Transmit antenna selection for massive MIMO: A knapsack problem formulation

Ryan Husbands, Qasim Ahmed, Junyuan Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Massive multiple input multiple output communication is now possible using millimeter wave frequency band. In this paper a transmit antenna selection algorithm is developed which satisfies a quality of service (QoS) for a given user. In order to achieve a particular level of QoS, the number of transmit antennas required is determined by remodeling it as a Knapsack Problem (KP). The smallest subset of antenna elements is found at the transmitter side to achieve the desired level of QoS using KP. Furthermore, we have compared our algorithm with the sorted and unsorted sequential selection algorithm (SSA). Our algorithm achieves similar performance as compared to SSA but with lower computational complexity than the sorted SSA. Moreover, the energy efficiency of our algorithm is similar to that of the sorted SSA but superior to unsorted SSA, as it is not sensitive to the arrangement of the antenna gains.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Communications, ICC 2017
EditorsMerouane Debbah, David Gesbert, Abdelhamid Mellouk
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781467389990
DOIs
Publication statusPublished - 28 Jul 2017
Externally publishedYes
Event2017 IEEE International Conference on Communications, ICC 2017 - Paris, France
Duration: 21 May 201725 May 2017

Conference

Conference2017 IEEE International Conference on Communications, ICC 2017
CountryFrance
CityParis
Period21/05/1725/05/17

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Husbands, R., Ahmed, Q., & Wang, J. (2017). Transmit antenna selection for massive MIMO: A knapsack problem formulation. In M. Debbah, D. Gesbert, & A. Mellouk (Eds.), 2017 IEEE International Conference on Communications, ICC 2017 [7996694] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICC.2017.7996694