Optimization of Power Consumption and Handover Margin of Sleep/Active Cells in Dynamic H-CRAN

Ahmed, Reem Farag; Ismail, Tawfik; Abd-Elal, L. F.; Sweilam, N. H.

doi:10.1109/csndsp.2018.8471750

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…By 2018, research [38] explored network sizing through dynamic adaptation to fluctuating demands using a self-organizing C-RAN framework, incorporating Cell Differentia-tion and Integration (CDI) for semi-static scaling of the BBU pool and Remote Radio Heads (RRHs).…”

Section: Related Workmentioning

confidence: 99%

Optimizing Path Loss Prediction: Advancements in Signal Parameter Estimation through Wavelet Neural Networks

2024

nano-ntp

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This study assesses the effectiveness of Neural Net-works (NN) and Wavelet Neural Networks (WNN) in predicting signal strength in wireless communication systems. WNNs, which integrate wavelet theory with NN architecture, demonstrate supe-rior performance. Significant reductions in Mean Squared Error (MSE) from 32.93 (NN) to 11.94 indicate improved precision for WNNs. Root Mean Squared Error (RMSE) decreases from 5.73 (NN) to 3.45 with WNNs, highlighting more consistent predictions. Mean Absolute Error (MAE) decreases from 4.62 (NN) to 2.75, showcasing enhanced accuracy in WNNs. For predicting Average Distance, WNNs outperform NNs with lower MSE (59.4 vs. 106.65), RMSE (7.7 vs. 10.32), and MAE (5.73 vs. 7.878). The study, conducted on the Colab platform using Python, emphasizes that incorporating wavelet transforms enhances the model's ability to recognize complex signal propagation patterns.

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Section: Related Workmentioning

confidence: 99%

Optimizing Path Loss Prediction: Advancements in Signal Parameter Estimation through Wavelet Neural Networks

2024

nano-ntp

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“…Several BS switching strategies have been discussed, but the power optimisation issues are critical for ultra-dense H-CRAN. Author in [64] proposed a handover scheme to minimise the power consumption of SBSs in ultra-dense H-CRAN. A margin-based genetic algorithm has been used to achieve optimal decision levels to minimise power consumption and the ping-pong effect.…”

Section: ) Traffic and Mobilitymentioning

confidence: 99%

Energy-Efficient Ultra-Dense 5G Networks: Recent Advances, Taxonomy and Future Research Directions

Mughees¹,

Tahir

Sheikh

et al. 2021

IEEE Access

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The global surge of connected devices and multimedia services necessitates increased capacity and coverage of communication networks. One approach to address the unprecedented rise in capacity and coverage requirement is deploying several small cells to create ultra-dense networks. This, however, exacerbates problems with energy consumption and network management due to the density and unplanned nature of the deployment. This review discusses various approaches to solving energy efficiency problems in ultra-dense networks, ranging from deployment to optimisation. Based on the review, we propose a taxonomy, summarise key findings, and discuss operational and implementation details of past research contributions. In particular, we focus on popular approaches such as machine learning, game theory, stochastic and heuristic techniques in the ultra-dense network from an energy perspective due to their promise in addressing the issue in future networks. Furthermore, we identify several challenges for improving energy efficiency in an ultra-dense network. Finally, future research directions are outlined for improving energy efficiency in ultra-dense networks in 5G and beyond 5G networks.

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“…Although there are several BSs switching policies proposed in the literature to minimize the cellular power consumption, ping-pong issue, i.e., the oscillations of small BSs between active and sleep modes, becomes a critical challenge towards minimizing the overall power consumption and to maintain the network stability. To this end, authors in [98] proposed a handover margin-based genetic algorithm to minimize the power consumption as well as to reduce the frequent switching mode of small BSs. It is shown that a significant reduction in power consumption and improvement in the network stability are obtained with the optimum switching decision level and the handover margin obtained from the proposed algorithms.…”

Section: A Heterogeneous Cran (H-cran)mentioning

confidence: 99%

A comprehensive survey on resource allocation for CRAN in 5G and beyond networks

Ejaz

Sharma

Saadat

et al. 2020

Journal of Network and Computer Applications

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The diverse service requirements coming with the advent of sophisticated applications as well as a large number of connected devices demand for revolutionary changes in the traditional distributed radio access network (RAN). To this end, Cloud-RAN (CRAN) is considered as an important paradigm to enhance the performance of the upcoming fifth generation (5G) and beyond wireless networks in terms of capacity, latency, and connectivity to a large number of devices. Out of several potential enablers, efficient resource allocation can mitigate various challenges related to user assignment, power allocation, and spectrum management in a CRAN, and is the focus of this paper. Herein, we provide a comprehensive review of resource allocation schemes in a CRAN along with a detailed optimization taxonomy on various aspects of resource allocation. More importantly, we identity and discuss the key elements for efficient resource allocation and management in CRAN, namely: user assignment, remote radio heads (RRH) selection, throughput maximization, spectrum management, network utility, and power allocation. Furthermore, we present emerging use-cases including heterogeneous CRAN, millimeter-wave CRAN, virtualized CRAN, Non-Orthogonal Multiple Access (NoMA)-based CRAN and fullduplex enabled CRAN to illustrate how their performance can be enhanced by adopting CRAN technology. We then classify and discuss objectives and constraints involved in CRAN-based 5G and beyond networks. Moreover, a detailed taxonomy of optimization methods and solution approaches with different objectives is presented and discussed. Finally, we conclude the paper with several open research issues and future directions.

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Optimization of Power Consumption and Handover Margin of Sleep/Active Cells in Dynamic H-CRAN

Cited by 7 publications

References 13 publications

Optimizing Path Loss Prediction: Advancements in Signal Parameter Estimation through Wavelet Neural Networks

Optimizing Path Loss Prediction: Advancements in Signal Parameter Estimation through Wavelet Neural Networks

Energy-Efficient Ultra-Dense 5G Networks: Recent Advances, Taxonomy and Future Research Directions

A comprehensive survey on resource allocation for CRAN in 5G and beyond networks

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