Using Clustering Techniques to Plan Indoor Femtocell Base Stations Layout in Multi-floors

Ibrahim, Lamiaa Fattouh; Salman, Hesham A.; Sery, Sara Y.; Taha, Zaki

doi:10.1093/comjnl/bxz007

Cited by 11 publications

(8 citation statements)

References 9 publications

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“…They achieved to reduce the number of femtocells while signal to interference/noise ratio (SINR) with a path‐loss threshold is maximized. The work in Reference 15 extended the work in Reference 14 in the multifloor buildings.…”

Section: Related Workmentioning

confidence: 72%

“…We use CLSTR, 15 ITP, 3 PLacILP, 16 and KMNS 29 models for comparing our proposed model. CLSTR model utilizes a clustering technique to locate the femtocells.…”

Section: Resultsmentioning

confidence: 99%

“…They did not provide an accurate mathematical model or an algorithm for the initial populations, which makes their proposed model highly complex and difficult to find the best solution in the large‐scale buildings. Ibrahim et al 14 proposed a clustering technique to find the best location for the femtocells. They achieved to reduce the number of femtocells while signal to interference/noise ratio (SINR) with a path‐loss threshold is maximized.…”

Section: Related Workmentioning

confidence: 99%

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A novel approach on femtocell placement in the commercial buildings using genetic algorithm

Javad

BorujeniShahram

2021

Trans Emerging Tel Tech

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The femtocell networks have been developed to solve the indoor coverage issues. In the large commercial buildings, finding the minimum number of femtocells and their locations with a full coverage of the building is a complicated problem. This article attempts to minimize the number of femtocells in a large building while providing maximum coverage. In this article, the locations of the femtocells are determined such that the total number of handovers in the building is minimal. For this purpose, the building area is divided into small and equal‐sized subregions, and a femtocell matrix coverage is obtained for each subregion using the path‐loss equation. We utilize the femtocell matrix coverage and propose a mathematical model to minimize the number of femtocells with a maximum (almost complete) coverage of the building area. We employ the genetic algorithm to solve this NP‐hard problem. An efficient algorithm is also presented to create the initial population for the genetic algorithm. Based on the past behavior of the users, we select the locations for the femtocells to reduce the number of handovers. Numerical results indicate that with almost complete coverage, our proposed method reduces the femtocell counts up to 55% and reduces the number of handovers up to 30% compared with the previous work.

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Section: Related Workmentioning

confidence: 72%

“…We use CLSTR, 15 ITP, 3 PLacILP, 16 and KMNS 29 models for comparing our proposed model. CLSTR model utilizes a clustering technique to locate the femtocells.…”

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A novel approach on femtocell placement in the commercial buildings using genetic algorithm

Javad

BorujeniShahram

2021

Trans Emerging Tel Tech

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“…To facilitate the process of cell planning that involves locating and configuring infrastructure for mobile networks a system using cluster techniques was proposed in [14]. A new hybrid method was proposed in [15] to increase the accuracy of an indoor positioning system using a hybrid of BLE fingerprinting and PDR.…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning Framework to Improve Wi-Fi Based Indoorpositioning

Pichaimani¹,

Manjula²

2022

Intelligent Automation &Amp; Soft Computing

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The indoor positioning system comprises portable wireless devices that aid in finding the location of people or objects within the buildings. Identification of the items is through the capacity level of the signal received from various access points (i.e., Wi-Fi routers). The positioning of the devices utilizing some algorithms has drawn more attention from the researchers. Yet, the designed algorithm still has problems for accurate floor planning. So, the accuracy of position estimation with minimum error is made possible by introducing Gaussian Distributive Feature Embedding based Deep Recurrent Perceptive Neural Learning (GDFE-DRPNL), a novel framework. Novel features from the dataset are through two processing stages dimensionality reduction and position estimation. Initially, the essential elements selection using the Gaussian Distributive Feature Embedding technique is the novel framework. The feature reduction process aims to reduce the time consumption and overhead for estimating the location of various devices. In the next stage, employ Deep Recurrent multilayer Perceptive Neural Learning to evaluate the device position with dimensionality reduced features. The proposed Deep-learning approach accurately learns the quality and the signal strength data with multiple layers by applying Deming Regressive Trilateral Positioning Model. As a result, the GDFE-DRPNL framework increases the positioning accuracy and minimizes the error rate. The experimental assessments with various factors such as positioning accuracy minimized by 70% and 60%, computation time minimized by 45% and 55% as well as overhead by 11% and 23% compared with PFRL and two-dimensional localization algorithm. Through the experiment and after analyzing the data, verify that the proposed GDFE-DRPNL algorithm in this paper is better than the previous methods.

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“…This study targets indoor environment because of the following reasons. Two‐thirds of phone calls (voice) and above 70% of data traffic arise from inside buildings 14 Significant femtocells deployment is expected in the near future. Operators generates more revenues as femtocells are providing enhanced data services for indoor. Indoor penetration is the major problem for > 2GHz operating frequencies. …”

Section: Introductionmentioning

confidence: 99%

Secure indoor traffic modeling in long‐term evolution femtocell networks using efficient Markov decision process

Iqra

Murawwat

Gulzar

et al. 2020

Trans Emerging Tel Tech

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Mobile users spent most of their time in an indoor environment, therefore it is crucial to develop indoor traffic models for next-generation networks. The significance of this paper is related to the predominant femtocell technology as it is anticipated to be a very crucial part of the modern mobile networks such as 5G and beyond networks. The Femtocell network can satisfy the increased bandwidth demand for video and data traffic with improved network capacity and security. Indoor data traffic exhibits different characteristics that need to be dealt with distinct criteria of the Poisson process and existing classifying techniques. Indoor traffic models, proposed in the literature do not consider long-range dependent characteristics for traffic modeling. Moreover, these traffic models have the ability to capture large flows only. In this paper, we have provided an efficient quality of service-based classifier for downlink transmission in long-term evolution (LTE)-femtocell network. Our proposed algorithm models the indoor traffic by Poisson Lomax Burst Process. Modeled traffic streams, after prioritization, are classified using Markov Decision Process (MDP) classifier. Furthermore, a detailed comparison of our proposed MDP algorithm with the state-of-the-art algorithms in the literature has also been presented. The network performance parameters have been assessed in terms of throughput, delay, and jitter. Simulation results depict a significant improvement in throughput in contrast with Round Robin (RR), which is approximately 8%, 6%, 6%, and 7% for voice, video, BE, and BG services, respectively, while modified least weighted delay first (MLWDF) gives least performance among them. MDP also provides an improvement in delay and average jitter of four different traffic flows and mixed traffic users when compared with RR and MLWDF algorithms.

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Using Clustering Techniques to Plan Indoor Femtocell Base Stations Layout in Multi-floors

Cited by 11 publications

References 9 publications

A novel approach on femtocell placement in the commercial buildings using genetic algorithm

A novel approach on femtocell placement in the commercial buildings using genetic algorithm

A Machine-Learning Framework to Improve Wi-Fi Based Indoorpositioning

Secure indoor traffic modeling in long‐term evolution femtocell networks using efficient Markov decision process

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