Handover management in high-dense femtocellular networks
© Chowdhury and Jang; licensee Springer. 2013
Received: 1 November 2011
Accepted: 15 November 2012
Published: 7 January 2013
Femtocell technology is envisioned to be widely deployed in subscribers’ homes to provide high data rate communications with quality of service. Dense deployment of femtocells will offload large amounts of traffic from the macrocellular network to the femtocellular network by the successful integration of macrocellular and femtocellular networks. Efficient handling of handover calls is the key for successful femtocell/macrocell integration. For dense femtocells, intelligent integrated femtocell/macrocell network architecture, a neighbor cell list with a minimum number of femtocells, effective call admission control (CAC), and handover processes with proper signaling are the open research issues. An appropriate traffic model for the integrated femtocell/macrocell network is also not yet developed. In this article, we present the major issues of mobility management for the integrated femtocell/macrocell network. We propose a novel algorithm to create a neighbor cell list with a minimum, but appropriate, number of cells for handover. We also propose detailed handover procedures and a novel traffic model for the integrated femtocell/macrocell network. The proposed CAC effectively handles various calls. The numerical and simulation results show the importance of the integrated femtocell/macrocell network and the performance improvement of the proposed schemes. Our proposed schemes for dense femtocells will be very effective for those in research and industry to implement.
Future wireless networks will necessitate high data rates with improved quality of service (QoS) and low cost. A femtocellular network [1–9] is one of the most promising technologies to meet the tremendous demand of increasing wireless capacity by various wireless applications for future wireless communications. Femtocells operate in the spectrum licensed for cellular service providers. The key feature of the femtocell technology is that users require no new equipment (UE). The deployment cost of the femtocell is very low while providing a high data rate. Thus, the deployment of femtocells at a large scale [5, 6] is the ultimate objective of this technology. Indeed, a well-designed femtocell/macrocell-integrated network can divert huge amounts of traffic from congested and expensive macrocellular networks to femtocellular networks. From the wireless operator point of view, the ability to offload a large amount of traffic from macrocellular networks to femtocellular networks is the most important advantage of the femtocell/macrocell-integrated network architecture. This will not only reduce the investment capital, the maintenance expenses, and the operational costs, but will also improve the reliability of the cellular networks .
The large- and dense-scale deployment of femtocells suffers from several challenges [2–5]. Handover is one challenging issue among several issues. For efficient handover management, four factors, namely, intelligent network support, signal flow control for the handovers, reduced neighbor cell list, and an effective call admission control (CAC) policy, are essential. To the best of the authors’ knowledge, complete research results regarding these issues are still unpublished. However, a few research groups (e.g., [10, 11]) have partially discussed some ideas regarding handover issues in femtocellular networks. Bai et al.  proposed a handover mechanism based on the decision made by an entity connected with a femtocell access point (FAP). This entity considers the user type, access mode of the FAP, and current load of the FAP to make a decision about the target femtocell. However, their scheme does not consider the creation of a neighbor cell list. Zhang et al.  presented a handover optimization algorithm based on the UE’s mobility state. They also presented an analytical model for the handover signaling cost analysis. Here, we propose some novel approaches to solve the mobility management issues for densely deployed femtocellular networks. We suggest self-organizing network (SON) features to support the dense femtocellular networks, detail handover call flows for different handovers, an algorithm to create an appropriate neighbor cell list (including the neighbor femtocell list and the neighbor macrocell list), and an efficient CAC to handle various calls. We also propose a novel traffic model for the integrated femtocell/macrocell scenario.
When the number of femtocells increases, the system architectures must support the efficient management of a large number of FAPs and a huge number of handover calls. The SON features [5, 12, 13] can support the coordination among the FAPs as well as among the FAPs and macrocellular BS to execute smooth handover.
The ability to seamlessly move between the macrocellular network and the femtocellular networks is a key driver for femtocell network deployment. Moreover, handover between two networks should be performed with minimum signaling. Owing to some modifications of the existing network and protocol architecture for integrated femtocell/macrocell networks, the proposed signal flows for handover procedures are slightly different as compared to the macrocellular case.
In a dense femtocellular network deployment, thousands of femtocells can be deployed within a small coverage area. As a result, this may present huge interference effects. Whenever a mobile station (MS) realizes that the received signal from the serving FAP is going down, the MS may receive multiple signals from several of the neighbor FAPs for handover. Thus, the neighbor cell list based on the received signal only will contain a large number of femtocells. In addition, a hidden FAP problem may arise. The hidden FAP problem arises when a neighbor FAP is very close to the MS but the MS cannot receive the signal owing to some barrier (e.g., a wall) between the MS and that FAP. Thus, the hidden FAPs will be out of the neighbor cell list if the neighbor femtocell list is designed on the basis of the received signals only. The same incidences are also applicable for the macrocell-to-femtocell handover case. The proposed algorithms are capable of providing a neighbor cell list that contains a minimum number of femtocells as well as includes the hidden FAPs.
The proposed CAC does not differentiate between the new originating calls and handover calls for the femtocellular networks owing to available resources in the femtocellular networks. The CAC provides higher priority for the handover calls in the overlaid macrocellular network by offering a QoS adaptation provision [14, 15]. The QoS adaptation provision is only available to accept handover calls in a macrocellular network. Thus, the macrocellular network can accept a large number of handover calls that are generated because of the femtocells and the neighbor macrocells. The CAC policy also offers two levels of signal-to-noise plus interference ratio (SNIR) thresholds to reduce some unnecessary macrocell-to-femtocell handovers.
The existing traffic model should be modified such that it can be applied to integrated networks. We propose a novel traffic model for femtocell/macrocell-integrated networks that is useful to analyze the performance of femtocell/macrocell-integrated networks.
The rest of this article is organized as follows. Section 2 suggests the system network architecture to support dense femtocells. The SON features of the network architecture are also proposed in this section. The neighbor cell list management algorithms are proposed in Section 3. In Section 4, we describe the call flows for the macrocell-to-femtocell, femtocell-to-femtocell, and femtocell-to-macrocell handovers. CAC policies are provided in Section 5. In Section 6, we derive the detailed traffic model and queuing analysis for the femtocell/macrocell-integrated networks. Performance evaluation results of the proposed schemes are presented and compared in Section 7. Finally, Section 8 concludes this study.
2. Network architecture to support dense femtocells
From the network operator’s perspective, the main requirement for dense femtocell deployment is that it fits into the network with minimum level of operator involvement in the deployment process while minimizing the impact of the femtocell on the existing network. For this purpose, the femtocell is required to boot up into a network by sniffing so that it can scan the air interface for available frequencies and other network resources. Self-organization of radio access networks is regarded as a new approach that enables cost-effective support of a range of high-quality mobile communication services and applications for acceptable prices. It enables deployment of dense femtocell clusters, providing advanced SON mechanisms [6, 12, 13] generally eliminating interference between femtocells, as well as reducing the size of the neighbor cell list and scanning for the handover to ensure fast and reliable handover.
The main functionalities of the SON for femtocellular networks are self-configuration, self-optimization, and self-healing [6, 13]. Self-configuration includes frequency allocation. Self-optimization includes transmission power optimization, neighbor cell list optimization, coverage optimization, and mobility robustness optimization. Self-healing includes automatic detection and solution of most of the failures. Neighbor FAPs as well as the macrocellular BS and the neighbor FAPs coordinate with each other. Whenever an MS desires handover in an overlaid macrocell environment, the MS detects multiple neighbor FAPs because of the dense deployment of femtocells along with the presence of macrocell coverage. Thus, during the handover phase, it is quite difficult to sense the actual FAP to which the user is going to be handed over to. The location information is exchanged among the neighbor FAPs as well as among the neighbor FAPs and macrocellular BS for building an optimized neighbor femtocell list. The handover processes are facilitated by the SON features of the network.
3. Neighbor femtocell list
Finding the neighbor FAPs and determining the appropriate FAP for the handover are the challenges for optimum handover decision . Macrocell-to-femtocell and femtocell-to-femtocell handovers in a dense femtocellular network environment suffer from some additional challenges because of dense neighbor femtocells. In these handovers, the MS needs to select the appropriate target FAP among many neighbor FAPs. These handovers create significant problems if there is no minimum number of femtocells in the neighbor femtocell list. The MSs use much more power consumption in order to scan multiple FAPs, and the MAC overhead becomes significant. This increased size of the neighbor femtocell list along with messaging and broadcasting a large amount of information causes too much overhead. Therefore, an appropriate and optimal neighbor femtocell list is essential for dense femtocellular network deployment.
Figure 4 describes the flow mechanism for the design of the optimal neighbor cell list for the handover when the MS is connected with an FAP. Figure 5 describes the flow mechanism for the design of the optimal neighbor cell list for the handover when the MS is connected with the overlaid macrocellular network. We use two threshold levels of a signal to design the flow mechanisms. The first threshold signal level ST 0 is the minimum level of RSSI that is required to detect the presence of an FAP. The second signal level ST 1 is higher than ST 0. This level of RSSI is considered in our proposed scheme to build up the neighbor cell list. The criterion used for determining the value of ST 1 is the density of femtocells. Therefore, by increasing the value of ST 1 with the increasing density of femtocells, the number of femtocells in the neighbor cell list can be reduced. This action also reduces unnecessary handovers and the ping-pong effect. After checking the open/closed access  system, the k th FAP is directly added to the neighbor cell list if the received signal S i from the k th FAP is greater than or equal to the second threshold ST 1. All N numbers of FAPs from where the MS receives signals are initially considered to create the neighbor cell list. Then, for the closed access case, all the non-accessible FAPs are removed from the number of initially considered femtocells. The frequency allocations are considered to find out the nearest FAPs for possible handover. The coordination among the neighbor FAPs as well as among the FAPs and macrocellular BS are performed to find hidden FAPs. Hidden FAPs are those from which the received signals are less than the second signal level ST 1; however, these FAPs are very close to the serving FAP. Even though these FAPs are very close to the MS, it receives a low level of signal or no signal from these FAPs owing to some obstacle between the MS and these FAPs. Thus, the addition of these hidden FAPs in the neighbor cell list reduces the chance that the MS fails to perfectly handover to the target FAP.
where FAPi(RSSI i ) represents that i th neighbor FAP from which the received RSSI level at the MS is greater than or equal to ST 0. ST 0 is the minimum level of the received signal from an FAP that can be detected by an MS.
Instead of considering only the RSSI level, we consider the RSSI level, frequency used by the serving FAP, and i th neighbor FAP, and the location information to construct an appropriate neighbor femtocell list.
where FAPk(f k ) represents the k th neighbor femtocell that uses frequency f k , whereas fs is the frequency used by the serving femtocell. N2 denotes the number of femtocells in this group. For the macrocell-to-femtocell handover case, if two or more neighbor femtocells from which the MS receives signals use the same frequency, then the femtocells except the nearest one will be included in this group.
where d m is the distance between the MS and the m th neighbor femtocell that uses frequency f m . The m th femtocell is included in this group only if the distance between the MS and the m th neighbor FAP is less than or equal to a pre-defined threshold distance dmax.
4. Handover call flow
To date, an effective and complete handover scheme for femtocell network deployment has been an open research issue. The handover procedures for existing 3GPP networks are presented in [21–27]. In our previous work , we presented the handover scheme for small-scale femtocellular network deployment. This section proposes the complete handover call flows for the integrated femtocell/macrocell network architecture in a dense femtocellular network deployment. The proposed handover schemes optimize the selection/reselection/radio resource control (RRC) management functionalities in the femtocell/macrocell handover.
Macrocell-to-femtocell and femtocell-to-femtocell handovers suffer from some additional challenges because each macrocell coverage area may have thousands of femtocells. In these handovers, the MS needs to select the appropriate target FAP among many FAPs. In addition, the interference level should be considered for handover decision. Handover from femtocell-to-macrocell does not have additional complexity as compared with traditional handovers. The basic procedures for handovers in the dense femtocellular network deployment include signal level measurement, SON configuration, optimized neighbor cell list, selection of appropriate access network for the handover, handover decision, and handover execution.
4.1. Femtocell-to-macrocell handover
4.2. Macrocell-to-femtocell handover
4.3. Femtocell-to-femtocell handover
5. CAC for femtocell/macrocell overlaid networks
For the femtocell/macrocell-integrated networks, the CAC can play a vital role in maximizing resource utilization, particularly for macrocellular networks, by efficiently controlling the admission of various traffic calls inside the macrocell coverage area. The main objective of our proposed scheme is to transfer a larger number of macrocell calls to femtocellular networks. We divide the proposed CAC into three parts. The first one is for the new originating calls, the second one is for the calls that are originally connected with the macrocellular BS, and the third one is for the calls that are originally connected with the FAPs. We also use two threshold levels of SNIR to admit a call in the system. The first threshold level Γ1 is the minimum level of the received SNIR that is needed to connect a call to any FAP. The second signal level Γ2 is higher than Γ1. The second threshold is used in the CAC to reduce the unnecessary macrocell-to-femtocell handovers. We offer QoS degradation [14, 15] of the QoS adaptive multimedia traffic to accommodate femtocell-to-macrocell and macrocell-to-macrocell handover calls. The existing QoS adaptive multimedia traffic in overlaid macrocellular network releases Crelease amount of bandwidth to accept the handover calls in the macrocellular network. This releasable amount depends on the number of running QoS adaptive multimedia calls and their maximum level of allowable QoS degradation and the total number of existing calls in the macrocellular network. Suppose β r,m and βmin,m are the requested bandwidth by a call and the minimum allocated bandwidth for a call of traffic class m, respectively. Then, each of the m th class QoS adaptive calls can release a maximum (β r,m – βmin,m) amount of bandwidth to accept a call in the macrocell system. If C and Coccupied are the macrocell system bandwidth capacity and the occupied bandwidth by the existing macrocell calls, respectively, then the available empty bandwidth Cavailable in the macrocellular network is (C – Coccupied,m).
5.1. New originating calls
5.2. Calls that are originally connected with the macrocellular BS
5.3. Calls that are originally connected with the FAPs
6. Queuing analysis and traffic model
The average channel release rate for the macrocell layer increases as the number of deployed femtocells increases. Because of the increasing number of femtocells, more macrocell users are handed over to femtocell networks. The average channel release rates  for the femtocell layer and the macrocell layer are calculated as follows.
where 1/μ, 1/η m , and 1/η f are the average call duration (exponentially distributed), average cell dwell time for the macrocell (exponentially distributed), and the average cell dwell time for the femtocell (exponentially distributed), respectively.
Equating the net rate of calls entering a cell and requiring handover to those leaving the cell, the handover call arrival rates are calculated as follows .
where Ph,mm, Ph,mf, Ph,ff, and Ph,fm are the macrocell-to-macrocell handover probability, macrocell-to-femtocell handover probability, femtocell-to-femtocell handover probability, and femtocell-to-macrocell handover probability, respectively.
7. Performance analysis
Summary of the parameter values used in our analysis
Radius of femtocell coverage area
Carrier frequency for femtocells
Transmit signal power by macrocellular BS
Maximum transmit power by an FAP
Height of macrocellular BS
Height of an FAP
Height of an MS
First threshold value of received signal (RSSI) from an FAP (S T0 )
Second threshold value of received signal (RSSI) from an FAP (S T1 )
Bandwidth capacity of a macrocell (C)
Required/allocated bandwidth for each of the QoS non-adaptive calls
Maximum required/allocated bandwidth for each of the QoS adaptive calls
Minimum required/allocated bandwidth for each of the QoS adaptive calls
Ratio of traffic arrivals (QoS non-adaptive calls: QoS adaptive calls)
First SNIR threshold (Γ1)
Second SNIR threshold (Γ2)
Number of deployed femtocells in a macrocell coverage area
Average call duration time (1/μ) considering all calls (exponentially distributed)
Average cell dwell time (1/η f ) for the femtocell (exponentially distributed)
Average cell dwell time (1/η m ) for the macrocell (exponentially distributed)
Density of call arrival rate (at femtocell coverage area:at macrocell only coverage area)
Standard deviation for the lognormal shadowing loss
The results in Figures 14, 15, 16, 17 and 18 show the improvement of the proposed schemes. Our proposed neighbor cell list algorithms provide an efficient way to manage the neighbor cell list. The reduced number of FAPs in the neighbor cell list results in reduced scanning and signaling. The inclusion of hidden FAPs in the neighbor cell list results in reduced handover failure probability to the femtocell. The proposed QoS adaptive/degradation policy is able to handle a large number of handover calls. The integration of a macrocell with the femtocells provides reduced overall forced call termination probability in the macrocell system. The integrated femtocell/macrocell network system also increases the macrocell channel release rate that results in an increased load transfer rate from the macrocellular network to the femtocellular networks.
8. Conclusion and future research
Femtocellular networks may have different sizes, and ultimately, we expect to see densely deployed networks with over thousands of femtocells overlaid by a single macrocell. Mobility management is one of the key issues for successful dense femtocellular network deployment. However, a complete solution for the mobility management for femtocellular networks is still an open research issue. We proposed novel approaches to solve the mobility management issues for densely deployed femtocellular networks. The proposed SON-based network architecture is capable of handling large numbers of FAPs inside the macrocell coverage. Our proposed algorithm helps to overcome the hidden FAP problem. The reduced neighbor cell list results in reduced power loss as well as reduced MAC overhead. The proposed handover call flows will be very effective to implement for handover processes in dense femtocellular network deployment. The suggested traffic model for the femtocell/macrocell-integrated network is quite different from the existing macrocellular network traffic model. This traffic model can be applied for the performance analysis of a femtocell/macrocell-integrated network. The results shown in this article clearly imply the advantages of our proposed schemes. The analyses also indicate the effect of femtocellular network deployment and performance improvement attributed to the integrated femtocell/macrocell network. Therefore, our performance analyses show that mobility management is a critical issue for dense femtocellular network deployment.
We studied major research issues concerning mobility management in integrated femtocellular/macrocellular networks. The research results were studied using several numerical and simulation analyses. A real-life experiment would require many FAPs as testing equipment. Therefore, experimental results for comparison to theory are saved for future research work. However, our proposed scheme provides a good basis for research as well as industry to implement dense femtocells successfully.
This study was supported by the IT R&D program of MKE/KEIT (10035362, Development of Home Network Technology based on LED-ID).
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