Location-aware self-organizing methods in femtocell networks

Aguilar-Garcı́a, Alejandro; Fortes, Sergio; Molina-García, Mariano; Calle-Sánchez, Jaime; Alonso, J.I.; Garrido, Aaron; Fernandez-Duran, A.; Barco, Raquel

doi:10.1016/j.comnet.2015.10.011

Cited by 15 publications

(12 citation statements)

References 13 publications

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“…However, these methods are based on propagation models, which could provide a high RSS error due to the variability of channel conditions. Additionally, the work proposed in [18] introduced the users' location and the RSS information measured from the network to support the SON mechanisms. Nevertheless, the self-optimization algorithm made use of the geometrical distances between the users in order to estimate the new transmission power of femtocells.…”

Section: Related Workmentioning

confidence: 99%

Coordinated location-based self-optimization for indoor femtocell networks

2016

Self Cite

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Section: Related Workmentioning

confidence: 99%

Coordinated location-based self-optimization for indoor femtocell networks

2016

Self Cite

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“…However, we model them in a specific way to retain the Markov property without any loss of generality, thereby making these problems tractable. Scenarios such as self organizing networks [23], 5G small cell network design [24], [25], supply chain networks, and last mile delivery problems [26] pose a parameterized MDP with a two-fold objective of determining simultaneously (a) the optimal control policy for the underlying stochastic process, and (b) the unknown parameters that the state and action variables depend upon such that the cumulative cost is minimized. The latter objective is akin to facility location problem [27]- [29], that is shown to be NP-hard [27], and where the associated cost function (non-convex) is riddled with multiple poor local minima.…”

Section: Introductionmentioning

confidence: 99%

Parameterized MDPs and Reinforcement Learning Problems—A Maximum Entropy Principle-Based Framework

Srivastava

Salapaka

2022

IEEE Trans. Cybern.

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We present a framework to address a class of sequential decision making problems. Our framework features learning the optimal control policy with robustness to noisy data, determining the unknown state and action parameters, and performing sensitivity analysis with respect to problem parameters. We consider two broad categories of sequential decision making problems modeled as infinite horizon Markov Decision Processes (MDPs) with (and without) an absorbing state. The central idea underlying our framework is to quantify exploration in terms of the Shannon Entropy of the trajectories under the MDP and determine the stochastic policy that maximizes it while guaranteeing a low value of the expected cost along a trajectory. This resulting policy enhances the quality of exploration early on in the learning process, and consequently allows faster convergence rates and robust solutions even in the presence of noisy data as demonstrated in our comparisons to popular algorithms such as Q-learning, Double Q-learning and entropy regularized Soft Q-learning. The framework extends to the class of parameterized MDP and RL problems, where states and actions are parameter dependent, and the objective is to determine the optimal parameters along with the corresponding optimal policy. Here, the associated cost function can possibly be non-convex with multiple poor local minima. Simulation results applied to a 5G small cell network problem demonstrate successful determination of communication routes and the small cell locations. We also obtain sensitivity measures to problem parameters and robustness to noisy environment data.

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“…However, the effect of obstacles and Non-Line-Of-Sight (NLOS) propagation [ 3 ] make GNSS essentially unavailable or very inaccurate in indoor scenarios [ 4 ]. This implies a huge barrier for the implementation of many applications of positioning, logistics [ 5 ], games and augmented reality applications [ 6 , 7 ] and even the management of cellular networks [ 8 , 9 , 10 , 11 ] indoors.…”

Section: Introductionmentioning

confidence: 99%

Accurate and Low-Complexity Auto-Fingerprinting for Enhanced Reliability of Indoor Localization Systems

Hatem

Fortes

Colin

et al. 2021

Sensors

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Indoor localization is one of the most important topics in wireless navigation systems. The large number of applications that rely on indoor positioning makes advancements in this field important. Fingerprinting is a popular technique that is widely adopted and induces many important localization approaches. Recently, fingerprinting based on mobile robots has received increasing attention. This work focuses on presenting a simple, cost-effective and accurate auto-fingerprinting method for an indoor localization system based on Radio Frequency Identification (RFID) technology and using a two-wheeled robot. With this objective, an assessment of the robot’s navigation is performed in order to investigate its displacement errors and elaborate the required corrections. The latter are integrated in our proposed localization system, which is divided into two stages. From there, the auto-fingerprinting method is implemented while modeling the tag-reader link by the Dual One Slope with Second Order propagation Model (DOSSOM) for environmental calibration, within the offline stage. During the online stage, the robot’s position is estimated by applying DOSSOM followed by multilateration. Experimental localization results show that the proposed method provides a positioning error of 1.22 m at the cumulative distribution function of 90%, while operating with only four RFID active tags and an architecture with reduced complexity.

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Location-aware self-organizing methods in femtocell networks

Cited by 15 publications

References 13 publications

Coordinated location-based self-optimization for indoor femtocell networks

Coordinated location-based self-optimization for indoor femtocell networks

Parameterized MDPs and Reinforcement Learning Problems—A Maximum Entropy Principle-Based Framework

Accurate and Low-Complexity Auto-Fingerprinting for Enhanced Reliability of Indoor Localization Systems

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