# A Light Signaling Approach to Node Grouping for Massive MIMO IoT Networks
## 2 Motivation  
Many IoT devices are battery-powered and transmit only infrequently, spending the rest of their time in a sleep state. (We recognise that some IoT devices may need to constantly send or receive data; however, here we focus on typical sensors with infrequent transmissions.) Among these devices, there is a large variation in the timescale when transmission and reception need to take place, with the absolute timescale varying from minutes up to tens of days or longer. At such timescales, efficient node grouping and scheduling become difficult, since information about the node traffic and propagation channels can quickly become outdated, unless additional signaling is introduced, which in turn requires nodes to wake up and transmit more often, consuming more energy. Node grouping consists of partitioning the nodes into groups such that each group can be accommodated for simultaneous transmission in the same coherence block, resulting in a combination of spatial and time multiplexing. This subsequently allows the scheduling of node groups rather than individual nodes, thus reducing the complexity of the scheduling problem. For low-power IoT devices, this partition should be based on node properties that are stable over a considerable time, so that schedules can cover a reasonable number of transmissions, thus reducing signaling and computation costs for scheduling. In addition, node properties used for grouping must also be informative for network performance. To this end, interference within the node groups should be minimized by exploiting characteristics of propagation channels that remain stable over a long time, ideally at least over some tens of node transmissions, and as long as possible to allow for long scheduling windows. This is the subject of this paper. One possible candidate for a stable channel property is long-term average signal strength, characterized by the average received power. This property can have a significant impact on the inter-node interference in massive MIMO networks, particularly when simple maximum ratio combining (MRC) is used for precoding [3]. Furthermore, the average received power can be effectively used for node grouping and scheduling [4]. Nevertheless, inter-node interference can also be strongly affected by the directional properties of the users' channels. Indeed, measurement campaigns have shown that propagation energy typically features dominant directions, and even in reflective environments, real channel responses are far from the \"ideal\" independent and identically distributed (i.i.d.) Rayleigh fading channels commonly assumed in theory [5; 6; 7]. This observation suggests that node partitioning that avoids simultaneously scheduling nodes with propagation energy coming from similar directions can actually prevent inter-node interference to a large extent. It is these solutions that we investigate in this work. In terms of the ergodic sum spectral efficiency, there is naturally a price to pay when reducing inter-node interference through user grouping. However, given the low data rates required by typical IoT applications, energy efficiency is a more important performance metric for this use case than spectral efficiency. We develop a node partitioning method for massive MIMO systems based on two directional properties of the nodes' channels, namely, the dominant direction--the angle of arrival of the strongest channel component--and the angular spectrum spread--how widely the channel components are distributed over the possible range of angles of arrival. These properties are stable over larger timescales than the full CSI, which changes according to the channel's coherence time, and are hence also robust against non-perfect CSI values, easy to collect at the base station with minimal signaling, and correlate with inter-node interference. Specifically, we seek to partition the nodes so as to maximize the minimum angular difference between nodes across multiple different groups according to their angular spectrum spreads.