Dynamic network configuration: Hotspot identification for Virtual Small Cells

Santana, Thomas Varela; Galindo-Serrano, Ana; Sayraç, Berna; Lopez, Sofia Martinez

doi:10.1109/iswcs.2016.7600873

Cited by 8 publications

(2 citation statements)

References 8 publications

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“…The location of clustered users or hotspots present in the network have also been utilized in [78], [120]- [122] wherein network configuration parameters (antenna parameters) are optimized w.r.t the identified hotspots. The underlying phenomenon is inherently the same as in case of dynamic CoMP Clustering since network parameters optimization is done based on hotspot location.…”

Section: Backhaul Bandwidth and Spectral Efficiency Jointly Optimisedmentioning

confidence: 99%

Coordinated Multi-Point Clustering Schemes: A Survey

Bassoy

Farooq

Imran

et al. 2017

IEEE Commun. Surv. Tutorials

162

109

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Abstract-Mobile data traffic grew by 74% in 2015 and it's expected to grow 8-fold by 2020. Future wireless networks will need to deploy massive number of small cells to cope with this increasing demand. Dense deployment of small cells will require advanced interference mitigation techniques to improve spectral efficiency and enhance much needed capacity. Coordinated multi-point (CoMP) is a key feature for mitigating intercell interference, improve throughput and cell edge performance. However, cooperation will need to be limited to few cells only due to additional overhead required by CoMP due to channel state information (CSI) exchange, scheduling complexity and additional backhaul limitation. Hence small CoMP clusters will need to be formed in the network. This article surveys the stateof-the-art on one of the key challenges of CoMP implementation: CoMP clustering. As a starting point, we present the need for CoMP, the clustering challenge for 5G wireless networks and provide a brief essential background about CoMP and the enabling network architectures. We then provide the key framework for CoMP clustering and introduce self organisation as an important concept for effective CoMP clustering to maximise CoMP gains. Next, we present two novel taxonomies on existing CoMP clustering solutions, based on self organisation and aimed objective function. Strengths and weaknesses of the available clustering solutions in the literature are critically discussed. We then discuss future research areas and potential approaches for CoMP clustering. We present a future outlook on the utilisation of Big Data in cellular context to support proactive CoMP clustering based on prediction modelling. Finally we conclude this paper with a summary of lessons learnt in this field. This article aims to be a key guide for anyone who wants to research on CoMP clustering for future wireless networks.

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Section: Backhaul Bandwidth and Spectral Efficiency Jointly Optimisedmentioning

confidence: 99%

Coordinated Multi-Point Clustering Schemes: A Survey

Bassoy

Farooq

Imran

et al. 2017

IEEE Commun. Surv. Tutorials

162

109

View full text Add to dashboard Cite

show abstract

“…• Dynamic Network Configuration. Dynamic network configuration can improve the energy efficiency of IoT devices through communication optimization [29]. It allows IoT devices to make adjustments according to changing network conditions, such as the signal strength, network load, and interference.…”

Section: Software Energy-saving Techniquesmentioning

confidence: 99%

A Survey on Energy-Aware Security Mechanisms for the Internet of Things

He,

Zhou,

Qin

2024

Future Internet

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The Internet of Things (IoT) employs sensors and the Internet for information exchange, enabling intelligent identification, monitoring, and management, which has deeply impacted various sectors such as power, medical care, and security, transforming social activities and lifestyles. Regrettably, IoT systems suffer from two main challenges, namely sustainability and security. Hence, pondering how to enhance sustainable and energy-efficient practices for IoT systems to mitigate risks becomes a worthwhile endeavor. To address this issue, we conduct a survey of energy-aware security mechanisms in the Internet of Things. Specifically, we examine the challenges that IoT is facing in terms of energy efficiency and security, and we inspect current energy-saving and privacy-preserving technologies for IoT systems. Moreover, we delineate a vision for the future of IoT, emphasizing energy-aware security mechanisms. Finally, we outline the challenges encountered in achieving energy-aware security mechanisms, as well as the direction of future research. Motivated by this study, we envision advancements in the IoT that not only harness the benefits of science and technology but also enhance the security and safety of our data.

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Hotspots Prediction Based on LSTM Neural Network for Cellular Networks

Zhou

Chen

Dong

et al. 2020

J. Phys.: Conf. Ser.

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The tremendous growth in data traffic usage is a prominent challenge to cellular network operators. The aggregation of a small number of users’ services may lead to considerably high loads on base stations, thus generating traffic hotspots in the networks. To provide better quality of service in dynamic network scenarios efficiently, it is necessary to accurately distinguish and predict potential hotspots, so as to adjust resource configuration and allocation in advance and keep the network running smoothly. In this paper, a network traffic hotspots prediction method is proposed based on the Long Short-Term Memory (LSTM) neural network framework. To verify its effectiveness, real-world data are utilized in our experiments. Numerical results demonstrate that a time series model based on the LSTM framework can achieve 92.2% success rate for future hotspots prediction.

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Dynamic network configuration: Hotspot identification for Virtual Small Cells

Cited by 8 publications

References 8 publications

Coordinated Multi-Point Clustering Schemes: A Survey

Coordinated Multi-Point Clustering Schemes: A Survey

A Survey on Energy-Aware Security Mechanisms for the Internet of Things

Hotspots Prediction Based on LSTM Neural Network for Cellular Networks

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