Optimized Handover Algorithm for Two-tier Macro-Femto Cellular LTE Networks

Transcription

1 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) Optimized Handover Algorithm for Two-tier Macro-Femto Cellular LTE Networks Ahlam Ben Cheikh 1,2, Mouna Ayari 1,2, Rami Langar 2, Guy Pujolle 2, and Leila Azouz Saidane 1 1 CRISTAL Lab., University of Manouba, 2010 Manouba,Tunisia. 2 LIP6 / UPMC University of Paris 6; 4 Place Jussieu, Paris, France. s:{ahlam.ben-cheikh, rami.langar, guy.pujolle}@lip6.fr; mouna.ayari@cristal.rnu.tn, leila.saidane@ensi.rnu.tn Abstract In the last few years, femtocells have gained a great deal of interest as an emerging wireless and mobile access technology to improve indoor coverage and network capacity. In such an environment, mobility management is one of the major concerns that may limit the wide deployment and adoption of such networks. In this paper, we investigate the handover procedure for the two-tier macro/femto LTE networks. An optimized handover algorithm with an efficient call admission control has been proposed and described. Our proposed scheme is mainly designed to reduce the number of unnecessary handovers and to maintain the communication quality during the handover. The choice of the femtocell target takes into account the direction of the mobile user, its velocity and the quality of the signal. Performance evaluation results show that our algorithm minimizes both the number of hand-in and the handover drop rate. Besides, the signal quality in terms of SINR after the hand-in is maintained higher than a fixed threshold, which maximizes the sojourn time of the mobile user within the selected femtocell. Index Terms Handover, Femtocell, Macrocell, LTE, Predicted direction. I. INTRODUCTION Low-power cellular base stations designed to be deployed in residential, enterprise, metropolitan hotspots or rural settings are known as femtocells. These Femtocells Access Points (FAPs), known also as Home enode B (HeNB) in 3GPP LTE terminology [3], are connected to an operator s network commonly through a digital subscriber line (DSL) connection or fiber. They are administered by operators and make use of licensed spectrum technology (e.g. UMTS, LTE, WiMAX). Compared with a macrocell base station (BS), FAPs present many differences mainly regarding the maximum power transmission range, the maximum number of users served by each BS and the access mode. Unlike macro-bs, FAPs can serve a few users. Besides, the access mode to a macro-bs is always open; i.e a mobile user has no restriction to access to a macro- BS. On the other hand, the access to a FAP can be controlled. Three access modes at FAPs have been defined: open, closed and hybrid. The deployment of femtocells presents interesting benefits either to mobile network operators or end users. In fact, with low-power transmissions, femtocells provide operators with low-cost infrastructure and significantly reduce energy costs. Besides, femtocells provide end users with very high data rates and a better indoor voice and data coverage. Moreover, this emerging network technology improves the macrocell reliability. In fact, a mobile user connected to a macro-bs can be redirected to a FAP to provide a better communication quality [1], [2]. So femtocells may relieve traffic from the macrocell network in order to provide a better quality of service to mobile users. This process refers to a handover scenario between two-tier macro and femto cells. In this context, three types of handover are defined: (i) a hand-in from macrocell to femtocell, (ii) a hand-out from femtocell to macrocell, and (iii) a femto-to-femto handover. Through recent years, few researches have been carried out to address the issue of mobility management in twotier macrocell/femtocell networks. The main objective is to enable the service continuity and provide a seamless handover. In [4], authors proposed a handover algorithm with a new hand-in procedure in LTE networks called SQ (Speed and QoS) based on the user equipment (UE) s velocity and QoS traffic. In [5], authors proposed a new handover scheme that optimized the selection of target BS for both hand-in and handout mobility scheme between WiMax and femtocells. Authors in [6] proposed a new CAC mechanism taking into account the residence time of the UE in a cell in the case of the hybrid access mode. In [7], authors present a framework with predictive timed QoS guarantees. In this paper, we focus on the hand-in procedure based on the 3GPP LTE specification. An UE which is initially connected to a macrocell and detects a decreased signal level will search the FAP that offers the best signal with the required QoS guarantees. To do so, we propose a new mechanism that reduces the number of unnecessary handovers and maintains the communication quality during user movement. Our approach takes into account not only the signal level but also the UE velocity, its movement direction, the access mode of the FAP target and the availability of resources in the femtocell target. To gauge the effectiveness of our proposal, we compare our optimized handover algorithm with the classical case where no filter on the FAP target candidates is used. The obtained results show that our algorithm minimizes both the number of handin and the handover drop rate. In addition, the signal quality /13/$ IEEE 608

2 in terms of Signal to Interference plus Noise Ratio (SINR) after the hand-in is maintained higher than a fixed threshold, which maximizes the sojourn time of the mobile user within the selected femtocell. The remainder of this paper is organized as follows. Section II describes the LTE two-tier macro/femtocell network architecture and presents the handover issue. In Section III, we detail our proposed handover algorithm that aims to optimize the list of femtocell candidates and reduce unnecessarily handovers. The performance evaluation results are presented and discussed in Section IV. Section V states our conclusions and future work plans. II. TWO-TIER LTE NETWORK ARCHITECTURE AND HANDOVER ISSUE The architecture of two-tier femtocell/macrocell network is shown in Fig. 1. In this figure, one tier contains femtocell that serves a small range data access point to perform indoor coverage whereas the second tier is served by macro-bs. We distinguish two parts in both macrocell and femtocell networks: the evolved UMTS Terrestrial Radio Access Network (E-UTRAN) and the Evolved Packet Core (EPC). In a femtocell network, the E-UTRAN part contains the HeNBs which are connected to the EPC via S1 interface. The EPC system consists of the Mobility Management Entity (MME), the Serving Gateway (SGW) and the HeNB Gateway (HeNB- GW). its sojourn time if the selected FAP is not in the UE direction. Hand-out: It represents the handover procedure when a UE switches from a FAP to a macro-bs. Handoff : It refers to an inter-fap (i.e. a femtocell to femtocell) handover. III. PROPOSED HANDOVER ALGORITHM The hand-in procedure can typically be divided into four essential phases [8]: the handover measurement and scan, the handover preparation, the handover execution and the handover completion. In order to optimize the handover algorithm, we propose modifications of the measurement, the selection of the FAP candidates and the Call Admission Control (CAC) procedures. Indeed, as illustrated in Fig. 2, in the first phase of handin, the serving macro-bs (e-nb) will periodically send a measurement request to the UE which will reply with a measurement report. In addition to the signal level and the channel information, we propose to add the following measurement parameters: the predicted direction of the UE and its speed. Based on the reported information, the MME will send the list of FAPs candidates to the concerning HeNB-GW. The HeNB- GW will optimize the list of FAPs candidates by selecting the ones located in the neighborhood of the UE direction (see subsection III-A). Once choosing the best FAP that offers the maximum signal level, the second phase will be initiated and the target HeNB will apply an optimized CAC to accept the handover request (see subsection III-B). After this control, a Handover response will be sent to the HeNB-GW, which forwards it to the MME. The latter will send the HO Response to the Serving enb to inform the UE (with HO command) about the termination of the connection. In the third phase, the Fig. 1: Macrocell/femtocell network architecture To better address the issue of mobility management in the case of two-tier LTE network, we briefly present the different handover procedures. As stated earlier, we distinguish three handover scenarios. Hand-in: It refers to a macrocell to femtocell handover. This type of handover procedure is considered to be the most complex. In fact, the access mode and the availability of resources must be checked before connecting to a FAP. Moreover, since FAPs are intended to be deployed massively, with a large number of femtocell candidates, during a handover process, the mobile user could be directed to a less congested FAP but without maximizing Fig. 2: Message sequence diagram of hand-in procedure 609

3 UE will execute the handover by sending synchronization to the target FAP and then will access to the target cell. Finally, during the handover completion, the MME will switch the path of downlink data to the target FAP. After the reception of the release resource by the serving enb, the target FAP can transmit the downlink packet data. In the following, we detail how the optimized FAPs candidates list is obtained and the proposed CAC procedure. A. Optimized FAPs candidates list Our objective is to optimize the choice of the target FAP. Initially, as shown in Fig. 3a, the UE is connected to an enb. When it detects a decrease of the signal level, it will handin to the suitable HeNB. The traditional list of HeNBs target (Fig. 3b) is defined based on the signal level. A HeNB will be designed as a target when the signal level of the HeNB is largely higher than the fixed threshold. In this case, the blue area, shown in Fig. 3b, defines the list of HeNBs target. We can model it as a circle, which center is the current position of the UE and which radius is equal to the distance between the center and the farthest HeNB target. angle between the current position of the UE i and that of FAP j. To determine this set, we define an angle β (Fig. 4) as the threshold of Φij. Set of F AP s Candidates = {FAPj/ Φij <β} (1) B. Call Admission Control procedure Before establishing the connection with the FAP target (HeNB-T), an admission control procedure will be carried out based on the following criteria: 1) The signal level of the best HeNB-T should be higher than a fixed threshold. 2) After the connection of the UE asking for the handin, the signal level of the others UEs connected to this HeNB-T should not decrease below the threshold already fixed. 3) The UE can be authorized to access to the HeNB-T only if the number of UE connected to the HeNB-T would not exceed the maximum number of allowed users according to the HeNB-T access mode. As illustrated in Fig. 5, the proposed CAC consists of three blocs. The first one details the measurement report sent by (a) UE connected to a macrocell.(b) UE initiate the handover measurement and scan. Fig. 3: Traditional Area of FAPs candidates When defining the list of HeNB target, the UE can have for example a FAP neighbor that offers the best communication signal level. However, it can be located in a different direction of the UE walk. So, in this case, the mobile user will spend a very short period in the target cell and will handoff to another FAP shortly. In order to overcome this issue, we define a list Fig. 4: The optimized FAPs candidates list of FAPs candidates as the set of all the FAPs having a level of signal up to a fixed threshold and are located in the UE s estimated direction as shown in formula (1), where Φij is the Fig. 5: Proposed Call Admission Control 610

4 the UE. The second bloc focus on the determination of the optimized list of FAP candidates. The final one presents the CAC core procedure. IV. PERFORMANCE EVALUATION In this section, we evaluate the efficiency of our proposed handover algorithm using Matlab. The non-optimized scheme (i.e the handover algorithm that does not use the predictive direction of UE and considers all FAP candidates as it was shown in Fig. 3b) is used as a benchmark to which the potential benefits of our proposal are compared. A. Simulation Environment In our simulations, we consider the case of an urban environment where both macro and femto base stations are installed. As shown in Fig. 6, we consider a dense environment of FAPs with the presence of only one macro-bs. The scenario TABLE I: Simulation Parameters Parameter Radius of circular FAP coverage area Power of FAP Number of max users by a FAP Number of femtocells within macrocell Mode Access of FAP Radius of circular enb coverage area Power of enb λ: Arrival rate of macro users (users/s) Mobility Model of macro UEs Velocity of macro UE in the macrocell area Walk Step of macro UE SINR Threshold Value 10 m 20 mw 6 users/femtocell 500 Open 250 m 50 W [1:1:10] Random WayPoint [0.2;4] m/s pedestrian Velocity 1s 10 db and 25dB β in degree [5:5:45] Simulation duration Area of simulation 100s 500m*500m Fig. 6: Simulation Environment consists of 500 FAPs within the macrocell coverage area. We assume that each FAP is initially serving four femtousers. In addition, calls are generated randomly according to a Poisson process with arrival rate λ. The arriving macro user could appear anywhere within the simulation area. Simulation parameters are reported in Table I. Fig. 7: The ratio of FAPs candidates versus the value of arrival rate of macro users B. Simulation Results 1) The ratio of FAP candidates: We present here the ratio of FAP candidates as function of λ and taking into account the variation of β. Recall that β is defined as the threshold of the angle Φij (see Fig. 4). The ratio of FAP candidates is defined as follows. Number of F AP in the optimized list T he ratio of F AP candidates = Number of F AP in the mobile coverage We can observe from Fig. 7 an enhancement up to 40% of the ratio of FAPs compared to the non-optimized one. In addition, with a high level of interference (Threshold = 25 db) this ratio is lower than that recorded in a low interference level (Threshold = 10dB). Moreover, with a high density of macro users and a small value of β, the ratio of FAPs candidates is reduced. 2) Number of Handovers: We measure here the total number of hand-in during the simulation. Fig 8 shows the effect of the variation of the arrival rate λ of macro-users on the number of hand-in. Considering our proposed CAC, the number of hand-in is significantly reduced. Consequently, the occurrence of unnecessary handin is reduced. Compared to the non-optimized algorithm, the optimized one shows an improvement up to 30% of the number of unnecessary handovers that is observed in the case of low interference level and particularly for λ =10. To better show the performance improvement of our proposed algorithm, we measure the hand-in gain as the ratio between the total number of hand-in in the optimized algorithm and the total number of hand-in in the non-optimized one, as follows. 611

5 T he optimized total number of hand in Hand in gain =1 The non optimized total number of hand in Fig. 9: The Handover Drop Rate versus the ratio of FAP candidates Fig. 8: The Number of hand-in versus the arrival rate of macro users TABLE II: hand-in gain (%) Threshold λ: Arrival rate db db As presented in Table. II, when the SINR threshold is about 25 db, a low value of the hand-in gain is observed. The lower the value of SINR threshold, the higher the hand-in gain is. For example, when λ =10, compared to a low level of interference (Threshold = 10 db), the hand-in gain is more than 28% when the SINR threshold is 25 db. 3) Handover Drop Rate (HDR): In our algorithm, we distinguish two types of handover drop: one is caused by the degradation of the signal level of femto users connected to the FAP that is elected as a target, and the other is caused by the non existence of a FAP that has a signal level higher than a fixed threshold. The HDR is defined as the ratio between the number of dropped Handover and the total number of handover that occurred in the system. Fig 9 reports the handover drop rate as function of the ratio of FAP candidates. From this figure, we can see that HDR is reduced when using our CAC. In addition, it is increased in the case of high interference level. This is caused by the degradation of femto users or the non existence of a FAP that has a signal level higher than a fixed threshold. The medium value of HDR is calculated for the ratio of FAPs candidates that is equal to 1, the HDR in this latter is higher for the non-optimized algorithm than for the optimized one. To further shows the performance of our algorithm, let us consider Table. III, where we present the different values of HDR for different values of FAP candidates ratio. From this table, we can see that in case of low interference level (Threshold = 10 db), our algorithm shows a lower HDR by TABLE III: Handover Drop Rate (HDR) FAP candidates ratio Threshold=10dB Threshold=25dB λ=1 λ=10 λ=1 λ=10 Optimized Algorithm Non-Optimized Algorithm respectively 99.92% and 66.18% for the two values of λ (1 and 10). However, in the high interference level case (Threshold = 25 db), the HDR values decrease down to 49.12% and 12.13% for λ =1and λ =10, respectively. Fig. 10: The Handover Drop Rate versus β Fig.10, shows the impact of β on HDR for different values of λ (1, 5 and 10). In all cases, the lowest β is, the lowest HDR is. This result confirms that our proposed scheme outperforms 612

6 the traditional handover algorithm for different values of λ and β. In fact, for a high density of macro-users (λ =10), our optimized algorithm reduces HDR more than 50% compared to the non-optimized one. 4) SINR vs Simulation time: We measure here the value of SINR (Signal to Interference plus Noise Ratio) after the hand-in. Recall that SINR is calculated as follows. SINR = P I + N,where : P: is the received power that represents the ratio between the transmitted power of the sender s (ψs) and PLs,r the path loss between the sender and receiver that is calculated based on the winnerii model [9]. P = ψs PLs,r I: it presents the interference power of other simultaneous transmissions that use the same resources as the sender s. N: is the noise power which is a constant of LTE networks and is equal to dbm. Fig. 12: The variation of the signal quality when the simulation time = 30 sec. Simulation results showed that our proposed algorithm minimizes the number of hand-in, the ratio of FAPs candidates and the Handover Drop Rate, when compared to the traditional handover one, which considers only signal strength. In addition, we showed through simulations that after the hand-in, the signal quality in terms of SINR is improved. Almost all hand-in have a SINR higher than a fixed threshold, which maximizes the residence time in the elected FAP. In our future work, we plan to analyze our proposal using Markov chains. In addition, we will study the case of adjusting dynamically the FAP power to prevent unnecessary Handover. ACKNOWLEDGMENT This work is supported by the Franco-Tunisian research cooperative projects PHC-Utique no. 12G1411 and DGRS- CNRS no. 12R1402, as well as by the Ferrari project funded by PICS CNRS under grant agreement no Fig. 11: The variation of the signal quality when the simulation time = 100 sec. Figs. 11 and 12 show the variation in time of the signal quality during simulations. We can observe that after hand-in, the signal quality using our optimized algorithm is enhanced. Compared to the non-optimized algorithm, we note that after hand-in the SINR is largely lower than that of the optimized one. In fact, for a simulation time that is high or equal to 15 sec and for a low level of interference, our optimized algorithm increases the SINR up to 14 db compared to the non-optimized one. This can significantly increase the residence time in the femtocell and is expected to provide better service and QoS. V. CONCLUSION In this paper, we proposed a new handover decision algorithm based on the prediction of the mobile user position in two tier macro/femto environment. Specifically, we exploited the predicted direction of the mobile user to identify a list of FAPs candidates that are most likely to be visited. REFERENCES [1] V. Chandrasekhar, J. Andrews and A. Gatherer, Femtocell networks: a survey, IEEE Communications Magazine, September [2] Femto Forum, The Femto Forum: HeNB (LTE Femto) Network Architecture, May [3] 3GPP, 3GPP TS , 3rd Generation Partnership Project, [4] H. Zhang, X. Wen, B. Wang, W. Zheng and Y. un, A Novel Handover Mechanism between Femtocell and Macrocell for LTE based Networks, Second International Conference on Communication Software and Networks 2010, ICCSN 10, pp [5] R. Singoria,T. Oliveira and D.P. Agrawal, Reducing Unnecessary Handovers: Call Admission Control Mechanism between WiMAX and Femtocells, 2011 IEEE Global Telecommunications Conference (GLOBECOM 2011), pp [6] J-S. Kim and T-J. Lee, Handover in UMTS networks with hybrid access femtocells, The 12th International Conference on Advanced Communication Technology (ICACT), 2010, pp [7] A. Aljadhai and T.F. Znati, Predictive mobility support for QoS provisioning in mobile wireless environments, IEEE Journal on Selected Areas in Communications, October 2001, pp [8] H. Kwak, P. Lee, Y. Kim, N. Saxena and J. Shin, Mobility Management Survey for Home-eNB Based 3GPP LTE Systems, Journal of Information Processing Systems, Vol.4, No.4, December 2008 [9] IST WINNER II: D1.1.2 V1.1 WINNER II channel models, September 2007, 613