# Handover Management in Virtualized Radio Access Networks

Solohaja Rabenjamina, Hervé Rivano, Razvan Stanica, Cezary Ziemlicki

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Solohaja Rabenjamina, Hervé Rivano, Razvan Stanica, Cezary Ziemlicki. Handover Management in Virtualized Radio Access Networks. WoWMoM 2025 - 26th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, May 2025, Fort Worth (TX), United States. hal-05095906

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Distributed under a Creative Commons Attribution 4.0 International License
Handover Management in Virtualized Radio Access
Networks

Solohaja Rabenjamina*, Hervé Rivano*, Razvan Stanica*, Cezary Ziemlicki†

*INSA Lyon, Inria, CITI, F-69621 Villeurbanne, France

†SENSE, Orange Innovation, 92320 Chatillon, France

***Abstract***—The evolution of mobile networks towards more diverse services and open architectures has led to the emergence of mobile network function virtualization. This allows to match the reserved resources for network operation to the actual resources that are needed in the network at a certain place and time. Mobility-related network functions, specifically handover, can also be virtualized in this new paradigm. This virtualization can be facilitated by studying handover behavior at the base station level. In this work, using agglomerative hierarchical clustering, we show the existence of different base station profiles in terms of handovers, including three primary profiles: producer, receiver, and balanced. We also show that the use of these profiles, in addition to the dynamic reconfiguration enabled by virtualization, can reduce reserved resources by more than 50% compared to the current static system.

# I. INTRODUCTION

For more than three decades, network functions in a mobile network have been linked to hardware. In this traditional architecture, each network function is mapped to a physical machine and the network is statically dimensioned, in order to satisfy the needs of each offered service. However, since various services require different types and amounts of resources, the network is dimensioned so that it can meet the demands of each service at peak times, i.e. when the maximum demand is recorded. As a consequence, mobile networks are over-dimensioned to be able to meet the needs of all the services at their peaks. Of course, this implies an over-provisioning of resources, as the physical machines hosting the network functions need to be able to handle the peaks of traffic. This means that, during periods of lower traffic, part of these resources are unused and wasted.

This over-dimensioning is also present in the case of user mobility, especially for micro-mobility management at the radio access network (RAN), where handovers (HO) are realized. A HO is performed when a user equipment (UE) moves between network cells while being connected to the RAN and exchanging traffic (e.g. ongoing call or data session). The fact that HOs occur while the UE is communicating implies that poor management or configuration of the network can lead to a degradation of the ongoing communication or even, in the most extreme cases, to its interruption. As HO duration and success are essential for the user quality of experience (QoE) [1], the required RAN resources for mobility management are over-dimensioned as well. Indeed, the number of HO follows user mobility in time and in space,

but the network is dimensioned to satisfy the peak of HO at each base station (BS).

Network function virtualization (NFV) is reshaping the mobile network architecture [2] and challenging these solutions based on over-dimensioning. Network functions are increasingly becoming virtualized in mobile networks [3], running on generic hardware under the management of an orchestrator [4]. This virtualization trend was also adopted at the RAN [5], which is composed in 5G New Radio (NR) networks of a distributed unit (DU), running locally on dedicated hardware, and a centralized unit (CU), which can be virtualized and hosted in an edge data center [6].

One significant advantage of a virtualized RAN is that the network functions can be activated only when they are needed. Virtualization also allows adapting the reserved resources to the actual user demand. The underlying idea of this study is that mobility-related network functions, specifically HO management, can also be virtualized. In terms of resources, the execution of a HO requires communication and computing resources. Communication is required for the UE to send channel quality information (CQI) to its serving base station (BS), to receive an HO command from the BS whenever required, and to connect to a new BS. Computing resources are needed by the BS in order to periodically process the CQI and decide whether an HO is required. From the point of view of a BS, there are two types of HOs: incoming HOs (IHO), where the BS acts as a destination for the UE, and outgoing HOs (OHO), where the BS is the source of the mobility process.

In a classical mobile network, the resources required for HOs are over-dimensioned, in order to accommodate peaks of user mobility. However, in a virtualized RAN, the amount and type of reserved resources can be dynamically adjusted to match the network needs. Moreover, the resources can be adapted to the type of HOs to be executed, either IHO or OHO. Indeed, the execution of these two types of HO involves different network functions and resources, so distinguishing them in the HO management process could be beneficial.

In this paper, we design a handover management framework for virtualized RAN and evaluate it using real data from a mobile operator network. The proposed framework is composed of: i) a BS profiling technique, and ii) a resource reconfiguration solution. The BS profiling technique classifies BS based on the type of HOs they execute. This allows us to observe that different classes of BS exist, e.g. BS with more
IHO, which act as HO receivers, and BS with more OHO, which act as HO producers. Based on this profiling technique, we show that a resource reconfiguration solution can reduce the number of resources reserved for HO in an urban region by around 70% during the night and 30% during the day.

We continue by discussing related works in Sec. II, before providing some important background information on mobility management in Sec. III. The proposed HO management framework is detailed in Sec. IV. Based on a real-world dataset, we obtain BS profiling results in Sec. V and we evaluate dynamic HO resource reconfiguration in Sec.VI. Finally, Sec. VII concludes the paper.

## II. RELATED WORK

As indicated by the technical nomenclature, user mobility management is the most distinguishable function of a mobile network. The HO mechanism is essential for mobility management, and it has been integrated very early in the cellular architecture: field tests with 1G technology in the 1970s already mention HO implementation and issues [7]. The execution of a HO implies the disruption of the radio resource control (RRC) connection at the RAN level, which, if too long, can have a significant impact on user QoE [8]. Therefore, accelerating the HO process has been a major objective throughout the decades of development of cellular technologies. Recent advancements and trends on this topic are summarized in [9].

With HO performance being an important metric in mobile networks, the HO management problem has been constantly revisited as the cellular network architecture evolved. In recent years, several studies focused on different HO scenarios and settings. Fakhreddine et al. [10] studied HOs in the context of cellular-connected drones, showing that these aerial UEs experience five times more HOs than terrestrial users, because of the way the cellular BS are configured. On the other hand, if BS are embarked on drones, [11] proposes a group handover technique that proves to be faster and more secure. The analysis and reduction of the so called ping-pong effect in HOs [12], [13] is a major topic in mobility management, even for terrestrial users. As a matter of fact, HO remains a highly important factor for user QoE, even in recent 5G deployments [14].

Indeed, as the cellular architecture evolves, HO management becomes more and more complex. Since a BS needs to continuously process CQI from users and take HO decisions, machine learning solutions appear as good candidates in these cases. For example, if millimeter-wave BS are deployed, the BS density needs to be increased and directional communications are required. This increases the HO frequency and the HO decision complexity, therefore reinforcement learning solutions are proposed in this context [15]. If reconfigurable intelligent surfaces are also considered in the network architecture, another layer of complexity is added to the problem, which is handled using deep reinforcement learning in [16].

Network virtualization also brings new challenges in HO management. Sun et al. [17] argue that the deployment of network slices as a consequence of virtualization needs to be accounted for in the HO process, as this brings new logical connection constraints. Their proposal relies on a distributed learning technique. At the RAN level, virtual machine migration and reconfiguration can actually produce a new type of reconfiguration handover, as pointed out in [18], where a graph-based heuristic is proposed to tackle this problem. A more elaborate solution to the problem is proposed in [19], where a long short-term memory (LSTM) machine learning model is used to predict HOs in a virtualized RAN scenario, in order to properly reconfigure the network.

However, these previous studies do not consider that the HO management function itself can be virtualized. Gharsallah et al. [20] are the first to point out this possibility and the advantages it brings. In their work, they design a software-defined HO management function, which they apply in an ultra-dense network scenario, where numerous HOs are expected. They show that this virtualization-based approach can divide the HO execution time by 50%.

Our work builds on this idea of a virtualized HO management function, showing the benefits it can bring at an urban scale by analyzing real-world HO data. This HO data analytics part is unique in the literature from our knowledge. While [18] and [19] use real-world traffic data in their studies, the HO data used in their analyses is synthetic. The same is true for the vehicular-oriented study in [21], where realistic UE mobility is considered, but the HO data is not available and needs to be artificially produced.

## III. HANDOVER BACKGROUND

Mobility management in cellular networks allows the continuity of the service while the user is moving, by changing its serving cell. HOs represent the main component of UE mobility management, and their operation is described below.

### A. Handover Procedure

The triggering of a HO is preceded by a preliminary phase consisting of the transmission of CQI measurements from the UE. If a HO is decided by the RAN, it is performed in three phases: i) HO preparation, ii) HO execution, and iii) HO completion.

**Preliminary phase:** In this phase, once an RRC connection is established, the UE continuously monitors the received signal quality on the downlink. Signal quality is assessed by several metrics: received signal strength indicator (RSSI), reference signal received power (RSRP), and reference signal received quality (RSRQ). The UE conducts these measurements on its current cell, as well as for neighboring BS. All these measurements are transmitted to the serving BS, either periodically or on its request. The BS combines this downlink feedback with its own measurements on the uplink and analyzes them to decide whether a HO is required. The HO decision algorithms are not standardized, but a HO is generally
triggered in two situations: i) if the RSRP and/or RSRQ are below some predefined thresholds, or ii) if a significantly better channel quality is observed on a neighboring BS.

**HO preparation:** Once the HO decision is made, the serving BS prepares its execution by reserving the required resources at the target BS. There are two ways for the source BS to contact the target BS: i) using a direct connection between BS on the Xn interface (or X2 in LTE), or ii) going through the core network, by using the N2 interface (or S1 in LTE). Of course, using the Xn interface is faster, but such a direct connection is not always available. While the used messages and involved equipment are different between the Xn and N2 HO procedures, the general idea is that a *HO Request* message is transmitted from the source BS to the target BS. This message contains information regarding the target cell identity, the cell radio network temporary identifier (C-RNTI) of the UE in the source BS, radio configuration parameters, which are necessary to prepare the handover towards the target BS. At the reception of the message, the target BS performs admission control, which consists of verifying whether it has enough available resources to serve a new UE. If the BS can admit the UE, the target BS chooses a preamble that will be used by the UE in the random access phase (RACH) with the target BS. This preamble is sent to the source BS in an acknowledgment message, which indicates that the target BS can receive the UE. A negative acknowledgment is transmitted otherwise, in which case a new target BS is selected.

**HO execution:** In this phase, the source BS sends the order to the UE to perform the HO, using an *RRC Connection Reconfiguration* message. This message contains information that will allow the UE to connect to the target BS, such as the new C-RNTI of the UE, the identifier of the target BS and the preamble previously provided by the target BS. After this point, the source BS no longer accepts traffic to/from the UE. The packets that continue arriving at the source BS will be rerouted towards the target BS. In parallel, the UE disconnects from the old cell, and it synchronizes with the target cell, through a contention-free random access procedure using the preamble received from the target BS. All the UE uplink data now passes through the target BS.

**HO completion:** The HO is now executed, but the core network is not yet aware of this, so the incoming packets are still forwarded to the source BS. A direct path from the core network to the target BS must therefore be set up. For this, the target BS contacts the access and mobility management function (AMF) on the core network, which further takes care of this reconfiguration by switching the downlink (DL) data path toward the target BS and establishing an NG-C interface toward the target BS. However, the HO is not completely finished until the resources on the old BS are released. For this, once all the tunnels are reconfigured, the target BS informs the source BS to release the resources.

The phases involved in the HO process and the messages exchanged between the different equipment, following the example of an intra-AMF/Xn HO in 5G, are depicted in Fig. 1.

<figure><img src="image_2.png" /><figcaption>Fig. 1: intra AMF/Xn HO basic procedure</figcaption></figure>

## B. Handover Cost

Considering the HO procedure described above, it is obvious that an incoming and an outgoing HO will not use the same amount and type of resources. We can easily see that the source BS and the target BS do not have the same interactions with the network, nor the same computation requirements. To differentiate the resources needed for IHO and OHO, we focus on two types of costs: computation and signaling.

1) **Computation cost:** This cost is represented by computation operations that the source and target BS need to perform in the HO process, whether it is for the decision, the command, or the admission control (i.e., when checking if the BS has the necessary resources to host a new UE). By analyzing the HO procedure, it is clear that the computation cost of an OHO is higher than the one of an IHO. Indeed, the source BS periodically runs an algorithm to check if the measurements reported by the UE require the triggering of an HO, and it also needs to make the choice of the target BS. This is much more significant than the computation cost of the target BS, which just checks if it has enough resources to accept the new UE.

In a virtualized RAN context, it is important to distinguish between IHO and OHO, so that we can allocate the right amount of central processing unit (CPU) time to the source and target BS. For modeling reasons, we can define the following relationship between IHO and OHO computation costs:

$$C_{\text{OHO}} = \beta \cdot C_{\text{IHO}} \quad (1)$$
In Eq. 1, we consider that the computation cost for the OHO is larger than the one for IHO, regulated by a multiplicative coefficient $\beta > 1$. The exact value of $\beta$ depends on the cellular technology and the configuration of the BS. For example, in the case of 4G technology, our estimation is that the coefficient $\beta = 1.2$.

2) **Signaling cost:** This cost corresponds to all the network communications that the two BS involved in the HO process need to conduct: to request the possibility of making an HO, to inform of the decision of an HO, or to exchange with the UE for the establishment of a new link. This communication can take place on different interfaces, with the core network, with the other BS, or with the UE. The communication with the UE, taking place on radio resources, is clearly the most costly from the BS perspective. Considering this, the HO procedure discussed above indicates that an IHO requires more network communications than an OHO. This is mainly because the target BS has to go through the random access procedure to establish a connection with the arriving UE, which requires the exchange of multiple messages on the radio interface.

As for the computation cost, we can model this cost difference as follows:

$$S_{\text{IHO}} = \gamma \cdot S_{\text{OHO}} \quad (2)$$

where the relationship is controlled by the multiplicative coefficient $\gamma > 1$. For example, in the case of 4G technology, we estimate $\gamma = 2.5$.

## IV. HANDOVER MANAGEMENT FRAMEWORK

In a virtualized RAN, computation and communication resources allocated to each BS no longer need to be statically over-dimensioned. This claim is valid for all the network functions provided by the BS, whether on the data plane (header compression, data encryption) or the control plane (radio resource allocation, active queue management, mobility control). In this section, we focus on a specific network function, proposing an HO management framework based on two phases: BS profiling and dynamic resource reconfiguration, depicted in Fig. 2. Our interest in HO management comes from the importance of this function, which represents the main reason behind the existence of mobile networks and has a significant impact on user QoS.

### A. BS Handover Profiling

The underlying idea of this first step is that BS have different behaviors in terms of the HOs they serve. For example, some BS might serve more OHO than IHO, basically producing HOs, while others, on the contrary, might act as HO receivers.

To build these BS profiles, we cluster time series data representing the HO behavior of each BS. We base this clustering step on weekly time series data. Indeed, in [22], they show that using a weekly basis allows a clustering algorithm to find differences in behavior that may occur on different days. Moreover, several other studies on mobile network data [23],

<figure><img src="image_3.png" /><figcaption>Fig. 2: Profiling and network reconfiguration framework.</figcaption></figure>

[24] use a weekly basis for the time series to validate their propositions. Therefore, we build a typical week time series, similarly to [23], in order to reduce the noise that individual weeks might present. The repetitiveness in human activities can also justify the use of the typical week, as the differences are negligible from one week to another. Finally, the use of the typical week can reduce the complexity of the clustering phase, since there are fewer elements to process compared to the use of an entire dataset.

For each BS, we have HO data over a set of days $D$, covering a set of geographical areas $C$. $A_c = \{a_c|c \in C\}$ represents the set of BS in each defined geographical area. Each day $d \in D$ contains a set $T$ of time intervals. For each time interval $t \in T$, and for each BS, we have the total number of IHO and OHO experienced during $t$.

We define $IN_{a_c}(d, t)$ as the number of IHO observed at BS $a_c$ during time $t$ of day $d$. Similarly, $OUT_{a_c}(d, t)$ is the number of OHO observed at BS $a_c$ during time $t$ of day $d$. As we are trying to understand the behavior of each BS in terms of IHO and OHO, we define a new metric, as follows:

$$\text{DIFF}_{a_c}(d, t) = \text{IN}_{a_c}(d, t) - \text{OUT}_{a_c}(d, t) \quad (3)$$

Practically, $\text{DIFF}_{a_c}(d, t)$ indicates the difference between the number of IHO and OHO that a BS handles at each $t$.

To build a typical day, we divide the different days $D$ in the dataset into subsets $D^\delta$, according to the days of the week, from Monday to Sunday, where $\delta = \{MON, TUE, WED, THU, FRI, SAT, SUN\}$. We have $D^\delta \subset D$, where $D^\delta$ contains all the days for a given day of the week. For example, $D^{MON}$ contains data for all Mondays. We can define a typical day $td$ as follows:

$$\text{DIFF}_{tda_c}(\delta, t) = \mu_{\frac{1}{2}} (\{\text{DIFF}_{a_c}(d, t)|d \in D^\delta\}) \quad (4)$$

In Eq.(4), $\mu_{\frac{1}{2}}(\cdot)$ represents the median. Indeed, we use the median to construct the typical day, resulting in a typical median week. The use of the median is interesting since it allows for avoiding outliers and exceptional values [25] that may occur on a given day. For example, 8:00 am on a typical
Monday of a given BS is the median of the HO values at 8:00 am of all Mondays in the dataset.

The typical week *tw* is built by concatenation of all typical median days for a given BS *a*<sub>c</sub> :

$$
\mathrm{DIFF}_{tw_{a_c}}(t) = \|_{\delta \in \delta} \mathrm{DIFF}_{td_{a_c}}(\delta, t), \forall c \in \mathbf{C}, a \in \mathbf{A}_c \quad (5)
$$

In Eq.(5), $\|_{\delta \in \delta}$ indicates the ordered concatenation of multiple elements. Thus, we use the typical median days, starting with a typical median Monday and ending with a typical median Sunday, to build a typical median week.

We are interested in finding profiles of BS based on the shape of DIFF<sub>tw<sub>a<sub>c</sub></sub>(t). Therefore, in order to cluster the BS based on their typical weeks, we have to conduct additional processing. First, we normalize the typical median week. Indeed, there can be a big difference between the values from one BS to another. This difference can affect the clustering results since high values can induce large variations in the time series, and the opposite for lower values. Normalization allows avoiding this, by considering only the evolution of the HO metric in the typical week. For this, we use standard score normalization, or Z-score, to normalize DIFF<sub>tw<sub>a<sub>c</sub></sub>. Formally, for a geographical area c and BS a<sub>c</sub>:

$$
\mathcal{P}_{a_c}(t) = \frac{\mathrm{DIFF}_{tw_{a_c}}(t) - \mu(\mathrm{DIFF}_{tw_{a_c}})}{\sigma(\mathrm{DIFF}_{tw_{a_c}})} \quad (6)
$$

In Eq.6, $\mu(\text{DIFF}_{tw_{a_c}})$ and $\sigma(\text{DIFF}_{tw_{a_c}})$ denote, respectively, the mean and the standard deviation of the typical median week $\text{DIFF}_{tw_{a_c}}$.

Next, we use agglomerative hierarchical clustering (AHC) for BS profiling. We aim to show the existence of profiles of BS which are more producer and/or more receiver of HO. Clustering based on $\mathcal{P}_{a_c}(t)$ results in placing the BS with a similar tendency of the evolution of the HOs in the same cluster. These different profiles will be used in the resource management part of the framework. The AHC allows us to obtain, in a simple way, these great tendencies described by the profiles. Despite its simplicity, AHC is often used in the literature [26], [23], as it obtains satisfactory clusters in terms of precision, using an explainable and simple process.

In AHC, we use the Pearson coefficient as a pairwise similarity and the average linkage method. The use of the Pearson correlation coefficient as a similarity metric is coherent since we aim to build clusters based on the general shape of the time series. The Pearson correlation coefficient is a good indicator of the similarity in this case, and it has a stable performance [25].

The result of AHC is a dendrogram, where BS are iteratively brought together in larger and larger clusters. To choose the best number of clusters, we conduct a visual analysis of the different levels of the dendrogram. Automated methods also exist for this task [25] and can be easily integrated into the framework. However, in our study, we easily distinguished visually at least three different behaviors: i) receiver BS, which present a higher number of IHO throughout the day, ii)

producer BS, where the number of OHO dominates, and iii) balanced BS, with a similar number of IHO and OHO. Since the labeling operation of the clusters was conducted by visual inspection of the time series, we also selected the number of clusters at the same time.

B. Resource Reconfiguration

We consider that virtualization allows the reconfiguration of resources during the day, with an objective of economy of these resources. In this case, since dedicated equipment is no longer installed, and the RAN functioning is based on resources coming from an edge data center, the amount of reserved resources no longer needs to be over-dimensioned to accommodate possible peaks. Instead, dynamic reconfiguration of resources can be conducted periodically, every *t* time.

In our study, we considered reconfiguration window sizes ranging from 0.5 to 12 hours. The resources reserved in each window need to cover the maximum number of resources consumed during the reconfiguration window. Thus, the larger the reconfiguration window size, the closer the number of reserved resources to the current static system, and fewer resources can be saved. Without loss of generality, we consider that, in the current system and in the proposed virtualization approach, the peak HO demand for a given period can be predicted with good accuracy [27].

However, since we further want to distinguish between IHO and OHO, in order to allocate just the right amount and type of resources, we use the BS profiles computed by our framework. We denote the proportion of IHO among all the HO observed by BS $a_c$ during time interval $t$ as $P_{a_c}^I(t)$. Then, each profile $p$ is characterized by the minimum and maximum proportion of IHO during the reconfiguration window $w$ among all the BS that belong to the profile in question. Based on these two values, we can compute, for each BS in profile $p$, three different proportions during the reconfiguration window $w$: the proportion of HOs that are certainly incoming, the proportion of HOs that are certainly outgoing, and the proportion of HOs with an uncertain type. We denote as uncertain HOs (UHO) this last class. These three values are computed as follows:

$$
P_{p,w}^{\text{IHO}} = \min_{b \in p} (\{P_{a_c}^I(t)\}_{t \in w}) \quad (7)
$$

$$
P_{p,w}^{\text{OHO}} = 1 - \max_{b \in p} (\{P_{a_c}^I(t)\}_{t \in w}) \quad (8)
$$

$$
P_{p,w}^{\text{UHO}} = \max_{b \in p} (\{P_{a_c}^I(t)\}_{t \in w}) - \min_{b \in p} (\{P_{a_c}^I(t)\}_{t \in w}) \quad (9)
$$

At the beginning of each reconfiguration window *w*, a BS *b* knows its peak HO demand per time interval *t*, and it computes $P_{p,w}^{\text{IHO}}$, $P_{p,w}^{\text{OHO}}$, and $P_{p,w}^{\text{UHO}}$ based on its predetermined profile. The BS will reserve a number of resources determined by these values: $C_{\text{OHO}}$ and $S_{\text{OHO}}$ for outgoing HOs, $C_{\text{IHO}}$ and $S_{\text{IHO}}$ for incoming HOs. For the HOs that remain uncertain, the BS reserves $\max(C_{\text{OHO}}, C_{\text{IHO}})$ and $\max(S_{\text{OHO}}, S_{\text{IHO}})$.
<figure><img src="image_4.png" /><figcaption>Fig. 3: The three major BS profiles in Paris.</figcaption></figure>

### C. *Baseline Solutions*

The proposed resource reconfiguration framework requires some input information, as explained above. First, it relies on knowledge regarding the peak number of HOs that a BS will observe during a reconfiguration window $w$. We note that there are already HO forecasting solutions with good performance [27], which focus on predicting the number of HOs for every time interval $t$. In our case, we simply need a prediction of the peak value among several intervals, which is a simpler problem than that addressed in the state of the art. Therefore, we do not focus on this part in our evaluation.

The second information required by our framework is the profile of every BS. This is computed based on historical data, and we do not see it as problematic. Finally, the framework needs to know the values $min_p^w$ and $max_p^w$, the minimum and maximum proportion of IHO among all the BS of a profile $p$ during a given window $w$. As shown below, these values can be easily estimated using historical data and pre-configured for every profile $p$. Moreover, in case there is an error in the profiling process or in the computation of the values, the direct consequence is an increase in the number of uncertain HOs. This reduces the performance of the proposed framework but, as shown below, the degradation is graceful.

To evaluate the resource reconfiguration gain using our BS profiling approach, we define a number of baseline solutions:

1) **Current system:** In this approach, there is no dynamic reconfiguration of resources and no differentiation between HO types. The same level of resources is used throughout an entire day. However, we consider a very favorable set-up for the current system, where the peak HO demand during the day is known (or perfectly estimated), and no over-dimensioning is used. We note that this is the best-case scenario for the current static system.

2) **No distinction between HO types:** In this case, we consider perfect information regarding the peak number of HOs during a reconfiguration window $w$. However, the system does not make any distinction between IHO and OH.O, and it always reserves the maximum number of resources $max(C_{OH.O}, C_{IHO})$ and $max(S_{OH.O}, S_{IHO})$. We note that, in our framework, as profiling and estimation errors appear and

the proportion of UHO increases, we converge towards this approach.

3) **Perfect HO type distinction:** We consider the peak number of IHO and OH.O can be perfectly predicted for each reconfiguration window $w$. We believe this forecasting problem to be more complex than the overall HO prediction. However, this represents the optimal scenario in terms of reserved resources, because the RAN can reserve just the right amount of resources required for HO management.

## V. BS PROFILING RESULTS

To study the performance of the proposed HO management framework, we use real-world HO data from a national French mobile operator. The data covers the city of Paris and consists of the number of IHO and OH.O experienced by the 4G BS deployed in the city, aggregated over a 30-minute interval. We note that a UE can perform more than one IHO and/or OH.O in this interval. To guarantee their privacy, we do not have any information about the users. The data covers 873 BS and a one-month period, from 16 March to 16 April 2019.

Running the BS profiling solution detailed in Sec. IV on this data, we obtain three major profiles in Paris, which we denote as receiver, producer, and balanced, for reasons detailed below. The average $P_{a,c}(t)$ time series for these profiles (as defined in Eq.(6)), together with the standard variation, computed over all BS belonging to a given profile, are shown in Fig. 3.

**Receiver profile.** For the receiver profile, we notice that the average time series has positive values. We recall that this time series depicts the difference between IHO and OH.O, meaning that positive values denote a higher proportion of incoming HOs, hence the receiver label. We observe three daily peaks in the HO data. These peaks seem to correspond to periods of high mobility in the city: the first morning peak, which takes place between 6:00 and 7:00 am, has a smaller amplitude than the peak appearing between 10:00 am and 12:00 am. The afternoon peak occurs between 5:00 and 6:00 pm and is the highest one. For the weekend days, there are only two smaller peaks. While the average evolution is always positive, both on weekdays and weekends, the standard deviation values
<figure><img src="image_5.png" /><figcaption>Fig. 4: Geographical distribution of profiles.</figcaption></figure>

indicate an increased heterogeneity during weekday morning hours.

**Producer profile.** The second profile in Fig. 3(b) shows a negative average time series, which indicates a more significant proportion of OHO when compared to IHO. Therefore, we denote it as *producer* of HOs. We also observe three inverse peaks for the days of the week. However, the central peak is the most important one this time. This peak, quite clearly, occurs daily at 12:00 pm. The time of appearance of the two other peaks, in the morning and afternoon, is similar to the times observed for the receiver profile. For the weekend days, only one midday peak appears. While the average time series values are always negative throughout the week, the standard deviation once again indicates some time intervals with a more heterogeneous behavior, where some BS might even register more IHO than OHO.

**Balanced profile.** The profile in Fig. 3(c) presents a noisy time series, which varies around 0, with relatively small positive and negative values. This means that the number of IHO and OHO for these BS is similar, so we denote the profile as *balanced*.

To visualize the spatial distribution of these profiles, we represent the theoretical coverage of the cells by Voronoi projection, considering as base points the locations of the BS from our dataset. The colored dots surrounding the cells correspond to the different locations of the edge BS, represented like this for visualization purposes. Fig. 4 represents this spatial distribution. We can see in Fig. 4 that the BS with receiving profiles, in green, are often adjacent to cells with a producer profile, in blue. Concerning the balanced profile, in orange, it covers the major communication axes, some freeway portions, and major entry points in the city of Paris. In addition to these major communication axes, the balanced profile also covers the main Parisian train station. These stations are the arrival points of trains coming from all over France and from abroad but also trains from the Paris suburbs and the surrounding region. The balanced profile also covers a big proportion of metro station entrances.

# VI. DYNAMIC RESOURCE RECONFIGURATION

For the dynamic resource reconfiguration part of the framework, we rely on the three major BS profiles uncovered in Sec.V: receiver, producer, and balanced. However, we note that the proposed solution is agnostic of the precise profiles and their number. To clarify this point, we do not consider here a completely autonomous and fined-grained solution, where each BS is monitored and the demand for communication and computation resources is done on a timescale of seconds or even minutes. While such a solution would be ideal from the point of view of the used resources, the current processes for activating and reconfiguring NFVs, starting and migrating VMs, do not yet scale to this level of reconfiguration performance. Instead, we consider that a number of predefined configurations are defined and validated by the mobile operator. Each BS is reconfigured at a timescale in the order of hours, based on these predefined configurations. In our case, the predefined configurations are based on the three BS profiles discussed above.

We begin by computing the proportions of IHO, OHO, and UHO for each of the three profiles, based on Eq. (7)-(9). Fig. 5 shows the results for a network reconfiguration window of 1 hour. In this example, we observe that, for the receiver profile, we constantly reserve more IHO resources compared to OHO ones. The opposite appears for the producer profile, starting from 5:00 am onwards. For the balanced profile, the importance of the OHO and IHO proportions changes during the day: more IHOs are reserved from 5:00 am to 1:00 pm, while the OHOs are dominant afterward. For the proportion of UHO, we see that it is significant in the three profiles, especially during the night. This comes from the impact that small variations can have in time intervals when there are not many HOs.

We test different BS reconfiguration windows, to understand the impact of this parameter. As shown in Fig. 6, the proportion of UHO decreases when we increase the reconfiguration window (3 hours in this case). Indeed, as we increase the reconfiguration window, the uncertainty level regarding the minimum and maximum IHO values decreases, especially during nighttime.

By using dynamic resource reconfiguration, the proposed framework and the baseline solutions described in Sec. IV-C obtain a gain in terms of reserved resources with respect to the current static system. If we denote the total cost of the current system during a time window $w$ as $cost_c$ (since it is the same for all $w$), and the cost of a dynamic solution as $cost_d^w$, we can define the gain $G^w = 1 - (cost_d^w/cost_c)$.

In Fig. 7, we present the cumulative distribution function (CDF) of this gain, for the proposed framework and the baseline solutions. We present computation and signaling gains, as discussed in Sec. III, for different reconfiguration windows: 1 h, 3 h, and 12 h. We notice that, even without making any distinction in the type of HO, a dynamic reconfiguration reaches significant gains: 60% of the BS have a gain of at least 60% with respect to a static system, for
<figure><img src="image_6.png" /><figcaption>Fig. 5: $P_{p,w}^{IHO}$, $P_{p,w}^{OHO}$, and $P_{p,w}^{UHO}$ proportions for different profiles for a reconfiguration window of 1h.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 6: $P_{p,w}^{IHO}$, $P_{p,w}^{OHO}$, and $P_{p,w}^{UHO}$ proportions for different profiles for a reconfiguration window of 3h.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 7: CDF of computation and signaling cost gain compared to the current system with different configuration windows.</figcaption></figure>

a reconfiguration window of 1 h. This is true whether we look at computation or signaling resources. As we increase the reconfiguration window, the gains are less important, but they are still significant for some BS. For example, 10% of the BS can obtain a computation gain above 30%, even for a reconfiguration window of 12 hours (meaning that the BS configuration changes only once during a day).

Our results show that further gains can be obtained by distinguishing between IHO and OHO in the HO management process. These complementary gains are more significant for the signaling part, and they increase as the reconfiguration

window increases. We note that the results of our framework, based on BS profiling, are very close to the optimal results (in orange), where a perfect prediction of the number of IHO and OHO for each BS is implied.

A temporal representation of the computation and signaling costs is shown in Fig. 8, for a reconfiguration window of 3 h. In this figure, the cost of the current system is, of course, static during the day. Our proposed framework and the two baselines bring significant gains, especially during the night, when the computation and signaling costs are practically divided by 6, underlying the motivation behind a dynamic HO management
<figure><img src="image_9.png" /><figcaption>Fig. 8: Total computation and signaling costs during the day.</figcaption></figure>

approach.

Finally, the results shown above are obtained for two specific values for the cost parameters $\beta$ and $\gamma$, as detailed in Sec. III. However, these values ($\beta$ = 1.2 and $\gamma$ = 2.5) are estimates based on our analysis of the 4G standards. Of course, for different (existing or future) technologies, the values might be different. Therefore, in Fig. 9, we show the computation and signaling cost reduction while varying the $\beta$ and $\gamma$ coefficients. The baseline solution where we do not distinguish the HO type obtains a constant gain, of around 50%, with respect to the current static system. The gain brought by distinguishing between IHO and OHO increases as the values of $\beta$ and $\gamma$ increase but in a logarithmic way. We also note that the results obtained by our profiling framework present a bigger gap to the optimal gain as we increase the two parameters. However, we recall that the optimal gain is obtained using online, perfect predictions of IHO and OHO, while our framework only implies a one-shot profiling and configuration operation.

<figure><img src="image_10.png" /><figcaption>Fig. 9: $\beta$ and $\gamma$ coefficients variation.</figcaption></figure>

Of course, a BS performs multiple network functions in parallel, and our analysis only targets one such function. However, we outline the fact that the gains obtained for this specific network function are significant for any current or future realistic combination of parameters (reconfiguration window, $\beta$, $\gamma$). Basically, we manage to cut in half, or even better, the computation and signaling costs for the handover management function, using a simple approach based on BS profiling and a limited number of pre-computed BS configurations.

## VII. CONCLUSION

Virtualization makes the network more modular. It allows resources to be added or removed to suit mobile network requirements. This facilitates dynamic network reconfiguration and reduces operational costs for mobile operators. In this work, we demonstrate this through the implementation of a dynamic reconfiguration framework for a virtualized HO management network function, which can save 50-70% of the computation and signaling costs of the current static system. The proposed reconfiguration framework is based on profiling BS using historical HO data and obtains gains comparable to an optimal reconfiguration solution, which assumes perfect knowledge and predictions of the user activity. We hope that the significant gains we demonstrate will kickstart the exploration of the management of other virtualized BS functions, allowing us to finally break up with the de facto standard of over-dimensioning networks.

## ACKNOWLEDGEMENT

This work was partially funded by the ANR COCO5G (ANR-22-CE25-0016) project.

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