20240049023. CHANNEL STATE FEEDBACK WITH DICTIONARY LEARNING simplified abstract (QUALCOMM Incorporated)
Contents
CHANNEL STATE FEEDBACK WITH DICTIONARY LEARNING
Organization Name
Inventor(s)
Hamed Pezeshki of San Diego CA (US)
Arash Behboodi of Amsterdam (NL)
Taesang Yoo of San Diego CA (US)
Mahmoud Taherzadeh Boroujeni of San Diego CA (US)
CHANNEL STATE FEEDBACK WITH DICTIONARY LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240049023 titled 'CHANNEL STATE FEEDBACK WITH DICTIONARY LEARNING
Simplified Explanation
The abstract of this patent application describes a wireless communication system where a user equipment (UE) can report channel state information (CSI) using a learned dictionary of sparse vectors. The UE determines the learned dictionary for CSI reporting, either by receiving a shared dictionary from a similar and nearby UE or by training the dictionary based on logged CSI measurements. The UE then indicates the learned dictionary to a serving base station and reports a sparse vector representing the CSI based on the learned dictionary.
- The UE in a wireless communication system can report CSI using a learned dictionary of sparse vectors.
- The UE determines the learned dictionary for CSI reporting.
- The learned dictionary can be received from a similar and nearby UE or trained based on logged CSI measurements.
- The UE indicates the learned dictionary to a serving base station.
- The UE measures CSI for multiple channels.
- The UE reports a sparse vector representing the CSI to the serving base station based on the learned dictionary.
Potential applications of this technology:
- Wireless communication systems where accurate CSI reporting is crucial, such as in 5G networks.
- Systems that require efficient and reliable transmission of channel state information.
- Communication systems that rely on sparse vector representations for data transmission.
Problems solved by this technology:
- Improves the accuracy and efficiency of CSI reporting in wireless communication systems.
- Reduces the amount of data needed to transmit CSI information.
- Enables better utilization of network resources by optimizing the transmission of CSI.
Benefits of this technology:
- Improved performance and reliability of wireless communication systems.
- Reduced bandwidth and energy consumption due to the use of sparse vector representations.
- Enhanced network capacity and efficiency by optimizing the transmission of CSI.
Original Abstract Submitted
in a wireless communication system, a user equipment (ue) may report channel state information (csi) using a learned dictionary defining a set of sparse vectors. the ue determines a learned dictionary for csi reporting. for example, the ue receives a shared dictionary from a similar and nearby ue or the ue trains the learned dictionary based on logged csi measurements. the ue indicates the learned dictionary to a serving base station. the ue measures csi for a plurality of channels. the ue reports a sparse vector representing the csi based on the learned dictionary to the serving base station.
