INTELLIGENT AUTOMATION's Patented Solution Revolutionizes Wireless Network Power Control
A recent patent filed by INTELLIGENT AUTOMATION, titled "SYSTEMS AND METHODS FOR DEEP REINFORCEMENT LEARNING FOR OPTIMAL POWER CONTROL IN WIRELESS NETWORKS" (patent number US20240089863A1), has the potential to address a core problem in wireless network communications. This patent introduces a system that employs deep reinforcement learning to optimize power consumption and transmission in wireless networks. By utilizing a special kind of brain that learns from past experiences, this invention aims to strike a balance between energy consumption and throughput in wireless communication networks.
The problem at hand stems from the challenge of managing power consumption and transmission efficiency in wireless network communications. Conventional approaches, such as heuristics-based optimization and linear programming, often rely on detailed models of the network topology, increasing complexity and time-consuming efforts. These approaches lack adaptability, generality, and the ability to consider total power consumption. Therefore, a need arises for a wireless network communication system that can tackle these issues effectively.
INTELLIGENT AUTOMATION's patent addresses this problem by introducing a system that deploys deep reinforcement learning techniques. In this system, a deep neural network is utilized, eliminating the need for a posteriori models. This approach not only provides better results compared to heuristics and linear programming but also adapts to the actual network environment. By training the network based on the observed network conditions, the system can optimize power control without relying on intricate models.
Once this problem is solved by the patent, the world of wireless network communications is expected to undergo significant improvements. The balance between energy consumption and throughput will see a remarkable enhancement, resulting in prolonged network lifetime and the ability to accommodate high throughput demands. Moreover, devices operating at limited power, such as battery-powered edge devices, will benefit from adaptively set power levels that consider this balance.
To better understand the practical applications of this invention, consider the example of WiFi communications. The patent's Deep Reinforcement Learning (DRL) approach enables effective interaction with the network environment, leading to enhanced energy efficiency and throughput. Notably, the use of DRL offers computational simplicity and does not impose excessive memory requirements. As a result, this method outperforms conventional fixed transmit power approaches, further enhancing energy efficiency and throughput of the wireless network.
It is important to note that, as with any patent, there is no guarantee that this invention will appear in the market. However, if implemented, it has the potential to revolutionize wireless network power control, bringing about significant improvements in energy efficiency and throughput.
P.S. Please be advised that the described invention is a patent, and its availability in the market is subject to further considerations and developments.
Figures:
- FIG. 1: Schematic illustration of a system demonstrating network communications nodes of a wireless network utilizing deep reinforcement learning for power control.
- FIG. 2: Flow chart diagram showcasing the method of deep reinforcement learning for power control in network communications.
- FIG. 3: Schematic diagram illustrating the process flow for deep reinforcement learning for power control in network communications.
- FIG. 4: Example collected parameters data (training set data) utilized for deep reinforcement learning for power control in network communications.
- FIG. 5: Example Q-Table matrix illustrating the exemplary embodiments of the patent.
DISCLAIMER: The presented article is based on the information available in the patent document and should not be considered as an endorsement or confirmation of the patent's appearance in the market.