Efficient Resource Management for Massive MIMO in High-Density Massive IoT Networks

Massive MIMO technology offers a promising solution for supporting the simultaneous connectivity of a large number of ultra high-density massive IoT devices. However, due to limited resources, effective channel estimation becomes a challenge. One approach is to reuse the orthogonal reference signal (ORS) in a repetitive manner, while another option is to employ random non-orthogonal reference signals (NORS) for distributed massive IoT devices. In order to enhance performance in the face of high RS congestion from massive IoT devices, the strategic utilization of several critical resources becomes imperative. In this paper, we delve into the methodology of resource management to effectively address the severe high-density scenario of massive IoT devices. Specifically, we examine the system bandwidth, which proves particularly advantageous in bandwidth-limited environments. Exploiting the spatial domain, we leverage the distinctive features of massive MIMO to enable simultaneous parallel transmission by increasing the number of service antennas at the base station (BS). Furthermore, we explore the potential benefits of sectorization, a technique that involves dividing a circular cell into multiple sectors, thereby reducing RS congestion. Nevertheless, it is crucial to acknowledge that increasing these resources may entail certain trade-offs and could potentially have adverse effects on overall system performance. To gain comprehensive insights, we conduct a thorough performance analysis under various scenarios, aiming to identify key characteristics that can facilitate the optimal operation of massive MIMO in high-density IoT environments. Building upon our findings, we devise an algorithm that efficiently manages resources, ultimately leading to improved system performance.