Publication Type : Journal Article
Publisher : Elsevier BV
Source : Computer Networks
Url : https://doi.org/10.1016/j.comnet.2025.111909
Keywords : 5G NR, 3GPP, Beam management, Energy consumption, Optimization, Smart agriculture
Campus : Haridwar
School : School of Computing
Department : Electronics and Communication
Year : 2026
Abstract : Beam management in the 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.
Cite this Research Publication : Manishika Rawat, Matteo Pagin, Marco Giordani, Louis-Adrien Dufrene, Quentin Lampin, Michele Zorzi, Energy efficient beam management for 5G RedCap devices in smart agriculture applications, Computer Networks, Elsevier BV, 2026, https://doi.org/10.1016/j.comnet.2025.111909