Optimizing Energy Efficiency of 5G RedCap Beam Management for Smart Agriculture Applications

TL;DR

This paper proposes an optimization framework for energy-efficient beam management in 5G RedCap IoT devices used in smart agriculture, focusing on UAV-based base stations to reduce energy consumption while maintaining QoS.

Contribution

It introduces a multi-variate optimization model for beam management parameters in UAV-based 5G IoT deployments, a novel approach for energy efficiency in RedCap devices.

Findings

Optimal beam management configurations depend on UE speed and network parameters.

Energy efficiency is maximized within specific feasibility regions of beamwidth and power.

Simulation shows tailored configurations significantly reduce energy consumption.

Abstract

Beam management in 5G NR involves the transmission and reception of control signals such as Synchronization Signal Blocks (SSBs), crucial for tasks like initial access and/or channel estimation. However, this procedure consumes energy, which is particularly challenging to handle for battery-constrained nodes such as RedCap devices. Specifically, in this work we study a mid-market Internet of Things (IoT) Smart Agriculture (SmA) deployment where an Unmanned Autonomous Vehicle (UAV) acts as a base station "from the sky" (UAV-gNB) to monitor and control ground User Equipments (UEs) in the field. Then, we formalize a multi-variate optimization problem to determine the optimal beam management design for RedCap SmA devices in order to reduce the energy consumption at the UAV-gNB. Specifically, we jointly optimize the transmission power and the beamwidth at the UAV-gNB. Based on the analysis, we derive the so-called "regions of feasibility," i.e., the upper limit(s) of the beam management parameters for which RedCap Quality of Service (QoS) and energy constraints are met. We study the impact of factors like the total transmission power at the gNB, the Signal-to-Noise Ratio (SNR) threshold for successful packet decoding, the number of UEs in the region, and the misdetection probability. Simulation results demonstrate that there exists an optimal configuration for beam management to promote energy efficiency, which depends on the speed of the UEs, the beamwidth, and other network parameters.