A Light Signaling Approach to Node Grouping for Massive MIMO IoT Networks
(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.
(Less)
- author
- Fitzgerald, Emma LU ; Pióro, Michał LU ; Tataria, Harsh LU ; Callebaut, Gilles ; Gunnarsson, Sara LU and Van der Perre, Liesbet LU
- organization
- publishing date
- 2022-06
- 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}}, }