CACI International. has been granted a patent for a method that utilizes deep reinforcement learning to train a neural network in a radio frequency (RF) network. The method involves receiving policies and features from telecommunication groups, observing graphical representations, assigning channels, and adjusting policies based on throughput changes. GlobalData’s report on CACI International gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on CACI International, was a key innovation area identified from patents. CACI International's grant share as of July 2024 was 66%. Grant share is based on the ratio of number of grants to total number of patents.
Neural network training using deep reinforcement learning in rf networks
The patent US12067487B2 outlines a method for training a neural network using deep reinforcement learning (DRL) to optimize the assignment of telecommunication groups within a radio frequency (RF) network. The process begins with the neural network receiving a policy from a third party and features from multiple telecommunication groups. It then observes a graphical representation of these features, which include pixel intensity related to transmit power, signal-to-noise ratios, and interference levels. Based on this observation, the neural network assigns a telecommunication group to a specific channel and determines the resulting change in throughput. Adjustments to the initial policy are made based on this throughput change, ensuring that the network operates efficiently.
Further claims detail additional functionalities of the method, such as assessing whether other telecommunication groups require channel assignments and revising the policy based on throughput changes. The method also includes mechanisms for determining if all groups have been allocated channels and whether any assigned groups need reassignment. The throughput changes are influenced by constraints like minimum rates and signal-to-interference ratios, and the method accounts for various factors, including power levels and bandwidth. Additionally, the graphical representation can include multi-dimensional images and assignment statuses, enhancing the neural network's ability to make informed decisions regarding channel assignments in the RF network. The patent also encompasses a non-transitory computer-readable medium that stores instructions for executing this method, further solidifying its applicability in telecommunications.
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