View source for 18523561. OPTIMIZATION METHOD AND APPARATUS IN UNMANNED AERIAL VEHICLE BASED COMMUNICATION NETWORK simplified abstract (ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE)

=OPTIMIZATION METHOD AND APPARATUS IN UNMANNED AERIAL VEHICLE BASED COMMUNICATION NETWORK=


==Organization Name==

[[:Category:ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE|ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE]]


[[Category:ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE]]

==Inventor(s)==

[[:Category:Ji Eun Yu of Seoul (KR)|Ji Eun Yu of Seoul (KR)]][[Category:Ji Eun Yu of Seoul (KR)]]

[[:Category:Kwang Myeong Yang of Seoul (KR)|Kwang Myeong Yang of Seoul (KR)]][[Category:Kwang Myeong Yang of Seoul (KR)]]

[[:Category:Kyu Yeon Lee of Seoul (KR)|Kyu Yeon Lee of Seoul (KR)]][[Category:Kyu Yeon Lee of Seoul (KR)]]

[[:Category:Cha Hyeon Eom of Seoul (KR)|Cha Hyeon Eom of Seoul (KR)]][[Category:Cha Hyeon Eom of Seoul (KR)]]

[[:Category:Hyung Je Lee of Seoul (KR)|Hyung Je Lee of Seoul (KR)]][[Category:Hyung Je Lee of Seoul (KR)]]

[[:Category:Chung Yong Lee of Seoul (KR)|Chung Yong Lee of Seoul (KR)]][[Category:Chung Yong Lee of Seoul (KR)]]

[[:Category:Duk Hyun You of Daejeon (KR)|Duk Hyun You of Daejeon (KR)]][[Category:Duk Hyun You of Daejeon (KR)]]

[[:Category:Hoon Dong Noh of Daejeon (KR)|Hoon Dong Noh of Daejeon (KR)]][[Category:Hoon Dong Noh of Daejeon (KR)]]

==OPTIMIZATION METHOD AND APPARATUS IN UNMANNED AERIAL VEHICLE BASED COMMUNICATION NETWORK - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18523561 titled 'OPTIMIZATION METHOD AND APPARATUS IN UNMANNED AERIAL VEHICLE BASED COMMUNICATION NETWORK

==Simplified Explanation==

The abstract describes a method for a communication node to initiate a collection procedure of training data for a neural network, train the neural network, determine optimal network parameters, and communicate with another node using these parameters.

* Transmit initiation signal to start training data collection
* Send network parameters to second node
* Receive training data from second node
* Train neural network with the data
* Determine optimal network parameters
* Communicate with second node using optimal parameters

== Potential Applications ==
This technology can be applied in various fields such as:
* Internet of Things (IoT) devices
* Autonomous vehicles
* Robotics

== Problems Solved ==
This technology helps in:
* Improving communication efficiency
* Enhancing network performance
* Facilitating data training processes

== Benefits ==
The benefits of this technology include:
* Faster data training for neural networks
* Optimized network parameters for improved performance
* Enhanced communication capabilities between nodes

== Potential Commercial Applications ==
This technology can be utilized in:
* Telecommunication networks
* Smart home devices
* Industrial automation systems

== Possible Prior Art ==
One possible prior art could be the use of similar methods in the field of machine learning and artificial intelligence research.

=== What are the potential security implications of implementing this technology? ===

Implementing this technology could raise concerns about data privacy and security, especially when training data is being transmitted between nodes. Ensuring secure communication protocols and encryption methods would be crucial to mitigate these risks.

=== How scalable is this technology for large-scale networks? ===

The scalability of this technology for large-scale networks would depend on factors such as the processing power of the nodes, the efficiency of the training algorithms, and the network infrastructure. Further research and development may be needed to optimize the scalability of this technology for complex networks.


==Original Abstract Submitted==

A method of a first communication node may comprise: transmitting, to a second communication node, an initiation signal indicating to initiate a collection procedure of training data for a neural network; transmitting, to the second communication node, an information signal including network parameters of the first communication node; receiving, from the second communication node, the training data in response to the information signal; training the neural network using the training data; determining optimal network parameters using the trained neural network; and performing communication with the second communication node using the optimal network parameters.



[[Category:H04W24/02]]
[[Category:H04B17/336]]
[[Category:H04L43/06]]