A Cell Outage Management Framework for Dense Heterogeneous Networks

Abstract

Abstract-In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks (HetNets) with split control and data planes -a candidate architecture for meeting future capacity, quality of service and energy efficiency demands. In such architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, while the data BSs handle UE data. An implication of this split architecture is that, an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both the data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large scale minimize drive testing (MDT) reports data, and detects outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly detecting algorithms, i.e. k− nearest neighbor and local outlier factor based anomaly detector, within the control COD. On the other hand, for data cells COD, we propose a heuristic grey-prediction based approach, which can work with the small number of UEs in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity, by receiving a periodic update of the received signal reference power (RSRP) statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of residual error that is inherent to grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm which can be applied to both planes. Our COC solution utilizes an actor critic (AC) based reinforcement learning (RL) algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage, and also compensate for the detected outage in a reliable manner.

Machine Learning Paradigms for Next-Generation Wireless Networks

“…The main objectives of the learning process are two-fold: first, to acquire spectrum allocation awareness and to identify the availability of unused spectral slots for the provision of opportunistic access; second, to select sub-channels from the available spectrum pool and to configure the terminals supported by femtocells to operate under carefully constructed restrictions to avoid interference and to meet the quality of service (QoS) requirements. Another example is constituted by dense small cell networks regarding their cell outage management and compensation [15]. The system's state was constituted by the specific allocation of users to the resource blocks of small cells, as well as by the channel quality, while the actions were constituted by the downlink power control actions, with the rewards being quantified in terms of signal-to-interference-plus-noise ratio (SINR) improvement.…”

“…• Unknown system transition model • Q-function maximization Femto and small cells [14,15] Multi-armed bandit…”

A method for identifying faulty cells using a classification tree-based UE diagnosis in LTE

“…On the other hand, there are some works in the literature that investigate the problem of cell outage by employing user-centric measurements [39][40][41]. Nevertheless, this information is commonly related to the radio environment (e.g., signal strength) derived from MDT functionality, while other measurements related to integrity performance such as user throughput are ignored.…”

Fault compensation algorithm based on handover margins in LTE networks

“…Then, the selected neighboring cells that are suffering from the fault are discarded from the compensating neighboring list. Thus, the faulty site is composed of cells 10, 11, and 12 and the definitive list of neighboring cells are 2, 3,8,9,13,15,28, and 29. The following step of the algorithm is to select the algorithm settings.…”

“…For instance, Saeed et al [12] present a COC algorithm based on fuzzy logic that modifies the antenna tilt and the transmission power of the compensating cells. The same set of configuration parameters is used by Onireti et al [13]. In that work, the compensation algorithm is based on reinforcement learning and it is applied to a heterogeneous network.…”