Encoding and decoding of information for wireless transmission using multi-antenna transceivers

Patent metadata

Field	Value
Publication number	US-2018367192-A1
Application number	US-201816012691-A
Country	US
Kind code	A1
Filing date	Jun 19, 2018
Priority date	Jun 19, 2017
Publication date	Dec 20, 2018
Grant date	—

Abstract

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned communication over multi-input-multi-output (MIMO) channels. One of the methods includes: determining a transmitter and a receiver, at least one of which implements a machine-learning network; determining a MIMO channel model; determining first information; using the transmitter to process the first information and generate first RF signals representing inputs to the MIMO channel model; determining second RF signals representing outputs of the MIMO channel model, each second RF signal representing aggregated reception of the first RF signals altered by transmission through the MIMO channel model; using the receiver to process the second RF signals and generate second information as a reconstruction of the first information; calculating a measure of distance between the second and first information; and updating the machine-learning network based on the measure of distance between the second and first information.

First claim

1 . A method performed by at least one processor to train at least one machine-learning network to communicate using multiple transmit antennas and multiple receive antennas over a multi-input-multi-output (MIMO) communication channel, the method comprising:
determining a transmitter and a receiver, at least one of which is configured to implement at least one machine-learning network; determining a MIMO channel model that represents transmission effects of a MIMO communication channel; determining first information for transmission over the MIMO channel model; using the transmitter to process the first information and generate a plurality of first RF signals representing inputs to the MIMO channel model; determining a plurality of second RF signals representing outputs of the MIMO channel model, each second RF signal of the plurality of second RF signals representing aggregated reception of the plurality of first RF signals having been altered by transmission through the MIMO channel model; using the receiver to process the plurality of second RF signals 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 the at least one machine-learning network based on the measure of distance between the second information and the first information.

2 . The method of claim 1 ,
wherein using the transmitter to process the first information and generate the plurality of first RF signals comprises:
determining, from the first information, a plurality of first information portions; and
generating, based at least in part on the plurality of first information portions, the plurality of first RF signals with each first RF signal corresponding to a respective one of the plurality of first information portions, and
wherein using the receiver to process the plurality of second RF signals and generate the second information as the reconstruction of the first information comprises:
determining, from the plurality of second RF signals, a plurality of second information portions with each second information portion corresponding to a respective one of the plurality of second RF signals; and
generating, from the plurality of second information portions, the second information.

3 . The method of claim 1 , further comprising:
determining channel state information (CSI) that indicates at least one of a state of the MIMO channel model, or spatial information or scheduling information regarding multiple users of the MIMO channel model; and based on determining the CSI, performing at least one of (i) using the transmitter to generate the plurality of first RF signals based on the CSI and the first information, or (ii) updating the MIMO channel model based on the CSI.

4 . The method of claim 3 , wherein determining the CSI comprises:
determining channel information regarding the at least one of a state of the MIMO channel model or spatial information or scheduling information regarding multiple users of a MIMO communication channel; and generating the CSI as a compact representation of the channel information by quantizing or classifying the channel information into one of a discrete number of states or finite number of bits as the CSI.

5 . The method of claim 1 , wherein updating the at least one 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 the at least one machine-learning network; selecting, based on the calculated rate of change of the objective function, a variation for the at least one machine-learning network; and updating the at least one machine-learning network based on the selected variation for the machine-learning network.

6 . 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 other probabilistic measure of distance, or (ii) a geometric distance metric between the second information and the first information.

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

8 . The method of claim 7 , wherein the at least one channel-modeling layer represents at least one of (i) additive Gaussian thermal noise in the MIMO communication channel, (ii) delay spread caused by time-varying effects of the MIMO communication channel, (iii) phase noise or other distortions caused by transmission and reception over the MIMO communication channel or hardware, or (iv) offsets in phase, frequency, rate, or timing caused by transmission and reception over the MIMO communication channel.

9 . The method of claim 1 , wherein the at least one 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.

10 . The method of claim 1 , further comprising:
training the at least one machine-learning network to communicate over a multi-user MIMO communication channel utilized by multiple users, wherein the transmitter comprises one or more encoder machine-learning networks, and the receiver comprises one or more decoder machine-learning networks, wherein using the transmitter to process the first information and generate the plurality of first RF signals comprises: using the one or more encoder machine-learning networks to (i) process at least a first portion of the first information to generate a first subset of the plurality of first RF signals; and (ii) process at least a second portion of the first information and generate a second subset of the plurality of first RF signals, wherein using the receiver to process the plurality of second RF signals and generate second information as a reconstruction of the first information comprises: using the one or more decoder machine-learning networks to (i) process a first subset of the plurality of second RF signals and generate at least a first portion of the second information as a reconstruction of the first portion of the first information; and (ii) process a second subset of the plurality of second RF signals and generate at least a second portion of the second information as a reconstruction of the second portion of the first information, wherein calculating the measure of distance between the second information and the first information comprises: (i) calculating a first measure of distance between the first portion of the second information and the first portion of the first information, and (ii) calculating a second measure of distance between the second portion of the second information and the second portion of the first information, and wherein updating the at least one machine-learning network based on the measure of distance between the second information and the first information comprises:
based on the first measure of distance and the second measure of distance, updating at least one of (i) the one or more encoder machine-learning networks, or (ii) the one or more d

Assignees

• Virginia Tech Intellectual Properties Inc

Inventors

• O`Shea Timothy James
• Erpek Tugba

Classifications

• H04B7/0456

  Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting · CPC title

• H04B7/0626

  Channel coefficients, e.g. channel state information [CSI] · CPC title

• G06N3/086

  using evolutionary algorithms, e.g. genetic algorithms or genetic programming · CPC title

• G06N3/088

  Non-supervised learning, e.g. competitive learning · CPC title

• G06N3/006

  based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO] · CPC title