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A Light Signaling Approach to Node Grouping for Massive MIMO IoT Networks

Fitzgerald, Emma LU orcid ; Pióro, Michał LU ; Tataria, Harsh LU ; Callebaut, Gilles ; Gunnarsson, Sara LU and Van der Perre, Liesbet LU (2022) In Computers 11(6).
Abstract

Massive MIMO is one of the leading technologies for connecting very large numbers of energy-constrained nodes, as it offers both extensive spatial multiplexing and large array gain. A challenge resides in partitioning the many nodes into groups that can communicate simultaneously such that the mutual interference is minimized. Here we propose node partitioning strategies that do not require full channel state information, but rather are based on nodes’ respective directional channel properties. In our considered scenarios, these typically have a time constant that is far larger than the coherence time of the channel. We developed both an optimal and an approximation algorithm to partition users based on directional channel properties,... (More)

Massive MIMO is one of the leading technologies for connecting very large numbers of energy-constrained nodes, as it offers both extensive spatial multiplexing and large array gain. A challenge resides in partitioning the many nodes into groups that can communicate simultaneously such that the mutual interference is minimized. Here we propose node partitioning strategies that do not require full channel state information, but rather are based on nodes’ respective directional channel properties. In our considered scenarios, these typically have a time constant that is far larger than the coherence time of the channel. We developed both an optimal and an approximation algorithm to partition users based on directional channel properties, and evaluated them numerically. Our results show that both algorithms, despite using only these directional channel properties, achieve similar performance in terms of the minimum signal-to-interference-plus-noise ratio for any user, compared with a reference method using full channel knowledge. In particular, we demonstrate that grouping nodes with related directional properties is to be avoided. We hence realize a simple partitioning method, requiring minimal information to be collected from the nodes, and in which this information typically remains stable over the long term, thus promoting the system’s autonomy and energy efficiency.

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Please use this url to cite or link to this publication:
author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
energy efficiency, IoT, massive MIMO, user grouping
in
Computers
volume
11
issue
6
article number
98
publisher
MDPI AG
external identifiers
  • scopus:85132763435
ISSN
2073-431X
DOI
10.3390/computers11060098
language
English
LU publication?
yes
additional info
Funding Information: Funding: The work of E. Fitzgerald was supported by the Celtic-Next project IMMINENCE, the SSF project SEC4FACTORY under grant no. SSF RIT17-0032, and the strategic research area ELLIIT. The work of M. Pióro was supported by the National Science Centre, Poland, under the grant no. 2017/25/B/ST7/02313: “Packet routing and transmission scheduling optimization in multi-hop wireless networks with multicast traffic”. The work of H. Tataria was partly supported by Ericsson AB, Sweden. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
id
0913f1d3-3dfb-4f05-90db-db80141c04df
date added to LUP
2023-04-05 12:02:11
date last changed
2023-11-21 08:20:20
@article{0913f1d3-3dfb-4f05-90db-db80141c04df,
  abstract     = {{<p>Massive MIMO is one of the leading technologies for connecting very large numbers of energy-constrained nodes, as it offers both extensive spatial multiplexing and large array gain. A challenge resides in partitioning the many nodes into groups that can communicate simultaneously such that the mutual interference is minimized. Here we propose node partitioning strategies that do not require full channel state information, but rather are based on nodes’ respective directional channel properties. In our considered scenarios, these typically have a time constant that is far larger than the coherence time of the channel. We developed both an optimal and an approximation algorithm to partition users based on directional channel properties, and evaluated them numerically. Our results show that both algorithms, despite using only these directional channel properties, achieve similar performance in terms of the minimum signal-to-interference-plus-noise ratio for any user, compared with a reference method using full channel knowledge. In particular, we demonstrate that grouping nodes with related directional properties is to be avoided. We hence realize a simple partitioning method, requiring minimal information to be collected from the nodes, and in which this information typically remains stable over the long term, thus promoting the system’s autonomy and energy efficiency.</p>}},
  author       = {{Fitzgerald, Emma and Pióro, Michał and Tataria, Harsh and Callebaut, Gilles and Gunnarsson, Sara and Van der Perre, Liesbet}},
  issn         = {{2073-431X}},
  keywords     = {{energy efficiency; IoT; massive MIMO; user grouping}},
  language     = {{eng}},
  number       = {{6}},
  publisher    = {{MDPI AG}},
  series       = {{Computers}},
  title        = {{A Light Signaling Approach to Node Grouping for Massive MIMO IoT Networks}},
  url          = {{http://dx.doi.org/10.3390/computers11060098}},
  doi          = {{10.3390/computers11060098}},
  volume       = {{11}},
  year         = {{2022}},
}