Consider a non-limiting example, whereby the AVM controller analyzes current environmental metadata associated with a DC power source and RRU, and in doing so, identifies an occurrence of a meteorological event. The AVM controller may further use one or more trained machine learning algorithms to correlate the current environmental metadata with data-points of the analysis model and infer that a voltage-boost is likely required to ensure that the QoS parameters associated with signal data (i.e. voice and data communications) transmitted by the RF antennas is not compromised by the meteorological event.

In another non-limiting example, the AVM controller may analyze current environmental metadata to predict an impending network congestion. For example, the current environmental metadata may include a current time of day or a current day of the week. The AVM controller may further use one or more trained machine learning algorithms to correlate the current environmental metadata with data-points of the analysis model to infer that a voltage-boost is likely required to overcome the impending network congestion. It is noteworthy that the historical environmental metadata used to develop the analysis model may include corresponding times of the day or current days of the week.

In various examples, the AVM controller may preemptively initiate a voltage-boost at a point-in-time prior to an impending network congestion or meteorological event. For example, the AVM controller may identify a step-up voltage rate that is associated with a potential transformer that is coupled between the DC power source and the RRU. The step-up voltage rate may correspond to an incremental voltage-boost that occurs over a one-minute time interval. The step-up voltage rate may be one volt-per-minute, two volts-per-minute, however, any step-up voltage rate is possible.