Pablo Muñoz

A unified framework for self-healing in wireless networks

Future wireless networks will be Heterogeneous Networks (HetNet), as they will comprise multiple Radio Access Technologies (RAT) and network architectures. The complexity of managing and operating such networks is forcing operators to find new strategies if they want to remain competitive. In this context, Self-Organizing Networks (SON) have arisen as one of the key technologies for enhancing operation and minimizing costs in future networks. SON functionalities can be classified as self-configuration, self-optimization, and self-healing. Although the first two categories have received considerable attention in the last years, the studies devoted to self-healing have been scarce. In addition, existing references use different terminology and concepts and they focus only on partial aspects of self-healing for a particular RAT. This article unifies previous research and proposes a reference model for self-healing in which terms and functions are unambiguously defined. In addition, this article presents a survey on the state of the art, in which the main references on self-healing are outlined. Finally, the main research challenges in self-healing that will have to be faced in the near future are summarized.

On the Potential of Handover Parameter Optimization for Self-Organizing Networks

Self-organizing networks (SONs) aim to raise the level of automated operation in next-generation networks. One of the use cases defined in this field is the optimization of the handover (HO) process, which involves a tradeoff between the amount of signaling load due to HOs and the quality of the active connections in the network. In this paper, first, a sensitivity analysis of the two main HO parameters, i.e., the HO margin (HOM) and the time-to-trigger (TTT), is carried out for different system load levels and user speeds in a Long-Term Evolution (LTE) network. Second, a fuzzy logic controller (FLC) that adaptively modifies HOMs is designed for HO optimization. In this case, different parameter optimization levels (network-wide, cell-wide, and cell-pair-wide) and the impact of measurement errors have been considered. Results of the sensitivity analysis show that tuning HOMs is an effective solution for HO optimization in LTE networks. In addition, the FLC is shown as an effective technique to adapt HOM to different network conditions so that the signaling load in the network is decreased while an admissible level of call dropping is achieved.

Optimization of a Fuzzy Logic Controller for Handover-Based Load Balancing

In Self-Organizing Networks (SON), load balancing has been recognized as an effective means to increase network performance. In cellular networks, cell load balancing can be achieved by tuning handover parameters, for which a Fuzzy Logic Controller (FLC) usually provides good performance and usability. Operator experience can be used to define the behavior of the FLCs. However, such a knowledge is not always available and hence optimization techniques must be applied in the controller design. In this work, a fuzzy

Fuzzy Rule-Based Reinforcement Learning for Load Balancing Techniques in Enterprise LTE Femtocells

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Mobility-based strategies for traffic steering in heterogeneous networks

-Learning algorithm is proposed to find the optimal set of fuzzy rules in an FLC for traffic balancing in GSM-EDGE Radio Access Network (GERAN). Load balancing is performed by modifying handover margins. Simulation results show that the optimized FLC provides a significant reduction in call blocking.

Optimization of load balancing using fuzzy Q-Learning for next generation wireless networks

Mobile-broadband traffic has experienced a large increase over the past few years. Femtocells are envisioned to cope with such a demand of capacity in indoor environments. Since those small cells are low-cost nodes, a thorough deployment is not typically performed, particularly in enterprise scenarios. As a result, the matching between traffic demand and network resources is rarely optimal. In this paper, several load balancing techniques based on self-tuning of femtocell parameters are designed to solve localized congestion problems. In particular, these techniques are implemented by fuzzy logic controllers (FLC) and fuzzy rule-based reinforcement learning systems (FRLSs). Performance assessment is carried out in a dynamic system-level simulator. Results show that the combination of FLC and FRLS produces an increase in performance that is significantly higher than if techniques are implemented alone. Both the response time and the final value of performance indicators are improved.

Load Balancing in a Realistic Urban Scenario for LTE Networks

The large increase in size and complexity experienced by cellular networks in recent years has led to a new paradigm known as heterogeneous networks, or HetNets. In this context, networks with different cell sizes, radio access technologies, or carrier frequencies can be deployed in the same environment. As the coverage area of each of these networks is typically overlapped, operators have some degree of freedom to modify user distributions across the networks (i.e., traffic steering) in order to improve network performance. This article introduces different mechanisms of traffic steering in HetNets, clarifying the specific goals that operators can set and focusing on those techniques that adjust mobility parameters, which are typically more attractive to achieve these goals. In addition, some challenging issues arising from particular HetNet deployments are discussed and illustrated by example use cases, which are applicable to an early stage of LTE deployment. Finally, a fuzzy-logic-based algorithm that optimizes network parameters for traffic steering is proposed.

Cell Outage Detection Based on Handover Statistics

Load balancing is considered by the 3GPP as an important issue in Self-Organizing Networks due to its effectiveness to increase network capacity. In next generation wireless networks, load balancing can be easily implemented by tuning handover (HO) margins, achieving a decrease in call blocking. However, call dropping can be increased as a negative effect of the HO-based load balancing, because users usually are handed over to cells where the radio conditions are worse. In this work, a Fuzzy Logic Controller (FLC) optimized by the fuzzy Q-Learning algorithm is proposed for the load balancing problem, with the aim of decreasing call blocking in congested cells, while at the same time restricting call dropping in neighboring cells according to the network policy. In particular, two different approaches for the FLC optimization are evaluated in this work, highlighting that one of the proposed methods allows to accurately preserve the call quality constraint during the load balancing, while the other can adapt to network variations. Results show that the optimized FLC provides a notable reduction in call blocking while preserving call dropping under the operator constraint.

Data mining for fuzzy diagnosis systems in LTE networks

In this paper the behavior and the self-optimization of an LTE network under realistic conditions are investigated. To enhance network performance in a urban environment a controller to auto-tune parameters has been proposed. An urban mobility model has also been defined in order to test the proposed method under realistic conditions. This model allows to investigate some performance features, wich are not visible with a simple mobility model. In this paper, we propose to use a Fuzzy Logic Controller (FLC) for the optimization of handover parameters for adaptive load balancing. Results show that under an agglomeration of vehicles in a main road of the scenario, the proposed method achieves an improvement in the global Call Blocking Ratio (CBR).

Automatic Root Cause Analysis for LTE Networks Based on Unsupervised Techniques

This letter presents a novel cell outage detection algorithm based on incoming handovers statistics. The main advantage of the proposed algorithm is that it uses neighbor measurements that allow to detect outage in two cases. First, when the cell in outage is able to report performance indicators; second, when these indicators are not available because the base station is affected. To evaluate the proposed algorithm and compare it with other approaches, a set of tests has been carried out using an LTE simulator and in a live LTE network.