US 10,552,738 B2Granted

Adaptive channel coding using machine-learned models

AssigneeGoogle Inc.

InventorsJason E. Holt, Marcello Herreshoff

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Abstract

The present disclosure provides systems and methods that enable adaptive training of a channel coding model including an encoder model, a channel model positioned structurally after the encoder model, and a decoder model positioned structurally after the channel model. The channel model can have been trained to emulate a communication channel, for example, by training the channel model on example data that has been transmitted via the communication channel. The channel coding model can be trained on a loss function that describes a difference between input data input into the encoder model and output data received from the decoder model. In particular, such a loss function can be backpropagated through the decoder model while modifying the decoder model, backpropagated through the channel model while the channel model is held constant, and then backpropagated through the encoder model while modifying the encoder model.

Claims20

Dependent claims

A computing system to perform machine learning, the computing system comprising:

at least one processor;
and
at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed by the at least one processor, cause the computing system to:
obtain data descriptive of a model that comprises an encoder model, a channel model and a decoder model, wherein the encoder model is configured to receive a first set of inputs and, in response to receipt of the first set of inputs, generate a first set of outputs, wherein the channel model is configured to receive the first set of outputs and, in response to receipt of the first set of outputs, generate a second set of outputs, and wherein the decoder model is configured to receive the second set of outputs and, in response to receipt of the second set of outputs, generate a third set of outputs;
determine a first loss function that describes a difference between the first set of inputs and the third set of outputs;
backpropagate the first loss function through the decoder model while modifying the decoder model to train the decoder model;
after backpropagating the first loss function through the decoder model, continue to backpropagate the first loss function through the channel model without modifying the channel model;
after backpropagating the first loss function through the channel model, continue to backpropagate the first loss function through the encoder model while modifying the encoder model to train the encoder model;
send a second set of inputs over a communication channel;
receive a fourth set of outputs from the communication channel;
and
train the channel model based at least in part from the second set of inputs and the fourth set of outputs.

9 more elements

A computing device that determines an encoding scheme for a communication channel, comprising:

at least one processor;
a machine-learned encoder model that is configured to receive a first set of inputs and output a first set of outputs, the encoder model having been trained by sequentially backpropagating a loss function through a decoder model to modify at least one weight of the decoder model, and then through a channel model without modifying the channel model, and then through the encoder model to modify at least one weight of the encoder model, the channel model configured to receive the first set of outputs and output a second set of outputs, the decoder model configured to receive the second set of outputs and output a third set of outputs, the loss function descriptive of a difference between the first set of inputs and the third set of outputs, wherein the channel model used to train the encoder model is trained by:
sending a second set of inputs over a communication channel;
receiving a fourth set of inputs from the communication channel;
providing the second set of inputs to the channel model and receiving a fifth set of outputs from the channel model in response to receipt of the second set of inputs;
determining a second loss function between the fourth set of outputs and the fifth set of outputs;
and
backpropagating the second loss function through the channel model while modifying the channel model to train the channel model;
and
at least one tangible, non-transitory computer-readable medium that stores instructions that, when executed by the at least one processor, cause the at least one processor to:
obtain a first set of transmit data for transmitting over a communication channel;
input the first set of transmit data into the machine-learned encoder model;
receive, as an output of the machine-learned channel encoder model, an encoded version of the transmit data;
and
transmit the encoded version of the transmit data over the communication channel.

12 more elements

One or more tangible, non-transitory computer-readable media storing computer-readable instructions that when executed by one or more processors cause the one or more processors to perform operations, the operations comprising:

obtaining data descriptive of a machine-learned decoder model, the decoder model having been trained by sequentially backpropagating a loss function through the decoder model to train the decoder model, and then through a channel model without modifying the channel model, and then through an encoder model to train the encoder model, the encoder model configured to receive a first set of inputs and output a first set of outputs, the channel model configured to receive the first set of outputs and output a second set of outputs, the decoder model configured to receive the second set of outputs and output a third set of outputs, the loss function descriptive of a difference between the first set of inputs and the third set of outputs, wherein the channel model used to train the decoder model is trained by:
sending a second set of inputs over a communication channel;
receiving a fourth set of outputs from the communication channel;
providing the second set of inputs to the channel model and receiving a fifth set of outputs from the channel model in response to receipt of the second set of inputs;
determining a second loss function between the fourth set of outputs and the fifth set of outputs;
and
backpropagating the second loss function through the channel model while modifying the channel model to train the channel model;
obtaining a first set of communication data received from a communication channel;
inputting the first set of communication data into the machine-learned decoder model;
and
receiving as an output of the machine-learned decoder model, in response to receipt of the first set of communication data, a decoded version of the communication data.

8 more elements

Dependencies are inferred from the claim text.

Facts

Application: 15/380,399Filed 15 Dec 2016
Published: US 2018/0174050 A121 Jun 2018
Granted: 4 Feb 2020
Term ends: Not estimatedThe source records don’t support an estimate.

Classification6 codes

G06N 3/084
Learning methods: backpropagation, e.g. using gradient descent
G06N 3/044
Architecture, e.g. interconnection topology: recurrent networks, e.g. Hopfield networks
G06N 3/0455
Combinations of networks: auto-encoder networks; Encoder-decoder networks
G06N 3/0464
Architecture, e.g. interconnection topology: convolutional networks [CNN, ConvNet]