Qualcomm Incorporated

California Institute of Technology

The State University of New York

Parham Noorzad

Michelle Effros

Michael Langberg

Taesang Yoo

Yang Yang

Siddhartha Mallik

Jamie Menjay Lin

Aspects described herein provide a method of performing phase selective convolution, including: receiving multi-phase pre-activation activation data; partitioning the multi-phase pre-activation data; applying a first activation function to the set of first phase pre-activation data to form a set of first phase activation output; convolving the set of first phase activation output with a first convolution kernel to form a first phase output feature map; negating the set of second phase activation data; applying a second activation function to the negated set of second phase pre-activation data to form a set of second phase activation output; convolving the set of second phase activation output with a second convolution kernel to form a second phase output feature map; negating the second phase output feature map; and training the neural network based on the first phase output feature map and the second phase output feature map.

Phase selective convolution with dynamic weight selection

A machine-learning based multiple-input, multiple-output (MIMO) demapper for a wireless device may include a classifier that selects which MIMO demapper to use for a sample for a particular tone. For example, a wireless device may receive via a plurality of antennas a plurality of signals including a plurality of tones. The wireless device may determine selection features for each tone of the plurality of tones. The wireless device may select, for each tone, by the classifier based on the selection features, a selected demapper from at least a first MIMO demapper and a second MIMO demapper. The second MIMO demapper may have a different performance characteristic than the first MIMO demapper. The wireless device may detect, for each tone, one or more streams using the selected demapper for the tone. A stream may refer to a sequence of bits.

Machine-learning based method for MIMO detection complexity reduction

Systems and methods for utilizing cooperation facilitators to achieve joint message and network state cooperation in accordance with various embodiments of the invention are disclosed. One embodiment of the invention includes: a plurality of transmitters; a receiver that has access to at least partial network state information and that includes a decoder configured to decode signals received via a multiple terminal channel from the plurality of transmitters using the at least partial network state information; and a cooperation facilitator. In addition, at least some of the plurality of transmitters are configured to transmit message information to the cooperation facilitator and the cooperation facilitator is configured to generate cooperation parameters based upon the message information and the manner in which state information is utilized by the receiver to decode signals received via the multiple terminal channel. In addition, the cooperation facilitator is configured to transmit cooperation parameters to the plurality of transmitters that select codewords based at least in part upon the received cooperation parameters.

Systems and methods for transmitting data using encoder cooperation in the presence of state information

Systems and methods for improved data transmission utilizing a communication facilitator are described in accordance with embodiments of the invention. One embodiment includes a plurality of nodes, that each comprise: a transmitter; a receiver; and an encoder that encodes message data for transmission using a plurality of codewords; a cooperation facilitator node comprising: a transmitter; and a receiver; wherein the nodes are configured to transmit data parameters to the cooperation facilitator; wherein the cooperation facilitator is configured to generate cooperation parameters based upon the data parameters received from the nodes; wherein the cooperation facilitator is configured to transmit cooperation parameters to the nodes; and wherein the encoder in each of the nodes selects a codeword from the plurality of codewords based at least in part upon the cooperation parameters received from the communication facilitator and transmit the selected codeword via the multiple access channel.

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San Diego, CA, United States of America

Parham Noorzad