Adaptive virtual MIMO single cluster optimization in a small cell

Kanakis, T., Opoku Agyeman, M. and Bakaoukas, A. G. (2017) Adaptive virtual MIMO single cluster optimization in a small cell. In: 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence :. IEEE.

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Item Type:	Book Section
Abstract:	Adaptive Virtual MIMO optimized in a single cluster of small cells is shown in this paper to achieve near Shannon channel capacity when operating with partial or no Channel State Information. Although, access links have enormously increased in the recent years, the operational system complexity remains linear regardless of the number of access nodes in the system proposed. Adaptive Virtual MIMO optimized in a single cluster performs a theoretical information spectral efficiency, almost equal to that of the upper bounds of a typical mesh network, up to 43 bits/s/Hz at a SNR of 30dB while the BER performance remains impressively low hitting the 10−6 at an SNR of about 13 dB when the theoretical upper bound of an ideal small cell mesh network achieves the 10−6 at a SNR of 12.5 dB. In addition, in a sub-optimum channel condition, the channel capacity and BER performance of the proposed solution is shown to drastically delay saturation even for the very high SNR.
Uncontrolled Keywords:	Adaptive multiuser detection, MIMO, small cell, single cluster, inter-cell interference, partial channel state information
Creators:	Kanakis, Triantafyllos, Opoku Agyeman, Michael and Bakaoukas, Anastasios G
Publisher:	IEEE
Faculties, Divisions and Institutes:	Faculties > Faculty of Arts, Science & Technology > Computing
Date:	13 January 2017
Date Type:	Publication
Title of Book:	2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence :
Event Title:	7th International Conference on Cloud Computing, Data Science & Engineering
Event Dates:	2017-01-13
Event Location:	Amity University, Noida, India
Language:	English
ISBN:	978-1-5090-3519-9
DOI:	https://doi.org/10.1109/CONFLUENCE.2017.7943198
Status:	Published / Disseminated
Refereed:	No
URI:	http://nectar.northampton.ac.uk/id/eprint/9026

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