Qualcomm incorporated (20240135231). REINFORCEMENT LEARNING-BASED ENHANCED DISTRIBUTED CHANNEL ACCESS simplified abstract
Contents
- 1 REINFORCEMENT LEARNING-BASED ENHANCED DISTRIBUTED CHANNEL ACCESS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 REINFORCEMENT LEARNING-BASED ENHANCED DISTRIBUTED CHANNEL ACCESS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
REINFORCEMENT LEARNING-BASED ENHANCED DISTRIBUTED CHANNEL ACCESS
Organization Name
Inventor(s)
Gaurang Naik of San Diego CA (US)
George Cherian of San Diego CA (US)
Sai Yiu Duncan Ho of San Diego CA (US)
Yanjun Sun of San Diego CA (US)
Abhishek Pramod Patil of San Diego CA (US)
Alfred Asterjadhi of San Diego CA (US)
Abdel Karim Ajami of Lakeside CA (US)
REINFORCEMENT LEARNING-BASED ENHANCED DISTRIBUTED CHANNEL ACCESS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240135231 titled 'REINFORCEMENT LEARNING-BASED ENHANCED DISTRIBUTED CHANNEL ACCESS
Simplified Explanation
The patent application describes a method for using a reinforcement learning model to optimize a wireless communication device's channel access procedure.
- The wireless communication device receives information associated with the reinforcement learning model.
- Based on the output of the model, the device transmits a protocol data unit during a specific time slot.
- The device uses the reinforcement learning model to perform a distributed channel access procedure.
- The information received configures the model and determines if the device can retrain the model.
Potential Applications
This technology could be applied in various wireless communication systems to improve channel access efficiency and performance.
Problems Solved
1. Optimization of channel access procedures in wireless communication devices. 2. Efficient utilization of available resources in wireless networks.
Benefits
1. Enhanced network performance and throughput. 2. Reduced interference and collisions in wireless communication. 3. Improved reliability and stability of wireless connections.
Potential Commercial Applications
Optimizing channel access procedures in IoT devices for smart homes and cities.
Possible Prior Art
One possible prior art could be the use of machine learning algorithms in wireless communication systems to optimize network performance.
Unanswered Questions
How does the reinforcement learning model adapt to changing network conditions?
The patent application does not provide details on how the model adjusts its parameters in response to dynamic network environments.
What is the impact of using a reinforcement learning model on the device's power consumption?
The application does not address the potential increase in power consumption associated with running a reinforcement learning model on a wireless communication device.
Original Abstract Submitted
this disclosure provides methods, components, devices and systems for use of a reinforcement learning (rl) model to obtain one or more parameters associated with a channel access procedure. some aspects more specifically relate to mechanisms according to which a wireless communication device may receive information associated with the rl model and transmit a protocol data unit (pdu) during a slot that is based on an output of the model. the wireless communication device may use the rl model to perform a distributed channel access procedure in accordance with the information and may further transmit the pdu, during the slot that is based on the output of the rl model, in accordance with the distributed channel access procedure. the information associated with the rl model may indicate or configure the rl model or may indicate whether the wireless communication is allowed to retrain the rl model.
