17952203. RECURRENT EQUIVARIANT INFERENCE MACHINES FOR CHANNEL ESTIMATION simplified abstract (QUALCOMM Incorporated)

RECURRENT EQUIVARIANT INFERENCE MACHINES FOR CHANNEL ESTIMATION

Organization Name

QUALCOMM Incorporated

Inventor(s)

Kumar Pratik of Amsterdam (NL)

Arash Behboodi of Amsterdam (NL)

Pouriya Sadeghi of San Diego CA (US)

Tharun Adithya Srikrishnan of La Jolla CA (US)

Alexandre Pierrot of San Diego CA (US)

Joseph Binamira Soriaga of San Diego CA (US)

Supratik Bhattacharjee of San Diego CA (US)

RECURRENT EQUIVARIANT INFERENCE MACHINES FOR CHANNEL ESTIMATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 17952203 titled 'RECURRENT EQUIVARIANT INFERENCE MACHINES FOR CHANNEL ESTIMATION

Simplified Explanation

The abstract describes methods, systems, and devices for wireless communications, specifically focusing on channel estimation and resource allocation in wireless devices.

• Wireless device receives a set of resources associated with a channel, including subsets for data transmission and reference signals.
• Device generates multiple channel estimations per layer of the channel and refines them to generate a channel estimation associated with multiple layers.
• Refinement operation includes generating gradients for each per layer channel estimation, modifying the channel estimations based on a current set of values of a latent variable.

Potential Applications

This technology could be applied in various wireless communication systems, such as 5G networks, IoT devices, and satellite communications.

Problems Solved

- Efficient resource allocation in wireless communications. - Accurate channel estimation for improved data transmission.

Benefits

- Enhanced data transmission reliability. - Optimal resource utilization. - Improved overall performance of wireless networks.

Potential Commercial Applications

"Wireless Channel Estimation and Resource Allocation Technology for Next-Gen Networks"

Possible Prior Art

There may be existing technologies related to channel estimation and resource allocation in wireless communications, but specific prior art is not provided in the abstract.

Unanswered Questions

How does this technology compare to existing methods for channel estimation in wireless communications?

The abstract does not provide a direct comparison to existing methods, leaving the reader to wonder about the advantages of this technology over current practices.

What specific wireless devices or systems could benefit the most from this innovation?

The abstract mentions wireless devices in general, but it would be helpful to know which specific devices or systems could see the most significant improvements from implementing this technology.

Original Abstract Submitted

Methods, systems, and devices for wireless communications are described. A wireless device may receive an assignment of a set of resources associated with a channel where the set of resources includes a first subset of resources allocated for data transmission and a second subset of resources allocated for a reference signal. The wireless device may generate multiple channel estimations per layer of the channel and perform a refinement operation utilizing the estimations to generate a channel estimation associated with multiple layers. Each iteration of the refinement operation may include generating respective gradients associated with each per layer channel estimation; generating a current set of values of a latent variable; and modifying the channel estimations.