A Joint Call Admission Control-Based Approach for. Initial RAT Selection in HetNets. Glaucio H. Carvalho, Isaac Woungang. Department of Computer Science.
A Joint Call Admission Control-Based Approach for Initial RAT Selection in HetNets Glaucio H. Carvalho, Isaac Woungang
Alagan Anpalagan
Department of Computer Science Ryerson University, Toronto, Canada
Department of Electrical and Computer Engineering Ryerson University, Toronto, Canada
Mohammad S. Obaidat, Fellow IEEE & Fellow SCS Department of Computer Science and Software Engineering Monmouth University, W. L. Branch, NJ 07764, USA
Md Mizanur Rahman Department of Computer Science Ryerson University, Toronto, Canada
Abstract— This paper proposes an optimal Joint-Call Admission Control (JCAC)-based approach for initial radio access technology (RAT) selection for Heterogeneous Wireless Networks (HetNets) composed of two co-located wireless networks supporting two different service classes. The framework of Semi-Markov Decision Process (SMDP) is used to formulate the problem as a JCAC optimization problem involving the design of a cost function that weights three criteria, namely, the blocking cost, the access price cost, and the energy consumption cost. Simulation results are provided, showing that the proposal network selection technique maximizes the system’s capacity while selecting the RAT that consumes less energy. Keywords— hetNets, radio resource management, SMDP, RAT selection, JCAC, optimal policy
I.
INTRODUCTION
The vision of HetNets is that of a new type of wireless networks where several different RATs are expected to coexist, with the goal to provide ubiquitous access with high data rates for mobile users through multi-mode terminals. From an operational prospective, such network must be designed following the System Architecture Evolution standards of the 3GPP [1], and appropriate radio resource management (RRM) approach should be devised to handle the complexity inherent to the allocation of resources among the different RATs and incoming service requests while attempting to achieve energy savings. The RRM should not only decide whether or not an incoming service request can be accepted, but should also decide which of the available RATs is best suited to accommodate that request, given a performance target; for instance access cost, security, data rate, mobility, energy efficiency, or a combination of them. Two initial benchmark RRM frameworks for HetNets that have been proposed for such purpose are the Common RRM (CRRM) [2] or the Joint RRM (JRRM) [3]. One of the key components of the JRRM framework is the Call Admission Control (CAC) mechanism, which defines in principle how the radio resources or wireless channels must be shared among the various incoming networks. Traditional CAC mechanisms for wireless networks are not suitable for HetNets [3] and an appropriate CAC design mechanism within JRRM is yet to be implemented. In this paper, we propose a
JCAC-based scheme for initial RAT selection in two colocated wireless networks which supports two different service classes (calls). To meet the JCAC goals, a cost function is proposed that weights three criteria: (1) a local blocking cost function which takes into account the priority of each service class in each RAT, thereby will reflect the overloaded RAT, (2) an energy consumption cost - which measures the battery power savings at nodes, and (3) a network access price cost. Our JCAC approach is made of two components: the framework of Semi-Markov Decision Process (SMDP) - which is used to formulate the JCAC optimization problem combined with an algorithm for computing the optimal JCAC policy. The rest of the paper is organized as follows. In Section 2, some related works are described. In Section 3, the formulation of the optimization problem is presented. In Section 4, our SMDP modelling approach is introduced. In Section 5, simulation results are presented. Finally, Section 6 concludes our work II.
RELATED WORK
Representative works on RAT selection techniques for achieving efficient radio resource management in HetNets are described as follows. In [4], Perez-Romero et al. proposed a policy based RAT selection algorithm, where a function selects an initial RAT from a set of available ones based on different inputs such as service class, traffic load in each RAT, node mobility speed. A policy-based mechanism is provided to handle the blocking of incoming requests when there is still some capacity available in other RATs than the targeted one. In [5], Falowo et al. proposed a dynamic RAT selection scheme that assigns a multimode terminal with a single or group of calls to the most suitable RAT. For selecting this suitable RAT, available RATs are rated using a multi-criteria group decision-making technique based on a modified fuzzy TOPSIS method that involves the specification of call priorities and certain weights criteria. In [6], Haldaret et al. proposed a cross-layer architectural framework for RAT and channel selection in heterogeneous cognitive wireless networks. Their scheme classifies the user’s application based on the Analytic Hierarchy Process algorithm. Channels are
categorized within an operating spectrum by using a probabilistic recurrence relation. Based on these assets, a suitable channel and a RAT are selected according to the user’s request. In [7], Jin et al. introduced a RAT selection method for HetNets that uses a fuzzy logic algorithm to balance the load in the network while attempting to select the most suitable RAT (in the form of a 3G network or a WLAN) that best accommodates the user’s request. In [8], Lucas et al. proposed an enhanced JRRM technique for HetNets that simultaneously finds out for each user an adequate combination of RATs and a number of radio resources within these RATs. By considering the current network load, their scheme selects the best RAT for the new incoming request while equally satisfying all users. In [9], Porjazoski et al. proposed a RAT selection method for HetNets that selects new incoming call as well as ongoing calls (so-called handover calls) based on service type, user mobility, and network load. A two-dimensional Markov chain analytical model is also introduced to assess the performance of their proposed scheme. In [10], Mohamed et al. proposed various weighted algorithms for assigning the weights to various criteria (velocity, user preferences, QoS) that are involved in available decision making algorithms for RAT selection in HetNets. In [11], Zhu et al. proposed an immune optimization algorithm for solving the JCAC problem in HetNets. Their model evaluates alternative RATs for each arriving call based on a set of selection criteria, which are themselves weighted according to the user’s preference. In [12], Si et al. proposed an optimal RAT selection method for HetNets, which is based on a multimedia distortion (as application layer QoS) and network access price. An optimal selection policy is designed to assign indices to candidate RATs; and the RAT with the lower index is selected as the most suitable one to accommodate the user’s request. The above schemes do not exploit the multi-criteria nature of RAT selection. In our approach, this is taken into account by considering a cost function that involves three optimization criteria: call blocking cost, energy consumption cost, and network access price cost. To address this optimization problem, the system dynamics are specified using a Semi-Markov Decision Model Process (SMDP) framework. III.
distribution with mean rate μi; and the traffic intensity is obtained as ρi= λi/μi A. SMDP Model Our proposed SMDP model is defined by five components: the states, the possible actions, the expected time until the next decision epoch, and the transition probabilities: (1) States: the states are defined as a five-tuple , , , , where the following constraints are associated to each RAT: 012
and where nij is the number of calls of type i connection in RAT-j, Nj is the capacity of RAT-j, bi is the bandwidth required by the type i connection (call), e = 0 is the departure of connection, and e = 1 (resp. e =2) is the arrival of connection of the type 1 (resp. type 2 call). (2) Actions: Three possible actions are considered for the JRRM policy, namely: block the call (B), accept the call in RAT-1 (denoted AR1), accept the call in RAT-2 (denoted AR2). In each state x 2 S, the controller can choose one action out of those possible actions, i.e. , 0,1,2 1, 1,2 , 2, 1,2 , (3) Expected Time until the next decision epoch: If the system is in the state x and the action is chosen, the expected time until the next decision epoch is determined by: 1 , (4) Transition probabilities: Let , , be the probability that at next decision epoch, system will be in state if action is chosen in state . And , be the expected time until next decision epoch if action is chosen in state . , , ,
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FORMULATION OF THE OPTIMIZATION PROBLEM
We consider a HetNet consisting of two co-located RATs in which the jth RAT (j=1,2) has Nj radio resources. When an incoming service connection requests an access to the network, the optimal RAT selection method must decide not only if it will be accepted, but also which RAT should be selected for handling it. The HetNet supports K classes of service connections, each class being categorized by its bandwidth requirement, arrival distribution, and channel holding time. Two types (i = 1,2 K) of service connections (calls) are considered. In addition, it is assumed that the ith service connection arrives according to a Poisson process with parameter λi and it requires bi radio resources. The channel holding time (connection duration + residence time) is assumed to follow an exponential
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in case of departure of calls. If the system is in the state and the action is chosen, the admission control is determined by means of the following total cost function , , , ,
where , , are the weights associated to the 1. The involved individual functions and values assigned to these weights are adjustable, and this function can be perceived from the wireless designers point of view as a way for deriving the relative importance of the targeted system performance, be it lower call blocking, lower access price, less energy consumption, or any combination of these performance objectives. The blocking cost function is defined as: , 1,2 , 0, where BCi is a quantity incurred whenever an incoming service request is rejected. The network access price cost function is defined as: ,
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0, where Pj is the power consumption of the RAT-j. B. JCAC Optimal Policy A policy specifies the action to be selected for each state of the MDP. Note that for a MDP with a finite state space and finite action sets, an optimal policy exist, which is stationary and deterministic. Such a policy is an application from S to A, defined by: for all x S, Rx . To determine the optimal policy, the continuous time SMDP model is converted into a discrete time MDP model such that for each stationary policy, the average cost per time unit in the discrete-time Markov model is the same as that in the semi-Markov model. After this step, the Value Iteration algorithm [13] is invoked to derive the JCAC optimal policy. IV.
1 ;
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where for all x, is the continuous time Markov chain steady state probability distribution under the optimal policy. A. Scenarios and Results We investigate three scenarios using the settings provided in Table II. TABLE II: SCENARIOS PARAMETERS SETTINGS Scenario I Param. Value b1 2 channels b2 1 channel 0.6
Scenario II Param. Value 5 ρ1 3 ρ2 0.6
0.2
0.2
0.2
0.2
Scenario III Param. Value b1 2 channels b2 1 channel 5 ρ1
ρ2 2
3 0.2
• Scenario I: The traffic intensities of class-1 and class-2 calls are varied and the impact of these variations on the RAT utilization are investigated. The traffic intensity describes the number of call requests received by fixed network elements in a unit area element during a time interval. The results are captured in Fig. 1. • Scenario II: The required bandwidth for class- 1 and class-2 calls are varied and the impact of these variations on the RAT utilizations are investigated. The results are captured in Fig. 2. • Scenario III: The weight of the energy consumption cost and the blocking cost in the cost function are varied and the weight of the access price cost is kept fixed. We study the impact of these variations on the RAT utilizations. The results are captured in Fig. 3.
SIMULATION RESULTS
Our designed simulation model is an event-driven system in Borland C++ v.5. To study the performances and structure of our proposed optimal RAT selection scheme, the co-located networks, i.e. RAT-1 and RAT-2, are considered to be representatives of GSM and UMTS technologies. Two types of service classes, referred to as class-1 and class-2 are considered for each RAT. Simulation parameters that are fixed are given in Table I. TABLE I: FIXED SIMULATION PARAMETERS Param. N1 N2 A11 A21
Value 20 channels 10 channels 20 10
Param. μ1 μ2 A12 A22
Value 1/120s (voice) 1/120s (voice) 10 5
Param. BC1 BC2 P1 P2
Value 1.0 0.8 3802W 300W
The total bandwidth utilization is used as metric to assess the performance of our proposed scheme. For the jth RAT, this is defined as:
Fig. 1. Scenario I: RAT utilization versus call intensities (a) RAT-1 utilization and (b) RAT-2 utilization
In Fig. 1 (a) and (b), it can be observed that when the values of ρ1 and ρ2 are smaller, the initial RAT selection policy decides to accept more calls in the less energy consuming RAT (i.e., RAT-2), in order to save the overall energy. For this reason, the utilization of RAT-2 is far better than that of RAT-1 at this step. But when both call intensities are getting high, in order to tackle the traffic volume, the optimal policy starts taking more calls in RAT-1 which makes its utilization high. Fig. 1 (b) shows that the RAT-2 utilization goes down and Fig. 1 (a) shows that the RAT-1 utilization goes up when both call intensities are high. This is due to the fact that the optimal policy accepts more calls in RAT-1 due to its higher capacity.
Fig. 3. Scenario III: RAT utilization versus weight for energy consumption cost in total cost (a) RAT-1 utilization and (b) RAT-2 utilization.
Fig. 2. Scenario II: RAT utilization versus required bandwidths (a) RAT-1 utilization and (b) RAT-2 utilization
In Fig. 2, it can be observed that the utilization of RAT-2 is far better than that of RAT-1, in particular when the required bandwidth of each call is low. This is attributed to the fact that RAT-2 consumes less energy compared to RAT-1, which forces the optimal policy to choose RAT-2 instead of RAT-1. But the optimal policy gradually accepts more and more calls into RAT-1 as the required bandwidth of each call increases. This is attributed to the fact that due to its less capacity (10 channels), RAT-2 is unable to accept more high band- width calls, thus RAT-1 utilization is high in Fig. 2 (a). On the other hand, Fig. 2 (b) shows that the utilization of RAT-2 goes up and down frequently as it has less capacity, which leaves more or less unused channels depending on the multiple of class-1 and class- 2 calls assigned to it.
Finally, the weight 2 of the access price cost is kept fixed at 20% of the total cost; and the weights 1 of the blocking cost and 3 of the energy cost are varied from 45% to 70% and 10% to 35% respectively. We study the impact of this variation on the RAT utilization. In Fig. 3, it can be observed that the optimal policy utilizes more channel of RAT-2 (79%) than that of RAT-1 (50%). This is attributed to the fact that RAT-1 requires more power than RAT-2 does for its operation, resulting to energy savings. But when 3 • 25%, RAT-1 channel utilization (shown in Fig. 3 (a)) reaches almost 0% and RAT-2 channel utilization (shown in Fig. 3 (b)) increases slightly due to the fact that more calls that should have been served by RAT-1 are now carried by RAT-2. We now analyze the behavior of our proposed optimal RAT selection policies under the system configuration shown in Table III, with the focus on determining how the policy allocates different calls over the existing RATs under different loads (i.e. existing number of class-1 and class-2 calls in each RAT) in order to achieve optimality. The results are captured in Table IV. TABLE III: SYSTEM CONFIGURATION FOR THE OPTIMAL POLICY Scenario I Param. Value N1 20 channels N2
10 channels
ρ1 ρ2
5 3
Scenario II Param. Value μ1 1/120s (voice) μ2 1/120s (voice) 0.6 0.2
Scenario III Param. Value b1 2 channels b2
1 channel
In Tables IV , the following convention is adopted to analyze the structure of the optimal policy for class-1 call when the RAT-1 channel load is less than 10: • •
’+’ denotes class-1 call accepted into RAT-1, ’*’ denotes class-1 call accepted into RAT-2.
[2] M. Lopez-Benitez and J. Gozalvez, ”Common Radio Resource Management Algorithms for Multimedia Heterogeneous Wireless Networks”, IEEE Transactions on Mobile Computing, Vol. 10, no. 9, Sept. 2011, pp. 1201-1213. [3] L. Giupponi, R. Agusti, J. Perez-Romero, and O. Sallent, ”A novel joint radio resource management approach with reinforcement learning mechanisms”, 24th IEEE International Conference on Performance, Computing, and Communications , Phoenix, AZ, USA, April 7-9, 2005. [4] J. Perez-Romero, O. Sallent, and R. Agust, Policy-based initial rat selection algorithms in heterogeneous networks”, in Proc. of the 7th Intl. Conference on Mobile and Wireless Communications Network (MWCN’05), Marrakesh, Morocco, Sept. 19-21, 2005. [5] O. Falowo and H. Anthony Chan, Dynamic rat selection for multiple calls in heterogeneous wireless networks using group decision-making technique, Computer Networks, Vol. 56, Issue 4, Mar. 16, pp. 13901401, 2012.
In Table IV, it can be observed that when the number of class-2 calls in RAT-2 is 0, regardless of the RAT-1 resource occupancy, the optimal JCAC decides to accept class-1 call in RAT-2 until its resources are fully occupied. The same Table also shows that when the number of class-2 calls in RAT-2 is greater than zero, and the radio resource occupancy of RAT-1 is low (note that RAT-1 load is less than 10 out of 20), the optimal JCAC decides to accept class-1 call in RAT-1. V.
CONCLUSION
In this paper, we have proposed an optimal initial RAT selection method for HetNets using a SMDP framework. Our method takes into account the maximization of the system capacity and energy consumption in the proposed cost function. Simulation results showed that the variations in the relative importance of parameters involved in the total cost function have a great impact on the system performance. In future, we intend to generalize our SMDP model to tackle more HetNets architecture designs. ACKNOWLEDGMENT This work is partially supported by the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ), Brazil, and by a grant from the Natural Sciences and Engineering Research Council of Canada (NSERC), held by the 2nd author, Reference number 119200. REFERENCES [1] J. Sachs and M. Olsson, ”Access network discovery and selection in the evolved 3GPP multi-access system architecture”, European Transactions on Telecommunications, Vol. 21, no. 6, pp. 544557, 2010.
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