Page 369 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

2 Use Case Description

2�1 Description

This use case introduces an AI-driven predictive beamforming system that fuses real-time 4.3 - 5G
meteorological feeds with adaptive network control to mitigate weather-induced signal
degradation in mmWave 5G and future 6G environments. The system utilizes Recurrent
Neural Networks (RNNs), specifically Long Short-Term Memory (LSTM) models, to analyze
time-series weather data such as rain rate, humidity, wind speed, and temperature. These
RNNs are trained on historical datasets where both weather patterns and corresponding signal
quality metrics (e.g., RSSI, SINR) are available. By learning temporal dependencies, the RNNs
can forecast short-term weather changes and infer the likely impact on signal attenuation based
on empirical relationships. For example, a predicted spike in rain intensity can be associated
with a proportional decrease in SINR, allowing the model to estimate future link degradation.

Once the RNN identifies a high probability of signal degradation, ensemble learning methods
(e.g., gradient boosting or random forests) are employed to cross-validate the forecast and flag
any anomalies such as abrupt changes in channel conditions not accounted for in the weather
model. Upon confirming a degradation event, the system transitions to a Reinforcement Learning
(RL) phase to determine the best counteractive measures. This ensures that beamforming
parameters are not adjusted based on single-model predictions alone but are supported by
a multi-model decision-making process.

The RL component is built using Proximal Policy Optimization (PPO) [8], a policy gradient
algorithm known for its stability and sample efficiency. The RL environment is a simulated
mmWave network where the agent receives observations including current weather state, CSI,
and per-beam signal quality. The action space includes beam steering angle, beam width,
transmission power, and rerouting decisions. The policy maps environment states to actions
that optimize connectivity. The reward function is designed to maximize signal reliability (e.g.,
maintaining SINR above a defined threshold), minimize power usage, and reduce latency.
Negative rewards are assigned for dropped links or excessive energy expenditure. Over time,
the RL agent learns to make preemptive, context-aware adjustments that improve network
robustness in adverse weather conditions [6].

Actions based on model inference include:

• Beam widening is selected in the action space when rain fade is predicted and tighter
beams are likely to fail.
• Frequency shifting is performed to avoid bands more susceptible to atmospheric
absorption.
• Power ramp-up, within regulatory limits, is chosen in low-SINR states as a compensatory
measure.
• Signal rerouting is triggered when the RL model predicts better connectivity in adjacent
cells. In severe cases where no optimal action exists, the system issues operator alerts,
representing a fallback policy for manual intervention.

This AI-based approach delivers context-aware, self-healing adjustments that enhance
adaptability, reduce downtime, and improve spectral efficiency. It depends on robust,
interoperable weather and link data as well as sufficient compute resources, challenges that
will be mitigated through data-validation pipelines and edge inference strategies, providing
a scalable foundation for climate-resilient 5G/6G networks aligned with ITU’s IMT-2030 vision.

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