Big data-driven optimization for mobile networks toward 5G

Zheng, Kan; Yang, Zhe; Zhang, Kuan; Chatzimisios, Periklis; Yang, Kang; Wang, Xiang

doi:10.1109/mnet.2016.7389830

Cited by 266 publications

(145 citation statements)

References 15 publications

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“…In terms of caching solutions, several approaches are being considered, such as in [17], [82], [85], [86], [189], [246].…”

Section: B Cachingmentioning

confidence: 99%

“…In [17], the authors explore various ways of integrating big data analytic with network resource optimization and caching deployment. The authors propose a big data-driven framework, which involves the collection, storage and analysis of the data and apply it to two different case studies.…”

Section: B Cachingmentioning

confidence: 99%

“…One example is the work in [17], in which the authors explore various ways of integrating big data in the mobile network. In this paper, the authors propose a big data-driven framework and analyze use cases in terms of resource management, caching and QoE.…”

Section: G Resource Optimizationmentioning

confidence: 99%

See 2 more Smart Citations

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

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

412

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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“…In terms of caching solutions, several approaches are being considered, such as in [17], [82], [85], [86], [189], [246].…”

Section: B Cachingmentioning

confidence: 99%

Section: B Cachingmentioning

confidence: 99%

See 1 more Smart Citation

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

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

412

200

View full text Add to dashboard Cite

show abstract

“…Among these latencies, some are deterministic processing latency, some are latencies related to signal detection, and others are random access latencies related to business activity [22]. Most projects of current research focus on computing of mean value and variance for random access latency; there are few projects of research that focus on probability density function (PDF) [23][24][25] of random access latency [26][27][28]. With quantity of waiting users and channel busy/idle as state variables, the moment generating function (MGF) for PDF of random access latency is deduced based on Markov process in [25,29,30].…”

Section: Related Workmentioning

confidence: 99%

RADB: Random Access with Differentiated Barring for Latency‐Constrained Applications in NB‐IoT Network

Miao

Tian

Cheng

et al. 2018

Wireless Communications and Mobile Computing

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With the development of LPWA (Low Power Wide Area) technology, the emerging NB-IoT (Narrowband Internet of Things) technology is becoming popular with wide area and low-data-rate services. In order to achieve objectives such as huge amount of connection and wide area coverage within NB-IoT, the problem of network congestion generated by random access of numerous devices should be solved. In this paper, we first introduce the background of NB-IoT and investigate the research on random access optimization algorithm. Then we summarize relevant features of NB-IoT uplink and narrowband physical random access channel and design random access with differentiated barring (RADB), which can improve the insufficiency of traditional dynamic access class barring method. At last, the algorithms proposed in this paper are realized with established NB-IoT model using OPNET Modeler platform, and simulations are conducted. The simulation results show that RADB is able to effectively solve preamble request conflict generated by random access of numerous devices and preferentially provide efficient and reliable random access for latency-sensitive devices.

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“…Anonymized user mobility traces are increasingly collected by Internet Service Providers (ISP) to assist various applications, ranging from network optimization [42] to user population estimation and urban planning [11]. Meanwhile, detailed location traces contain sensitive information about individual users (e.g., home and work location, personal habits).…”

Section: Introductionmentioning

confidence: 99%

De-anonymization of Mobility Trajectories: Dissecting the Gaps between Theory and Practice

Wang

Gao

Yong

et al. 2018

Proceedings 2018 Network and Distributed System Security Symposium

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Abstract-Human mobility trajectories are increasingly collected by ISPs to assist academic research and commercial applications. Meanwhile, there is a growing concern that individual trajectories can be de-anonymized when the data is shared, using information from external sources (e.g. online social networks). To understand this risk, prior works either estimate the theoretical privacy bound or simulate de-anonymization attacks on synthetically created (small) datasets. However, it is not clear how well the theoretical estimations are preserved in practice.In this paper, we collected a large-scale ground-truth trajectory dataset from 2,161,500 users of a cellular network, and two matched external trajectory datasets from a large social network (56,683 users) and a check-in/review service (45,790 users) on the same user population. The two sets of large ground-truth data provide a rare opportunity to extensively evaluate a variety of de-anonymization algorithms (7 in total). We find that their performance in the real-world dataset is far from the theoretical bound. Further analysis shows that most algorithms have underestimated the impact of spatio-temporal mismatches between the data from different sources, and the high sparsity of user generated data also contributes to the underperformance. Based on these insights, we propose 4 new algorithms that are specially designed to tolerate spatial or temporal mismatches (or both) and model user behavior. Extensive evaluations show that our algorithms achieve more than 17% performance gain over the best existing algorithms, confirming our insights.

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Big data-driven optimization for mobile networks toward 5G

Cited by 266 publications

References 15 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

RADB: Random Access with Differentiated Barring for Latency‐Constrained Applications in NB‐IoT Network

De-anonymization of Mobility Trajectories: Dissecting the Gaps between Theory and Practice

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