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Computationally Intelligent Techniques for Resource Management in MmWave Small Cell Networks

Munir, H and Pervaiz, H and Hassan, SA and Musavian, L and Ni, Q and Imran, MA and Tafazolli, R (2018) 'Computationally Intelligent Techniques for Resource Management in MmWave Small Cell Networks.' IEEE Wireless Communications, 25 (4). 32 - 39. ISSN 1070-9916

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Computationally%20Intelligent%20Techniques%20for%20Resource%20Management%20in%20mmWave%20Small%20Cell%20Networks.pdf - Accepted Version

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Abstract

Ultra densification in HetNets and the advent of mmWave technology for 5G networks have led researchers to redesign the existing resource management techniques. A salient feature of this activity is to accentuate the importance of CI resource allocation schemes offering less complexity and overhead. This article overviews the existing literature on resource management in mmWave-based Het- Nets with a special emphasis on CI techniques and further proposes frameworks that ensure quality of service requirements for all network entities. More specifically, HetNets with mmWave-based small cells pose different challenges compared to an all-microwave- based system. Similarly, various modes of small cell access policies and operations of base stations in dual mode, that is, operating both mmWave and microwave links simultaneously, offer unique challenges to resource allocation. Furthermore, the use of multi-slope path loss models becomes inevitable for analysis due to irregular cell patterns and blocking characteristics of mmWave communications. This article amalgamates the unique challenges posed because of the aforementioned recent developments and proposes various CI-based techniques, including game theory and optimization routines, to perform efficient resource management.

Item Type: Article
Uncontrolled Keywords: Resource management, 5G mobile communication, Optimization, Cellular networks, Game theory, Complexity theory, Long Term Evolution, Millimeter wave technology, Self-organizing networks, Ultra-dense networks
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 24 Sep 2018 09:34
Last Modified: 24 Sep 2018 09:34
URI: http://repository.essex.ac.uk/id/eprint/23089

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