Today's WLANs rely on a centralized Access Controller (AC) entity for managing distributed wireless Access Points (APs) to which user devices connect. The availability of real-time analytics at the AC opens the possibility to automate the allocation of scarce radio resources, continuously adapting to changes in traffic demands. Often, the allocation problem is formulated in terms of weighted graph coloring, which is NP-hard, and custom heuristics are used to find satisfactory solutions. In this paper, we contrast solutions that are based on (and even improve) state of the art heuristics to a data-driven solution that leverages Deep Reinforcement Learning (DRL). Based on both simulation results as well as experiments in a real deployment, we show that our DRL-based scheme not only learns to solve the complex combinatorial problem in bounded time, outperforming heuristics, but it also exhibits appealing generalization properties, e.g. to different network sizes and densities.

Optimization of mobile networks is a crucial task for operators aiming to offer a high quality of service. In this paper, we focus on the handover (HO) problem. An HO is the procedure through which a user changes its serving cell, ensuring service continuity for mobile users. The HO procedure is typically governed by several variables: the received signals powers and the HO parameters (offsets and hysteresis) corresponding to the different cells. HO parameters allow to modify the cell borders in order to balance loads, and prevent radio link failures and HO ping-pongs. There are two approaches for defining HO parameters: one is to use per-cell parameters and the other is to use per-cell-pair parameters. Per-cell-pair parameters provide more degrees of freedom in HO parameter optimization as we benefit from independent parameters for each neighbour. However, we show that it can lead to instabilities in the form of continuous chained HO, i.e., several HOs that follow one shortly after another, among three or more cells. Then, we show that using the same HO parameters toward all neighbouring cells, i.e., per-cell parameters, is risk free of such problems. We provide numerical results outlining the tendencies of this risk.

An important problem of today's mobile network operators is to bring down the capital expenditures and operational expenditures. One strategy is to automate the parameter tuning on the small cells through the so-called self-organizing network (SON) functionalities, such as cell range expansion, mobility robustness optimization, or enhanced Inter-Cell Interference Coordination. Having several of these functionalities in the network will surely create conflicts, as, for example, they may try to change the same parameter in the opposite directions. This raises that the need for an SON COordinator (SONCO) meant to arbitrate the parameter change requests of the SON functions, ensuring some degree of fairness. It is difficult to anticipate the impact of accepting several simultaneous requests. In this paper, we provide a SONCO design based on reinforcement learning (RL) as it allows us to learn from previous experiences and improve our future decisions. Typically, RL algorithms are complex. To reduce this complexity, we employ two flavors of function approximation and provide a study-case. Results show that the proposed SONCO design is capable of biasing this fairness among the SON functions by means of weights attributed to the SON functions. Also, we evaluate the tracking capability of the algorithms.

In trying to meet the demands of traffic hungry users, mobile network operators are faced with increased CAPital EXpenditures (CAPEX) and OPerational EXpenditures (OPEX). The Self Organizing Network (SON) functions have been introduced by 3GPP as a means to cut down these costs. There are mainly 3 categories of such functions: self-configuration, self-optimization and self-healing. In this paper we focus on the second which represents the SON functions performing a runtime optimization of the network. We center our attention on LTE heterogeneous networks. Having several SON functions in a network may lead to conflicts and potentially to bad network Key Performance Indicators (KPIs). Thus a troubleshooting mechanism has to be envisaged. Such a mechanism typically contains 3 steps: fault detection, cause diagnosis and solution deployment. In this paper we tackle the first two and we study the feasibility of using the Naive Bayes Classifier (NBC) in order to build a framework for SON Conflict Diagnosis (SONCD). We provide numeric results proving the feasibility of the framework.

The continuously increasing traffic demand faces us with increased CAPital Expenditures (CAPEX) and Operational Expenditure (OPEX). Self Organizing Network (SON) functions aim to lower these costs by automating the network tuning. A SON instance is a realization of a SON function which can tune one or a set of cells. Having several uncoordinated SON functions in the network creates a risk for conflicts and instability. This raises the need for a SON Coordinator (SONCO) meant to deal with these issues. In this work we consider that on each cell we have one SON instance of each SON function. We present the design of a SONCO which arbitrates conflicts based on weights attributed to the SON functions. The design makes use of Reinforcement Learning (RL) with function approximation. We provide a low complexity approximation of the action-value function based on a number of parameters that scales linearly with the number of cells. We present a study case with the Mobility Load Balancing (tuning the Cell Individual Offset (CIO)) and Mobility Robustness Optimization (tuning the CIO and the handover hysteresis) functions, where the SONCO deals with the conflicts on the CIOs. Numerical results prove that we can orchestrate the SON functions through SONCO configurations that reflect different operator policies.

With the emergence of Self-Organizing Network (SON) functions network operators are faced with a practical problem: coordination of SON instances. The SON functions are usually designed in a standalone manner, i.e. they do not take into account the possibility that other instances of the same or different SON functions may be running in the network. This creates the risk of conflicts and network instability. Therefore a SON COordinator (SONCO) is needed. In this paper we design an operator centric SONCO that sees the SON instances as black-boxes, i.e. it does not know the algorithm inside the SON functions. Our aim is to improve the network stability (i.e. number of parameter changes) for SON instances of the same SON function. We employ Reinforcement Learning (RL) in order to profit from the information on the past SONCO decisions. We simplify the expression of the action-value function and we use state aggregation to further reduce the required state space, making it scale linearly with the number of coordinated cells. We provide a study case with the Mobility Load Balancing (MLB) function independently instantiated on every cell. The results show that the proposed SONCO improves the network stability.

In Long Term Evolution(LTE) networks one of the main focuses is on automating the network optimization. This is done through so called Self Organizing Network (SON) functions like Mobility Load Balancing (MLB), Mobility Robustness Optimization(MRO) and others. A SON instance is a realization of a SON function that governs (optimizes) one or a cluster of eNBs. The SON functions are built in a standalone manner, i.e. without considering the existence of other SON instances. So they do not necessarily operate in a coordinated fashion, especially in a network where different SON instances may come from different vendors. Thus we face a risk of generating conflicts and instabilities in the network and so this raises the need for a SON COordinator (SONCO) . The SONCO, built from an operator point of view, sees the SON instances as black boxes and has a very limited amount of information on them. In these conditions the SONCO has to solve conflicts and improve the network stability. In this paper we propose a Reinforcement Learning (RL) based solution for coordinating SON instances that run independently on neighboring eNBs and we provide results for a case study with MLB instances. We analyze the scalability of our solution and we provide numerical results showing how improvements in network stability can be obtained.