Challenges in 5G: how to empower SON with big data for enabling 5G

By Muhammad Ali Imran, Ahmed Zoha. IEEE Network, 2014

434 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/3329f0d0cd0d01ae00a0bf58afc151d555d53bc0

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Next Generation 5G Wireless Networks: A Comprehensive Survey (2016, background): “by reducing human intervention [185], [186].”
A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions (2017, methodology): “latency (i.e., network and processing latency). The optimization problem is formulated as a resource constrained shortest path problem on an auxiliary layered graph followed by initial evaluation. In [185], the authors proposed a detailed approach to TABLE XV:PROPOSED CORE NETWORK ENTITIES FOR 5G VI”
Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both? (2019, background): “At present, exactly the exponential increase of wireless devices results in a corresponding growth of traffic data [25]–[27].”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, background): “of wireless communication is studied in [291] and the study shows that without (supervised as well as unsupervised) ML support, SON is not possible.”
Intelligent 5G: When Cellular Networks Meet Artificial Intelligence (2017, background): “[4].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “[55] An introductions to big data analysis for self-organizing networks (SON) in 5G.”
A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks (2017, methodology): “MDS, on the other hand, tries to reduce the dimension of the original data set such that the distance between the items in the transformed space reflects the proximity in the original data.”
Security for 5G and Beyond (2019, methodology): “Using the concepts of AI or big data in physical layer as described in [396] for improving the data or network security needs further research.”
Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions (2018, background): “Optimizing the operation of cellular networks in dynamic wireless environments has been challenging over the generations since the number of conﬁgurable parameters of a cellular network has been rapidly increasing from one cellular generation to the next one [15, 16].”
Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions (2019, background): “In fact, the influence of ML-enabled mobile and wireless network communications has already been made apparent by a number of recent networking paradigms such as locationbased services [5], mobile edge caching [6], [7], contextaware networking [8], big data analytics [9], [10], mobile edge computing”
Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues (2016, background): “In the context of mobile networks, the big data, described by volume, variety, velocity, and value, includes subscriberlevel, cell-level, core-network-level, and other level data, which can facilitate the network towards a more proactive one [138].”