Deployment strategies and performance analysis of Macrocell and Femtocell networks in suburban environment with modern buildings

Yunas, Syed Fahad; Asp, Ari; Niemelä, Jarno; Valkama, Mikko

doi:10.1109/lcnw.2014.6927715

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…For analyzing the energy-efficiency of both homogeneous macro-only and heterogeneous macrofemto deployments, we use the same methodology as described in [17,26,27]. In this paper, the energy-efficiency of a network is defined as the aggregate data rate that is achievable while consuming a given power, for example, 1 kW.…”

Section: Energy-efficiencymentioning

confidence: 99%

Technoeconomical Analysis of Macrocell and Femtocell Based HetNet under Different Deployment Constraints

Yunas

Ansari²,

Valkama³

2016

Mobile Information Systems

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Ultradense deployment of small cells is being considered as one of the key flavors of the emerging 5G cellular networks to address the future data capacity challenges. A large share of these deployments will be indoor, as this is the arena where the majority of the data traffic is believed to originate from in the future. Indoor small cell solutions (e.g., femtocell or WiFi) are well positioned for delivering superior indoor coverage and capacity. However, due to relatively smaller coverage footprint compared to traditional macrocells, a very dense deployment of small cells will be needed in order to have a ubiquitous indoor coverage. Such dense deployment triggers cost and energy efficiency concerns for mobile operators. In this paper, we analyze and compare the technoeconomic performance of two deployment strategies: homogeneous macrocellular densification and heterogeneous macro-femto deployment strategy, from an indoor service provisioning perspective. Particularly, we analyze and contrast the performance of macro-femto based deployment, with varying femtocell market penetration rate and under different femtocell backhaul connectivity constraints, with that of homogeneous macrocellular densification. The results indicate superior performance of indoor femtocell based deployment as compared to macrocellular-only densification, due to better indoor coverage, radio channel conditions, and high degree of spatial reuse.

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Section: Energy-efficiencymentioning

confidence: 99%

Technoeconomical Analysis of Macrocell and Femtocell Based HetNet under Different Deployment Constraints

Yunas

Ansari²,

Valkama³

2016

Mobile Information Systems

Self Cite

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“…Indeed, existing studies on UDN planning are based on computationally expensive ray tracing techniques, which constrains them to only explore static deployment strategies. For instance, in [4], [5], capacity, spectrum and energy efficiency metrics were used to evaluate the performance of a UDN, employing ray tracing software [6] to model radio wave propagation under a predetermined set of deployments. However, approaches based on ray tracing, such as the one above, can only explore a limited number of manually defined deployments, which are most likely sub-optimal with respect to what could be achieved in practice.…”

Section: Introductionmentioning

confidence: 99%

“…Expedient and near-optimal UDN planning can be achieved by bringing together three components: (i) computationally efficient and accurate modeling of indoor propagation; (ii) a suitable target KPI metric that quantitatively represents the performance of the deployment; (iii) an optimization problem formulation that can return a high-performing deployment of femto Txs [2], [4], [5]. By coupling propagation solvers with optimization algorithms, one can conduct iterative simulations to gradually adapt the network topology towards meeting the target criteria or optimizing the specific KPI.…”

Section: Introductionmentioning

confidence: 99%

Expedient AI-assisted Indoor Wireless Network Planning with Data-Driven Propagation Models

Bakirtzis¹,

Fiore²,

Wassell³

et al. 2023

Preprint

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<p>Propelled by rapid advances in artificial intelligence (AI), the design and operation of 5G and beyond networks are anticipated to be radically different from those of legacy communication systems. Indeed, AI can be exploited to automate and optimize various functionalities that are essential for the wireless ecosystem, such as resource allocation, channel modeling, or network planning. This article explores how data-driven propagation models can be leveraged for the automated and expedient deployment of small cells in indoor environments. In particular, a generalizable data-driven propagation model is entwined with AI-based optimizers aiming at determining the optimal network topology with respect to some target key performance indicators. The data-driven model combines the accuracy of a high-performance propagation solver with the computational efficiency of deep neural networks, inferring the signal level spatial distribution based on physics-based information of the indoor geometry. The capability of the data-driven model to conduct multiple simulations within a few seconds is vital for the optimization procedure, enabling accurate network planning that would be extremely expensive to produce with a conventional indoor propagation tool, especially for ultra-dense networks where macro-base stations coexist with numerous small cells.</p>

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“…Co‐channel deployment is the default option due to operators having limited spectrum resources 9 . The research carried out in Yunas et al 10 suggests a successful deployment of indoors FBSs in modern buildings under outdoor interference conditions. The authors shows that co‐channel deployment might be an ideal scenario for deploying FBSs inside buildings.…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithm for biobjective optimization of indoor LTE femtocell deployment

Figueras-Benítez

Badra

2020

Int J Communication

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The optimization of indoor femtocell base stations (FBS) placement is a highly complex problem in which multiple performance figures can be considered. In this paper, we propose a biobjective optimization approach based on nondominated sorting genetic algorithm II (NSGA-II) to solve the indoor FBS deployment problem in residential scenarios where a single cell is deployed within the coverage of a single dominant interfering external macro base station (MBS). The optimization problem is formulated with the objective of jointly minimizing outage probability and maximizing normalized downlink average throughput. Several conditions are simultaneously considered under the framework of Monte Carlo simulations, such as the effect of co-channel interference in uplink and downlink, buildings with arbitrary layouts, realistic transmission schemes and antenna radiation patterns, random user distributions, and a well-established empirical path loss model. The proposed algorithm, which determines the location and orientation of the FBS's antenna that provides a suitable compromise between the two objective functions, is evaluated in this paper. First, two different experiments are proposed to investigate the algorithm convergence; then, statistical analyses are formulated to characterize the relation between the values achieved for both objective functions; finally, the computational load is evaluated. Results indicate the convergence of the proposed method towards the real Pareto front and a significant reduction of the computational time with respect to a reference method based on exhaustive search, while pointing at new criteria for the selection of the objective functions.

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Deployment strategies and performance analysis of Macrocell and Femtocell networks in suburban environment with modern buildings

Cited by 10 publications

References 14 publications

Technoeconomical Analysis of Macrocell and Femtocell Based HetNet under Different Deployment Constraints

Technoeconomical Analysis of Macrocell and Femtocell Based HetNet under Different Deployment Constraints

Expedient AI-assisted Indoor Wireless Network Planning with Data-Driven Propagation Models

Genetic algorithm for biobjective optimization of indoor LTE femtocell deployment

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