Melhem El Helou

Marc Ibrahim

Samer Lahoud

Kinda Khawam

Radio Access Selection Approaches in Heterogeneous Wireless Networks

Keywords: Radio access technology selection, heterogeneous wireless networks, hybrid approach

Along with the rapid growth of mobile broadband traffic, multiple radio access technologies (RATs) are being integrated and jointly managed. To optimize heterogeneous network performance, efficient Common Radio Resource Management (CRRM) mechanisms need to be defined. This paper tackles the access technology selection -a key CRRM functionality -and proposes a hybrid approach that combines benefits from both network-centric and user-centric methods. Network information, that is periodically broadcasted, assists mobile users in their decisions. By broadcasting appropriate decisional information, the network tries to globally control users decision in a way to meet operator objectives. On the other hand, mobiles also integrate their needs and preferences to select their access technology so as to maximize their own utility. In comparison with other RAT selection techniques, including network-centric, hybrid and user-centric methods, simulation results prove the efficiency of our hybrid approach in enhancing resource utilization and maximizing user satisfaction.

I. INTRODUCTION

To cope with the rapid growth of mobile broadband traffic, different radio access technologies (e.g., HSPA, LTE, WiFi and WiMAX) are being integrated and jointly managed. So as to optimize resource utilization, while enhancing user experience (Always Best Connected concept [START_REF] Gustafsson | Always Best Connected[END_REF]), efficient Common Radio Resource Management (CRRM) mechanisms need to be defined. Typically, when a new or a handover session arrives, a decision must be made as to what technology it should be associated with. This is known as the Radio Access Technology (RAT) selection, a key CRRM functionnality.

In order to consider operator objectives, including efficient exploitation of radio resources, network-centric schemes have been proposed: network elements collect necessary measurements and information. They take selection decisions transparently to end-users in a way to enhance heterogeneous network performance [START_REF] Sang | Coordinated Load Balancing, Handoff/Cell-Site Selection, and Scheduling in Multi-Cell Packet Data Systems[END_REF]- [START_REF] Khawam | Individual vs. Global Radio Resource Management in a Hybrid Broadband Network[END_REF]. However, to reduce network complexity, signaling and processing load, user-centric methods have also gained in importance: based on their individual needs and preferences, rational users select their access technology in a way to selfishly maximize their payoff (utility) [START_REF] Khawam | Individual vs. Global Radio Resource Management in a Hybrid Broadband Network[END_REF]- [START_REF] Chamodrakas | A Utility-Based Fuzzy TOPSIS Method for Energy Efficient Network Selection in Heterogeneous Wireless Networks[END_REF]. Because individual users have no information on the global network state (i.e., network load conditions), user-centric approaches are known for their potential inefficiency.

In this article, we propose a hybrid decision method that combines benefits from both network-centric and user-centric approaches. Network information is designed to assist mobile users in their decisions: mobiles make their selection decision based on their individual needs and preferences as well as on the cost and partial QoS parameters signaled by the network. To maximize user experience, we present a satisfaction-based Multi-Criteria Decision-Making (MCDM) method. In comparison with existing MCDM methods, namely Simple Additive Weighting (SAW) [START_REF] Stevens-Navarro | Comparison between vertical handoff decision algorithms for heterogeneous wireless networks[END_REF], Multiplicative Exponent Weighting (MEW) [START_REF] Stevens-Navarro | Comparison between vertical handoff decision algorithms for heterogeneous wireless networks[END_REF], Grey Relational Analysis (GRA) [START_REF] Song | Network Selection in an Integrated Wireless LAN and UMTS Environment Using Mathematical Modeling and Computing Techniques[END_REF] and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) [START_REF] Chamodrakas | A Utility-Based Fuzzy TOPSIS Method for Energy Efficient Network Selection in Heterogeneous Wireless Networks[END_REF], our algorithm meets the exact needs of mobile users (e.g., traffic class, throughput demand, cost tolerance), thus avoiding oversized and undersized alternatives.

A particular attention is then addressed to the network to make sure it broadcasts appropriate decisional information, so as to enhance resource utilization while individual users are maximizing their own utility. We therefore introduce the slope tuning policy to dynamically derive what to signal to mobiles.

The rest of this paper is organized as follows: Section II describes our hybrid decision framework. Our satisfactionbased decision-making method is presented in section III. Section IV introduces our tuning policy. Simulation parameters and results are discussed in section V. Section VI concludes the document.

II. HYBRID DECISION FRAMEWORK A. Network topology

Consider a heterogeneous wireless network composed of N T radio access technologies. The user-perceived signal-tonoise ratio (SN R x , x = 1, ..., N T ) determines the user modulation and coding scheme, and subsequently its instantaneous peak rate (i.e., its perceived throughput when connected alone to RAT x). Since practically the set of achievable instantaneous peak rates is not continuous, RAT x cell is logically divided into N x Z concentric rings (Fig. 1). Users in ring Z x k , k = 1, ..., N x Z , with SN R x between δ x k and δ x k-1 , have a peak rate of D x k when connected to RAT x. The user peak rate in RAT x, namely D x , is then expressed as a function of the user-perceived SNR as follows:

D x =        0 if SN R x < δ x N x Z , D x N x Z if δ x N x Z ≤ SN R x < δ x N x Z -1 , ... D x 1 if δ x 1 ≤ SN R x < δ x 0 = ∞. (1)
where

δ x N x Z
is the minimum SNR that allows transmission, at the lowest bit rate D x N x Z , given a target error probability.

B. Network resources

In RAT x, the radio resource is divided into elementary resource units (RU). Typically, in OFDM(A)-based technologies (e.g., LTE and WiMAX technologies), resource units are defined as OFDM symbols (one-dimensional allocations) or OFDMA slots (two-dimensional allocations: m subchannels by n OFDMA symbols). However, in CDMA-based technologies (e.g., HSPA technology), codes, power and allocation time are treated as RUs.

C. Network information

Network information is periodically sent to all mobile users using the logical communication channel (i.e., radio enabler) proposed by the IEEE standard 1900.4 [START_REF]IEEE Standard for Architectural Building Blocks Enabling Network-Device Distributed Decision Making for Optimized Radio Resource Usage in Heterogeneous Wireless Access Networks[END_REF]. In our work, this information is assumed to implicitly integrate operator objectives, guiding users decision. It may be static or variable so as to dynamically optimize short-or long-term network performance.

When a new or a handover session arrives, the mobile decodes the decisional information, evaluates available alternatives, and selects the technology that best suits it.

In this setting, we assume that the network information provides cost and some QoS parameters: they can be seen as incentives to join available alternatives.

• Cost parameters: Because flat-rate pricing strategies waste resources, result in network congestion and thus degrade network performance [START_REF] Edell | Providing internet access: what we learn from index[END_REF], they are not optimal in supporting QoS. A volume-based model is therefore proposed: mobile users are charged based on the amount of traffic they consume; in our work, costs are defined on a per kbyte basis. • QoS parameters: The number of resource units (RUs) that need to be allocated to future arrivals are broadcasted:

-Mobiles are guaranteed an average minimum number of RUs, denoted by n min . -They also have priority to occupy up to an average maximum number of RUs, denoted by n max . The network load conditions and capacity are, however, masked. In fact, n min and n max reveal the operator intention to serve future arrivals: they do not exclusively reflect the load conditions, but also other potential operator objectives. Since the smallest allocation unit (i.e., RU) may be different from one technology to another, there is a need to homogenize the QoS information. QoS parameters are then expressed as throughputs: d min and d max instead of n min and n max . Yet, because perceived throughputs highly depend on radio conditions (or equivalently on adopted modulation types and FEC coding rates), d min and d max are derived for the most robust modulation and coding scheme (i.e., as perceived by users in ring Z N Z ). Consequently, when evaluating available alternatives, mobiles should combine their individual radio conditions with the provided QoS parameters: for that they multiply d min and d max with a given modulation and coding gain, denoted by g(M, C).

D. RAT selection

For each incoming session, the network proposes one or more alternatives, which are the available access technologies. For each alternative (a), the network broadcasts the three parameters: d min (a), d max (a), and cost(a). From the user point of view, these parameters are the decision criteria that will be used to evaluate the different access technologies. As in all multi-criteria decision making methods, the mobile has to define a utility function that will be computed for all of the available alternatives. This utility is obtained after normalizing and weighting the decision criteria.

The particularity of our RAT selection process resides in the normalization step that takes into account the traffic class and throughput demand as detailed in the next section. Such approach overcomes some limitations of classical methods due to undersized and oversized decisions.

III. SATISFACTION-BASED DECISION METHOD

A. Normalization and Traffic classes

As stated above, the normalization of decision criteria d min (a), d max (a), and cost(a) depends on the session traffic class and throughput demand. For traffic class c and alternative a, the normalization is a mapping of d min (a), d max (a), and cost(a) to dc min (a), dc max (a), and cost c (a) respectively.

In our work, we define two traffic classes : streaming, and elastic classes. Before we give the normalizing functions for each traffic class, let us note that pc (a), p ∈ {d min , d max , cost}, can be viewed as the satisfaction of a class c session with respect to criterion p for alternative a:

• Streaming sessions (c = S): since designed to support real-time variable bit rate services (e.g., MPEG-4 video service), streaming sessions are fairly flexible and usually characterized by a minimum, an average and a maximum bandwidth requirement. Their throughput satisfaction is therefore modelled as an S-shaped function (Fig. 2(a)):

d′ S (a) = 1 -exp( -α( d ′ (a).g(M,C) Rav ) 2 β + ( d ′ (a).g(M,C) Rav ) ) (2)
where d ′ = {d min , d max }. R av represents session needs: an average throughput demand. α and β are two positive constants to determine the shape of the S-shaped function.

dE max (a) = 1 -exp(- d max (a).g(M, C) R c ) (3)
The monetary cost satisfaction is, however, modelled as a Z-shaped function (Fig. 3): the slope of the satisfaction curve increases rapidly with the cost.

cost c (a) = exp(- cost(a) 2 λ c ), c ∈ {S, E} (4)
λ c represents the cost tolerance parameter: a positive constant to determine the shape of the Z-shaped function.

Satisfaction Cost

B. User Profile and Utility Function

The user profile defines the cost tolerance parameter and the weights that a given session will apply to normalized criteria. More precisely, the user profile is the set of vectors • Next, it combines the user preferences (i.e., w c dmin , w c dmax and w c cost ) to the normalized decision criteria, so as to compute the weighted normalized criteria: w c dmin . dc min (a), w c dmax . dc max (a) and w c cost . cost c (a).

• Finally, it computes the utility function for each alternative (a) and selects the alternative with the highest score. By broadcasting appropriate decisional information, the network tries to globally control users decision in a way to enhance resource utilization. On the other hand, mobiles make their decisions so as to maximize their own satisfaction. Selection decisions take then into account both user needs and preferences and operator objectives. Network complexity and processing load are, however, reduced.

IV. THE SLOPE TUNING POLICY

Because mobile users also rely on their needs and preferences to select their best alternative, the network does not completely control individual decisions. However, by dynamically tuning its broadcasted information, the network tries to globally influence users decision in a way to enhance resource utilization.

When a RAT dominates all the others (i.e., provides higher QoS parameters for the same cost or the same QoS parameters for a lower cost), common radio resources are inefficiently utilized causing performance degradation. In fact, mobile users would select the dominant alternative, leading to unevenly distributed traffic load. While a technology is overcrowded, the others are almost unexploited. This inefficiency is very similar to that of the user-centric approaches. To avoid it, QoS parameters, signaled by the network, needs to be modulated as a function of the load conditions.

Actually, as technologies are progressively loaded, QoS parameters (i.e., d min and d max ) are gradually tuned. When the operator bandwidth guarantees -identified as a generic load factor -exceed a predefined threshold S 1 , these parameters are linearly reduced down to zero, as shown in Fig. 5. The slope helps to well respond to traffic load fluctuations. In order to reduce network complexity and processing load (one of the drawbacks of the network-centric approaches), the proposed policy is basic and simple. Yet, it helps to efficiently distribute traffic load over the available RATs and thus to better utilize radio resources.

Initial parameters

Load factor S1 S2

QoS incentives

V. PERFORMANCE EVALUATION

For illustration, we consider a heterogeneous wireless network composed of Mobile WiMAX and LTE radio access technologies. They are supposed to utilize a channel bandwidth of 5 and 10 MHz respectively. For economical and technical reasons, WiMAX base station and LTE Evolved Node B are assumed to be co-localized, having the same coverage. As a consequence, and since both technologies use OFDMA on the downlink, their cell may be divided into overlayed concentric rings.

For the sake of simplicity, users are of two types: users with good radio conditions adopting the 64-QAM: 3/4 (4.5 bits/symbol) and users with bad radio conditions adopting the 16-QAM: 1/2 (2 bits/symbol). Their peak rates (i.e., their perceived throughput when connected alone to WiMAX and LTE technologies) are reported in Table I.

We compare our hybrid approach with other networkcentric, hybrid and user-centric methods. network utility reflects operator objectives: it is defined as the total offered throughput. However, the users utility reflects the average user-perceived satisfaction: it depends on their needs and preferences and thus take into account both QoS and cost considerations.

Radio resources are actually allocated using fair time scheduling. Yet, when our hybrid method is employed, mobiles are first provided with their minimum guaranteed throughput given by d min . Then, fair time scheduling is used to provide them with up to their maximum throughput given by d max . The remaining resources may afterwards be equitably shared (i.e., after receiving their maximum throughput, all mobiles have the same priority leading to fair time scheduling).

Mobiles arrive in sequence and are uniformly ready either to pay for better performances, or to sacrifice within limits their service quality seeking to save up money. When users decision needs to be evaluated, or typically when their perceived satisfaction is to be computed, a set of cost tolerance parameter and QoS and cost weights is used according to user preferences (cf.

D x = D x 1 + N x (5)
where D x represents the user peak rate when connected to RAT x and N x represents the number of users that are connected to RAT x at the time of selection. • Satisfaction-based using peak rate (SB -PR): Using their peak rates, mobiles adopt the Satisfaction-based multi-criteria decision-making method to select their best RAT. In order to evaluate the different technologies, the provided QoS parameters in Eq. 2 and 3 are replaced with the peak rate that mobiles can achieve when connected to these technologies.

• Satisfaction-based using instantaneous rate (SB -IR): Mobiles use the Satisfaction-based multi-criteria decision-making method to select the RAT that maximizes their expected utility. In Eq. 2 and 3, the provided QoS parameters are replaced with the average throughput that mobiles estimate to obtain (cf. Eq. 5). • Exhaustive search: The network considers all possible associations involving all users. It finally selects the combination that optimizes its own utility. Actually, it assigns mobiles to either WiMAX or LTE technologies in a way to maximize the total offered throughput. This is known to be the optimal method with respect to operator objectives: it leads to the highest network utility.

• Our hybrid approach: The network periodically sends decisional information (i.e., cost and QoS parameters) to assist mobile users in their decisions. QoS parameters are signaled with the following thresholds: S 1 = 0.3 and S 2 = 0.8. Thereby, a network is considered to be low-loaded when its load factor is below 0.3. Initial d min and d max are then signaled (cf. Table III). Yet, when its load factor exceeds 0.8, a network is considered to be highly loaded, providing no QoS guarantees.

RAT d min (Mb/s) dmax (Mb/s) cost (unit/kB) Mobile WiMAX 1 1.5 4 LTE 1.5 2 6

TABLE III INITIAL QOS AND COST PARAMETERS

When using the peak rate maximization and the SB -PR methods, mobiles select their RAT without any network assistance. Decisions are then user-centric. However, when employing the instantaneous rate maximization and the SB -IR methods, load conditions signaled by the network assist mobile users in their decisions. The latter two methods are thus considered to be hybrid. Finally, when adopting the exhaustive search method, decisions are network-centric since they are made by the network transparently to end-users.

Since in practice telecom operators will not reveal neither the exact numbers of users that are connected to their RATs nor the scheduling algorithm they adopt, the instantaneous rate maximization and the SB -IR methods are not realistic. Yet, they serve to illustrate the gain from masking network load conditions and only signaling cost and some QoS parameters in order to enhance resource utilization.

A. Streaming sessions

We assume that streaming sessions have an average longterm throughput of 1 Mb/s. So as to improve content quality, they can furthermore benefit from throughputs up to 1.5 Mb/s (i.e., R av = 1 Mb/s and R max = 1.5 Mb/s).

When our proposed hybrid approach is used, the cost tolerance parameter and the weights that are assigned to the decision criteria (i.e., d min , d max and cost) are put forward in Table IV. When profile no. 1 is assigned to users that are ready to pay for better performances, profile no. 2 is attributed to those that seek to save up money. The network utility, defined as the total offered throughput, generally increases with the total throughput demand. Yet, when a RAT gets overloaded, its offered throughput stagnates and no longer increases with additional throughput demand. When the SB -PR method is used, all users select the mobile WiMAX technology (i.e., Mobile WiMAX is their best trade-off between cost and QoS decision criteria). Regardless of user preferences and radio conditions, mobile WiMAX is expected to provide mobile users with the highest utility. Since mobiles use their peak rate in estimating their utility, their decisions do not depend on network load conditions. As a result, mobiles continue to select the WiMAX technology even when it gets overloaded.

At low traffic load, mobile WiMAX can meet users QoS needs, while charging them less. When users benefit from throughputs up to their R max and pay less, they have the highest utility (i.e., satisfaction). However, when WiMAX gets loaded, it becomes no longer able to fulfill users QoS needs. Typically, at medium and high traffic load, WiMAX becomes saturated leading to a significant decrease of the user-perceived throughput below R av (cf. Fig. 6). As a consequence, userperceived satisfaction will also dramatically decrease (cf. Fig. 7).

Furthermore, when the peak rate maximization method is adopted, all users select the LTE technology. Actually, independently of their modulation and coding scheme, mobiles can achieve the best peak rate when connected to the LTE technology. Here again, their decisions do not change with network load conditions. As a consequence, at high traffic load, user-perceived throughput goes below R max . Yet, it continues to be greater than R av .

On the other hand, since LTE charges more than WiMAX does, mobile users experience the lowest satisfaction level at low traffic load. As a matter of fact, when all RAT selection techniques meet users QoS needs, the peak rate maximization method assign all users to the LTE technology, thus charging them more. At high traffic load, because userperceived throughput decreases, their experienced utility also diminishes.

Moreover, when the SB -IR method is employed, users combine their needs and preferences with network load conditions to select their best RAT. As a consequence, at low traffic load and regardless of their radio conditions, all users select the mobile WiMAX technology: their QoS needs are perfeclty met while paying less. This leads to the highest user-perceived utility, as in the case of the SB -PR method. However, when the mobile WiMAX gets loaded, users may start to join the LTE technology according to their radio conditions and preferences (i.e., their willingness to pay for better performances). Precisely, based on their modulation and coding scheme, as well as on their cost tolerance parameter and QoS and cost weights (cf. Table II), users estimate the utility they can obtain in both of the available RATs. They then select the technology with the highest expected utility. In fact, users with bad radio conditions that are ready to pay for better performances are the first to start to join the LTE technology. Besides, users with good radio conditions that seek to save up money are the last to start to join the LTE technology.

Consequently, since users are not proportionally distributed over the two RATs, mobile WiMAX gets overloaded before the LTE. Thus, the growth rate of the network utility decreases as the total throughput demand increases (cf. Fig. 6). This means that the average user-perceived throughput decreases. Yet, it remains greater than R av . When some users start to join LTE and so pay more, and others that are connected to WiMAX start to perceive lower throughputs, the average user satisfaction also decreases as the total throughput demand increases (cf. Fig. 7).

Furthermore, our hybrid approach and the instantaneous rate maximization method perfectly meet users QoS needs, even at high traffic load. Their network utility, as depicted in Fig. 6, is so close to that of the exhaustive search method, known to be the optimal one with respect to resource utilization. Yet, as shown in Fig. 7, our hybrid approach provides the highest user utility.

In fact, when the instantaneous rate maximization method is used, mobiles select the RAT that offers them the best throughput. Therefore, a kind of load balancing is achieved: Mobile WiMAX and LTE are similarly occupied with respect to their maximum capacity. As a result, the network utility can likely follow the throughput demand increase. On the other hand, when our hybrid approach is employed, the network modulates the broadcasted QoS parameters as a function of its load conditions. It tries to push future arrivals to less loaded RATs, thus enhancing resource utilization. By integrating their needs and preferences, mobiles can avoid oversized decisions and so improve their perceived-satisfaction. Typically, at low traffic load, when both RATs can perfectly meet users QoS needs, mobile WiMAX will be preferred since it charges less. This explains why, when using our hybrid method, user utility is constantly higher than when adopting the instantaneous rate maximization method. The latter ignores user preferences (i.e., its willingness to pay for better performances or to save up money) and mainly deals with load balancing. However, because the proportion of users that are connected to the LTE technology is almost constant and the user-perceived throughput is always close to R max , user utility hardly changes as a function of the total throughput demand. On the other side, when using our hybrid method, since the proportion of users that are connected to the LTE increases with the total throughput demand, the average user utility decreases since LTE charges more than WiMAX. Yet, it always remains greater than that of the instantaneous rate maximization method. Moreover, when using the exhaustive search method, the network involves all users at each decision epoch: it considers all possible combinations and selects the one that maximizes its own utility. Since user needs and preferences are ignored, and RATs are not statistically similarly occupied, this network-centric method provides the lowest user utility amongst the instantaneous rate maximization method and our hybrid approach. As a matter of fact, the network seeks to optimize its own utility, regardless of user preferences. In other words, when different combinations lead to the same network utility, they are assumed equivalent. The one that better distributes mobiles over the two RATs has no priority. As a result, statistically, the proportion of users that are connected to the LTE is higher than those of the instantaneous rate maximization and our hybrid methods, leading to lower user-perceived satisfaction (cf. Fig. 7).

To conclude, so as to illustrate the gain from masking network load conditions and only signaling cost and some QoS parameters, we compare our hybrid approach with the SB -IR one. Actually, when using our hybrid method, we can push users to LTE long before WiMAX really gets overloaded. By reducing the broadcasted QoS parameters in WiMAX, typically with S 1 = 0.3 and S 2 = 0.8, future arrivals are encouraged to join LTE well in advance in comparison with the SB -IR scenario. Thereby, sessions are better distributed over the two RATs, leading to higher network utility as shown in Fig. 6.

At low traffic load, both methods perfectly meet users QoS needs. Yet, since the proportion of users that are connected to the most expensive RAT (i.e., LTE) is higher when our hybrid approach is used, user-perceived satisfaction is lower than that of the SB -IR method. However, at high throughput demand, because future arrivals start to join LTE much earlier than the SB -IR case, WiMAX is on average less loaded when using our hybrid approach. As a consequence, WiMAX can better serve its on-going sessions leading to higher user-perceived throughput. Therefore, although mobiles may pay more (i.e., the proportion of users that are connected to LTE is higher), they experience significantly better performances leading to higher satisfaction (Fig. 7). After all, by dynamically tuning QoS parameters, the network enhances resource utilization while mobiles maximize their satisfaction.

B. Elastic sessions

Elastic sessions adapt to resource availability. Their needs are expressed as comfort throughput, denoted by R c . We assume in the following that R c is related to the user willingness to pay and thus imposed by the user profile (cf. Table V). Typically, when users are ready to pay for better performances, they have a comfort throughput of 1.25 Mb/s. Yet, when they seek to save up money, they are content with a comfort throughput of 0.75 Mb/s. We respectively depict in figures 8 and 9 the network utility and the average user utility as a function of the total number of users denoted by N total . When connected alone to a RAT, an elastic session can occupy all of the available resources. However, when several sessions are present, they all share these resources. As a result, the network utility, defined as the total offered throughput, do not usually change as a function of the total number of users N total (cf. Fig 8). Yet, the average user-perceived throughput is reduced.

As in the case of streaming sessions, when the SB -PR method is used, all users are connected to the mobile WiMAX technology regardless of the network load conditions. As shown in Fig. 8, the total offered throughput (i.e., the network utility) is close to 12 Mb/s independently of N total : it actually corresponds to the weighted average total throughput taking into account both users with good and bad radio conditions. However, the average user-perceived throughput linearly decreases with N total , leading to a significant decrease of the user-perceived satisfaction (cf. Fig. 9).

Moreover, when the peak rate maximization method is adopted, all users select the LTE technology. The network utility is then, on average, higher than that of the SB -PR method. As a consequence, user-perceived throughput is also higher. But, since all users are connected to the most expensive RAT (i.e., LTE), the satisfaction improvement with respect to the perceived throughput criterion fails to offset the satisfaction decrease with respect to the cost criterion. This leads to a lower user-perceived satisfaction in comparison with the SB -PR case (cf. Fig. 9).

Furthermore, when the exhaustive search method is employed, optimal resource utilization is achieved as shown in Fig. 8. Yet, the average user utility is not that interesting. First, when assigning mobiles to the available RATs, this networkcentric method do not consider user preferences. It actually ignores user willingness to pay for better performances or to save up money, and only seeks to maximize the network offered throughput. Second, in order to better exploit the available resources, only few users with good radio conditions may be assigned to LTE. The majority, with bad and also good radio conditions, will be connected to mobile WiMAX, all competing for the same resources. As a result, few users connected to LTE will have excellent throughputs that far outweigh their R c . The others will experience relatively low throughputs that may be well below their R c . This asso-ciation optimizes the total offered throughput, but not the user-perceived satisfaction (cf. Fig. 9). Actually, because of its concave form, even the satisfaction with respect to the throughput criterion is not maximized.

In comparison with the exhaustive search method, mobiles are better distributed over the two RATs when the instantaneous rate maximization method is adopted. In fact, users select the RAT that offers them the best throughput, leading to a kind of load balancing as in the streaming case. As a result, mobiles with equivalent radio conditions will have close throughputs regardless of their access technology. Since even users with bad radio conditions may be connected to LTE, the network utility is on average lower than that of the exhaustive search method known to be the optimal one. However, because on average perceived throughputs better meet user needs (i.e., their R c ), the user utility is significantly higher than that of the exhaustive search approach.

On the other hand, when the SB -IR method is used, mobile users combine their needs and preferences with the network load conditions so as to select their best RAT. At low traffic load (typically for N total = 5), more users select the mobile WiMAX technology in comparison with the instantaneous rate maximization method. When WiMAX can meet user needs very well, it charges them less. Occasionally, based on the current load conditions, a user with bad radio conditions, that is ready to pay for better performances, would select the LTE technology. As N total increases, more users including those with good radio conditions start to join LTE, leading to higher network utility. The latter remain almost constant at medium and high load conditions. On average, it is slightly lower than that of the instantaneous rate maximization method. Yet, since selection decisions take into account user needs and preferences, typically their cost considerations, the user utility is significantly better than that of the instantaneous rate maximization method. Lastly, by masking network load conditions and only signaling some cost and QoS parameters, our hybrid approach drives users decision in a way to enhance resource utilization. At low traffic load, more users typically those with bad radio conditions, that are ready to pay, select LTE. This leads to a higher network utility in comparison with the SB -IR method where, as explained before, users may occasionally join LTE (cf. Fig. 8). As a result, and although users pay on average more, they experience higher satisfaction since they have quite better throughput.

As N total increases, QoS parameters are reduced with S 1 = 0.3 and S 2 = 0.8. As a consequence, future arrivals are encouraged to join LTE much earlier than the SB -IR case. However, users with good radio conditions that seek to save up money are the last to start to join LTE. In comparison with the SB -IR method, most users that are connected to WiMAX have good radio conditions, and more users with good and bad radio conditions are connected to LTE. This leads to higher total offered throughput, as shown in Fig. 8. Yet, the user utility is so close to that of the SB -IR scenario, since users having better performances pay on average more.

VI. CONCLUSION

In this paper, we address the access technology selection in heterogeneous wireless networks and proposes a hybrid decision method. Cost and QoS parameters, periodically signaled by the network, assists mobile users in their decisions. Our proposed solution combines benefits from both networkcentric and user-centric approaches: it considers operator objectives as well as user needs and preferences, without unduly complicating the network. In comparison with different RAT selection techniques, including network-centric, hybrid and user-centric approaches, simulation results prove the efficiency of our hybrid method in enhancing resource utilization and maximizing user satisfaction. In the streaming sessions scenario, it optimizes the total offered throughput and maximizes the average user utility (except at low traffic load, where the non-realistic SB -IR method provides higher user satisfaction). Also, in the elastic sessions scenario, our hybrid approach significantly enhances resource utilization and maximizes user utilities in comparison with various user-centric and hybrid methods. Furthermore, compared with the exhaustive search method, known to be the optimal one with respect to resource utilization, our hybrid approach provides significantly higher user satisfaction.

Fig. 1 .

 1 Fig. 1. A cell divided into N Z concentric rings

Fig. 2 .

 2 Fig. 2. Throughput satisfaction function forms

Fig. 3 .

 3 Fig. 3. Monetary cost satisfaction function form

Figure 4

 4 Figure 4 summarizes the decision process.

Fig. 4 .

 4 Fig. 4. Satisfaction-based multi-criteria decision process • For each alternative (a), the mobile combines its radio conditions with the QoS parameters signaled by the network: it multiplies d min (a) and d max (a) with a given modulation and coding gain to determine its perceived QoS parameters, as provided by the network. • Then, based on the user needs (i.e., traffic class c, throughput demand and cost tolerance λ), it computes the normalized decision criteria: dc min (a), dc max (a) and cost c (a).

Fig. 5 .

 5 Fig. 5. Bandwidth guarantees reduction -Slope tuning

Figures 6 and 7

 7 Figures 6 and 7 respectively show the network utility and the average user utility as a function of the total throughput demand.The network utility, defined as the total offered throughput, generally increases with the total throughput demand. Yet, when a RAT gets overloaded, its offered throughput stagnates and no longer increases with additional throughput demand.

Fig. 6 .Fig. 7 .

 67 Fig. 6. Network utility: Streaming sessions scenario

Fig. 8 .

 8 Fig. 8. Network utility: Elastic sessions scenario

Fig. 9 .

 9 Fig. 9. User utility: Elastic sessions scenario

  To evaluate selection decisions, network and users utilities are introduced. The

	RAT	64-QAM: 3/4	16-QAM: 1/2
	Mobile WiMAX (5 MHz)	16.6 Mb/s	7.4 Mb/s
	LTE (10 MHz)	33.5 Mb/s	14.9 Mb/s
		TABLE I
	PEAK RATES IN MOBILE WIMAX AND LTE

  Table II).

	Set No.	λ	w QoS	wcost
	1	60	0.7	0.3
	2	45	0.3	0.7

  Instantaneous rate maximization: Mobiles are assumed to know the exact numbers of users that are connected to available technologies. Assuming that fair time scheduling is employed, they select the RAT that offers them the best throughput. Their estimated throughput in RAT x, D x , at the time of selection, is computed as:

	TABLE II
	COST TOLERANCE PARAMETER AND QOS AND COST WEIGHTS
	For comparison purposes, six different RAT selection tech-
	niques are considered:

• Peak rate maximization: Mobile users have no information on the global network state. Based on their radio conditions, they select the RAT that offers them the best peak rate. •


Melhem El Helou

Samer Lahoud

Marc Ibrahim

Kinda Khawam

A Hybrid Approach for Radio Access Technology Selection in Heterogeneous Wireless Networks

Keywords: Radio access technology selection, heterogeneous wireless networks, hybrid decision-making approach

In heterogeneous wireless networks, different radio access technologies are integrated and may be jointly managed. To optimize composite network performance and capacity, Common Radio Resource Management (CRRM) mechanisms need to be defined. This paper tackles the access technology selection -a key CRRM functionality -and proposes a hybrid decision framework to dynamically integrate operator objectives and user preferences. Mobile users make their selection decision based on their needs and preferences as well as on the cost and QoS information signaled by the network. Appropriate decisional information should then be derived so that the network better utilizes its radio resources, while mobile users maximize their own utility. We thus present two tuning policies, namely the staircase and the slope tuning policies, to dynamically modulate this information. Simulation results illustrate the gain from using our tuning policies in comparison with a static one: they lead to better network performance, larger operator gain and higher user satisfaction.

I. INTRODUCTION

Multiple radio access technologies (RATs), such as IEEE 802.11 WLANs, mobile WiMAX, HSPA+ and LTE, are being integrated to form a heterogeneous wireless network. This cost-effective solution provides high capacity and global service coverage. However, radio resources need to be jointly managed. Typically, when a new or a handover session arrives, a decision must be made as to what technology it should be associated with. Robust decisions inevitably help to enhance resource utilization and user satisfaction.

So as to consider operator objectives, including efficient exploitation of radio resources, network-centric schemes have been proposed: network elements collect necessary measurements and information. They take selection decisions transparently to end-users in a way to enhance heterogeneous network performance. In [START_REF] Sang | Coordinated load balancing, handoff/cell-site selection, and scheduling in multi-cell packet data systems[END_REF] and [START_REF] Khawam | Individual vs. global radio resource management in a hybrid broadband network[END_REF], network selection is formulated as an optimization problem. The best assignment is derived to optimize the associated objective function, defined as a heterogeneous network performance metric: perceived throughput in [START_REF] Sang | Coordinated load balancing, handoff/cell-site selection, and scheduling in multi-cell packet data systems[END_REF] and service time in [START_REF] Khawam | Individual vs. global radio resource management in a hybrid broadband network[END_REF]. In [START_REF] Ibrahim | Network-centric joint radio resource policy in heterogeneous wimax-umts networks for streaming and elastic traffic[END_REF] and [START_REF] Kumar | User-Network Association in a WLAN-UMTS Hybrid Cell: Global & Individual Optimality[END_REF], Semi-Markov Decision Process (SMDP) is proposed to find the optimal access policy that maximizes the long-term reward function. In [START_REF] Giupponi | A framework for JRRM with resource reservation and multiservice provisioning in heterogeneous networks[END_REF], a fuzzy neural methodology is used to jointly decide of the network association and the bandwidth allocation. A reinforcement signal is also generated to optimize the decision-making process: the means and standard deviations of the input and output bell-shaped membership functions are adjusted accordingly.

However, to reduce network complexity, signaling and processing load, mobile-terminal-centric methods have also gained in importance: based on their individual needs and preferences, rational users select their access technology in a way to selfishly maximize their payoff (utility). Since their payoff does not only depend on their own decision, but also on the decisions of other mobiles, game theory is widely adopted as a theoretical decision-making framework ( [START_REF] Khawam | Individual vs. global radio resource management in a hybrid broadband network[END_REF], [START_REF] Kumar | User-Network Association in a WLAN-UMTS Hybrid Cell: Global & Individual Optimality[END_REF] and [START_REF] Niyato | Dynamics of network selection in heterogeneous wireless networks: An evolutionary game approach[END_REF]). Players (i.e., the individual users) will try to reach a mutually agreeable solution, or equivalently, a set of strategies they will unlikely want to change. Also, in [START_REF] Stevens-Navarro | Comparison between vertical handoff decision algorithms for heterogeneous wireless networks[END_REF] and [START_REF] Song | A network selection mechanism for next generation networks[END_REF], Simple Additive Weighting (SAW), Multiplicative Exponent Weighting (MEW), Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are used as multi-criteria decision-making methods. Individual users combine their QoS parameters (e.g., instantaneous peak rates), calculate decision metrics, and select their access technology accordingly. Because individual users have no information on the global network state (i.e., network load conditions), mobile-terminal-centric approaches are known for their potential inefficiency.

In this article, we propose a hybrid decision method that combines benefit from both network-centric and mobileterminal-centric approaches. The network information, that is periodically broadcasted, assists mobile users in their decisions: mobiles make their selection decision based on their individual needs and preferences as well as on the cost and partial QoS information signaled by the network. A particular attention is then addressed to the network to make sure it broadcasts appropriate decisional information so as to better exploit its radio resources, while individual users are maximizing their own utility. We thus present two tuning policies, namely the staircase and the slope tuning policies, to dynamically derive what to signal to the mobiles. Our hybrid framework may be naturally integrated into Self-Organizing Networks (SON) [START_REF]Telecommunication management; Self-Organizing Networks (SON) Policy Network Resource Model (NRM) Integration Reference Point (IRP); Requirements[END_REF].

The rest of this paper is organized as follows: Section II describes our hybrid decision framework. Section III presents our tuning policies. Our system model is detailed in section IV. Section V discusses simulation parameters and results. Section VI concludes the document.

II. HYBRID DECISION FRAMEWORK

Network information is periodically sent to all mobile users using the logical communication channel (i.e., radio enabler) proposed by the IEEE standard 1900.4 [START_REF]IEEE standard for architectural building blocks enabling networkdevice distributed decision making for optimized radio resource usage in heterogeneous wireless access networks[END_REF]. This information implicitly integrates operator objectives. It may be static or variable so as to dynamically optimize short-or long-term network performance.

When a new or a handover session arrives, the mobile decodes the decisional information, evaluates available alternatives, and selects the technology that best suits it.

A. Network information

The network information provides cost and some QoS parameters: they can be seen as incentives to join available alternatives.

• The cost parameters: Because flat-rate pricing strategies waste resources [START_REF] Edell | Providing internet access: what we learn from index[END_REF], result in network congestion and thus degrade network performance [START_REF] Zhang | Pricing for efficient usage in wired and wireless networks[END_REF], they are not optimal in supporting QoS. A volume-based model is therefore proposed: mobile users are charged based on the amount of traffic they consume; in our work, costs are defined on a per kbyte basis.

• The QoS parameters: The number of radio resource units (RRUs) (e.g., OFDM symbols or OFDMA slots) that need to be allocated to future arrivals are broadcasted:

-Mobiles are guaranteed an average minimum number of RRUs, denoted by n min . -They also have priority to occupy up to an average maximum number of RRUs, denoted by n max . The network loading conditions and capacity are, however, masked. In fact, n min and n max reveal the operator intention to serve future arrivals: they do not exclusively reflect the loading conditions, but also other potential operator objectives (e.g., energy consumption). Since the smallest allocation unit (i.e., RRU) may be different from one technology to another, there is a need to homogenize the QoS information. The QoS parameters are then expressed as throughputs: d min and d max instead of n min and n max . Yet, because perceived throughputs highly depend on radio conditions (or equivalently on adopted modulation types and FEC coding rates), d min and d max are derived for the most robust modulation and coding scheme. Consequently, when evaluating available alternatives, mobiles should combine their individual radio conditions with the provided QoS parameters: for that they multiply d min and d max with a given modulation and coding gain. Although QoS parameters are provided, our decision framework is independent of local resource allocation schemes. First, enough RRUs are allocated to meet all of the operator commitments (i.e., the minimum guaranteed throughput given by d min ). Then, any priority scheduling algorithm (including opportunistic schemes) could be adopted to allocate to each session up to its maximum prioritized throughput given by d max . The remaining resources may afterwards be equitably granted to all sessions.

B. Technology selection

For each incoming session, the network proposes one or more alternatives, which are the available access technologies. For each alternative (a), the network broadcasts the three parameters: d min (a), d max (a), and cost(a). From the user's point of view, the network parameters are decision criteria that will be used by the mobile to rank the access technologies. For that, the mobile has to adopt a multi-criteria decision making method: it defines a utility function that will be computed for all available alternatives. This utility is obtained after normalizing and weighting the decision criteria.

III. TUNING POLICIES

Because mobile users also rely on their needs and preferences to select their best alternative, the network does not completely control individual decisions. However, by broadcasting appropriate decisional information, the network tries to globally influence users decision in a way to satisfy its own objectives.

In our work, we focus on efficient resource utilization: the network information is dynamically derived in a way to enhance heterogeneous network performance. On the other hand, mobile users make their decisions so as to maximize their own utility.

When a radio access technology dominates all the others (i.e., provides higher QoS parameters for the same cost or the same QoS parameters for a lower cost), common radio resources are inefficiently utilized causing performance degradation. In fact, mobile users would select the dominant alternative, leading to unevenly distributed traffic load. While a technology is overcrowded, the others are almost unexploited. This inefficiency is very similar to that of the mobile-terminalcentric approaches. To remedy it, the QoS information, signaled by the network, needs to be modulated as a function of the loading conditions.

In this section, we present two tuning policies, namely the staircase and the slope tuning policies, to dynamically derive the QoS information. In order to reduce network complexity and processing load (one of the drawbacks of the networkcentric approaches), our policies are basic and simple. Yet, they help to efficiently distribute traffic load and thus to better utilize radio resources.

A. Staircase tuning

Initial QoS parameters (i.e., d min and d max ) are specified. When the operator bandwidth guarantees -identified as a generic load factor -exceed a predefined threshold S 1 , these parameters are reduced in the corresponding technology following a step function, as shown in Fig. 1. Yet, when S 2 is reached, they are set to zero.

Initial parameters

Load factor S1 S2

QoS incentives

B. Slope tuning

As technologies are progressively loaded, the QoS parameters are gradually tuned. When S 1 is reached, these parameters are linearly reduced down to zero, as shown in Fig. 2. The slope helps to better respond to traffic load fluctuations.

Initial parameters

Load factor S1 S2

QoS incentives

Fig. 2. Bandwidth guarantees reduction -Slope tuning

When the QoS parameters are dynamically modulated, future arrival decisions are pushed to less loaded technologies, thus enhancing long-term network performance.

IV. SYSTEM MODEL

The radio resource is divided into multiple radio resource units (RRU), thus compatible with OFDM-based technologies (e.g., LTE and WiMAX technologies). In the time domain, transmissions are further organized into (radio) frames of length 10 ms. At each scheduling epoch, resource units are allocated to individual users based on their priority and current needs. Actually, before any scheduling is applied, the minimum guaranteed resource units (the operator guaranteed commitments) are directly allocated. Then, the Weighted Fair Queueing is adopted to share out the remaining resources; grants are however limited to d max . Session priorities are based on the cost they pay for one unit of traffic. Finally, when all active sessions have been allocated enough resources as to meet their d max , the remaining resources are equitably distributed (according to the Round Robin service discipline).

Mobile users arrive sequentially. The total number of arrivals is limited to N total ; it sets the traffic load. Their sojourn time is considered to be much greater in comparison with the simulation time. Consequently, the system dynamics will then slow down until a pseudo-stationary regime is attained, where all measurements are performed. Results are validated through extensive simulations.

After they decode cost and QoS parameters, mobiles adopt a satisfaction-based decision making method to evaluate and then rank the different alternatives. The normalization of decision criteria d min (a), d max (a), and cost(a) depends on the session traffic class and throughput demand. For traffic class c and alternative a, the normalization is a mapping of d min (a), d max (a), and cost(a) to dc min (a), dc max (a), and ĉost c (a) respectively. In our work, we define three traffic classes : inelastic, streaming, and elastic classes. Before we give the normalizing functions for each traffic class, we note that pc (a), p ∈ {d min , d max , cost}, can be viewed as the satisfaction of a class c session with respect to criterion p for alternative a:

• Inelastic sessions (c = I): since designed to support constant bit rate circuit emulation services, inelastic sessions require stringent and deterministic bandwidth guarantees. Thus, d max should not have any impact on the final decision. Besides, the satisfaction with respect to d min has a step shape (Fig. 3(a)): mobiles expect to be satisfied when d min is greater or equal to their fixed throughput demand R f ; otherwise, they are not satisfied.

dI min (a) = 0 if d min (a) < R f 1 if d min (a) ≥ R f (1)
• Streaming sessions (c = S): since designed to support real-time variable bit rate services (e.g., MPEG-4 video service), streaming sessions are fairly flexible and usually characterized by a minimum, an average and a maximum bandwidth requirement. Their throughput satisfaction is therefore modelled as an S-shaped function (Fig. 3(b)):

d′ S (a) = 1 -exp -α( d ′ (a) Rav ) 2 β + ( d ′ (a) Rav ) (2)
where d ′ = {d min , d max }. R av represents session needs: an average throughput demand. α and β are two positive constants to determine the shape of the sigmoid function. • Elastic sessions (c = E): since designed to support traditional data services (e.g., file transfer, email and web traffic), elastic sessions adapt to resource availability (i.e., load conditions), requiring no QoS guarantees. Thus, d min is completely ignored. Moreover, the satisfaction with respect to d max has a concave shape (Fig. 3(c)): the satisfaction increases slowly as the throughput exceeds the comfort throughput demand R c of the user (i.e., the mean throughput beyond which, user satisfaction exceeds 63% of maximum satisfaction).

dE max (a) = 1 -exp - d max (a) R c (3)
The monetary cost satisfaction is, however, modelled as a Zshaped function for the three traffic classes (Fig. 4): the slope of the satisfaction curve increases rapidly with the cost.

ĉost c (a) = exp - cost(a) 2 λ c , c ∈ {I, S, E} (4)
λ c represents the cost tolerance parameter: a positive constant to determine the shape of the Z-shaped function. • Finally, it computes the utility function for each alternative a and selects the alternative with the highest score. By broadcasting appropriate decisional information, the network tries to globally control users decision in a way to enhance resource utilization. On the other hand, mobiles make their decisions so as to maximize their own satisfaction. The selection decisions take then into account both the user needs and preferences and the operator objectives. Network complexity and processing load are, however, reduced.

V. SIMULATION PARAMETERS AND RESULTS

For illustration, we consider two access technologies each with a capacity of 70 Mb/s. Each is assumed to propose three different service classes, namely Premium, Regular and Economic. Initial QoS and cost parameters are depicted in Table I.

TABLE I INITIAL QOS AND COST PARAMETERS

After they arrive, mobiles are uniformly associated with a user profile. Detailed user profiles are presented in Table II. Inelastic and streaming session needs are respectively expressed as fixed (i.e., R f ) and average long-term throughput (i.e., R av ). We assume that the set of possible throughput demands is given by D = {0.5, 1, 1.5, 2} Mb/s. Inelastic sessions generate packets according to a deterministic distribution, whereas streaming sessions generate packets according to a poisson process. In our work, we fix delay constraints for the latter session types. A maximum delay requirement of 100 ms is fixed. Since resources are limited, some packets may miss their deadline; they will be dropped as they are no longer useful. Furthermore, elastic session needs are expressed as comfort throughput (i.e., R c ). We suppose that the set of possible comfort throughputs is given by C = {0.75, 1.25} We also assume that mobiles are uniformly associated with a set of modulation and coding gains. These multiplicative factors reflect the user radio conditions in the different technologies and are supposed to remain constant in time. Two sets of gains are considered and reported in Table III.

Set No. RAT 1 RAT 2 1 1.5 1.5 2 2 1

TABLE III MODULATION AND CODING GAIN

When the two access technologies provide the same QoS parameters, users that are associated with set no. 2 would select RAT 1: they expect to have better radio conditions and thus to perceive higher throughputs in RAT 1. All other alternatives (proposed by RAT 2) are subsequently dominated. Also, users that are associated with set no. 1 randomly join their access technology, since they expect to perceive similar throughputs in the two available technologies. This situation leads to unevenly distributed traffic load. However, when the network information is dynamically modulated according to the staircase or to the slope tuning policies, the QoS parameters are changed in a way to drive future arrivals to the less loaded RAT: loaded technologies provide lower QoS parameters and thus push future users to less loaded technologies.

When staircase policy is adopted, reduced QoS parameters are presented in Table IV.

To analyze long-term network performance, six major key performance indicators are defined: mean delay, packet loss rate (for inelastic and streaming sessions), comfort metric (for elastic sessions), throughput, operator gain and perceived satisfaction level.

A. Performance Results

The proposed tuning policies are compared with the static one. In the latter case, initial QoS parameters are maintained fixed, except when the access technology is no longer able to guarantee to future arrivals the initial QoS parameters.

In the following, we assume that S 1 and S 2 are respectively set to 0.5 and 0.9 times the access technology capacity. Before S 1 , the network provides constant QoS parameters. After S 2 , QoS incentives are no longer provided to future arrivals: the network keeps a margin of about 10% of the RAT capacity to provide on-going sessions with more than their minimum guaranteed throughputs.

1) Real-time sessions: Because real-time (RT) sessions (i.e., inelastic and streaming sessions) require tight delay constraints, access technologies should meet their throughput demands. However, users with a demand of 2 Mb/s may suffer: even the Premium guarantees may be lower than their throughput demand. When the access technology is highly loaded, the resource scheduler will not be able to provide them with more than their minimum guaranteed throughputs, thus leading to packet loss. So as to reduce the packet drop probability, we should avoid that a technology gets overloaded long before the others. User decisions should then be driven.

Figures 6(a) and 6(b) respectively show the mean waiting delay and the packet drop probability as a function of the total number of arrivals. When the slope intervention policy denoted as Dynamic information [START_REF] Khawam | Individual vs. global radio resource management in a hybrid broadband network[END_REF] is adopted, it best responds to traffic load fluctuations and thus provides a shorter delay, a lower drop probability and a subsequently better overall QoS level. On the other hand, the staircase intervention policy denoted as Dynamic information [START_REF] Sang | Coordinated load balancing, handoff/cell-site selection, and scheduling in multi-cell packet data systems[END_REF] is disadvantageous when (c) Perceived user satisfaction Fig. 7. Operator gain and global network performance all technologies have exceeded their S 1 : while load conditions are critical, RAT 1 is once again privileged until the operator guarantees exceed S 2 (i.e., until RAT 1 no longer provides QoS guarantees to future arrivals). Yet, real-time sessions performance are always significantly enhanced in comparison with the static scenario (denoted as Static information).

2) Elastic sessions: We define the comfort metric as the ratio of the perceived throughput to the comfort throughput. When the network information is dynamically variable, sessions are better distributed over the two technologies. More RRUs are then on average allocated to on-going sessions. Typically, elastic sessions would experience higher throughput and subsequently higher comfort metric, as shown in Fig. 6(c). However, at low traffic load (since tuning policies are not yet triggered) and at high traffic load (since all technologies becomes similarly occupied regardless of the tuning policy), performance enhancement is not that significant for elastic sessions.

3) Operator gain and global network performance: When tuning policies are triggered, QoS parameters are reduced. To benefit from the same initial bandwidth guarantees, mobile users may have to select a higher priority service class, and thus have to pay more. Also because fewer real-time packets are dropped (cf. Fig. 6(b)) and more elastic packets are served (cf. Fig. 6(c)), users consume on average a larger amount of traffic (Fig. 7(a)) and once again pay more. We illustrate in Fig. 7(b) the average operator gain. When operators dynamically intervene, they gain more.

We depict in Fig. 7(c) the average user-perceived satisfaction. Although mobiles may pay more, we notice a higher satisfaction when tuning policies are implemented. Higher costs are then justified since users benefit from significantly better performances. At low traffic load, tuning policies are not yet triggered. Equivalent performances, costs and subsequently satisfactions are intuitively observed. However, at very high traffic load, the performance gain over the static scheme begins to reduce; henceforth, it slightly offsets the cost considerations, leading to close user satisfaction.

VI. CONCLUSION

In this article, we address the access technology selection in heterogeneous wireless networks. We first propose a hybrid decision framework: the cost and QoS information, signaled by the network, assists mobile users in their decisions. Our proposed approach takes into account both the user needs and preferences and the operator objectives, without unduly complicating the network. We further present two tuning policies, namely the staircase and the slope tuning policies, to adjust the decisional information in a way to enhance resource utilization, while individual users are maximizing their own satisfaction. In comparison with the static scheme, performance results show that our tuning policies enhance network performance, provide larger operator gain and higher user satisfaction. Since it best responds to traffic load fluctuations, the slope tuning policy has proved to be an efficient strategy that enhances resource utilization.

Fig. 1 .

 1 Fig. 1. Bandwidth guarantees reduction -Staircase tuning

Fig. 3 .Fig. 4 .

 34 Fig. 3. Throughput satisfaction function forms

Fig. 5 .

 5 Fig. 5. Satisfaction-based multi-criteria decision process • For each alternative a, the mobile combines its radio conditions with the QoS parameters signaled by the network: it multiplies d min (a) and d max (a) with a given modulation and coding gain to determine its perceived QoS parameters, as provided by the network. • Then, based on the user needs (i.e., traffic class c, throughput demand and cost tolerance λ), it computes

  Service class d min (Mb/s) dmax (Mb/s) Cost (unit/kB)

  Fig. 6. Experienced QoSMb/s. While inelastic and streaming sessions uniformly choose one of the possible throughput demands (regardless of the user cost tolerance parameter), we assume in the following that elastic session comfort throughput is related to the user willingness to pay and thus imposed by the user profile.

  Service class d min (Mb/s) dmax (Mb/s)

	Premium	0.5	0.7
	Regular	0.35	0.5
	Economic	0.2	0.5


Melhem El Helou

Samer Lahoud

Marc Ibrahim

Kinda Khawam

Satisfaction-based Radio Access Technology Selection in Heterogeneous Wireless Networks

Keywords: Radio access technology selection, heterogeneous wireless networks, multi-criteria decision-making

In this paper, a hybrid approach for Radio Access Technology (RAT) selection in heterogeneous wireless networks is proposed. This decision framework dynamically integrates operator objectives and user preferences, with a relatively reduced network complexity, signaling and processing load. By broadcasting cost and QoS parameters, the network assists mobile users in their decisions. Focusing on the user side, we present a satisfaction-based multi-criteria decision-making (MCDM) method. Based on their needs and preferences, individual users select their RAT avoiding inadequate decisions. Simulation results show that our MCDM method maximizes user utility and outperforms existing solutions.

I. INTRODUCTION

Along with the rapid growth of mobile broadband traffic, efficient RAT selection techniques are increasingly required. In the recent few years, many network-centric approaches [START_REF] Ibrahim | Network-Centric Joint Radio Resource Policy in Heterogeneous WiMAX-UMTS Networks for Streaming and Elastic traffic[END_REF]- [START_REF] Khawam | Individual vs. Global Radio Resource Management in a Hybrid Broadband Network[END_REF] have been studied. When network elements collect necessary measurements and information, they take selection decisions transparently to end-users in a way to meet operator objectives (e.g., enhance resource utilization, lower energy consumption). However, to reduce network complexity, signaling and processing load, mobile-terminal-centric approaches [START_REF] Khawam | Individual vs. Global Radio Resource Management in a Hybrid Broadband Network[END_REF]- [START_REF] Falowo | Dynamic RAT Selection for Multiple Calls in Heterogeneous Wireless Networks Using Group Decision-Making Technique[END_REF] have also gained in importance. Based on their individual needs and preferences, rational users select their RAT so as to selfishly maximize their utility. Yet, because mobiles have no information on the global network state (i.e., dynamic load conditions), mobile-terminal-centric methods are known for their potential inefficiency.

In this article, we propose a hybrid approach that combines benefits from both network-centric and mobile-terminalcentric methods. On the one side, by broadcasting appropriate cost and QoS parameters, the network tries to globally control users decision in a way to meet operator objectives. On the other side, based on their needs and preferences as well as on the signaled network information, individual users select their RAT so as to maximize their own utility.

We have previously introduced in [START_REF] Helou | A Hybrid Approach for Radio Access Technology Selection in Heterogeneous Wireless Networks[END_REF] two tuning policies that dynamically derive network information as a function of the load conditions. In the present contribution, we focus on the user side and propose a satisfaction-based Multi-Criteria Decision-Making (MCDM) method. In comparison with existing MCDM algorithms, namely the Simple Additive Weighting (SAW) [START_REF] Stevens-Navarro | Comparison between Vertical Handoff Decision Algorithms for Heterogeneous Wireless Networks[END_REF], and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) [START_REF] Falowo | Dynamic RAT Selection for Multiple Calls in Heterogeneous Wireless Networks Using Group Decision-Making Technique[END_REF], our method avoids undersized and oversized decisions providing better performances and higher user satisfaction.

II. HYBRID DECISION FRAMEWORK

A. Network information

Network information is periodically sent to all mobile users using the logical communication channel (i.e., radio enabler) proposed by the IEEE standard 1900.4 [START_REF]IEEE Standard for Architectural Building Blocks Enabling Network-Device Distributed Decision Making for Optimized Radio Resource Usage in Heterogeneous Wireless Access Networks[END_REF]. When a new or a handover session arrives, the mobile decodes this decisional information, evaluates available alternatives, and selects the RAT that best suits it.

In this setting, we assume that the network information provides cost and some QoS parameters: they can be seen as incentives to join available RATs.

• Cost parameters: A volume-based fixed pricing strategy is proposed. Mobiles are then charged based on the amount of traffic they consume. • QoS parameters: Throughputs that can be allocated to future arrivals are broadcasted:

-Mobiles are guaranteed an average minimum throughput, denoted by d min .

-They also have priority to benefit from an average maximum throughput, denoted by d max . However, since perceived throughputs highly depend on radio conditions (or equivalently on adopted modulation types and FEC coding rates), d min and d max are derived for the most robust modulation and coding scheme. Therefore, when evaluating available alternatives, mobiles should combine their individual radio conditions with the provided QoS parameters: for that they multiply d min and d max with a given modulation and coding gain, denoted by g(M, C).

B. RAT selection

For alternative a, the network broadcasts the three parameters: d min (a), d max (a), and cost(a). New arrivals then compute a utility function for each of the available alternatives, and select the one with the highest score. This utility, obtained after normalizing and weighting the decision criteria, depends on user radio conditions, needs and preferences (e.g., QoSmaximizing, cost-minimizing preferences) as well as on the cost and QoS parameters signaled by the network. The particularity of our RAT selection process resides in the normalization step that takes into account user traffic class and throughput demand, thus avoiding inadequate decisions.

III. SATISFACTION-BASED DECISION METHOD

A. Normalization and Traffic classes

In our work, we consider three traffic classes : inelastic, streaming, and elastic classes. For traffic class c and alternative a, dc min (a), dc max (a), and cost c (a) are respectively the normalized values of d min (a), d max (a), and cost(a). Before we give the normalizing functions for each traffic class, we note that pc (a), p ∈ {d min , d max , cost}, can be viewed as the satisfaction of a class c session with respect to criterion p for alternative a:

• Inelastic sessions (c = I): Since they require stringent and deterministic bandwidth guarantees, d max should not have any impact on the final decision. Besides, the satisfaction with respect to d min has a step shape: mobiles expect to be satisfied when d min is greater or equal to their fixed throughput demand R f . Otherwise, they are not satisfied.

dI min (a) = 0 if d min (a).g(M, C) < R f 1 if d min (a).g(M, C) ≥ R f (1)
• Streaming sessions (c = S): Since they are usually characterized by a minimum, an average and a maximum bandwidth requirement, their throughput satisfaction is modeled as an sigmoid function:

d′ S (a) = 1 -exp( -α( d ′ (a).g(M,C) Rav ) 2 β + ( d ′ (a).g(M,C) Rav ) ) (2)
where d ′ = {d min , d max }. R av represents session needs: an average throughput demand. α and β are two positive constants that determine the shape of the sigmoid function. • Elastic sessions (c = E): Since they adapt to resource availability (i.e., load conditions), requiring no QoS guarantees, d min is completely ignored. Moreover, the satisfaction with respect to d max has a concave shape: the satisfaction increases slowly as the throughput exceeds the comfort throughput demand R c of the user (i.e., the mean throughput beyond which, user satisfaction exceeds 63% of maximum satisfaction).

dE max (a) = 1 -exp(- d max (a).g(M, C) R c ) (3)
The monetary cost satisfaction is, however, modeled as a Zshaped function: the slope of the satisfaction curve increases rapidly with the cost.

cost c (a) = exp(- cost(a) 2 λ c ), c ∈ {I, S, E}) (4)
λ c represents the cost tolerance parameter: a positive constant that determines the shape of the Z-shaped function.

B. User Profile and Utility Function

The user profile defines the cost tolerance parameter and the weights that a given session will apply to normalized criteria. More precisely, the user profile is the set of vectors (λ c , w c dmin , w c dmax , w c cost ), c ∈ {I, S, E}, where w c p is the weight of pc , p ∈ {d min , d max , cost}. The utility function of a class c session for alternative a is defined by :

U c (a) = w c dmin . dc min (a) + w c dmax . dc max (a) + w c cost . cost c (a)
IV. SIMULATION PARAMETERS AND RESULTS

For illustration, three generic OFDM(A)-based RATs are considered; each is assumed to propose three different service classes, namely Premium, Regular and Economic. For the sake of simplicity, all mobiles are supposed to have the same modulation and coding scheme. Cost and QoS parameters, as perceived by mobile users, are depicted in Table I. They are supposed fixed and do not change as the RAT load changes (except when the RAT is no longer able to guarantee to future arrivals the initial QoS parameters).

TABLE I STATIC QOS AND COST PARAMETERS

We assume that RAT capacity is fixed to 35 Mb/s. The radio resource is however divided into 700 resource units (RU). In the time domain, transmissions are further organized into radio frames of 10 ms length. At each scheduling epoch, resource units are allocated to individual users based on their priority and current needs (i.e., amount of traffic waiting for transmission). Before any scheduling is applied, the minimum guaranteed throughputs (the operator guaranteed commitments) are directly granted. Then, the Weighted Fair Queuing is adopted to share out the remaining resources; grants are however limited to d max . Session priorities are based on the cost they pay for one unit of traffic. The residual resources are afterwards equitably distributed (according to the Round Robin service discipline).

We further suppose that mobile users arrive sequentially. The total number of users is however limited to N total ; it sets the traffic load. Their sojourn time is considered to be much greater in comparison with the simulation time. Consequently, the network dynamics will progressively slow down until a pseudo-stationary regime is attained, where all measurements are performed. Results are validated through extensive simulations.

After they arrive, mobiles are uniformly associated with a user profile (cf. Table II). The needs of inelastic and streaming sessions are respectively expressed as fixed (i.e., R f ) and average long-term throughput (i.e., R av ). We assume that the set of possible throughput demands is given by D = {0.5, 1, 1.5, 2} Mb/s. Inelastic sessions generate packets according to a deterministic distribution, whereas streaming sessions generate packets according to a Poisson process. In our work, we fix delay constraints for the latter session types. A maximum delay requirement of 100 ms is fixed. Since resources are limited, some packets may miss their deadline; they will be dropped as they are no longer useful. Furthermore, the needs of elastic sessions are expressed as comfort throughput (i.e., R c ). We suppose that the set of possible comfort throughputs is given by C = {0.75, 1.25} Mb/s. While inelastic and streaming sessions uniformly choose one of the possible throughput demands (regardless of the user cost tolerance parameter), we assume in the following that the comfort throughput of elastic sessions is related to the user willingness to pay and thus imposed by the user profile.

The proposed Satisfaction-Based (SB) MCDM method is compared with the well-known SAW and TOPSIS schemes. Because they ignore user needs, SAW and TOPSIS often lead to undersized and oversized decisions. Their decisions exclusively depend on user preferences (i.e., weights of the decision criteria) as well as on the available alternatives, without aligning with user throughput demands. So as to make the comparison more fair, enhanced SAW and TOPSIS are used: they only explore feasible alternatives. When their throughput demand is greater than the provided d max , the alternative opted for is considered to be infeasible and thus rejected. This will prevent SAW and TOPSIS from making some undersized decisions. However, as shown in the following, our proposed method continues to outperform them.

A. Performance Evaluation

To better analyze performance results, inelastic, streaming and elastic sessions are individually addressed.

1) Inelastic sessions: When SB is used, selection decisions are optimized to meet the exact session requirements, regardless of the user cost tolerance (willingness to pay for better performances or to save up money). However, because their normalization process ignores traffic class and needs, enhanced SAW and TOPSIS still lead to undersized and oversized decisions.

When users are ready to pay for better performances (i.e., users with profile no. 1), SAW and TOPSIS always single out to the Premium service class. Intuitively and since inelastic session needs are constantly fixed, this decision is oversized for 0.5 and 1 Mb/s sessions. As SB respectively opts for the Economic and the Regular service classes, QoS requirements are always perfectly satisfied, while cost is reduced. Also, when users seek to save up money (i.e., users with profile no. 4), enhanced SAW and TOPSIS lead to the Economic service class for 1 Mb/s sessions and to the Regular one for 1.5 Mb/s sessions (undersized decisions). When the RAT is highly loaded, fixed QoS requirements are not satisfied, thus dramatically degrading session performances. We depict in Fig. 3(a) the average user satisfaction. We notice that, at low traffic load, enhanced SAW and TOPSIS provide higher satisfaction: First, undersized decisions are able to fulfill strict QoS requirements, while charging mobile users less. Second, although oversized decisions decrease user satisfaction, the reduction is not significant enough to offset the impact of undersized decisions. In other words, at low traffic load, undersized decisions considerably increase user satisfaction because the corresponding users seek to save up money; their QoS needs are perfectly met, while paying less. However, oversized decisions do not significantly decrease user satisfaction because users in question are originally ready to pay. We further note that, when traffic load is moderate, SB brings the largest satisfaction since it always meets the strict QoS requirements. Actually, under SAW and TOPSIS, undersized decisions are no more able to meet the QoS needs when traffic load is relatively high.

2) Streaming sessions: When users look for better performances and are ready to pay (i.e., users with profile no. 2), they are interested in higher throughputs (up to their maximum requirement). However, when they seek to save up money (i.e., users with profile no. 5), they may sacrifice their service quality and look for lower throughputs (until When users are ready to pay for better performances, SAW and TOPSIS lead to the Premium service class and SB to the Regular one for 0.5 Mb/s sessions. SAW and TOPSIS decisions are considered to be oversized; the Regular service class actually provides users with twice their average longterm throughput.

The mean waiting delay and the packet drop probability are respectively depicted in Fig. 4 and5. Since all methods provide the same QoS level, we deduce that the Premium service class is oversized for 0.5 Mb/s sessions. In comparison with SB, no performance improvement is observed. Therefore, on average, SB charges less (avoids oversized decisions) and carries out higher user satisfaction (Fig. 3(b)). 3) Elastic sessions: Because elastic sessions accommodate with available bandwidth, undersized and oversized decisions do not technically exist. Although theoretically SB may reach different solutions from SAW and TOPSIS (since SB takes into account the comfort throughput), they practically all lead to the same decisions, given our simulation model and parameters. As a consequence, they all ensure the same performances and user satisfaction (Fig. 3(c)).

V. CONCLUSION

In this paper, a hybrid approach for RAT selection is presented. Focusing on the user side, we introduce a satisfactionbased (SB) MCDM method. Since it considers traffic class and needs, SB leads to the most appropriate decisions. Simulation results show that SB brings better performances and charges, on average, less than existing multi-criteria decisionmaking methods, thus providing higher user satisfaction. It actually best meets QoS requirements for the best cost: In comparison with enhanced SAW and TOPSIS methods, when session needs are stringent and inflexible, SB avoids undersized choices and leads to a high enough priority service class. Furthermore, when higher bandwidth guarantees do not improve session performances, SB avoids oversized choices and leads to a low enough priority service class.

  Service class d min (Mb/s) dmax (Mb/s) cost (unit/kB)

Fig. 1 .Fig. 2 .

 12 Fig. 1. Mean packet delay for inelastic sessions Figures 1 and 2 respectively show the mean waiting delay and the packet drop probability as a function of the total number of arrivals. Since it avoids undersized decisions, SB provides a shorter delay, a lower drop probability and subsequently a better overall QoS level.

Fig. 3 .

 3 Fig.3. User-perceived satisfaction their minimum requirement). While session requirements are transparent to SAW and TOPSIS, they are integrated within the QoS satisfaction function when using SB (Eq. 2) : α and β determine the shape of the sigmoid function and thus depend on the minimum and the maximum throughput requirements.When users are ready to pay for better performances, SAW and TOPSIS lead to the Premium service class and SB to the Regular one for 0.5 Mb/s sessions. SAW and TOPSIS decisions are considered to be oversized; the Regular service class actually provides users with twice their average longterm throughput.The mean waiting delay and the packet drop probability are respectively depicted in Fig.4 and 5. Since all methods provide the same QoS level, we deduce that the Premium service class is oversized for 0.5 Mb/s sessions. In comparison with SB, no performance improvement is observed. Therefore, on average, SB charges less (avoids oversized decisions) and carries out higher user satisfaction (Fig.3(b)).

Fig. 4 .Fig. 5 .

 45 Fig. 4. Mean packet delay for streaming sessions