In future, more devices such as wearable devices will be connected to the networks. This will increase simultaneous handovers. The coverage of a cell will be small because a superhigh frequency used in 5G wireless networks does not propagate very far. This trend will increase the number of neighbour cell lists and it will accelerate the change of neighbour cell lists since the coverage of cells can be altered by the environment. Meanwhile, the ANR technology will be essential in 5G networks. Since the network environment in the future is not similar to the present, the strategy of ANR should also be different from the present. First, since practical neighbour cell lists in each cell are changed frequently and individually, it is necessary to optimize them frequently and individually. Second, since the neighbour cell lists in each cell are not changed similarly, it is necessary to operate ANR flexibly. To respond to these issues, we propose to use network function virtualization (NFV) for ANR. To evaluate the proposed strategies, we measured additional resource consumption and the latency of handover if neighbour cell lists are not optimized when UEs perform handover simultaneously. These experiments are conducted using Amarisoft LTE-100 Platform.
In the IoT era, the mass use of devices such as wearable devices and wireless sensors in mobility (e.g., vehicle, personal mobility, and watch) will be connected to the networks for convenience. This will cause an increase of simultaneous handover. Thus, the handover performance will become more important for ceaseless connection and QoS. In particular, if there will be an urgent situation to receive data from networks, the handover performance will be critical. Moreover, in the 5G era, the coverage area of cells will become smaller because a superhigh frequency will be used for high throughput. The superhigh frequency cannot propagate very far. Hence, more cells are necessary to cover the same area (i.e., massive small cells). These features will increase the number of neighbour cell lists, and it will accelerate the change of neighbour cell lists because the coverage of cells can be altered by the environment, which includes reflection, diffraction, and shadowing effects. In addition, these small cells will also increase the simultaneous handover because the boundary among cells will increase. Moreover, nowadays, moving cells and small cells are usually used for data traffic rushes and radio shadow areas [
Meanwhile, it is necessary to inquire assumption that an increase of neighbour cell lists will accelerate the change of neighbour cell lists. First, practical neighbour cell lists, according to the strength of the signal from other cells, are not fixed. This means that a strong signal from far-off cells can be received in the serving cell, and it propagates sufficiently far because radio signal strength is affected by environmental factors such as temperature and humidity [
Due to massive small cells in 5G networks, automatic neighbour relation (ANR) technology, which automatically detects and configures neighbour cells, will be essential and become more important in 5G networks because manual configuration and optimization of neighbour cell lists in each individual small cell will become more costly and difficult. ANR detects a new cell’s information including physical cell ID (PCI), E-UTRAN cell global identifier (ECGI), and the IP address from operations, administration, and maintenance (OAM) in order to execute handover to a new cell when a serving cell does not know the new cell and the serving cell receives the strong signal of the new cell. This handover method is called UE-Triggered ANR with OAM Support [
Finally, since the network environment in the future (i.e., an increase of simultaneous handovers and a frequent change of neighbour cell lists) will not be the same as the present, the strategy of ANR should also be changed as we needed a new strategy of self-organizing networks in the past due to enterprise femtocells [
For these strategies, we propose using network function virtualization (NFV) [
The remainder of this paper is organized as follows. Section
Operating radio networks is a challenging task, especially in cellular mobile communication systems due to their latent complexity. This complexity arises from the number of network elements and interconnections between their configurations. In a heterogeneous network, it is difficult to handle the variety of technologies and their precise operational paradigms. Today, planning and optimization tools are typically semiautomated and management tasks need to be tightly supervised by human operators. This manual effort by the human operator is time-consuming, expensive, and error-prone and requires a high degree of expertise. SON can be used to reduce operating costs by reducing tasks at hand and to protect proceeds by minimising human error. The subsection below details SON taxonomies.
Configuration of base stations (eNBs), relay stations (RS), and femtocells is required during deployment, extension, and upgrade of network terminals. Configurations may also be needed when there is a change in the system, such as the failure of a node or a drop in network performance. In future systems, the conventional process of manual configuration needs to be replaced with self-configuration. It is predictable that nodes in future cellular networks should be able to self-configure all of their initial parameters including IP addresses, neighbour list, and radio access parameters.
After the initial self-configuration phase, it is significant to continuously optimize system parameters to ensure efficient performance of the system if all its optimization objectives are to be maintained. Optimization in legacy systems can be done through periodic drive tests or analysis from log reports generated from network operating centers. Self-optimization includes load balancing, interference control, coverage extension, and capacity optimization.
Wireless cellular systems are prone to faults and failures because of component malfunctions or natural disasters. In traditional systems, failures are mainly detected by the centralized O&M (Operation and Maintenance) software. Events are recorded and necessary alarms are set off. When alarms cannot be cleared remotely, radio network engineers are usually mobilized and sent to cell sites. This process could take days or even weeks before the system returns to normal operation. In future self-organized cellular systems, this process needs to be improved by consolidating the self-healing functionality. Self-healing is a process that relates the remote detection, diagnosis, and triggering of compensation or recovery actions to blunt the effect of faults in the network’s equipment.
The coverage of cells is limited because the cell cannot emit radio frequency with unlimited power, so there are many cells for covering a wide area. If one mobile phone moves from the coverage area of one cell to that of another, it would connect to the new cell and disconnect from the old cell. This procedure is called handover. In LTE radio access network, the cell consists only of eNodeB which communicates with each other directly via the X2 interface. Over this X2 interface, neighbouring eNodeBs communicate with each other to prepare and execute handovers. In order to provide seamless mobility in LTE, it is important to set up the X2 interface without omission because there will be no handover between neighbouring eNodeBs unless the X2 interface is set up and functioning.
The X2 interface is set up by using the neighbour cell lists in the Neighbour Relation Tables (NRT) of each eNodeB, so there is neither X2 interface nor handover between neighbour cells if one eNodeB omits the neighbour cell lists in its own NRT, which is caused by a moving cell or a newly added small cell. In this case, Automatic neighbour Relation (ANR) functionality can detect the new neighbour cell and add its list to the NRT automatically.
Service provision within the telecommunications industry has traditionally been based on network operators providing physical proprietary devices and equipment for each function. These dedicated requirements for high quality, stability, and stringent protocol adherence have led to long product cycles, very low service agility, and heavy dependence on specialized hardware.
However, the requirements by users for more diverse and new (short-lived) services with high data rates continue to increase. Therefore, Telecommunication Service Providers (TSPs) must correspondingly and continuously purchase, store, and operate new physical equipment. All these factors lead to high CAPEX and OPEX for TSPs. Moreover, the resulting increase in capital and operational costs cannot result in higher subscription fees, so TSPs have been forced to find ways of building more dynamic and service-aware networks with the objective of reducing product cycles, operating, and capital expenses and improving service agility.
NFV [
With this, NFV allows TSPs to get more flexibility to further open up their network capabilities and services to users and other services and the ability to deploy or support new network services faster and cheaper so as to realize better service agility. To achieve these benefits, NFV paves the way for a number of differences in the way network service provisioning is realized in comparison to current practice.
A basic architecture of LTE networks without NFV shows that the UE is connected to the Evolved Packet Core (EPC) over the LTE access network (E-UTRAN), in which the eNodeB is the base station for LTE radio. The EPC is made up of the Serving Gateway (S-GW), the Packet Data Network (PDN), the Gateway (P-GW), the Mobility Management Entity (MME), and the Policy and Charging Rules Function (PCRF). All these functions are based on dedicated equipment.
In virtualized cellular infrastructure shown in Figure
The architecture of virtualized cellular infrastructure.
The Amarisoft LTE-100 platform is a software-based LTE station running on a PC. Like a virtualized cellular infrastructure, the Amarisoft LTE-100 platform provides LTE Enhanced Packet Core (EPC) and base station (eNB) on each PC. The EPC includes Mobility Management Entity (MME) with built-in Packet Gateway (P-GW), Serving Gateway (S-GW), and Home Subscriber Servers (HSS). The radio interface in the software-based LTE solution was handled by Ettus Research USRP N210. The antenna configuration used in the experiments was a Single-Input Single-Output (SISO) scheme.
The PHY layer complies with LTE release 13 and supports closed-loop power control, and the protocol layer also complies with LTE release 13 and implements the MAC, RLC, PDCP, and RRC layers. Also, it supports intra-eNodeB, S1, or X2 handovers. For network interface, it supports standard S1AP and GTP-U interfaces to the Core Network and the X2AP interface between eNodeBs.
Like Figure
Amari LTE 100 platform configuration.
Since small cells and macrocells are mixed in the transition period to 5G networks and the cell coverage is affected by the surrounding environment, neighbour cell lists will not be fixed in practice and will not be small in number; that is, the number of neighbour cell lists in the NRT of each eNodeB can be increased over the number of physically adjacent cells or can be decreased due to the environmental effects such as shadowing and fading. Furthermore, since the simultaneous handovers will increase due to IoT and massive small cells, it is expected that the handover performance will be more important and has hazard issues. Thus, it is necessary to optimize the NRT (i.e., neighbour cell lists) because redundant neighbour cell lists could be burdensome to perform handover. In order to estimate the performance issues of ANR in 5G, we performed several experiments.
In these experiments, the Amarisoft LTE-100 Platform [
For executing handover, the UEs are at a similar distance from two cells and perform handover simultaneously by decreasing the power of the serving cells with command at the same time. In order words, UEs do not move from serving cell to target cell; they are just fixed in the same position with light of sight (LOS). Other power adaptation is not considered. This forces the UEs to have the same handover condition (e.g., the power of the serving cell is less than that of neighbour cell). We increase the number of UEs that perform handover simultaneously (e.g., 2, 3, and 4 UEs), and all these handover experiments are executed over 10 times. During handover, to measure CPU usage, the Linux “top” command is used to measure the CPU usage of the eNodeB program during handover. This “top” command measures CPU usage per second which makes it possible to evaluate the fluctuation of CPU usage for the running the eNodeB program.
For evaluating handover latency, the log information is used. For that, the handover procedure is necessary. Authors in [
Handover procedure.
With the handover procedure log, we measure each UE’s handover latency, which means the period of each UE’s handover when all UEs complete handover simultaneously. We also measure total handover latency, which means the period from the start time of the first UE’s handover to the end time of the last UE’s handover when all UEs complete handover simultaneously.
In Table
During handover, the average fluctuation of target eNodeB’s CPU usage (%) with respect to the number of neighbour cell lists.
Number of neighbour cell lists | Average fluctuation of CPU usage [%] | SEM [%] |
---|---|---|
1 | 0.6125 | 0.280 |
11 | 1.1 | 0.558 |
Meanwhile, if a UE receives packets during handover, the CPU usage for download is transferred from serving cell to target cell. Figure
Transition of CPU usage [%] during handover from node 1 to node 2.
Table
Average handover latency with respect to the number of neighbour cell lists when 1 UE performs handover.
Number of neighbour cell lists | Average latency [msec] | SEM [msec] |
---|---|---|
1 | 72.8 | 2.4 |
11 | 73.3 | 1.9 |
21 | 77.2 | 2.1 |
Table
Each UE’s average handover latency with respect to the number of neighbour cell lists when 2 UEs, 3 UEs, or 4 UEs perform handover simultaneously.
Number of neighbour cell lists | Average latency of handover [msec] | SEM [msec] | |
---|---|---|---|
2 UEs | 11 | 66.1 | 1.3 |
21 | 83 | 3.5 | |
3 UEs | 11 | 68.9 | 1.2 |
21 | 74.3 | 1.6 | |
4 UEs | 11 | 79.8 | 1.5 |
21 | 79.3 | 1.4 |
Each UE’s handover latency and total handover latency when UEs perform handover simultaneously.
Table
Average total handover latency with respect to the number of neighbour cell lists when 2, 3, or 4 UEs perform handover simultaneously.
Number of neighbour cell lists | Average total latency [msec] | SEM [msec] | |
---|---|---|---|
2 UEs | 11 | 324.3 | 79.9 |
21 | 357.2 | 152.2 | |
3 UEs | 11 | 385.4 | 49 |
21 | 529.4 | 32.7 | |
4 UEs | 11 | 513.2 | 25.9 |
21 | 621.6 | 22.6 |
Average delay until other UEs start handover since the start of the 1st handover.
Number of neighbour cell lists | Average delay [msec] | SEM [msec] | |
---|---|---|---|
2nd UE | 11 | 28.2 | 6.3 |
21 | 31.7 | 6.4 | |
3rd UE | 11 | 137.3 | 10.1 |
21 | 134.2 | 25.9 | |
4th UE | 11 | 433.6 | 26.7 |
21 | 506.6 | 23.5 |
As shown in Table
Therefore, it seems that the average of total handover latency will increase considerably when the number of the neighbour cell lists will be large and these will be not optimized in 5G networks and the number of UEs that perform simultaneous handover will increase dramatically due to IoT and massive small cells. As a result, some UEs will have a delayed handover. This will result in the degradation of QoS.
As we mentioned, it will be essential to optimize neighbour cell lists in 5G networks. Although recent ANR technology includes the algorithms of optimization of neighbour cell lists and these algorithms are researched continuously, these algorithms are limited (e.g., overreached scenario) and are not sufficient to consider the future network conditions (i.e., an increase of simultaneous handovers and a frequent change of neighbour cell lists due to a natural phenomenon). In this paper, although there is not the algorithm to solve these issues, it is certain that more resources are needed to consider all of them.
In addition, it is also necessary to operate ANR technology flexibly. In some cells, since neighbour cell lists are frequently modified, it is necessary to extend the capacity of the ANR function to quickly optimize neighbour cell lists. Also, it is necessary to diminish the capacity of the ANR function in order to save resources when neighbour cell lists are rarely changed. For this flexible operation of ANR technology, we propose to use NFV for operating ANR. In this case, ANR is an important function for self-optimization as well as self- configuration. Like distributed SON [
Figure
Conceptual diagram of proposed ANR model [
For ANR-VNF, it seems burdensome to deploy typical VMs (virtual machine). Thus, it is necessary to use a Linux container (e.g., Docker) to use ANR-VNF portably [
In 5G networks, the network environment will change in several cases. Small cells and moving cells will increase due to the frequency property and data offload. These changes will cause neighbour cell lists to be modified more frequently as we mentioned. In addition, since the coverage of cells will become small and many devices will be connected to networks due to IoT, there is no doubt that the number of simultaneous handovers will rise.
These changes will degrade handover performance if neighbour cell lists are not optimized frequently and individually, and this optimization will need more resources. The hazard from these changes is proved through several experiments. Therefore, we propose a new strategy of ANR (i.e., ANR-VNF) by using NFV to overcome this hazard. This strategy can make ANR able to respond to the change of network environments flexibly and efficiently for resource management and handover performance. In future work, we will focus on the algorithm to solve these issues and compare it with any other algorithm. In addition, it is necessary to implement and operate ANR-VNF in each eNodeB.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the “ICT Consilience Creative Program” (IITP-R0346-16-1008), supervised by the IITP (Institute for Information & communications Technology Promotion).