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Massive MIMO is a leading technology to connect very large numbers of energyconstrained nodes, as it offers both extensive spatial multiplexing and largearray gain. A challenge resides in partitioning the many nodes into groups thatcan communicate simultaneously such that the mutual interference is minimized.We here propose node partitioning strategies that do not require full channelstate information, but rather are based on nodes' respective directionalchannel properties. In our considered scenarios, these typically have a timeconstant that is far larger than the coherence time of the channel. Wedeveloped both an optimal and an approximation algorithm to partition usersbased on directional channel properties, and evaluated them numerically. Ourresults show that both algorithms, despite using only these directional channelproperties, achieve similar performance in terms of the minimumsignal-to-interference-plus-noise ratio for any user, compared with a referencemethod using full channel knowledge. In particular, we demonstrate thatgrouping nodes with related directional properties is to be avoided. We hencerealise a simple partitioning method requiring minimal information to becollected from the nodes, and where this information typically remains stableover a long term, thus promoting their autonomy and energy efficiency.

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