, and how that is affected by measurement errors. A. Combined Capacity Regions Figures 7 and 8 show combined capacity regions for GSM and WCDMA-based multi-access networks. The combined capacity regions are calculated using the combined capacity definition of Equation (5), and either the service-based user assignment algorithm of Equation (9), or the random user assignment algorithm of Equation (8). In addition to the combined capacity regions, also the sub-system capacity regions, taken from [13], and the service allocations per sub-system used to achieve the combined capacity region in the service-

TABLE III A CCESS S ELECTION A LGORITHMS : R EQUIRED Q O S AND S YSTEM S TATUS I NPUT PARAMETERS Algorithm Random Selection (Alg 1) Bearer service based (Alg 2)

QoS (Wireless) Hints — Bearer service type and associated (see Table I) Bearer service type and associated (see Table I)



Measured Individual Resource Consumption based (Alg 3)


    
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300 GSM/GPRS WCDMA/HSDPA Combined Equal mix

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120

200 Data Users

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150

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Fig. 8. The combined (overall) capacity region of GPRS and HSDPA sub-systems, i.e. assuming 2 different service allocation policies: ”combined best ( )”, ”equal mix”



based case are plotted. In Figure 7, standard GPRS data bearers (with a bit-rate requirement of 50kbps) are used in the GSM sub-system. As compared to the GSM/EDGE case of Figure 6, this yields a larger difference between the capacity region slopes, and hence a larger capacity gain (up to 35% for certain service mixes) compared to the random assignment. The service allocation rule is however not affected; voice users are preferably assigned to GSM, and data users to WCDMA. Figure 8 shows the case of combining GSM/GPRS with WCDMA/HSDPA. HSDPA enhances the WCDMA data capacity, and emphasizes the differences in the capacity region slopes. This renders the same service allocation rule as before but results in an increased capacity gain for service-based assignment. Notably, with the convex WCDMA/HSDPA capacity region, the capacity achieved with equal mixing of bearer services in the sub-systems is somewhat improved as compared to the case with linear capacity regions. This compensates for the large differences in single-service data capacity, and thus reduces the gain with service-based assignment. In total, capacity gains of up to 50% are achieved. In rough terms, the more different the sub-system capacity regions are, the greater the capacity gain with service-based user assignment can be.

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Fig. 9. The combined (overall) capacity region of an EDGE/WCDMA system obtained by Algorithms 1, 2 and 3 of Section VII assuming correlation coefficients of and

B. Employing the Resource Consumption Based User Assignment Algorithm To compare the performance of Algorithms 2 and 3, we consider a GSM/EDGE and WCDMA multi-access system supporting voice and WWW services. The actual radio resource consumptions (i.e. of Equation (7)) are randomly generated according to the statistical properties described in Subsection VII-D. To model the impact of measurement inaccuracies and protocol delays, a log-normally distributed random error with standard deviation of 0 to 6dB is added to the actual radio resource consumptions before using them for access selection. Users are sequentially assigned to a sub-system until one of the sub-systems is fully loaded. In the resource consumption based case, users may subsequently be re-allocated as new users enter the system. The full range of service mixes is covered. For each service mix the simulation was repeated 16 times. Figure 9 shows the combined capacity regions achieved with Algorithm 1, 2 and 3 of Section VII. In the case of Algorithm 3, a radio resource consumption correlation in the two sub-systems is assumed with coefficients of and . Here, error-free radio resource consumption estimates are assumed. It is seen that the random and bearer service-based assignment algorithms yield combined capacity regions similar to the calculated ones depicted in Figure 6. The actual radio








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400

Fig. 10. Combined capacity region of an EDGE/WCDMA system obtained by Algorithms 1, 2 and 3 of Section VII assuming a correlaand taking into account measurement tion coefficient of errors with various standard deviations ( dB)





resource consumption-based assignment algorithm provides a capacity gain of about 40% compared to random assignment. The gain over bearer service-based assignment varies between 20% and 40% depending on the service mix. In the case when the correlation between the radio resource consumptions is , the capacity increase for actual radio resource consumption based user assignment is somewhat greater than with . Figure 10 depicts the capacity regions achieved with errors added to the actual radio resource consumption estimates for the case with a radio resource consump. It is seen that an error with a tion correlation of standard deviation of 1.0dB yields a combined capacity that for some service mixes is lower than that achieved with service-based user assignment. Hence, although actual radio resource consumption-based user assignment has a large capacity potential, service-based user assignment may be preferable if very accurate and rapid estimates of the actual radio resource consumptions are not available.







IX. C ONCLUSIONS

rameters and aim to realize the capacity near-optimum bearer allocation. The first algorithm employs the optimum service-based policy %* which makes access selection based only on the type of the requested bearer service and associated QoS parameters. The second algorithm makes use of measurements on the individual user’s resource cost to determine the most resource efficient subsystem at the time of the user’s arrival. Numerical results in the case of GSM/WCDMA show that employing these simple algorithms indeed out-perform our reference selection method which selects accesses with equal probability. We found that the actual resource consumption based assignment is sensitive to measurement errors. Therefore, whether the service-based or the resource consumption based algorithm should be used in a specific scenario depends on the available measurement methods and other system requirements. The development of algorithms that take into account other user preferences (such as service pricing) and also studying numerical results when incorporating future access technologies are important future research topics. A PPENDIX : S OLUTION OF THE P ROBLEM S UBSECTION V-D

$  *    % *


      D           )
           )
           )
 >    

    $  *   $      $ *  $       $ * $       $ $ *         $ *     *   *   *   *                    @             @                                '       '                                                          '       '      

                    



        

                

  #   


Recall that policy is specified when the associated for each bearer service is specified. Denote . Recall that

For a fixed ( ) pair the combined capacity is maximized when is maximized, and we therefore need to find the sub-system bearer service allocations , , , , , , , , , (9 unknowns) fulfilling 





(12)

maximize the sustain-able data load . For fixed voice and streaming loads and , is given by:



Access selection in Always Best Connected networks plays an important role both from the end-user’s and the operator’s point of view. In this paper we concentrated on the case when the user parameters only include QoS information. We argued that it is important to take into account both QoS and (access) network status, because it can help maximize overall network capacity. We referred to previous work and assumed that these two sets of information are part of an end-to-end bearer service definition and can be made available to the access selection entity using the previously proposed QoS distribution mechanism. Next, we identified the key input parameters to the bearer service allocation method that can significantly improve the overall capacity of a multi-access system. Finally we proposed two simple user assignment algorithms that receive these pa-

IN



(14)

To maximize , its partial derivatives with respect to , , , , and need to be taken. Beginning with :









  ,   , and   :                               #                                #                                #   At a local extreme, all partial derivatives equal to  * zero. $   $  , 

 

The   , zeros are thus found at *  $  *    and  $  *    such that:                    

          

      '       '      (23)                    

          

      '       '      (25) Whether these correspond to local maximum, minimum or saddle points of  depends on the characteristics of the second order partial derivatives of  .     The three sub-system capacity regions (    Similarly, for







   



) (see Subsection V-D), (12) and (23)-(25) provide the necessary 9 equations and determine %* . We refer to [13] and [14] for a general treatment of [ subsystems and \ services. 

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[11] S. Dixit, R. Prasad (eds), G. Fodor, B. Olin, F. Persson, C. Roobol and B. Williams, ”Providing Differentiated and Integrated Services for IP Applications over UMTS Access Networks”, in Wireless IP and Building the Mobile Internet, Artech House, ISBN 158053354X, 2002. [12] A. Furusk¨ar, ”Allocation of Multiple Services in Multi-Access Wireless Networks”, in proceedings of the IEEE Mobile and Wireless Communication Networks, MWCN ’02, pp. 261-265, September 2002. [13] A. Furusk¨ar, ”Radio Resource Sharing and Bearer Service Allocation for Multi-bearer Service, Multi-access Wireless Networks - Methods to Improve Capacity”, Ph.D. dissertation, Royal Institute of Technology, TRITA-S3RST-0302, ISSN 1400-9137, ISRN KTH/RST/R–03/02– SE. http://media.lib.kth.se:8080/dissengrefhit.asp?dissnr=3502, May 2003. [14] A. Furusk¨ar, J. Zander, ”Multi-service Allocation for Multiaccess Wireless Systems”, to appear in the IEEE Transactions on Wireless Communication. [15] D. Ayyagari and A. Ephremides, ”Cellular Multi-code CDMA Capacity for Integrated (Voice and Data) Services”, IEEE Journal on Selected Areas in Communications, Vol 17, No. 5, pp. 928-938, May 1999. [16] T. Halonen, J. Romero, J. Melero, ”GSM, GPRS and EDGE Performance - Evolution Towards 3G/UMTS”, John Wiley & Sons, ISBN 0470 84457 4, 2002. [17] H. Soliman and T. Ernst, ”Internet Engineering Task Force Network Mobility Working Group Charter”, http://www.ietf.org/html.charters/nemo-charter.html, March 2003. [18] S. Blake, et al., ”An Architecture for Differentiated Services”, IETF Request for Comments 2475, http://www.ietf.org/rfc/rfc2475.txt, IETF RFC 2475, December 1998. [19] J. Wroclawski, ”The Use of RSVP with IETF Integrated Services”, IETF Request for Comments 2210, http://www.ietf.org/rfc/rfc2475.txt, IETF RFC 2210, September 1997. [20] J. Wroclawski, ”Specification of the Controlled Load Quality of Service”, IETF Request For Comments 2211, http://www.ietf.org/rfc/rfc2211.txt, September 1997.