Energy-Efficient Mobile Networks: A Deep Learning Approach to Traffic Analysis and Base Station Switching

In this article, we explore approaches to reduce energy consumption in 5G networks using LSTM (Long Short-Term Memory) models. Our analysis shows that these models can save an average of 8% to 21% of energy consumption during working days by adapting to traffic demands and optimizing energy usage. However, there is a trade-off between extra delay and energy consumption in both cases, with the LSTM model consistently exhibiting higher delay than the reference strategy.
To understand how these models work, imagine a smart thermostat that adjusts the temperature in your home based on your daily routine. Just like this thermostat, an LSTM model can learn to adapt to your energy usage patterns and optimize energy consumption. By using deep learning algorithms, these models can analyze traffic demands and adjust energy usage accordingly, similar to how you would turn up the heating when you expect more people in your home.
However, just like a smart thermostat has limitations on how much it can reduce energy consumption without compromising comfort, an LSTM model also has limitations on how much energy it can save without increasing extra delay. In other words, there is a balance between reducing energy consumption and extra delay that must be carefully managed.
Overall, our findings demonstrate the potential of using LSTM models to reduce energy consumption in 5G networks, but also highlight the need for careful consideration of trade-offs between energy savings and extra delay. By leveraging these approaches, we can create more efficient and sustainable mobile networks that reduce their carbon footprint while meeting user demands for faster speeds and lower latency.