# US 2018/0322388 A1 · Learning and deployment of adaptive wireless communications

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- Number: US 2018/0322388 A1
- Document ID: US20180322388A1
- Status: Published application
- Kind: A1
- Application number: 15/970,324
- Filed: 2018-05-03
- Published: 2018-11-08
- Earliest claimed: 2017-05-03
- Assignees: Virginia Tech Intellectual Properties, Inc.
- Applicants: Virginia Tech Intellectual Properties, Inc.
- Inventors: Timothy James O`Shea
- Web page: https://patentgrep.com/patent/US20180322388A1

## CPC

- G06N 3/08: Learning methods
- H04L 1/0001: {Systems modifying transmission characteristics according to link quality, e.g. power backoff (adaptive data allocation for multicarrier modulation H04L5/0044; controlling transmission power for radio systems H04W52/04)}
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/0442: characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
- G06N 3/045: Combinations of networks
- G06N 3/0455: Auto-encoder networks; Encoder-decoder networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/0895: Weakly supervised learning, e.g. semi-supervised or self-supervised learning
- G06N 3/09: Supervised learning
- H04L 1/0026: {Transmission of channel quality indication}
- H04L 1/0033: {arrangements specific to the transmitter}
- H04L 1/0036: {arrangements specific to the receiver}
- G06N 3/082: modifying the architecture, e.g. adding, deleting or silencing nodes or connections
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned communication over radio frequency (RF) channels. One of the methods includes: determining first information; using an encoder machine-learning network to process the first information and generate a first RF signal for transmission through a communication channel; determining a second RF signal that represents the first RF signal having been altered by transmission through the communication channel; using a decoder machine-learning network to process the second RF signal and generate second information as a reconstruction of the first information; calculating a measure of distance between the second information and the first information; and updating at least one of the encoder machine-learning network or the decoder machine-learning network based on the measure of distance between the second information and the first information.

## Claims

### Claim 1

A method performed by at least one processor to train at least one machine-learning network to communicate over a communication channel, the method comprising:
determining first information;
using an encoder machine-learning network to process the first information and generate a first RF signal for transmission through a communication channel;
determining a second RF signal that represents the first RF signal having been altered by transmission through the communication channel;
using a decoder machine-learning network to process the second RF signal and generate second information as a reconstruction of the first information;
calculating a measure of distance between the second information and the first information; and
updating at least one of the encoder machine-learning network or the decoder machine-learning network based on the measure of distance between the second information and the first information.

### Claim 2 (depends on 1)

The method of claim 1, wherein updating at least one of the encoder machine-learning network or the decoder machine-learning network based on the measure of distance between the second information and the first information comprises:
determining an objective function comprising the measure of distance between the second information and the first information;
calculating a rate of change of the objective function relative to variations in at least one of the encoder machine-learning network or the decoder machine-learning network;
selecting, based on the calculated rate of change of the objective function, at least one of a first variation for the encoder machine-learning network or a second variation for the decoder machine-learning network; and
updating at least one of the encoder machine-learning network or the decoder machine-learning network based on the at least one of the selected first variation for the encoder machine-learning network or the selected second variation for the decoder machine-learning network.

### Claim 3 (depends on 1)

The method of claim 1, wherein the measure of distance between the second information and the first information comprises at least one of (i) a cross-entropy between the second information and the first information, or (ii) a geometric distance metric between the second information and the first information.

### Claim 4 (depends on 1)

The method of claim 1, wherein updating at least one of the encoder machine-learning network or the decoder machine-learning network comprises at least one of:
updating at least one encoding network weight or network connectivity in one or more layers of the encoder machine-learning network, or
updating at least one decoding network weight or network connectivity in one or more layers of the decoder machine-learning network.

### Claim 5 (depends on 1)

The method of claim 1, wherein updating at least one of the encoder machine-learning network or the decoder machine-learning network further comprises:
determining a channel mode, from among a plurality of channel modes, that represents a state of the communication channel; and
updating at least one of the encoder machine-learning network or the decoder machine-learning network based on the channel mode of the communication channel.

### Claim 6 (depends on 1)

The method of claim 1, wherein the encoder machine-learning network and the decoder machine-learning network are jointly trained as an auto-encoder to learn communication over a communication channel, and
wherein the auto-encoder comprises at least one channel-modeling layer representing effects of the communication channel on transmitted waveforms.

### Claim 7 (depends on 6)

The method of claim 6, wherein the at least one channel-modeling layer represents at least one of (i) additive Gaussian thermal noise in the communication channel, (ii) delay spread caused by time-varying effects of the communication channel, (iii) phase noise caused by transmission and reception over the communication channel, or (iv) offsets in phase, frequency, or timing caused by transmission and reception over the communication channel.

### Claim 8 (depends on 1)

The method of claim 1, wherein at least one of the encoder machine-learning network or the decoder machine-learning network comprises at least one of a deep dense neural network (DNN), a convolutional neural network (CNN), or a recurrent neural network (RNN) comprising parametric multiplications, additions, and non-linearities.

### Claim 9 (depends on 1)

The method of claim 1, further comprising:
processing the first RF signal to generate a first analog RF waveform that is input into the communication channel;
receiving a second analog RF waveform as an output of the communication channel that represents the first analog RF waveform having been altered by the communication channel; and
processing the second analog RF waveform to generate the second RF signal.

### Claim 10 (depends on 1)

The method of claim 1, wherein the communication channel comprises at least one of a radio communication channel, an acoustic communication channel, or an optical communication channel.

### Claim 11

A method of transmitting and receiving information through a communication channel, the method comprising:
determining an encoder and a decoder, at least one of which is configured to implement an encoding or decoding that is based on at least one of an encoder machine-learning network or a decoder machine-learning network that has been trained to encode or decode information over a communication channel;
determining first information;
using the encoder to process the first information and generate a first RF signal;
transmitting, by at least one transmitter, the first RF signal through the communication channel;
receiving, by at least one receiver, a second RF signal that represents the first RF signal altered by transmission through the communication channel; and
using the decoder to process the second RF signal and generate second information as a reconstruction of the first information.

### Claim 12 (depends on 11)

The method of claim 11, further comprising:
determining feedback information that indicates at least one of (i) a measure of distance between the second information and the first information, or (ii) channel state information regarding the communication channel; and
updating at least one of the encoder or the decoder based on the feedback information.

### Claim 13 (depends on 12)

The method of claim 12, wherein updating at least one of the encoder or the decoder based on the feedback information further comprises:
determining a channel mode, from among a plurality of channel modes, that represents a state of the communication channel based on the feedback information; and
updating at least one of the encoder or the decoder based on the channel mode of the communication channel.

### Claim 14 (depends on 11)

The method of claim 11, wherein the encoder implements an encoding mapping that is based on results of training an encoder machine-learning network and the decoder implements a decoding mapping that is based on results of training a decoder machine-learning network,
wherein the encoder machine-learning network and the decoder machine-learning network have been jointly trained as an auto-encoder to learn communication over a communication channel.

### Claim 15 (depends on 11)

The method of claim 11, further comprising:
processing the first RF signal to generate a first analog RF waveform;
using one or more transmit antennas to transmit the first analog RF waveform over the communication channel;
using one or more receive antennas to receive a second analog RF waveform that represents the first analog RF waveform having been altered by the communication channel; and
processing the second analog RF waveform to generate the second RF signal.

### Claim 16

A system comprising:
at least one processor; and
at least one computer memory coupled to the at least one processor having stored thereon instructions which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
determining first information;
using an encoder machine-learning network to process the first information and generate a first RF signal for transmission through a communication channel;
determining a second RF signal that represents the first RF signal having been altered by transmission through the communication channel;
using a decoder machine-learning network to process the second RF signal and generate second information as a reconstruction of the first information;
calculating a measure of distance between the second information and the first information; and
updating at least one of the encoder machine-learning network or the decoder machine-learning network based on the measure of distance between the second information and the first information.

### Claim 17 (depends on 16)

The system of claim 16, wherein updating at least one of the encoder machine-learning network or the decoder machine-learning network based on the measure of distance between the second information and the first information comprises:
determining an objective function comprising the measure of distance between the second information and the first information;
calculating a rate of change of the objective function relative to variations in at least one of the encoder machine-learning network or the decoder machine-learning network;
selecting, based on the calculated rate of change of the objective function, at least one of a first variation for the encoder machine-learning network or a second variation for the decoder machine-learning network; and
updating at least one of the encoder machine-learning network or the decoder machine-learning network based on the at least one of the selected first variation for the encoder machine-learning network or the selected second variation for the decoder machine-learning network.

### Claim 18 (depends on 16)

The system of claim 16, wherein the measure of distance between the second information and the first information comprises at least one of (i) a cross-entropy between the second information and the first information, or (ii) a geometric distance metric between the second information and the first information.

### Claim 19 (depends on 16)

The system of claim 16, wherein updating at least one of the encoder machine-learning network or the decoder machine-learning network comprises at least one of:
updating at least one encoding network weight or network connectivity in one or more layers of the encoder machine-learning network, or
updating at least one decoding network weight or network connectivity in one or more layers of the decoder machine-learning network.

### Claim 20 (depends on 16)

The system of claim 16, wherein updating at least one of the encoder machine-learning network or the decoder machine-learning network further comprises:
determining a channel mode, from among a plurality of channel modes, that represents a state of the communication channel; and
updating at least one of the encoder machine-learning network or the decoder machine-learning network based on the channel mode of the communication channel.

### Claim 21 (depends on 16)

The system of claim 16, wherein the encoder machine-learning network and the decoder machine-learning network are jointly trained as an auto-encoder to learn communication over a communication channel, and
wherein the auto-encoder comprises at least one channel-modeling layer representing effects of the communication channel on transmitted waveforms.

### Claim 22 (depends on 21)

The system of claim 21, wherein the at least one channel-modeling layer represents at least one of (i) additive Gaussian thermal noise in the communication channel, (ii) delay spread caused by time-varying effects of the communication channel, (iii) phase noise caused by transmission and reception over the communication channel, or (iv) offsets in phase, frequency, or timing caused by transmission and reception over the communication channel.

### Claim 23 (depends on 16)

The system of claim 16, wherein at least one of the encoder machine-learning network or the decoder machine-learning network comprises at least one of a deep dense neural network (DNN) a convolutional neural network (CNN), or a recurrent neural network (RNN) comprising parametric multiplications, additions, and non-linearities.

### Claim 24 (depends on 16)

The system of claim 16, wherein the operations further comprise:
processing the first RF signal to generate a first analog RF waveform that is input into the communication channel;
receiving a second analog RF waveform as an output of the communication channel that represents the first analog RF waveform having been altered by the communication channel; and
processing the second analog RF waveform to generate the second RF signal.

### Claim 25 (depends on 16)

The system of claim 16, wherein the communication channel comprises at least one of a radio communication channel, an acoustic communication channel, or an optical communication channel.

### Claim 26

A system comprising:
at least one processor; and
at least one computer memory coupled to the at least one processor having stored thereon instructions which, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
determining an encoder and a decoder, at least one of which is configured to implement an encoding or decoding that is based on at least one of an encoder machine-learning network or a decoder machine-learning network that has been trained to encode or decode information over a communication channel;
determining first information;
using the encoder to process the first information and generate a first RF signal;
transmitting, by at least one transmitter, the first RF signal through the communication channel;
receiving, by at least one receiver, a second RF signal that represents the first RF signal altered by transmission through the communication channel; and
using the decoder to process the second RF signal and generate second information as a reconstruction of the first information.

### Claim 27 (depends on 26)

The system of claim 26, wherein the operations further comprise:
determining feedback information that indicates at least one of (i) a measure of distance between the second information and the first information, or (ii) channel state information regarding the communication channel; and
updating at least one of the encoder or the decoder based on the feedback information.

### Claim 28 (depends on 27)

The system of claim 27, wherein updating at least one of the encoder or the decoder based on the feedback information further comprises:
determining a channel mode, from among a plurality of channel modes, that represents a state of the communication channel based on the feedback information; and
updating at least one of the encoder or the decoder on the channel mode of the communication channel.

### Claim 29 (depends on 27)

The system of claim 27, wherein the encoder implements an encoding mapping that is based on results of training an encoder machine-learning network and the decoder implements a decoding mapping that is based on results of training a decoder machine-learning network,
wherein the encoder machine-learning network and the decoder machine-learning network have been jointly trained as an auto-encoder to learn communication over a communication channel.

### Claim 30 (depends on 27)

The system of claim 27, wherein the operations further comprise:
processing the first RF signal to generate a first analog RF waveform;
using one or more transmit antennas to transmit the first analog RF waveform over the communication channel;
using one or more receive antennas to receive a second analog RF waveform that represents the first analog RF waveform having been altered by the communication channel; and
processing the second analog RF waveform to generate the second RF signal.