Mobile Networks on the Move: Optimizing Moving Base Stations Dynamics in Urban Scenarios

TL;DR

This work investigates moving base stations (MBSs) as a dynamic alternative to dense static deployments in urban radio access networks, aiming to reduce CAPEX while meeting a target mean per-bit delay $\tau_0$. It introduces a stochastic-geometry–based system model and a two-stage linear optimization framework: first, to estimate the minimum total BS density under QoS constraints, and second, to optimally split infrastructure between moving and static BSs to minimize total deployed BSs. Applied to a two-district urban scenario with time-varying traffic, the approach shows substantial infrastructure savings, with up to $21\%$ fewer BSs and robustness to mean user density variations, largely due to shifting MBS resources to meet peaks in demand. The results support the potential of MBS-driven densification reduction, while highlighting future needs for realistic backhaul modeling and energy-efficiency considerations to translate these savings into operational benefits.

Abstract

Base station densification is one of the key approaches for delivering high capacity in radio access networks. However, current static deployments are often impractical and financially unsustainable, as they increase both capital and operational expenditures of the network. An alternative paradigm is the moving base stations (MBSs) approach, by which part of base stations are installed on vehicles. However, to the best of our knowledge, it is still unclear if and up to which point MBSs allow decreasing the number of static base stations (BSs) deployed in urban settings. In this work, we start tackling this issue by proposing a modeling approach for a first-order evaluation of potential infrastructure savings enabled by the MBSs paradigm. Starting from a set of stochastic geometry results, and a traffic demand profile over time, we formulate an optimization problem for the derivation of the optimal combination of moving and static BSs which minimizes the overall amount of BSs deployed, while guaranteeing a target mean QoS for users. Initial results on a two-district scenario with measurement-based network traffic profiles suggest that substantial infrastructure savings are achievable. We show that these results are robust against different values of user density.

Figures (2)

• Figure 1: (a) Normalized daily mobile traffic profiles in the resident (red) and office (blue) district; (b), (c): Optimal number of MBS (green), total number of BS (orange) with respect to the baseline (red), and installed BS in the static scenario (black) for the two regions over 24 hours. The total number of active base stations over the all area is given by the superposition of the orange dashed lines in the 2 plots.
• Figure 2: (a) Fraction of the total amount of MBS in the scenario allocated to the residential area, over the $24$ hours; (b) Saving with respect to the fully static scenario, in terms of installed SBS in the residential and office districts and in terms of both moving and static BS in the whole area, as a function of the ratio between the values of maximum density of connected users in the two regions.