Cell outage management in LTE networks

Amirijoo, Mehdi; Jorguseski, L.; Kürner, Thomas; Litjens, Remco; Neuland, M.; Schmelz, Lars Christoph; Türke, Ulrich

doi:10.1109/iswcs.2009.5285232

Cited by 58 publications

(34 citation statements)

References 7 publications

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“…The self-coordinator module receives as input the actions selected by each subMDP, and based on that, the possible situations to face are the following: If the actions are the same, the coordinator just executes the action, otherwise, the conflict is solved by mixed strategies through the reward matrix depicted inside the coordination box in Figure 4, where A, a, are the rewards of subMDP 1 and subMDP 2 , respectively, when executing for both subMDP α; B, b, are the rewards of subMDP 1 and subMDP 2 , respectively, when executing action α for subMDP 2 and action β for subMDP 1 ; C, c, are the rewards of subMDP 1 and subMDP 2 , respectively, when executing Hence, each player is not actually choosing α, β directly, but choosing a probability with which a player will play α. A given number p means that player 1 will play α with probability p and β with probability 1 − p. Similar considerations can be done for player 2.…”

Section: Resolution Of Policy Conflictsmentioning

confidence: 99%

“…Let subMDP 1 In this section, we provide an example about how the proposed functional architecture can be used to deal with the conflicts generated by the concurrent execution of multiple SON functions. The objective of this section is to show that the proposed approach is general enough to model all the SON functions and their derived conflicts.…”

Section: Algorithm 1 Coordination Game (S a T R)mentioning

confidence: 99%

“…In 3GPP, this kind of negative interactions, which affect the system performances, is referred to as SON function conflict, and the general framework to solve them is referred to as self-coordination. The literature on SON mainly focuses on individual SON function design, [1][2][3] while some initial work on self-coordination functions can be found in [4][5][6] and [7] where the authors focused on the identification and classification of different conflict types. Algorithmic solutions [8,9] and implementation challenges [10] have been recently discussed in literature.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Self-coordination of parameter conflicts in D-SON architectures: a Markov decision process framework

Moysen

Giupponi

2015

J Wireless Com Network

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We consider a distributed SON (D-SON) architecture where the interaction of different self-organizing network (SON) functions negatively affect the performances of the system. This is referred to in 3rd Generation Partnership Project (3GPP) as a SON conflict, which needs to be handled by means of a self-coordination framework. We focus on a functional architecture and a theoretical framework based on the theory of Markov decision process (MDP) for the self-coordination of different actions taken by different SON functions. In order to cope with the complexity of the overall SON problem, we subdivide the global MDP modeling the long-term evolution (LTE)-enhanced node base station (eNB) onto simpler subMDPs modeling the different SON functions. Each sub-problem is defined as a subMDP and solved independently by means of reinforcement learning (RL), and their individual policies are combined to obtain a global policy. This combined policy can execute several actions per state but can introduce policy conflicts. We focus on the specific SON conflict generated by the concurrent execution of coverage and capacity optimization (CCO) and inter-cell interference coordination (ICIC) SON functions, which may require to update the same parameter, i.e., the transmission power level. The coordination among the different actions selected by the conflicting use cases is achieved by means of a coordination game where the players are the subMDPs and the actions and rewards are those provided by means of a RL approach. Performance evaluation is carried out in a ns3 release 8 compliant LTE system simulator, and it shows that our self-coordination approach provides satisfying solutions in terms of system performances for both the conflicting SON functions.

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Section: Resolution Of Policy Conflictsmentioning

confidence: 99%

Section: Algorithm 1 Coordination Game (S a T R)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Self-coordination of parameter conflicts in D-SON architectures: a Markov decision process framework

Moysen

Giupponi

2015

J Wireless Com Network

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“…After detection, compensation from other nodes can occur through relay assisted handover for ongoing calls, adjustments of neighboring cell sizes via power compensation or antenna tilt. In [1], the authors not only propose a cell outage management description but also describe compensation schemes. These steps of monitoring and detection, then compensation of nodes failures are comprised under the self-healing functions of future cellular networks.…”

Section: Recovery In Cellular Networkmentioning

confidence: 99%

Disaster recovery in wireless networks: A homology-based algorithm

Vergne

Flint

Decreusefond

et al. 2014

2014 21st International Conference on Telecommunications (ICT)

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Abstract-In this paper, we present an algorithm for the recovery of wireless networks after a disaster. Considering a damaged wireless network, presenting coverage holes or/and many disconnected components, we propose a disaster recovery algorithm which repairs the network. It provides the list of locations where to put new nodes in order to patch the coverage holes and mend the disconnected components. In order to do this we first consider the simplicial complex representation of the network, then the algorithm adds supplementary vertices in excessive number, and afterwards runs a reduction algorithm in order to reach an optimal result. One of the novelty of this work resides in the proposed method for the addition of vertices. We use a determinantal point process: the Ginibre point process which has inherent repulsion between vertices, and has never been simulated before for wireless networks representation. We compare both the determinantal point process addition method with other vertices addition methods, and the whole disaster recovery algorithm to the greedy algorithm for the set cover problem.

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“…With increasing scale of networks, automatic detection and compensation of cell outage has become a necessity, and, it has been included in recent 3GPP releases [6]. Therefore, the SON paradigm aims to replace these manual tasks with an autonomous process referred to cell outage management (COM) [7]- [12]. COM can be further subdivided into cell outage detection (COD) and cell outage compensation (COC).…”

Section: Introductionmentioning

confidence: 99%

A Cell Outage Management Framework for Dense Heterogeneous Networks

Onireti

Zoha

Moysen

et al. 2016

IEEE Trans. Veh. Technol.

106

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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.

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Cell outage management in LTE networks

Cited by 58 publications

References 7 publications

Self-coordination of parameter conflicts in D-SON architectures: a Markov decision process framework

Self-coordination of parameter conflicts in D-SON architectures: a Markov decision process framework

Disaster recovery in wireless networks: A homology-based algorithm

A Cell Outage Management Framework for Dense Heterogeneous Networks

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