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

TL;DR

This work tackles the energy challenge of 5G NR beam management for battery-powered RedCap devices in smart agriculture using a UAV-based gNB. It formalizes a MINLP to jointly optimize the UAV transmit power $P_t$ and beamwidth via the antenna count $N_{ m gNB}$, subject to QoS constraints; it also generalizes to allow a misdetection probability $P_{ m MD}$ and develops a custom algorithm to find feasible solutions. The authors characterize regions of feasibility and provide design guidelines, showing there exists an optimal beam-management configuration that minimizes UAV energy consumption while satisfying SNR and misalignment constraints. The findings enable practical, energy-efficient beam management for aerial gNBs in autonomous farming scenarios and point to future work in joint optimization with data transmission and ML-based methods.

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.

Figures (12)

• Figure 1: UE mobility model (left) and SMa scenario (right). During beam management, UE$_k$ accumulates an angular offset ${\theta}_k$ due to initial misalignment ($\theta_{i,k}$) and mobility ($\theta_{v,k}$).
• Figure 2: Average angular offset $\bar{\theta}$ as a function of $N_{\rm gNB}$, $v$, and $T_{\rm SS}$. In the two bottom figures, solid lines and filled markers represent $v=2$ m/s, while dashed lines and empty markers represent $v=4$ m/s.
• Figure 3: Average angular offset $\bar{\theta}$ obtained from Eq. \ref{['an_long']} (dashed lines and empty markers) and via Monte Carlo simulation (solid lines and filled markers), as a function of $N_{\rm gNB}$ and $T_{\rm SS}$, for $v=2$ m/s and $N_{\rm SS}=8$.
• Figure 4: $N^*_{\rm gNB}$ and $P_t^*$ as a function of $v$ and $T_{\rm SS}$, for $N_{\rm SS}=8$, $P_T=18$ dBm, $\tau=7$ dB, and $K=50$. Missing values represent infeasibility.
• Figure 5: Average beamforming gain $G_{\rm gNB}$ at the as a function of $N_{\rm gNB}$, $v$ and $T_{\rm SS}$, for $N_{\rm SS}=8$. Solid lines and filled markers represent $v=2$ m/s, while dashed lines and empty markers represent $v=4$ m/s.