Self-organizing networks in next generation radio access networks: Application to fractional power control

Dirani, Mariana; Altman, Zwi

doi:10.1016/j.comnet.2010.08.012

Cited by 29 publications

(20 citation statements)

References 7 publications

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“…see [15,16] to introduce autonomic capabilities in network control systems, and is a combination of fuzzy logic [17] with Q-learning (type of Reinforcement Learning (RL)) [18] that aims to combine the robustness of a rule based fuzzy system with the adaptability and learning capabilities of Q-learning. In this Section we highlight the main concepts and benefits of this approach and its applicability in the context of PCN-based AC.…”

Section: Fuzzy Q-learning Pcn-based Admission Controlmentioning

confidence: 99%

“…The objective of an agent is to find, by trying out all the possible actions when being in a given state, the action that maximizes its long term reward. The detailed mathematical foundation and formulation of Q-learning can be found in [15,16,18] therefore it is not repeated here, due to space limitations; the core Q-learning algorithm [18] is provided though, so as to highlight the parameters involved in it and consequently in our evaluation in the following Session.…”

Section: Fuzzy Q-learning Conceptsmentioning

confidence: 99%

“…It can also be easily combined with fuzzy logic and provide the association between the states and the available actions, which is the same as constructing the fuzzy logic "if…then" engine; the only additional step required being the distribution of the reinforcement/reward signal among multiple simultaneously triggered "if…then" rules due to the overlapping of the fuzzy input sets. This distribution can be done proportionally to the strength with which each rule is triggered so that rules (states) triggered with high strength -and contributing therefore more to the output action-are rewarded (or penalized) more compared to rules triggered with lower strength [15,16].…”

Section: Fuzzy Q-learning Conceptsmentioning

confidence: 99%

See 2 more Smart Citations

A Fuzzy Reinforcement Learning Approach for Pre-Congestion Notification Based Admission Control

Georgoulas

Moessner

Mansour

et al. 2012

Dependable Networks and Services

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Abstract. Admission control aims to compensate for the inability of slowchanging network configurations to react rapidly enough to load fluctuations. Even though many admission control approaches exist, most of them suffer from the fact that they are based on some very rigid assumptions about the perflow and aggregate underlying traffic models, requiring manual reconfiguration of their parameters in a "trial and error" fashion when these original assumptions stop being valid. In this paper we present a fuzzy reinforcement learning admission control approach based on the increasingly popular Pre-Congestion Notification framework that requires no a priori knowledge about traffic flow characteristics, traffic models and flow dynamics. By means of simulations we show that the scheme can perform well under a variety of traffic and load conditions and adapt its behavior accordingly without requiring any overly complicated operations and with no need for manual and frequent reconfigurations.

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Section: Fuzzy Q-learning Pcn-based Admission Controlmentioning

confidence: 99%

Section: Fuzzy Q-learning Conceptsmentioning

confidence: 99%

Section: Fuzzy Q-learning Conceptsmentioning

confidence: 99%

See 1 more Smart Citation

A Fuzzy Reinforcement Learning Approach for Pre-Congestion Notification Based Admission Control

Georgoulas

Moessner

Mansour

et al. 2012

Dependable Networks and Services

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show abstract

“…In LTE, fractional power control is used in the UpLink (UL) to dynamically change UE transmit power [5]. Moreover, several self-planning methods have been proposed to adapt UpLink Power Control (ULPC) parameters in LTE to local network conditions [6][7][8][9][10]. However, for the downlink (DL) of LTE, power planning is the simplest solution to solve CCO issues in the absence of a power control scheme.…”

Section: Introductionmentioning

confidence: 99%

Self-Planning of Base Station Transmit Power for Coverage and Capacity Optimization in LTE

Buenestado

Toril

Luna-Ramírez

et al. 2017

Mobile Information Systems

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A computationally efficient self-planning algorithm for adjusting base station transmit power in a LTE system on a cell-by-cell basis is presented. The aim of the algorithm is to improve the overall network spectral efficiency in the downlink by reducing the transmit power of specific cells to eliminate interference problems. The main driver of the algorithm is a new indicator that predicts the impact of changes in the transmit power of individual cells on the overall network Signal to Interference plus Noise Ratio (SINR) for the downlink. Algorithm assessment is carried out over a static system-level simulator implementing a live LTE network scenario. During assessment, the proposed algorithm is compared with a state-of-the-art self-planning algorithm based on the modification of antenna tilt angles. Results show that the proposed algorithm can improve both network coverage and capacity significantly compared to other automatic planning methods.

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“…For instance, a self-tuning algorithm is proposed in [24] to dynamically adjust nominal power parameter based on overload indicator [25] so as to control the overall interference in the network. Similarly, in [26], a self-tuning algorithm for FPC is proposed based on fuzzy-reinforcement learning techniques. These self-tuning algorithms, conceived to be executed during network operation, can also be applied to network planning, provided that a system performance model is available.…”

mentioning

confidence: 99%

A computationally efficient method for self-planning uplink power control parameters in LTE

Fernández-Segovia

Luna-Ramírez

Toril

et al. 2015

J Wireless Com Network

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Uplink power control has a strong impact on the performance of mobile communication networks. In this work, an automatic parameter planning algorithm for the standardized power control scheme in the physical uplink shared channel (PUSCH) of long term evolution LTE is proposed. The method is conceived for the network design stage, when network measurements are still not available. The proposed heuristic algorithm can handle irregular scenarios at a low computational complexity. For this purpose, the parameter planning problem in a cell is formulated analytically through the combination of multiple regular scenarios built on a per-adjacency basis. Method assessment is carried out over a static system-level simulator implementing a real scenario. Results show that the proposed method can improve average user throughput and cell-edge user throughput when compared to current vendor approaches, which provide network-wide uniform parameter settings.

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Self-organizing networks in next generation radio access networks: Application to fractional power control

Cited by 29 publications

References 7 publications

A Fuzzy Reinforcement Learning Approach for Pre-Congestion Notification Based Admission Control

A Fuzzy Reinforcement Learning Approach for Pre-Congestion Notification Based Admission Control

Self-Planning of Base Station Transmit Power for Coverage and Capacity Optimization in LTE

A computationally efficient method for self-planning uplink power control parameters in LTE

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