In this example, the AVM controller may quantify the voltage-boost based at least in part on the current environmental metadata. For example, the AVM controller may retrieve historical instances of voltage-boost that occur in response to historical environmental metadata, such as network congestion, network (i.e. hardware or software) impediments, DC power source impediments, or meteorological events. Meteorological events may include weather events, such as electrical storms, that impede the transmission of signal data (i.e. voice and data communications) by the RF antennas.

The AVM controller may use one or more trained machine learning algorithms to correlate current environmental metadata with historical environmental metadata to quantify a voltage-boost that is required to maintain a threshold QoS for signal data (i.e. voice and data communications) transmitted by the RF antennas. The one or more trained machine learning algorithms may make use of techniques such as supervised learning, unsupervised learning, semi-supervised learning, naive Bayes, Bayesian networks, decision trees, neural networks, fuzzy logic models, and/or probabilistic classification models.