Weather-Aware 5G Network Management for Rain Attenuation
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Solution Overview
Problem
5G networks using millimeter-wave frequencies are susceptible to weather-induced attenuation, particularly from rain, which can cause significant outages in mission-critical applications, necessitating predictive and dynamic network management to maintain quality of service.
Innovation Solution
Implementing predictive network management using wireless node measurements and machine learning to estimate future attenuation, enabling real-time adjustments in transmission power, modulation, and network topology to mitigate weather-induced disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If millimeter-wave frequencies are used to achieve higher throughput, then network capacity and speed are improved, but weather-induced attenuation and reliability deteriorate
Solution Approach 1:
The system performs preliminary actions by predicting future network conditions based on historical data and current measurements. The predictive model forecasts attenuation patterns before they occur, allowing the network to proactively adjust parameters such as modulation schemes, coding rates, and transmission power to maintain reliable communication during weather events.
Solution Approach 2:
The network dynamically adapts its operational parameters based on predicted weather conditions. The system continuously monitors current network performance and adjusts transmission characteristics in real-time according to forecasted attenuation levels, enabling the network to optimize between throughput and reliability as conditions change.
2Quantity of substance
If higher frequencies are used to increase capacity, then network capacity is improved, but path loss and blockage potential worsen
Solution Approach 1:
The system predicts future path loss conditions based on historical data and current measurements, allowing proactive adjustment of transmission parameters before severe attenuation occurs. This enables the network to maintain capacity by pre-adapting to expected blockage conditions.
Solution Approach 2:
The network changes operational parameters such as modulation order, coding rate, and transmission power based on predicted weather conditions. By dynamically adjusting these parameters, the system compensates for increased path loss at higher frequencies while maintaining network capacity.
3Device complexity
If traditional reactive network management is used, then system complexity is reduced, but response time to weather disturbances worsens
Solution Approach 1:
Instead of reacting to disturbances after they occur, the system performs preliminary actions by predicting future network conditions based on historical data and current measurements. This allows the network to prepare and adjust parameters before weather disturbances impact performance, reducing the effective response time.
Solution Approach 2:
The system implements a feedback mechanism where predicted weather conditions inform network management decisions. The predictive model continuously receives input from current network performance data and translates it into actionable adjustments, creating a closed-loop system that proactively responds to anticipated disturbances.
Data Source
AI summary
Disclosed are methods, systems, devices, and other implementations, including a method that includes obtaining measurement data from at least part of a wireless communication network during a first interval of time, with the measurement data including measurements indicating whether there are potential disturbances to operation of the wireless communication network. The method further includes determining, based on the obtained measurement data, predictive data representative of future performance of the wireless communication network at one or more future time instances subsequent to an end of the first interval of time, and managing resources of the wireless communication network based, at least in part, on the determined predictive data representative of the future performance of the wireless communication network at the one or more future time instances subsequent to the end of the first interval.