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Published in: Wireless Networks 5/2019

14-05-2019

Resource allocation and interference suppression with PCA for multicell MU-MIMO systems

Authors: Szu-Lin Su, Tsung-Hsiu Chih, Tai-Yeh Wu

Published in: Wireless Networks | Issue 5/2019

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Abstract

This paper studies a resource allocation scheme and precoding design to increase the number of quality of service (QoS)-satisfied user equipments (UEs) for multicell multiuser multiple-input-multiple-output downlink systems. The main challenge in such multicell systems is the co-channel interference, including intra-user interference (IUI) and inter-cell interference (ICI). To deal with both IUI and ICI, most conventional schemes aim to maximize the signal to interference plus noise ratio or throughputs through enormous channel state information (CSI) exchange among base stations. In order to avoid such tremendous overhead, this work proposes a novel framework, containing a distributed subcarrier assignment and a block diagonalization (BD) precoding, to cope with both types of interferences based on limited exchanged information of subcarrier-assignment and local CSIs (channel gains). In the subcarrier-assignment process, each sector’s base station independently assigns a subset of the served UEs to be served over the subcarrier. The channel gains of a particular subcarrier hold a semi-orthogonal property for that subset. Then, on the basis of limited exchanges of information, a BD-type precoding is presented to completely eliminate all IUI and suppress the major part of the leakage interference to the adjacent sectors by exploiting principal component analysis. The simulation results indicate that the proposed framework can provide more QoS-satisfied UEs than that obtained in a previous work.

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Metadata
Title
Resource allocation and interference suppression with PCA for multicell MU-MIMO systems
Authors
Szu-Lin Su
Tsung-Hsiu Chih
Tai-Yeh Wu
Publication date
14-05-2019
Publisher
Springer US
Published in
Wireless Networks / Issue 5/2019
Print ISSN: 1022-0038
Electronic ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-019-02004-8

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