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

Research output: Contribution to journalArticle

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.

Details

Authors
Organisations
External organisations
  • Catholic University of Leuven
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Communication Systems
  • Signal Processing

Keywords

  • Massive MIMO, IoT, user grouping, energy efficiency
Original languageEnglish
Number of pages13
JournalIEEE Transactions on Wireless Communications
Publication statusSubmitted - 2020 May 11
Publication categoryResearch
Peer-reviewedYes

Bibliographic note

Submitted for publication to the IEEE Transactions on Wireless Communications

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