A survey of self-coordination in self-organizing network
Introduction
In the last decade, the automation of cellular network management and optimization has attracted the attention of both academic and industrial fields. This tendency has increased with the introduction of 5G, where the complexity of network management and optimization is expected to increase significantly in accordance with the requirements of 5G [1]. Handling immense volume of transmitted data including signaling messages, controlling myriad connections within small area, configuring and optimizing massive numbers of multi-vendor multi-tier network nodes, all of which should be treated by the network operator efficiently in order to provide the desired Quality of Service (QoS) according to different user demands with consistency to the operator policies. While the requirements of 5G can be provided by the underlying network infrastructure [2], ensuring the effective network operation can not only be provided by qualified engineers as in previous generations. In addition, while the 5G intends to connect anything-to-anything and works with the legacy ancestors like LTE/LTE-Advance, and other wireless networks [3], the network optimization becomes a challenging task. In order to reduce the Capital Expenditure (CAPEX) and Operational Expenditure (OPEX) associated with management tasks and at the same time enhance the efficiency of optimization tasks even with its complexity, the concept of SON has been introduced. It is defined according to the 3rd Generation Partnership Project (3GPP) as a set of functions that perform automatic configuration, optimization, fault diagnostic and healing of cellular network [4]. In general, SON relies on the principles of autonomic networking [5], and the self-x functionalities of SON are classified into three major groups standardized by 3GPP:
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Self-configuration: starts once the Base Stations (BS) are physically powered on with the capability of connection to the transport links, and provides Plug-and-Play (PnP) operation for network BSs by initial assignment of Network Control Parameters (NCP) [6].
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Self-optimization: performs continuous monitoring and performance analysis of network BSs and provides on-demand optimization of theradio NCPs on the affected BSs in order to maintain the desired quality and performance [6].
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Self-healing: provides fault management and outage compensation for network BSs. By continuous monitoring of BS status, an appropriate recovery algorithm is triggered after fault detection and diagnosis in order to maintain the service and revenue [7].
In practice, each self-x functionality is achieved by a group of use cases [8], and each use case is realized by a stand-alone SON Function (SONF) as depicted in Fig. 1. Since the earlier standardization of SON in Release 8, many SONFs have been introduced. The stand-alone SONFs, however, are not allowed to be executed arbitrarily on a live network whenever their triggering conditions are met as these SONFs may interact with each other negatively provoking a SONF conflict (according to the 3GPP definition [9]). With its negative effects, the conflict can seriously impact the network Key Performance Indicators (KPI) and the QoS and compromise the overall gain of SON [5]. In order to reduce the risk of SON conflicts, 3GPP has introduced the functionality of self-coordination in Release 10 [9]. The term of SONF coordination is defined, with respect to the 3GPP specifications [9], as the methods, techniques and algorithms used in order to avoid and resolve conflicts or negative impacts between SONFs and ensure that the execution of these functionalities is coordinated according to the operator's policies. By means of self-coordination, the conflict-free execution of self-x functionalities is maintained simultaneously with the stability of NCPs and the network optimization is performed according the operator's objectives.
In this paper, we present a survey of self- coordination, motivated by the major challenges and research keys in this field as the following:
- a)
Increased number of network parameters: with each new generation of cellular network, the number of overall NCPs related to the operational generations on the network is increased. As a result, the optimization efforts are increased qualitatively from the side of Mobile Network Operator (MNO), making the optimization processes of the overall NCPs more complex. Since the human-based optimization process is naturally time consuming and prone to error, the probability of parameter misconfiguration also increases [10]. Considering that a typical 5G node has about 2000 parameters to be optimized [11], the insurance of conflict-free optimization may not be achieved by operator's staff. When the network configuration and optimization tasks are achieved automatically by SON, the potential parametric misconfigurations are referred to as SON conflict [9]. Thus, the functionality of self-coordination should be able to anticipate the results of interacting the requested SONFs (before execution) and evaluate them according to the network state in order to avoid the well-known conflicts. In addition, the self-coordination is also responsible for resolving unexpected conflict by verifying the execution of SONFs in order to detect, diagnose and resolve any unexpected conflict and try to avoid it in the future.
- b)
Network Cell Densification: small-cells are one of the key enablers for 5G as they are used to enhance coverage and capacity especially for indoor users [12]. Usually, these kinds of cells are widely distributed and operate in a plug-and-play manner according to user demand which can quantitatively increases the configuration and optimization efforts in the densified area. In this context, three major challenges are identified. The first one is represented by the initial configuration of the operational NCPs to a massive number of small-cells in the network. Another challenge is represented by the optimization of these NCPs in densified areas, which requires fast and continuous responses in order to maintain the desired QoS and Quality of Experience (QoE). Taking into the account the difficulty of human-based intervention under the operational requirements of small-cells, the previous challenges can, to some extent, be overcome by the functionalities of self-configuration and self-optimization. However, the third challenge, which is represented by ensuring the conflict-free configuration and optimization, also needs to be handled, considering the major problems of small-cells which are represented by interference management and power consumption [12]. As a consequence, the third challenge should be addressed by self-coordination which is responsible for coordinating a large number of simultaneously executed SONFs with different time-scale within small areas in a conflict-free manner with minimum latency. In addition, the scalability issues of coordination algorithms should also be maintained in terms of Heterogeneous Network (HetNet) considering the indispensable interaction between the small-cell layer and the macro layer, as the user can be handed over between these layers, and any conflicting configuration can affect the QoS and QoE significantly.
- c)
Increased attention of QoE-based schemes: unlike the traditional optimization schemes, which are intended to optimize the NCPs according to the major network KPIs, the QoE-based optimization which considers QoE indicators (bitrate, latency, jitter, etc.) is important as the 5G is expected to deliver high QoE [13]. However, SON conflicts can defect the user QoE and compromise the optimization performance, as sometimes providing the desired QoS may not mean that the required QoE is achieved. For example, in an overloaded cell, increasing the Transmission Power (TXP) in order to provide better coverage and QoS on the cell edge can negatively impact the user QoE. Since the SON conflicts can directly affect the user QoS and QoE, the coordination mechanisms have to be aware of user QoE and anticipate the user demands which can help in resolving the conflicts in a fast and intelligent manner and maintain the user satisfaction.
- d)
Extending the Utilized Spectrum Bands: spectrum sharing and new spectrum bands utilization are useful technologies to fulfill the greedy demand of higher capacity and data rate in 5G [14]. However, the problems of interference management, congestion avoidance, high spectral efficiency in terms of these new technologies should be seriously considered in order to provide the required QoS and QoE. Without SON, it is hardly possible to overcome these problems under dynamic conditions like user mobility, changeable environment and coexistence with other wireless systems. However, the challenge here is that the traditional optimization algorithms might not be compatible for operating on these new bands, especially in case of physical units dependency. There is also an additional complexity as some subsides of these technologies are recently discovered or even need more investigation as in mm-wave. In order to provide conflict-free optimization, the coordination algorithms should also be modified and tested under the conditions of these new spectrum bands in order to provide the optimal investment of these technologies and avoid compromising the desired performance.
- e)
Multi-RAT Operation and Optimization: the coexistence of 5G with its currently operating ancestors (e.g. LTE, LTE-Advanced) makes the network optimization as an extremely delicate task, since the user can be attached to one of the provided Radio Access Technologies (RAT) according to the connection and performance requirements [15]. The problem here is not only with the qualitative or quantitative increase of optimization tasks, but also with harmonizing the optimization objectives over different RATs. In addition, the legacy SON algorithms, which are operable with specific environment, may not be compatible to operate in a unified environment [16]. The coordination function is then responsible for coordinating RAT-specific SONFs simultaneously and ensuring that the NCPs is consistently optimized in a unified manner in a way that a smooth performance and mobility can be achieved under the umbrella of these technologies.
- f)
Increase the interest of green networks and energy consumption: the requirements of 5G and its implementation considerations reproduce a challenge of sustainability and energy efficiency. Densifying the network cells based on the current architecture is energy dissipative as well as interference stimulant. Hence, for economical and environmental concerns along with operational demands, the Energy Efficiency (EE) in wireless network has attracted a significant attention in 5G. In general, the major identified solutions to EE can be divided into: Hardware solutions, Energy Harvesting, Resource allocation and Planning-based solutions [17]. While the former two solutions are conflict-free, as they are out of the optimization loop, the remaining solutions are subjected to conflicts due to the dependency between physical units, i.e., the trade-off between EE, interference and power consumption [17] which directly provoke a conflict between coverage and QoS. For example, providing a high data rate with low-latency requires a permanent activity of network resources; whereas the objective of EE is to reduce the energy losses by turning-off underutilized resources. The challenge of achieving an adequate EE in a conflict-free manner can be addressed by adopting a robust coordination scheme, where the coordinator is aware of power consumption, interference levels, cell throughput, cell resources and the required QoS in line with anticipating the user consumption. In this way, the network optimization can be done in a real-time operation without contradictions between operator's objectives, and even in the case of outage or power fluctuation.
- g)
Reduce the OPEX and misconfiguration losses: SON is essentially intended for reducing OPEX by minimizing human intervention which can decrease planning, deployment, optimization, and maintenance costs [5]. However, SON conflicts can negatively affect this fundamental characteristic since the misconfiguration of NCPs compromises the performance gain and even can lead to an outage [18, 10]. This negative effect can reduce the revenue and increase the human intervention during the monitoring of SONF performance, conflict detection and diagnosis. In this case, the self-coordination functionality should have a conflict detection and diagnosis mechanisms in order to detect unexpected conflicts and resolve the conflicting misconfiguration and avoid them in the future without human intervention. In this way reducing OPEX can be achieved and revenue losses caused by unexpected performance degradation can be avoided.
- h)
The heterogeneous architecture of 5G: the 5G architecture supports diverse Network Elements (NE) such as macro-cells, micro-cells, small-cells and relays. In addition, other technologies like Device-to-Device (D2D), Internet of Things (IoT), Internet of Vehicles (IoV), etc, should also be consolidated and managed by the same architecture [14]. Due to the limitations of current architecture, which is hardware and software restricted, dealing with heterogeneous data sources from all around the network is a real problem. For this purpose, and in order to overcome the complexity of network management, new technologies such as Network Function Virtualization (NFV), Self-Defined Network (SDN) and big data analysis can be utilized in 5G core network [19]. With these technologies, collecting and analyzing heterogenous data from different resources with low-latency can be achieved. This achievement enables more sophisticated self-coordination paradigms in order to provide unified management of network resources.
- i)
The need for reliable self-coordination in multi-vendor network: the typical mobile 5G network contains millions of NEs and billions of connected devices, which make it possible for the existence of a multi-vendor equipment within the same area. As a consequence, the problem of increasing vendor-specific NCPs makes the consistent configuration and optimization of these parameters more confusing. This can increase the probability of misconfiguration and compromise the scalability of SONFs as well as the coordination scheme. This challenge can be addressed by the functionality of self-coordination which is responsible for automatic translation of high-level operator's policy into low levels of SON policy. Then, harmonizing the optimization objectives can be achieved, and the SONFs which operate in parallel on different vendor-specific NEs can be coordinated in a conflict-free manner.
The issue of SONF coordination remains a challenge in 5G, and many 5G surveys have focused on the important role of SON in the future cellular network without going deeper into details [2, 14]. In terms of SON in cellular networks, some major contributions can be found where the functionalities of self-optimization and self-configuration have received the major attention. Aliu, et al., present the first comprehensive survey of SON in [20]. However, the coordination between SONFs has received little attention and has been discussed under the future research directions, whereas the SON conflicts are not considered. A similar contribution in terms of SON in HetNet can be found in [21]. In [22], the authors provide the first classification of five types of SON conflicts with examples, and they give an extended classification and additional examples of conflicts in [23]. In [24], the applied Machine Learning (ML) techniques in the field of SON can be found, where Valente, et al., list some of ML algorithms in self-coordination with some contributions in this field. Furthermore, they briefly point out to the role of ML techniques in future self-coordination researches. The functionality of self-healing and its methodologies are surveyed in [10] with more attention being given to the common issues with self-coordination. However, the authors do not discuss in-depth how the coordination can happen between self-healing functionalities and other SONFs. In addition, they do not provide the type of conflicts that are likely to appear in terms of self-healing. In the SOCRATES [25] and SEMAFOR projects [26], new coordination mechanisms are introduced and integrated into SON frameworks. However, the coordination problem itself is generally treated with limited use cases and applied on specific environments.
In terms of SON in 5G, we can say that the previous works are limited in at least one of the following points: (1) One limitation is that the works [24, 10, 20] discuss the methodologies, criteria, challenges and research fields in SON with less attention being paid to self-coordination. (2) The concepts of self-coordination are dissipated in literature and none of the previous works encompasses these concepts in line with the methodologies of self-coordination. Even with the contribution provided in [25, 26], however, it is immature in terms of 5G and its requirements. (3) Another limitation is that the classifications in [22] and [23] contain well-known conflicts, whereas the new types of conflicts introduced in this paper make the previous works limited in terms of 5G self-coordination. (4) In addition, to the best of our knowledge, a comprehensive survey of the state-of-the-art contributions and challenges in the field of SON self-coordination and conflict resolution has not been provided yet.
The contributions that this paper presents are summarized as the follows:
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This paper concentrates on the important role of self-coordination in current and future cellular network.
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Presents a brief historical overview of SON and its terminologies that are usually used with coordination, and explores the evolution of self-coordination in SON.
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Provides a tutorial on SON conflicts and its classification as an extension to recently provided works.
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Provides seven new types of SONF conflicts from current literature to be considered in 5G self-coordination works.
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Organizes the coordination logics in current literature into three main categories, i.e., protective, reactive and proactive, and puts them in a framework.
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This paper also provides a comprehensive review of state-of-the-art contributions in the field of SONF coordination.
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Identifies major coordination challenges to be addressed in future researches.
The remainder of the paper is organized as follows:
In Section 2, and in order to simplify the comprehension of coordination issues, we present a brief historical overview and background on the most important SON terminologies that are frequently used in the field of self-coordination. The coordination logics are presented in Section 3, and we put them in a framework and discuss the implementation issues of the coordination function. In Section 4, we provide a tutorial on SON conflicts based on the previous works on the classification of SON conflicts, and mention additional new types of conflicts. In Section 5, we present a review of general methodologies used in the field of conflict avoidance and resolution. The surveys of SON self-coordination proposals in the fields of self-configuration, self-optimization and self-healing are provided in Section 6, Section 7 and Section 8 respectively. Then, we point out major challenges and open issues that should be considered in the future of self-coordination researches in Section 9. Finally, we conclude our survey in Section 10.
A summary of the paper structure and organization is presented in Fig. 2, and a list of acronyms used in this paper is provided in Table 1.
Section snippets
A brief historical overview of SON
In the earlier deployment of 2nd Generation (2G), the expertise engineers carried out network operational tasks such as planning, configuration, optimization, performance analysis and maintenance. Since the mobile network was consisted of tractable number of BSs, the optimization tasks were handled manually and the performance was ensured by Drive Tests (DT), which took a long time (several days to several weeks) due to the limitation of data analysis and diagnostic tools [27]. The increased
Protective coordination logic
The proactive coordination logic is dedicated for avoiding the well-known conflicts at design-time before the SONFs are deployed. For this purpose, the coordination rules are developed in order to guide the coordination process during the run-time operation [48]. These rules, which must be in consistence with the operator's policy, describe the essential coordination constrains e.g., the allowed interactions between SONFs, default priorities, time guards, parameter configuration restrictions
Characterization of SON function conflicts
SON in mobile network relies on autonomic networking concepts. It tries to mimic the self-x properties of humans through its functionalities [73]. A simple analogy between the resolution of a SON conflict and the treatment of a human disease shows that the start must be from the diagnosis of the problem, then an appropriate remedy should be given accordingly. The analogy also shows that there may be interfering symptoms not only of human diseases, but also of SON conflicts. As a consequence,
The applied methodologies in self-coordination
In this section, we explore the proposed solutions in the field of SON self-coordination from starting from thier earlier foundation, going through their improvements. For the sake of clarity and simplicity, we have categorized these solutions according to the main concept or methodology used in each. The difference between these solutions and sometimes their contradictions refer to many factors such as, the fuzziness nature of conflict, lack of standardization, available network data to be
State-of-the-art self-coordination approaches in self-configuration
The functionality of self-configuration is responsible for enabling the new NEs to join the network successfully in a plug-and-play (PnP) manner by an automatic assignment of their NCPs [15]. This assignment, in general, is done in three essential stages as follows:
- •
At the first stage, the operational NCPs of BS are configured such as the IP address, access GateWay (aGW), Cell IDentity (CID), and PCI [155].
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At the second stage, the cell neighbors are discovered and the NCL table is created
State-of-the-art self-coordination approaches in self-optimization
The self-optimization is realized by a group of functionalities that continuously optimize the radio NCPs. These functionalities according to the 3GPP defined use cases are: MLB, MRO, EE, CCO and ICIC\eICIC. Table 4 shows the NCPs that are under the control of each functionalities. As discussed in Section 4, many reasons put these functionalities in conflict with each other. This means that the coordination between these SONFs is mandatory and not a luxury. In this section, we explore the
State-of-the-art self-coordination approaches in self-healing
Self-healing in cellular networks is considered with outage detection and compensation and realized by three functions: outage detection, outage diagnosis and COC [7, 241]. Once the outage is detected, the root cause should be diagnosed correctly in order to request the most appropriate recovery algorithms by the COC. Since the recovery action may include a modification on NCPs, it is prone to potential conflicts which means that the compensation actions should get the SONFC approval before the
Challenges and future research directions
So far, we have surveyed the available literature on self-coordination in cellular networks. In order for the SON to provide a conflict-free configuration, optimization and healing under the requirements of future networks, many challenges still need further research attention in the field of self-coordination. In this section, we discuss these challenges along with future research directions.
Conclusion
Cellular communication networks are undergoing continuous evolutions wherein the human-based operational tasks, in terms of configuration, optimization and maintenance, have been gradually delivered to the SON which promises to provide an advanced and cost-effective optimization in 5G. As the SON in its nature relies on individual functions to perform interrelated tasks, the self-coordination plays a pivotal role in providing the consistent and conflict-free execution for these SONFs. The
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Adnan BAYAZEED, Engineering Degree in Electronic and Telecommunication from Damascus University, Damascus, Syria, 2014.
Master Degree in Telecommunication Networks from the Higher Institute of Applied Sciences and Technology (HIAST), Damascus, Syria, 2017.
Network Management Center (NMC) Engineer from 2016 to 2018 and Packet Switching (PS) Core Engineer from 2019 to 2020 at MTN_Syria for mobile telecommunication.
Now PhD student at the Higher Institute of Applied Sciences and Technology (HIAST),
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Adnan BAYAZEED, Engineering Degree in Electronic and Telecommunication from Damascus University, Damascus, Syria, 2014.
Master Degree in Telecommunication Networks from the Higher Institute of Applied Sciences and Technology (HIAST), Damascus, Syria, 2017.
Network Management Center (NMC) Engineer from 2016 to 2018 and Packet Switching (PS) Core Engineer from 2019 to 2020 at MTN_Syria for mobile telecommunication.
Now PhD student at the Higher Institute of Applied Sciences and Technology (HIAST), Damascus, Syria
Khaldoun I. Khorzom was born in 1959, Damascus, Syria.
He received his PhD degree in Computer Science (fault tolerant on-board computers), in 2000 from Moscow State Technical University (MSTU) n.a.m.e. Bauman, Moscow, Russia, and a Master degree in Informatics in 1990 from Nancy Informatics Research Center, Nancy, France.
He is currently a senior researcher in the communications department of the Higher institute for applied sciences and technology.
He authored and co-authored several papers in local and international journals and conferences.
His major research interests include: Wireless networks, cellular Networks and Internet of Things, Wireless Sensors Networks and Ad Hoc routing algorithms, Networks Infrastructure, SDN Networks, Software and hardware reliability.
Mohamad ALJNIDI, Engineering Degree in Computer Science from the Higher Institute of Applied Sciences and Technology (HIAST), Damascus, Syria, 1996.
Master Degree in Computer Networks from Pierre et Marie Curie University (Paris VI), Paris, France, 2005.
PhD in Computer Network Security from TELECOM Paris (ENST: Ecole Nationale Superieurs des Telecommunications), Paris, France, 2009.
Specialized in Information System Security and Network Security.
Associate professor at the Higher Institute of Applied Sciences and Technology (HIAST), the Arab Academy for E-Business (ARAEB), and the Syrian Virtual University (SVU).
Research, teaching and consultancy in a number of institutions in academia and industry in a number of domains: Computer Networks, Information and Network Security, Autonomic Communications, Software Defined Networks, Network Functions Virtualization, Internet of Things, Software Defined Data Centers and Cloud Computing.
Director of Information Technology Engineering program at the Syrian Virtual University (SVU).
Member of the administrative committee in the Syrian Computer Society (SCS).
Member of the administration board of Syrian Telecommunication (ST) Company.
Member of the administration board of the Syrian National Agency of Network Services (NANS).