A Light Signalling Approach to Node Grouping for Massive MIMO IoT Networks
(2020) In IEEE Transactions on Communications- Abstract
- Massive MIMO is a promising technology to connect 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 in groups that can communicate simultaneously such that the mutual interference is minimized. We here 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... (More)
- Massive MIMO is a promising technology to connect 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 in groups that can communicate simultaneously such that the mutual interference is minimized. We here 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 realise a simple partitioning method requiring minimal information to be collected from the nodes, and where this information typically remains stable over a long term, thus promoting their autonomy and energy efficiency. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/aa725d1e-769f-4587-844b-82b882f245e4
- author
- Fitzgerald, Emma LU ; Pioro, Michal LU ; Tataria, Harsh LU ; Callebaut, Gilles ; Gunnarsson, Sara LU and van Der Perre, Liesbet LU
- organization
- publishing date
- 2020-05-11
- type
- Contribution to journal
- publication status
- submitted
- subject
- keywords
- Massive MIMO, IoT, user grouping, energy efficiency
- in
- IEEE Transactions on Communications
- pages
- 13 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- ISSN
- 0096-1965
- project
- Cyber Security for Next Generation Factory (SEC4FACTORY)
- Massive Mimo Technology and Applications
- language
- English
- LU publication?
- yes
- additional info
- Submitted for publication to the IEEE Transactions on Wireless Communications
- id
- aa725d1e-769f-4587-844b-82b882f245e4
- alternative location
- https://arxiv.org/pdf/2005.05048.pdf
- date added to LUP
- 2020-05-16 17:50:12
- date last changed
- 2021-10-29 16:19:38
@article{aa725d1e-769f-4587-844b-82b882f245e4, abstract = {{Massive MIMO is a promising technology to connect 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 in groups that can communicate simultaneously such that the mutual interference is minimized. We here 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 realise a simple partitioning method requiring minimal information to be collected from the nodes, and where this information typically remains stable over a long term, thus promoting their autonomy and energy efficiency.}}, author = {{Fitzgerald, Emma and Pioro, Michal and Tataria, Harsh and Callebaut, Gilles and Gunnarsson, Sara and van Der Perre, Liesbet}}, issn = {{0096-1965}}, keywords = {{Massive MIMO; IoT; user grouping; energy efficiency}}, language = {{eng}}, month = {{05}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, series = {{IEEE Transactions on Communications}}, title = {{A Light Signalling Approach to Node Grouping for Massive MIMO IoT Networks}}, url = {{https://arxiv.org/pdf/2005.05048.pdf}}, year = {{2020}}, }