Leveraging Massive MIMO for Enhanced Energy Efficiency in High-Density IoT Networks

Abstract

Maximizing energy efficiency (EE) in massive multiple-input multiple-output (MIMO) systems, while supporting the rapid expansion of Internet of Things (IoT) devices, is a critical challenge. In this paper, we delve into the intricate operations geared toward enhancing EE in such complex environments. To effectively support a multitude of IoT devices, we adopt a strategy of heavy reference signal (RS) reuse, and in this circumstance, we formulate the EE metrics and their corresponding inverses to determine pivotal operational parameters. These EE-centric parameters encompass factors such as the number of service antennas in the base station (BS), the number of IoT devices, and permissible coverage extents. Our objective is to calibrate these parameters to meet a predefined EE threshold, ensuring optimal system performance. Additionally, we recognize the indispensable role of Peak-to-Average Power Ratio (PAPR) reduction techniques, particularly in multicarrier systems, to further enhance EE. As such, we employ clipping-based PAPR reduction methods to mitigate signal distortions and bolster overall efficiency. Theoretical EE metrics are derived based on formulated signal-to-interference-plus-noise ratios (SINRs), yielding insightful closed-form expressions for the operational parameters. Leveraging two distinct EE metric models, we undertake parameter determinations, accounting for the levels of approximation. Intriguingly, our analysis reveals that even simplified models exhibit remarkable applicability in real-world scenarios, with a minimal margin of error. The results not only underscore the practical applicability of our theoretical constructs but also highlight the potential for significant EE enhancements in massive MIMO systems, thereby contributing to sustainable evolution in the IoT era.