Mobile networks are managed by means of operations support systems (OSS) which facilitate performance, fault, and configuration management. Network complexity is increasing due to the heterogeneity of cell types, devices, and applications. Characterization and configuration of networks optimally in such a scenario is challenging task. This paper introduces an experimental platform that combines statistical relational learning and semantic technologies by integrating a mobile network simulator, Markov Logic Network model (MLN) and an OWL 2 ontology into a runtime environment tool. Our experiments, based on a prototype implementation, indicate that the combination of an ontology and MLN model can be utilized in network status characterization, optimization and visualization.
Mobile networks have become a crucial part of our society, and yet their significance
will increase in the future, as the number of users, devices, and applications are
expected to drastically increase [
Networks need to be configured optimally to provide customers a high service
quality with low operational expenses. In legacy systems, network configurations have been
handled manually, but due to the increasing complexity of networks, more automation
is needed from Operations and Support Systems (OSS) [
Machine Learning (ML) is expected to increase the level of automation in the OSS field by, for example, analyzing traffic patterns and cell-related data to learn statistical correlations. The output of an ML system can be characterized as hypothetical in contrast to deterministic results of a traditional rule-based system. In the long run, it has been argued that knowledge models would bring benefits as a basis of future telecommunication systems both in view of systems design and also from the perspective of value networks [18].
In this paper we propose a new approach to automated mobile network management by using statistical relational learning with a Markov Logic Network model (MLN) [20] for handling uncertainty in mobile network analysis. In addition to the MLN model, we propose an OWL 2 ontology above it to provide global meaning and description logic (DL) reasoning capabilities to the system.
The paper is divided as follows: after presenting a short view of our system, section 3 briefly presents the MLN and how it is applied to our implementation. After this, section 4 describes our OWL 2 model and section 5 presents dataset statistics and how MLN reasoning results can be examined via an RDF-based GUI. Finally, section 6 discusses related work and section 7 concludes the paper and presents ideas for future work. 2
The novel idea of our system is to combine modeling of uncertainty and semantics in an automated OSS system. The system contains a mobile network (LTE) simulator, an MLN model for statistical reasoning, an OWL 2 model for semantic modeling and a GUI for representing and interacting with the system. We simulate a small urban area with 5000 citizens (network users) and with 32 cells.
The interface between the LTE simulator and the MLN model contains OSS management activities, such as reading performance data from the simulator and sending configuration data back to it. The performance data contains key performance indicators (KPIs) for various measurement cases. KPIs utilized in the MLN model are channel quality indicator (CQI) for measuring the signal quality of a cell and radio link failures (RLF) for measuring the amount of connection failures per cell. The configuration management contains changes in the transmission power (TXP) and angle (remote electrical tilt, RET) of a cell antenna.
The MLN model processes the performance data into evidence that is used in the MLN reasoning. The reasoner infers posterior marginal probabilities for potential network configuration changes. The model parameters of the MLN reasoner can be fitted based on historical performance data and executed configuration actions. The OWL 2 model is constructed by transforming the MLN reasoner’s model into a SHIF OWL 2 DL ontology that can be utilized with a Pellet reasoner1. In addition to DL reasoning capabilities, the OWL 2 ontology is published as an RDF graph in a SPARQL endpoint. An operator interface for managing the system and the underlying mobile network is implemented on top of the SPARQL endpoint in HTML5. 3
This section briefly introduces the Markov logic [20] by giving a definition of the MLN, explaining the inference of marginal probabilities, and describing how the MLN is adapted to our OSS system.
3.1 MLNs allow uncertain and contradictory knowledge in a first-order logic (FOL) model by introducing a weight parameter for each formula in the FOL knowledge base. The weighted set of formulas defines a template for a Markov network, in which the cliques and clique potentials are determined by the formulas and formula weights. Definition 1. A Markov logic network L is a set of pairs (Fi; wi), where Fi is a firstorder formula and wi is a real-valued weight parameter. Together with a set C of constant terms, over which the formulas in L are applied, it defines a Markov network ML;C with a binary variable for each possible grounding of each predicate appearing in L and a feature for each possible grounding of each formula in L. The value of the feature corresponding to a grounding of formula Fi is 1 if the ground formula is true, and 0 otherwise. The weight of the feature is wi, the weight associated with Fi in L. Each state of the variables in a Markov network ML;C represents a possible world, i.e., a truth assignment for each of the ground atoms for (L; C). The probability distribution over possible worlds x 2 X specified by ML;C is
P (X = x) = where ni(x) is the number of true groundings of Fi in x and Z is a partition function given by Z = Px2X exp (Pi wini(x)). Intuitively this means that the weights of the true ground formulas give the logarithmized factors of the distribution function. If two worlds differ only on a single ground formula, then the weight of the formula gives the logarithmic odds of choosing one world over the other.
A typical inference task is to infer the most likely state or a marginal distribution of some subset of the variables using the values of all other variables as evidence. In practice, exact inference over an MLN model is infeasible. Richardson and Domingos [20] introduce an efficient stochastic algorithm for this problem. 3.3
– Context predicates indicate the current status of the network and the environment.
A context predicate can indicate, for example, that some KPI value for a cell is currently below the acceptable level or that two cells are neighbors in the network topology. – Objective predicates indicate required changes to KPI values to achieve performance targets defined by the operator. For example, an objective predicate can indicate that a particular KPI value for some cell is too low and needs to be increased. – Action predicates indicate changes to network configuration parameter values. Each predicate represents an attribute of a cell in the network or a relation among the cells. The domain of a predicate can be either the set of cells X or an n-ary Cartesian product of X.
The MLN model is composed of rules with these predicates. We wish the rules to describe a correlation between a set of Objectives and a set of Actions in a certain Context. A typical inference task is to query for appropriate actions using the current context data and objective requirements as evidence. Therefore, the rule format is defined as
C(x) )
O(x) , A(x) : Example 1. Let L be a simple MLN model consisting of the weighted rules defined in Table 1. Here variables c and d denote a cell in the mobile network. Suppose that we have a mobile network with two neighbor cells named C1 and C2 and that we measured a low CQI value for cell C1 and high RLF value for C2. We would like to use this information to infer proper configuration actions to get the CQI and RLF values to a normal level. We use the MLN reasoner to query the MLN model L for marginal probability distributions for action proposals IncT xp(c), DecT xp(c), IncRet(c), and DecRet(c) for each cell c given the evidence:
An example of the reasoning output is shown in Table 2, which shows inferred marginal probabilities for cell configurations given the model L and the evidence E. The output indicates that decreasing RET for cell C1 and increasing TXP for cell C2 are the most likely actions to achieve the objectives according to the model.
Action P (ActionjL; E) Action P (ActionjL; E) IncT xp(C1) 0:32 IncRet(C1) 0:27 DecT xp(C1) 0:36 DecRet(C1) 0:50 IncT xp(C2) 0:56 IncRet(C2) 0:31 DecT xp(C2) 0:24 DecRet(C2) 0:37
Table 2. Marginal probabilities for cell configurations
The OWL 22 ontology generalizes (with URIs and semantic annotation) terms and variables used in the MLN reasoning into a semantic model. Logically, the ontology consists of two subontologies: the OSS and MLN ontology. A semantic mapping between the OSS and MLN ontologies will enhance the interoperability between the heterogeneous mobile network environment and the MLN rule base extracted from it. 4.1
The OSS ontology describes network context retrieved from the LTE simulator and used by the MLN reasoner as evidence. Figure 1 briefly depicts the TBox structure of the OSS ontology. The most fundamental class in the model is Cell which defines 1) network topology with a cellHasNeighbor relation to other Cell instances, 2) configuration parameters with a cellHasParameter relation to Parameter instances, and 3) performance measurements with a cellHasIndicator relation to Indicator instances. The current version of the OSS ontology models the MLN evidence rather than network context generally. Thus, the only subclasses for Parameter are Txp and Ret and for Indicator, Rlf and Cqi. The Parameter and Indicator instances can be related to events by the property cellHasEvent, such as configurations inferred by the MLN model. Events are instances of the class Event where the value of the property eventHasImpact is an instance of Impact (Decrease or Increase) with a numerical value indicating the current and previous values.
The MLN ontology can be seen as an OWL 2 interpretation of the MLN evidence and action proposals that are semantically bound to mobile network concepts. Figure 2 depicts a TBox model of an MLN rule. The MLNRule class defines a rule and it has a numerical value ruleWeight defining its weight and relations to rule parts MLNContext, MLNObjective, and MLNAction. The figure also shows that the rule parts are bound to network classes Parameter, Indicator, and Impact. For example, an MLNAction class has a relation to a subclass of Parameter (e.g. Txp) and to a subclass of Impact (e.g. Increase). Similarly, MLNObjective have relations to subclasses of Indicator and Impact and MLNContext have relations to a subclass of Indicator and to a crisp value of an indicator (High, Medium, or Low).
Figure 3 shows how an ActionProposal class is modeled in the MLN ontology. Cell has a relation to ActionProposal whose content is defined with relations to an Impact and Parameter (for example Increase and Txp). Moreover, a data property hasActionProbability defines the marginal probability of the ActionProposal.
The model presented above has been implemented and tested using the LTE simulator. This section presents evaluation of our system by analyzing sizes of the MLN and OWL model and then showing a visualization of the reasoning outcome. 5.1
We have built an HTML5 application [
Reasoning under uncertainty has in the past been performed with a variety of methods,
including Markov Networks [11], Bayesian Networks [14], as well as probabilistic
extensions to description logic ontologies such as to OWL [
In mobile network research, ontologies have been used to model general concepts of the telecommunication field [17] as well as to model context in mobile network management [21][24]. The Linked Open Data (LOD)3 paradigm has also been addressed in [22], where cells and terminals are modeled and combined with other data sources, for example, with event data. However, there exists no research of using ontologies and statistical reasoning together to analyze and configure the mobile network, as in this paper.
In other problem domains there have been experiments of combining an ontological
approach with statistical relational learning. For example, Bayesian Networks and their
relational extensions, such as multi-entity Bayesian Networks (MEBN), have been
applied with OWL ontologies in order to infer probabilistic results from a certain domain
[
MLNs have been applied with semantic technologies mainly in problem domains
for ontology matching [13] and for natural language processing, in which ontological
concepts are extracted from text [
We have generated a mapping from our MLN model into a consistent SHIF OWL 2 DL ontology. The OWL 2 model is currently utilized only as an RDF graph by using SPARQL queries (faceted navigation) in the HTML5 GUI. Advanced DL reasoning tasks have not yet been implemented.
The next step in this project is to enhance the OWL-MLN interaction so that MLN model settings can be dynamically modified by a human or a DL reasoner (Pellet). Model settings include selection of measurement variables, their threshold values and rules to be generated from the set of variables. Model settings could even include some
initial rule weights with respect to prior knowledge. Moreover, our system will be enhanced by creating high-level goals which the user can use to modify the behaviour of the reasoning system. For example, high-level goals could be mapped to corresponding MLN model settings.
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