Bi-SON: Big-Data Self Organizing Network for Energy Efficient Ultra-Dense Small Cells

Wang, Li-Chun; Cheng, Shao-Hung; Tsai, Ang-Hsun

doi:10.1109/vtcfall.2016.7880924

Cited by 12 publications

(14 citation statements)

References 7 publications

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“…Lastly, the work in [258] utilizes big data, together with supervised learning (polynomial regression), in order to optimize the energy of ultra dense cellular networks. The authors show that the proposed solution can achieve the highest cell throughput while maintaining energy efficiency, when compared to conventional approaches.…”

Section: G Resource Optimizationmentioning

confidence: 99%

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

416

200

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Abstract-In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future.

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Section: G Resource Optimizationmentioning

confidence: 99%

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

416

200

View full text Add to dashboard Cite

show abstract

“…Thus, how to determine the appropriate number of low power cells in UDSC is an interesting research problem. In our previous work [9], a base station data-driven supervised learning method was proposed to model the throughput performance of UDSC, taking into account of dynamic interference and nonuniform traffic loads. It assumes that the labeled throughput data for a set of system parameters is available.…”

Section: Motivationmentioning

confidence: 99%

“…In addition, we further compare the performance of the proposed APPC mechanism with the optimal solution based on the exhaustive searching algorithm. Furthermore, we compare APPC with a data-driven scheme that jointly considers the optimization of interference reduction and power saving [9]. Fig.…”

Section: ) We Observe That Increasing the Ratio Of User Densitymentioning

confidence: 99%

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Data-Driven Resource Management for Ultra-Dense Small Cells: An Affinity Propagation Clustering Approach

Wang

Cheng

2019

IEEE Trans. Netw. Sci. Eng.

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Deploying dense small cells is the key to providing high capacity, but raise the serious issue of energy consumption and inter-cell interference. To understand the behaviors of ultra-dense small cells (UDSC) with dynamic interference and traffic patterns, this paper presents a data-driven resource management (DDRM) framework to implement power control and channel rearrangement in UDSC. We find that the inter-cell interference can be used to describe the affinity of cells. Thus, we propose an unsupervised learning algorithm for UDSC, called affinity propagation power control (APPC) mechanism. In principle, APPC first groups small cells into different clusters and identifies cluster centers. Next, the transmission power of a cluster center is decreased to reduce the interference to the neighboring cells' users in this cluster. Since lowering transmission power of a cluster center cell may cause the performance degradation to the users at the cell edge, a victim-aware channel rearrangement (VACR) mechanism is further designed to adjust the channel usage bandwidth of the neighboring cells in order to guarantee the quality of service of these victimized users. Our simulation results show that the DDRM framework can significantly improve energy efficiency and throughput in UDSC compared to the existing approaches.

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“…For simplicity, we assume that the polynomial curve of the static model is generated for 1300 users/km 2 . For comparison, the performance of UDSC with IA cell ranking in our previous work [42] was shown in the figure. From the figure, we have the following observations.…”

Section: A Effect Of Rss Thresholdmentioning

confidence: 99%

Self-Organizing Ultra-Dense Small Cells in Dynamic Environments: A Data-Driven Approach

Wang

Cheng

2019

IEEE Systems Journal

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This paper presents a data-driven biadaptive selforganizing network (Bi-SON) for ultra-dense small cells (UDSC), which can improve energy efficiency and reduce interference in dynamic environments, taking account of cell switching ON/OFF, transmission power adjustment, and traffic loads simultaneously. In the first adaptation of BiSON , a joint traffic load and interference aware cell ranking mechanism first determines the necessary active small cells based on traffic loads, and then ranks all the active small cells based on their carried traffic load and resulting interference. Top ranked cells will transmit at the maximum power. The last ranked K cells will adjust the transmission power for interference reduction in the second adaptation function of BiSON , while maintaining the required quality of service. According to a polynomial regression learning approach, the total system throughput of UDSC is characterized as a function of K. Compared to the baseline case when all the cells transmit with the maximum power, our proposed BiSON framework can improve the throughput and energy efficiency of UDSC by 73% and 169%, respectively. However, the pure switching ON/OFF approach can only improve the throughput and the energy efficiency of UDSC by 52% and 115%, respectively. As demonstrated, even with a simple power adaptation algorithm, a learningbased BiSON framework can improve the performance of UDSC by taking advantage of the pervasive availability of voluminous data.

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Bi-SON: Big-Data Self Organizing Network for Energy Efficient Ultra-Dense Small Cells

Cited by 12 publications

References 7 publications

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Data-Driven Resource Management for Ultra-Dense Small Cells: An Affinity Propagation Clustering Approach

Self-Organizing Ultra-Dense Small Cells in Dynamic Environments: A Data-Driven Approach

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