A Location-Based Vertical Handoff Decision Algorithm for ... - UBC ECE

A Location-Based Vertical Handoff Decision Algorithm for Heterogeneous Mobile Networks Jie Zhang1, Henry C. B. Chan2 and Victor C. M. Leung1 1

Department of Electrical & Computer Engineering, The University of British Columbia, Vancouver, BC, Canada 2 Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

Abstract — Next-generation mobile communication systems will provide “always best connected” services to mobile users via cellular networks that provide wide area coverage for global access, complemented by broadband wireless networks (BWNs) at hotspots to accommodate higher traffic intensities. In such a heterogeneous network, a user entering a hotspot has the option to handoff to the BWN for better services. However, each vertical handoff also causes some momentary service degradations. Since the enhancement of user satisfaction by a vertical handoff depends on the user’s sojourn time in the BWN, the handoff decision should consider the user’s location and mobility, in order to optimize the user’s satisfaction. In this paper, we propose a novel vertical handoff decision algorithm based on dynamic programming, utilizing the location information of mobile users. Simulation results show the effectiveness of the algorithm compared with other recently proposed schemes. Keywords — Vertical handoff decision, B3G/4G networks, heterogeneous networks, location-based service

I.

INTRODUCTION

Next-generation mobile networks will integrate various wireless networks to provide “always best connected” (ABC) [1] services to mobile users. Generally, cellular networks offer wide-area services with a lower bandwidth and at a relatively higher cost, compared with broadband wireless networks (BWNs) such as wireless local area networks (WLANs), which provide lower cost and higher speed services at hotspots to accommodate higher traffic intensities. Due to their complementary nature, BWN and cellular networks can work together to provide highly effective and efficient services to mobile users. Several standards on the integration of WLAN and cellular networks have been released [2][3]. One of the most important issues in B3G/4G wireless networks concerns vertical handoff support, which allows mobile users to switch between different access networks during calls. While conventional horizontal handoffs keep ongoing calls connected with the same cellular network, a (downward) vertical handoff from a cellular network to a BWN is optional in that a mobile user can decide whether to execute it or not based on his/her requirements, particularly from the quality of service (QoS) point of view. Moreover, a vertical handoff usually takes a longer time to complete and causes a larger QoS degradation than a horizontal handoff during the handoff process. Therefore, a vertical handoff is beneficial only This work was supported by Bell Canada, the Canadian Natural Sciences and Engineering Research Council under grant CRDPJ 328202-05, and The Hong Kong Polytechnic University under account number 4-Z079 and Z09Z.

if the short-term service degradation can be compensated by the long-term service enhancement, measured in terms of the user’s utility, which is time- and location-dependent. Therefore the sojourn periods of mobile terminal (MTs) in a BWN are crucial for rational handoff decisions. Many vertical handoff decision algorithms have recently been proposed [4-9]. Comprehensive evaluations of services offered by heterogeneous networks are given in [4-6]. Vertical handoff decisions based on fuzzy logics are considered in [7-8]. These schemes account for the momentary service degradations caused by handoffs by varying the handoff decision probability according a MT’s velocity. In [9], the minimum duration that a MT should stay in a BWN to compensate for handoff degradation is used as the delay-trigger for a downward handoff. Whereas [4-6] focus on network service comparisons but do not consider the service degradations experienced during handoffs, [7-9] take the service degradations into account by considering the radio signal strength (RSS) of a BWN and the MT’s velocity. However, these papers fail to consider all the factors that determine the sojourn period of a MT in the BWN, such as the BWN coverage area and the moving pattern of the MT. For example, the sojourn period of a MT moving along a straight line at a constant speed of 1 m/s in a BWN with a 100m diameter varies between (0, 100] s depending on its direction. These variations are further increased if MTs may change directions in the BWN. In the future, MTs are expected to be enabled with low-cost sensors [10] for position awareness. Systems capable of position-tracking in outdoor/indoor environments with ±1m accuracy have been considered [11]. This capability is extremely valuable for mobility management and there have been several mobility and resource management schemes proposed based on such techniques [11][12]. In this paper, we develop a general framework for evaluating user satisfaction by means of a utility function, and based on this framework, we propose a novel vertical handoff decision scheme utilizing the above positioning technique. The key is to predict the mobility pattern of a MT based on its movement and location topology, and utilize this information for handoff decisions based on the user satisfaction framework. An optimal handoff decision problem is formulated as a Markov decision process and solved by dynamic programming. The organization of the paper is as follows. A utility function to measure user satisfaction is derived in Section 2.

Section 3 describes the decision scheme. Section 4 presents and discusses the simulation results. Section 5 concludes the paper. II.

DECISION METRICS AND UTILITY FUNCTION

We consider a B3G/4G network architecture in which a location service server (LSS) is introduced to facilitate MTs’ access to different networks [6]. Periodically, each MT notifies the LSS its position, and in turn the LSS replies with information on available BWNs around the MT. These messages include the essential information such as the identity of BWNs and their service parameters (i.e., bandwidth, security, coverage area). As the implementation of LSS relates to the business agreement between operators, it is beyond the scope of this paper and will not be discussed further in the paper. When it receives an LSS message, the MT evaluates the services offered by nearby BWNs. Many factors affect the user’s preference on access networks, including the QoS requirements of the user’s application, power consumption and usage charges; e.g., conversational traffic is delay sensitive, but background data traffic is throughput sensitive. On the other hand, different access networks offer different services; e.g., some networks provide delay and/or throughput guarantees but others only offer best-effort services. In addition, the offered data rates and network access charges may be different too. This means that when a MT is served by a cellular network and a BWN becomes available, the MT needs to make a downward vertical handoff decision based on different factors. In this section, we describe some general ways to measure user satisfaction as the basis for making handoff decisions. In this paper, we adopt a discrete time model. We consider the situation in a potential handoff cycle, which starts when a MT engaged in a call enters a BWN and ends when the call terminates or the MT leaves the BWN. Note that the MT needs to make the handoff decision only when it is within a BWN. If it is not within a BWN, it must be served by the cellular network. To evaluate the user satisfaction, we make use of a utility function. Specifically, we denote ui as the utility provided during the i-th time unit. Our aim is to maximize the expected total utility over each potential handoff cycle. In general, the expected utility per unit time of a user in the potential handoff cycle can be found by dividing the expected total utility by the expected duration of the handoff cycle. Since the expected duration of a handoff cycle is independent of the decision made, the decision rule to maximize the expected total utility will also maximize the expected utility per unit time. The utility ui provides a general way to measure a user’s satisfaction by using different functions. For example, in [6], various network factors including throughput and mean latency are normalized linearly according to the user requirements. In this case, the utility can be found by calculating the weighted sum of the normalized factors. In [5] and [9], the positive network factors (i.e., the ones which favor a larger value, such as throughput) are normalized with a natural log function and the negative network factors (i.e., the ones which favor a smaller value, such as usage charge and power consumption)

are normalized using a negative natural log function. Similarly, the utility can be found by computing the weighted sum of the normalized factors. Another possible way to compute the utility is by means of the grey relational coefficient method and the analytical hierarchy process [4]. A comparison of the above functions can be found in [13]. In this paper, we assume that we can use one of these functions to compute ui. Although the utility may be improved when the MT switches to a BWN, there are also some service degradations that may be attributed to the handoff, e.g., extra packet losses. We quantify the service degradations here as a deduction on the service utility. When the MT crosses a BWN area, a downward handoff (from cellular to BWN) may be executed, possibly followed later by an upward handoff (from BWN to cellular network). In this paper, we denote ucellular and uBWN as the utility that the MT receives in one time unit when it is connected to the cellular network and BWN, respectively. The service degradation caused by an upward or downward handoff is dup or ddown, respectively. In summary, we have  u cellular u  cellular − d up ui =   u BWN u BWN − d down

if if if if

connect with cellular network , not initiate handoff at time i handoff from BWN to cellular network at time i connect with BWN , not initiate handoff at time i handoff from cellular network to BWN at time i

(1) The aim of the handoff decision algorithm proposed in this paper is to determine whether and when to initiate a downward handoff in order to maximize the user’s expected total utility. III.

THE PROPOSED ALGORITHM

In this section, we propose a novel network-assisted handoff decision scheme to maximize the expected total utility of a user. An MT always tracks its position at a given frequency when there are some BWNs nearby. Once the MT enters a BWN, it notifies the LSS of its mobility information and satisfaction utilities of both networks, and the LSS then returns the handoff decision. To facilitate quick responses, decisions can be precalculated and stored in the LSS. If deciding to handoff, the MT communicates with the BWN and initiates the downward handoff procedure. A corresponding upward handoff will be executed if the MT moves out of the BWN during the call. To provide rational decisions, the LSS should be aware of both the coverage area of the BWN and the mobility pattern of the MT since they determine the MT’s sojourn period in the BWN. In the following subsections, we explain how these two factors are estimated and utilized to formulate a Markov decision process for the downward handoff problem, which enables optimal handoff decisions to maximize the expected total satisfaction utility experienced by the MT. A. Coverage Area of BWN We cannot simply approximate the coverage area of a BWN (denoted as AreaBWN) as a circle since there may be multiple base stations or access points, and the propagation effects may not be uniform in different directions. So, we require MTs to report their positions to the LSS whenever they make upward

handoffs. Since an upward handoff is executed only when MTs leave the BWN, the position information allows the LSS to estimate AreaBWN. B. Mobility Pattern Estimation The LSS should predict the user’s future movement within the BWN based on its mobility history, using an appropriate mobility model. Many mobility models have been proposed, e.g., the fluid-flow model [14] and Gauss-Markov model [15]. It is generally agreed that Markovian models can model user movements effectively because a MT’s current location and velocity depend highly on the previous values [15]. Based on the previous work discussed above, we propose to use a discrete time Markov model to estimate users’ mobility. Time is discretized into units; likewise distance and speed are also quantized into discrete untis. The mobility information of a user in the beginning of time unit i is denoted as (Li, Vi ), where Li and Vi are vectors representing the user’s location and velocity, respectively. By considering only twodimensional movements, we have Li = (lxi, lyi) and Vi = (vxi, vyi). The mobility information at the beginning of every time unit can be used to construct a Markov chain. The location of the user at the next time unit, Li+1, is Li+Vi ∆T where ∆T is the length of a time unit. We assume that the velocity of the next time unit Vi+1 can be derived from a given probability distribution conditioned on Vi. Here we assume that Vi does not change during the time unit (it is reasonable when ∆T is small enough). The goal of the mobility pattern estimation is to predict the state transition probability from time i to i+1, which is denoted as pi,i+1 = P((Li+1, Vi+1)|(Li , Vi )), based on the user’s mobility history. In practice, users’ mobility within the BWN is also limited by the terrain. Here we propose two models for open areas and roads. Open Area: In an open area, we assume that the velocity is independent of the location. Given the user’s previous locations Lk, k∈{0,1,…,w}, we estimate the transition probability of the velocity as follows: w−1

P(Vi+1 Vi ) =

∑χ k =1

true

  Lk − Lk −1    L − Lk   == Vi +1   == Vi  &  k +1    ∆T   ∆T w−1  Lk − Lk −1  χ V == ∑ true  i  ∆T  k =1

(2)

where χtrue(x) = 1 or 0 when x is true or false, respectively. Then we can calculate pi,i+1 as:  P (V V ) if pi ,i +1 =  i +1 i 0 if 

Li +1 = Li + ∆TVi Li +1 ≠ Li + ∆TVi

(3)

Roads: In situations where the users move along roads , we can consider the velocity as a scalar v rather than the vector V. Similar to (2), we have: w−1

P (vi +1 vi ) =

∑χ k =1

true

  Lk − Lk −1   ∆T 

  Lk +1 − Lk == vi  &    ∆T   w−1  L − Lk −1 2  == vi  χ true  k ∑  ∆T k =1   2

2

 == vi+1    

(4)

At an intersection, the user may make a turn with certain probability (e.g,. randomly or based on some statistical data). Similar to (3), we can determine pi,i+1. Besides the estimation of MT’s mobility pattern, we should also estimate of MT’s current mobility information: its location and velocity. Basically, the MT’s location can be tracked by positioning techniques like GPS and the velocity can be estimated by the current and the previous locations of the MT. C. Handoff Decision A (potential) vertical handoff cycle starts when a MT enters AreaBWN while engaged in a call via the cellular network, and ends when the call ends or when the MT exits AreaBWN. Based on the estimation of AreaBWN and pi,i+1, the LSS makes downward handoff decisions for each MT during its handoff cycle until the cycle ends or the MT handoffs to the BWN, i.e., at each time unit the LSS decides on the MT’s action: to “Handoff” (HO) or “Not Handoff” (NHO), and informs the MT if the decision is HO. To facilitate formulation of the decision process at the LSS, we augment the above Markovian mobility model by including an absorbing state Sout that the MT enters when it moves outside AreaBWN. Denote mi,i+1 as the probability that the MT moves from state Si to Si+1; then we have:

mi ,i+1

pi ,i+1  1 − ∑ pi ,i+1  =  ( Li +1 ,Vi +1 )∈AreaBWN 1   0 

if if

S i ≠ S out , S i+1 ≠ S out S i ≠ S out , S i +1 = S out

if if

S i = S i+1 = S out S i = S out , S i+1 ≠ S out

(5)

We denote the utility received by a MT during time unit i of its handoff cycle as RCellular(Si, A(Si)), where Si is the mobility state of the MT at the beginning time unit i and A(Si) is the action of the MT decided by the LSS. From (1), we have: uBWN − ddown if RCellular (Si , A(Si )) =  if  uCellular

Si ≠ Sout , A(Si ) = HO (6) Si ≠ Sout , A(Si ) = NHO

For a MT already connected to the BWN, we denote RBWN(Si ) as the utility it received during time unit i where its mobility state is Si at the beginning of the time unit. Note that when the MT attaches to the BWN, it will make an upward handoff only if it leaves the BWN before the call terminates. The corresponding utility can be found as follows: uCellular − d up if RBWN (Si ) =  if  u BWN

S i ≠ Sout , Si+1 = Sout Si ≠ S out , Si +1 ≠ S out

(7)

We also define RCellular(Sout) = RBWN(Sout) = 0 since in this state the handoff cycle and hence the decision process have ended. By discretizing the exponential call holding time with average λ, the call is still active for the next time unit with probability:

λe −λ (t + ∆T ) (8) = e −λ∆T λe −λt If the call terminates at the current time unit, it will cease to P(x ≥ t + ∆T | x ≥ t ) =

∑m Si +1

U BWN (S i +1 )

i ,i +1

(9)

We can also express (9) in vector format as:

(

U BWN = RBWN + e −λ∆T MU BWN = I − e −λ∆T M

)

−1

U cellular (S i ) = max {RCellular (S i , HO ) + e − λ∆T ∑ mi ,i+1U BWN (S i +1 ), RCellular (S i , NHO ) + e

Si +1

−λ∆T

∑m Si +1

(10)

(11)

U Cellular (S i +1 )}

i ,i +1

The optimal decision A*(Si) is the argument of the above optimization, which can be solved by dynamic programming methods such as the value iteration algorithm [16]. IV.

30 25 20 15 10

RBWN

Here UBWN and RBWN are the respective vectors of UBWN(Si) and RBWN(Si) considering all possible Si, while M is the matrix of transition probabilities mi,i+1 for all possible Si and Si+1. During the handoff cycle before a handoff is executed, the LSS will make a handoff decision for the MT at beginning of each time unit i. According to the Markov decision theory, the optimal decision A*(Si) should maximize the expected total utility based on the following equation: HO , NHO

35

SIMULATION RESULTS

We implement a simulator to compare the performance of the proposed scheme with the “fuzzy scheme” in [7], where a handoff occurs when the RSS of the BWN exceeds a threshold determined by fuzzy logic based on the MT’s speed and the BWN traffic load, and the “dwelling scheme” in [9], where a handoff is made after the MT stays in the BWN area for a period that compensates for the degradation during the downward handoff. For comparison purpose and to provide an upper bound of the performance, we also implement an ideal handoff decision scenario, in which the future movements of MTs are assumed to be known in advance so that perfect handoff decisions can be made. We randomly locate 20 BWNs covering circular areas with 50m radius, in a 3km×3km open area covered entirely by the cellular network. We use “Open Area” terrains in the experiment. Note that “Roads” terrains yield better results for our scheme due to better predictability of movements. We set ∆T = 1s, UCellular = 0.543 and UBWN = 0.864 [4]. Assuming an 80% reduction in utility during handoff periods of 3s [17], Dup = 1.3 and Ddown = 2.07. The Gauss-Markov model [15] is used to simulate the mobility of the users. Initially, 100 MTs are randomly distributed in non-BWN area and move at a random speed. Each MT holds an ongoing call with expected duration of 2 minutes. Results are averaged from 100,000 experiments. The performances of various handoff schemes with different MT velocities and utility degradation ratios are compared in Figs. 1-3 and 4-5, respectively.

Ideal scheme Proposed scheme Fuzzy scheme Dwelling scheme

5 0 0.5

1

1.5

2

2.5 3 MT velocity (m/s)

3.5

4

4.5

5

Figure 1. Satisfaction utilities comparison when velocity of MT varies

Fig. 1 shows the proposed scheme substantially increases the average total utility to a level closed to that of the ideal scheme, compared to the “fuzzy” and “dwelling” schemes. The average utility decreases as the MT moves faster, due to a corresponding reduction of the average sojourn time in the BWN that reduces the gain in utility by connecting with it. If the MT initiates a downward handoff and crosses the BWN in a short time, an upward handoff soon occurs. The utility loss during the two handoff periods may not be compensated by the utility gain due to connecting to the BWN, resulting in an overall loss of total utility caused by such “harmful” handoffs. Fig. 2 compares the probabilities of harmful handoffs between different handoff schemes. Obviously, the ideal scheme never initiates harmful handoffs because it knows the users’ future movements. For the “fuzzy” and “dwelling” schemes, harmful handoff probabilities are low only when the MT moves with a medium velocity, as a slow moving MT may stay near the BWN boundary over a longer time period to cause frequent handoffs, while a fast moving MT may have a low sojourn period depending on its moving direction. Thus, the proposed scheme yields much lower harmful handoff probabilities than the other schemes. Fig. 3 shows the probabilities of initiating a vertical handoff 60 Ideal scheme Proposed scheme Fuzzy scheme Dwelling scheme

50 Harmful Handoff Probability (%)

U BWN (S i ) = RBWN (S i ) + e

− λ∆T

40

Utility Enhancement (%)

receive any service. According to (8), the expected utility for the next time unit should be factored by e-λ∆T. We denote UCellular(Si) and UBWN(Si) as the expected total utility received after time unit i for the MT connected to the cellular network and BWN, respectively. So, we have:

40

30

20

10

0 0.5

1

1.5

2

2.5 3 MT velocity (m/s)

3.5

4

4.5

5

Figure 2. Harmful handoff probability when velocity of MT varies

25

90 85 Harmful Handoff Probability (%)

20 Handoff Probability (%)

80 75 70 65 60

Ideal scheme Proposed scheme Fuzzy scheme Dwelling scheme

55 50 0.5

1

1.5

2

2.5 3 MT velocity (m/s)

3.5

4

4.5

[2]

[3] [4]

[5] [6]

[7]

[8]

[9]

[10]

[11]

Utility Enhancement (%)

[12] [13]

35

30

[14] 25

20

[15]

15

[16] 40

50

60 70 Degradation Ratio (%)

80

90

100

Figure 4. Satisfaction utilities with different degradation ratio

50

60 70 Degradation Ratio (%)

80

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100

REFERENCES [1]

45 Ideal scheme Proposed scheme Fuzzy scheme Dwelling scheme

40

Figure 5. Harmful handoff probability with different degradation ratio

CONCLUSION

In this paper, we have presented a framework for evaluating user satisfaction in B3G/4G heterogeneous networks by a utility function. Based on the framework, we have proposed an optimal handoff decision scheme to maximize users’ satisfaction by determining when and whether to initiate a vertical handoff based on a MT’s mobility information. Simulations results show that, by utilizing MTs’ mobility information, the proposed scheme enhances MTs’ satisfaction and reduces harmful handoffs compared with other existing schemes. Work is on-going to further enhance our scheme.

10 30

10

0 30

5

when a MT enters the BWN. While we have considered maximizing users’ utilities, each handoff also incurs additional signaling cost in the network. Fig. 3 shows that the proposed scheme achieves lower handoff probabilities than the “fuzzy” and “dwelling” schemes, thus minimizing signaling costs. Figs. 4 and 5 compare, respectively, the utility enhancements and the probabilities of harmful handoffs for different utility degradation ratios between downward and upward handoffs when MTs move at the speed of 3 m/s. Obviously, the proposed scheme gives much better performance than the “fuzzy” and “dwelling” schemes, and its harmful handoff probability is relatively insensitive to changes in the utility degradation ratio.

40

15

5

Figure 3. Handoff probability when velocity of MT varies

V.

Ideal scheme Proposed scheme Fuzzy scheme Dwelling scheme

[17]

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