Load balancing algorithm by vertical handover for integrated heterogeneous wireless networks
 Mohammad Ali Pourmina^{1}Email author and
 Navid MirMotahhary^{1}
https://doi.org/10.1186/16871499201214
© Pourmina and MirMotahhary; licensee Springer. 2012
Received: 23 February 2011
Accepted: 16 January 2012
Published: 16 January 2012
Abstract
In this paper, an adaptive resource management scheme for hybrid WWAN/WLAN is proposed. Based on proposed joint velocity and average received power (ARP) estimation algorithms, a novel vertical handoff (VHO) for efficient load balancing in multitier network is developed. Simulation results show that proposed scheme achieves significant improvements over conventional schemes.
Keywords
mobility aware vertical handover load balancing heterogeneous networks power estimation1. Introduction
Future wireless communication systems can be visualized as the integration of different radio access network (RAN) technologies, to provide always best connected. Heterogeneous wireless networks (HWN) will give the service provider, a chance to provide sufficient capacity, needed to support the temporally and spatially fluctuating traffic demands generated by mobile users. A practical benefit is that users can be served at lower cost and with the better quality of service (QoS). To support freedom of movement between HWNs and seamless roaming, several VHO management architectures and decisionmaking algorithms have already been proposed [1]. These will allow full exploitation of flexible HWN infrastructure, resources, and services. Although individual radio resource management (RRM) schemes can be tuned to optimally perform within their respective RANs, they may not efficiently perform in an HWN if the different RRM schemes are not properly managed. Hence, a major issue is how to jointly utilize the resources of the different RANs in an efficient manner while simultaneously achieving the desired QoS and minimizing the service cost from both user and service provider perspectives. In an integrated heterogeneous wireless network (IHWN), a mobile terminal (MT) is equipped with heterogeneous network interface, which is called the multimode terminal. When a multimode MT generates or originates a new call in an IHWN, it can select connections among different types of the IHWN based on network selection strategies. An active multimode MT can also change its connections among different types of IHWN. Such a process is called the vertical handoff (VHO). Traditional handoff algorithms are based on link quality or estimate of ARP. However, this measure is not sufficient for VHO, and other factors like mobile user velocity, network condition, and user preferences should be considered. Also because of complex structure of nextgeneration networks, more precise and sophisticated method for link measurements is required. In multipath channel, received signal strength (RSS) is consisted of three different phenomena (path loss, shadow fading, and fast fading). Because of MT mobility, multipath effect, and shadowing, the RSS has fluctuations, which make raw signal strength an unstable criterion for triggering vertical and horizontal handoffs (HHOs). Shadowing, largescale variation in path loss, is caused by obstacles in the propagation path between the MT and the base station (BS). The smallscale variation is due to the Doppler shift along the different signal paths and the time dispersion caused by the multipath propagation delays. As one primary indicator of channel quality, the power of the slowly varying shadow component is important for handoff decisions and power control. Most existing handoff algorithms assume that multipath fluctuations can be adequately filtered and base their handoff decisions on local mean power estimates [2, 3]. Although these variations bring back uncertainty in the act of VHO decision making, they can be utilized to extract precious information about propagation environment and mobility behavior of mobile user [4]. In order to mitigate these variations in RSS, efficient smoothing techniques must be considered. If the averaging interval is too short, fluctuations may not be effectively removed, or if the interval is too long, it may cause delay in handoff procedure, or in nonline of site (NLOS) scenarios, it can average out useful information of corner's positions. To fully exploit the capacity of the wireless channel, and to overcome pingpong effect, an efficient power estimation method is required [5, 6]. The pingpong effect occurs if factors for VHO decision are changing rapidly and an MT performs the handoff as soon as it detects a more suitable BS. Because of heterogeneity, PHY and MAC layer of different IHWN are different, so a unified approach must be taken into consideration for collection of specific measures from different networks. As a result, more sophisticated VHO algorithm is required to extend the throughput of multilayer network and to increase efficiency of resource management for next generation of HWNs. In this paper, utilizing an accurate joint velocity and ARP estimation algorithms, a novel VHO algorithm is proposed which can effectively be used for load balancing and internetwork pingpong effect reduction in HWNs. Also based on Markov model, an analytical model for performance evaluation of the VHO algorithm is proposed. This paper is organized as follows. Section 2 reviews related work on VHO algorithms. In Section 3, propagation model for an IHWN is introduced. In Section 4, proposed load balancing algorithm is discussed. VHO algorithm analysis framework is described in Section 5. The performance of the proposed VHO algorithm is analyzed through a theoretical model and simulations based on probability of blocking and probability of false network layer assignment in Section 6. Finally, paper is concluded in Section 7.
2. Previous works
Early works on VHO considered multitier homogeneous networks and used the RSS as the main factor of the handoff decisionmaking process [7]. However, the VHO needs to be triggered considering a few more factors [8]. In [9], a VHO algorithm is proposed based on a assumption that a data call is kept in the higher bandwidth network as long as possible and voice calls are vertically handed over as soon as possible to avoid handoff delay. In [10], a network selection strategy that only considers mobile users' power consumption is introduced. To maximize the battery life, the mobile user selects the uplink or downlink that has the lowest power consumption from all of the available networks. In [11], a policyenabled network selection strategy is proposed, which combines several factors such as bandwidth provision, price, and power consumption. By setting different weights over different factors based on the user's preference, a mobile user can connect to the most desired network. Reference [12] presents a signaling protocol for the exchange of information between a network management system and intelligent multimodal wireless terminals in a heterogeneous environment; some preliminary measurement works were done mainly between WLANs, general packet radio service (GPRS), and digital video broadcasting for terrestrials (DVBT), but the described algorithms were very simple, and it did not present how such redistribution would be performed in detailed steps. Efforts on standardization of the VHO operation can be review on [13–15]. Although the above network selection strategies have their own advantages, they did not put much attention on system performance, such as the blocking probability of originating calls and the forced termination probabilities of horizontal and VHO calls. They fail to address any ARP and also velocity estimation method in order to perform an accurate and seamless VHO. Also these have not considered corner effect and effect of low SNR in cell boundaries. In a homogenous environment, the pingpong effect is a phenomenon that rapidly repeats HHOs between two BSs and can be mitigated by means of dwell timer (DT) or hysteresis margin [16]. In a heterogeneous environment, the pingpong effect occurs if factors for the VHO decision are changing rapidly and an MT performs handoff as soon as the MT detects the better BS [17]. The DT scheme has been used to avoid such pingpong effects due to the fact that RSS from HWNs is not comparable to each other [5, 6]. The pingpong effect can also occur if the MT's speed is high or its moving direction is irregular. Therefore, the proposed VHO algorithm balances the traffic load in each network based on efficient mitigation of internetwork pingpong effect and also based on MT mobility behavior.
3. Propagation and noise model
4. Proposed traffic load balancing algorithm
Knowledge of MT's position and velocity plays an important role on offering efficient network controlling mechanisms and variety of offered services in IHWNs. Mainly in IHWNs structure, WLANs have less coverage than WWAN, so a reliable mobility tracking algorithm is desirable to reduce the number of handoffs and waste of bandwidth due to unnecessary signaling. Researches about MT speed estimation are divided into two different groups. First one uses statistics of RSS from different BSs measured at an MT and second one uses corresponding propagation times called cell sojourn time (CST) in order to show if user speed is slow, medium, or fast. Both categories are subjected to strong irregular variations caused by Rayleigh fading and shadowing [17, 20]. Many of fading distribution property (FDP)based methods give an accurate estimate in noiseless environments, but in noisy environment, results are unreliable [17, 20]. In urban area which is modeled as Manhattan structured microcell, speed estimation is more complicated, due to complexity introduced by severe fading and noise. On the other side, although the CSTbased methods work well in noisy area [17], but these algorithms lose their accuracy when they are used on highly dense urban area with variety of building structures. Because CSTbased methods calculate MT speed by comparing the CST with a predefined time threshold, a manoeuvering user with variable speed might have more (less) actual speed than what is estimated based on comparing CST with predefined threshold. High MT mobility in cell borders is another issue which can result in many cell border crossings. The sojourn timer resets every time MT crosses the cell border, thus a slowmoving MT that is repeatedly crosses cell border is considered as a fastmoving MT by CSTbased schemes [17]. In addition, CSTbased algorithms fail to estimate variable speed. When variable speed MT is circulating around the cell boundary, CSTbased algorithms classify MT as a slowmoving MT. CSTbased speed estimation algorithms require estimation and calculation of statistical properties of CST in coverage area [17]. In order to calculate probability and cumulative distribution functions (PDF and CDF) of CST, while taking into account presence of fast fading, shadowing, corner effects, and uncertainty region between neighboring BSs, it is required to perform simplifications and assumptions, which results in lack of generality. Hence, an FDPbased method for MT speed estimation is proposed in this paper. A simple type of windowbased ARP estimators, namely weighted sample average estimators of local mean power, is currently deployed in many commercial communication systems, and various other windowbased estimators (WBEs) have been proposed in [21]. These WBEs work well under the assumption that the shadowing is constant over the duration of the averaging window, and in this case, their performance improves as the window size increases. In practice, however, the shadow process varies with time (albeit slowly relative to the fastfading process), and this variation must be considered since both analysis (developed herein) and experiment show that the mean square error (MSE) performance of these WBEs deteriorates severely when the window size increases beyond a certain value. For variable speed, the observation window must be adapted constantly, and the rate of adaptation depends not only on the MT speed but also on the sampling period and shadow fading characteristics. In particular, errors in the estimates could propagate due to suboptimal observation windows. A joint velocity and power estimator are proposed in this paper to calculate mobility behavior of manoeuvering MT in dense urban area also to mitigate fluctuations of RSS in order to minimize number of VHOs and simultaneously assign different service requests and MTs efficiently to different networks in a IHWN. This in turn can minimize probability of blocking and probability of false network assignment.
4.1. Joint velocity and average received power estimation
4.2. Load balancing by VHO algorithm
5. VHO algorithm analysis
In case of 2 integrated networks, we have

State0) The user has no active session in progress and is not occupying any channel (i.e., the user is idle), independent of its location.

State1) The user in the WLAN coverage area is occupying the WWAN resource.

State2) The user in the WLAN coverage area is occupying the WLAN resource.

State3) The user in the out of WLAN coverage area is occupying the WWAN resource.
Based on Algorithm 1, a user can change state from state0 to any nonzero state when a new connection is made. If a session is completed while a user is currently in a nonzero state, the user changes to state0. The Markov chain depicted in Figure 5 is irreducible and aperiodic, and all the states are recurrent nonnull, so that the equilibrium state probabilities can be determined by solving the (9), subject to the normalization condition ${\sum}_{i=0}^{{N}_{\mathsf{\text{state}}}}{P}_{si}=1$; however, the expressions for the transition probabilities remain to be determined; these form the topic of discussion in the succeeding sections.
5.1. Service model
 1.
Due to wide coverage that WWAN has, we assume that probability of MT being inside the coverage area of WWAN, P_{WWAN} = 1 and as the coverage area of WLAN is subset of WWAN; it is clear that P_{WLAN} < 1.
 2.
New calls arrive in the macrocell and microcell according to a Poisson process with mean arrival rates λ_{WW} and λ_{WL}, respectively. A new call is randomly determined as the real time (RT) and nonRT (NRT) calls with probabilities P_{rt} and P_{nrt}, respectively. Similarly, a call is also independently determined as a low (high)bandwidth application with probability P_{bwl}(P_{bwh}). Clearly, P_{rt} + P_{nrt} = 1 and P_{bwl} + P_{bwh} = 1
Algorithm 1 Proposed VHO algorithm
1: loop
2: if MS_{ i }∈ {MS_{ i } Connected to WWAN} then
3: if ${\u015c}_{W\phantom{\rule{0.3em}{0ex}}L\phantom{\rule{0.3em}{0ex}}A\phantom{\rule{0.3em}{0ex}}N}\ge {T}_{{r}_{W\phantom{\rule{0.3em}{0ex}}L\phantom{\rule{0.3em}{0ex}}A\phantom{\rule{0.3em}{0ex}}N}}\left(dB\right)$} then
4: MS_{ i }∈ {Downward VHO probability set}
5: if $\widehat{\upsilon}\le {V}_{th}$ then
6: MT_{ i }∈ {Pedestrain Class} and MT_{ i }∈
{VHO Candidate set}
7: Start(t_{ DT })$\left\{{t}_{DT}\propto 1/\widehat{\upsilon}\right\}$
8: if $\widehat{\upsilon}\le {V}_{th}$ untill the timer expires then
9: at the end of
${t}_{DT}\widehat{\upsilon}=\sum _{i=1}^{n}\frac{{\widehat{\upsilon}}_{i}}{n}\left(t\right)\phantom{\rule{1em}{0ex}}0\le t\le {t}_{DT}$
10: MS_{ i }∈ {VHO Active Set}
11: VHO to target Network
VHO(targ_{ network })
12: else
13: Reset Dwell Timer {Reset (t_{ DT })}
14: end if
15: else
16: MS_{ i }∉ {VHO Candidate set} → Unbeneficial
VHO (MT Stays in Current Serving Network)
17: end if
18: end if
19: else if MT_{ i }∈ {MT is Connected to WLAN} then
20: if ${\u015c}_{WLAN}\le {T}_{rWLA{N}_{Add}}\left(dB\right)$ and
${\u015c}_{WWAN}\ge {T}_{{r}_{WWAN}}\left(dB\right)$ then
21: MT_{ i }∈ {Upward VHO probability set}
22: Start(t_{ DT })$\left\{{t}_{DT}\propto 1/\widehat{\upsilon}\right\}$
23: if $\widehat{\upsilon}\ge {V}_{th}$ untill the timer expires or
${\u015c}_{WLAN}\le {T}_{{r}_{WLA{N}_{Drop}}}\left(dB\right)$ then
24: MT_{ i }∈ {VHO Active Set}
25: VHO to target Network VHO(targ_{ network })
26: else
27: Reset Dwell Timer {Reset (t_{ DT })}
28: end if
29: end if
30: end if
 3.
Call duration T_{call} is exponentially distributed with a mean of 1/μ, where μ is the average call completion rate. Hence, the call completion (termination) probability P_{term} = P (T_{call} ≤ T_{th}), where T_{th} is the time unit for the user state transition diagram, as shown in Figure 5.
 4.
From ${\sum}_{i=0}^{{N}_{\mathsf{\text{state}}}}{P}_{si}=1$, it is clear that for scenario depicted in Figure 5. ${P}_{\mathsf{\text{WWAN}}}={\sum}_{i=0}^{{N}_{\mathsf{\text{state}}}}{P}_{si}$ and P_{WLAN} = P_{s0} + P_{s1} + P_{s2} = 1  P_{s3}.
5.2. State transition probabilities for VHO
Other transition probabilities can be determined likewise. To calculate a close form for transition probabilities, it is required to calculate joint and conditional PDF of RSS which is given in Appendix B.
6. Performance evaluation
7. Conclusion
This paper proposed a load balancing scheme that minimizes the VHO rate while achieving the desired service quality (i.e., low call blocking probabilities) for highly dense urban area. The performance of the proposed scheme was analyzed via user level Markov chains. Numerical results show that the proposed scheme achieves low VHO rate and low call blocking probability in comparison with the currently existing servicebased and sojourn timebased load balancing schemes. Results show that, under different mobility conditions, the proposed scheme exhibits more recourse utilization in integrated networks while achieving low blocking probability in highly dense urban area. Based on these results, it is concluded that the proposed scheme exhibits a good service quality and, hence, serves as a viable alternative for practical IHWN deployment.
Appendix A. Derivation of bias and MSE for proposed ARP estimator
Appendix B. Derivation of joint and conditional PDF of received signal estimates
Declarations
Authors’ Affiliations
References
 Akyildiz IF, Xie J, Mohanty S: A survey of mobility management in nextgeneration allIPbased wireless systems. IEEE Trans Wirel Commun 2004, 11(4):1628. 10.1109/MWC.2004.1325888View ArticleGoogle Scholar
 Enrique SN, Lin YX, Vincent WSW: An MDP based vertical handoff decision algorithm for heterogeneous wireless networks. IEEE Trans Veh Technol 2008, 57(2):12431254.View ArticleGoogle Scholar
 Shen W, Zeng QA: Costfunctionbased network selection strategy in integrated wireless and mobile networks. IEEE Trans Veh Technol 2008, 57(6):37783788.View ArticleGoogle Scholar
 Mirmotahhary N, Kohansal A, ZamiriJafarian H, Mirsalehi M: Discrete mobile user tracking algorithm via velocity estimation for microcellular urban environment. IEEE Vehicular Technology Conference, Singapore 2008, 26312635.Google Scholar
 Ylianttila M, Pande M, Makela J, Mahonen P: Optimization scheme for mobile users performing vertical handoffs between IEEE 802.11 and GPRS/EDGE networks. In IEEE Global Telecommunication Conference. San Antonio, TX; 2001.Google Scholar
 Hsieh R, Zhou ZG, Senevirante A: SMIP: A seamless handoff architecture for mobile IP. In IEEE International Conference on Computer Communication. San Francisco, CA; 2003.Google Scholar
 Pollini GP: Trends in handover design. IEEE Commun Mag 1996, 34(3):8290. 10.1109/35.486807View ArticleGoogle Scholar
 Mirmotahhary N, Mafinejad Y, Atbaei F, Kouzani A: An adaptive policybased vertical handoff algorithm for heterogeneous wireless networks. In IEEE 8th International Conference on Computer and Information Technology. Sidney; 2008:188193.Google Scholar
 Zhang Q, Guo C, Guo Z, Zhu W: Efficient mobility management for vertical handoff between WWAN and WLAN. IEEE Commun Mag 2003, 41(11):102108. 10.1109/MCOM.2003.1244929View ArticleGoogle Scholar
 Nam N, Choi N, Seok Y, Choi Y: WISE: energyefficient interface selection on vertical handoff between 3G networks and WLANs. IEEE Int Symp Pers Indoor Mobile Radio Commun 2004, 692698.Google Scholar
 Wang H, Katz R, Giese J: Policyenabled handoffs across heterogeneous wireless networks. IEEE Workshop on Mobile Computing Systems and Applications 1999, 5160.Google Scholar
 Yang Xiaodong, Owens ThomasJ: Intersystem soft handover for Converged DVBH and UMTS Networks. IEEE Trans Veh Technol 2008, 57: 18871898.View ArticleGoogle Scholar
 Lampropoulos G, Skianis C, Neves P: Optimized Fusion of Heterogeneous Wireless Networks based on Media Independent Handover Operations. IEEE Wirel Commun Mag 2010, 17(4):7887.View ArticleGoogle Scholar
 Dimitriou N, Sarakis L, Loukatos D, Kormentzas G, Skianis C: Vertical handover framework for future collaborative wireless networks. In Int J Netw Manag. Wiley;Google Scholar
 ElSadek WF, Mikhail MN: Universal mobility with global identity (UMGI) architecture. IEEE International Conference on Wireless Networks and Information Systems, WNIS 09 Los Alamitos 389394.Google Scholar
 Zhang N, Holtzman J: Analysis of handoff algorithms using both absolute and relative measurements. 44th IEEE Vehicular Technology Conference 1994, 8286.Google Scholar
 Chung Y, Lee DJ, Cho DH, Shin BC: Macrocell/microcell selection schemes based on a new velocity estimation in multitier cellular system. IEEE Trans Veh Technol 2002, 51(5):893903. 10.1109/TVT.2002.801764View ArticleGoogle Scholar
 Narasimhan R, Cox DC: Estimation of mobile speed and average received power in wireless systems using best basis methods. IEEE Trans Commun 2001, 49(12):21722183. 10.1109/26.974264View ArticleGoogle Scholar
 Wong D, Cox DC: Optimal local mean signal power level estimator for rayleigh fading environments. IEEE International Conference on Information, Communications Signal Processing 1997, 17011704.Google Scholar
 Appiah KDA: On generalized covariancebased velocity estimation IEEE Trans. Veh Technol 1999, 48: 15461557. 10.1109/25.790529View ArticleGoogle Scholar
 Jaing T, Sidiropulos ND, Giannakis G: Kalman filtering for power estimation in mobile communications. IEEE Trans Wirel Commun 2003, 2(1):151161. 10.1109/TWC.2002.806386View ArticleGoogle Scholar
 Leu AE, Mark BL: An efficient timerbased hard handoff algorithm for cellular networks. IEEE Wireless Communication and Networking Conference 2003, 19691974.Google Scholar
 Papoulis A, Pillai SU: Probability, Random Variables and Stochastic Processes. 4th edition. McGrawHill, New York; 2002.Google Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.