Learning and deploying compression of radio signals
First Claim
1. A method of training at least one machine-learning network to learn compact representations of radio frequency (RF) signals, the method performed by at least one processor executing instructions stored on at least one computer memory coupled to the at least one processor, the method comprising:
- determining a first RF signal to be compressed;
using an encoder machine-learning network to process the first RF signal and generate a compressed signal;
using a decoder machine-learning network to process the compressed signal and generate a second RF signal that represents a reconstruction of the first RF signal;
calculating a measure of distance between the second RF signal and the first RF signal;
obtaining a measure of compression in the compressed signal; and
updating at least one of the encoder machine-learning network or the decoder machine-learning network based on (i) the measure of distance between the second RF signal and the first RF signal, and (ii) the measure of compression in the compressed signal, wherein the updating comprises;
determining an objective function that comprises at least;
(i) the measure of distance between the second RF signal and the first RF signal, and (ii) the measure of compression in the compressed signal,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, andupdating at least one of the encoder machine-learning network or the decoder machine-learning network based on the selected at least one of the first variation or the second variation.
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Accused Products
Abstract
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned compact representations of radio frequency (RF) signals. One of the methods includes: determining a first RF signal to be compressed; using an encoder machine-learning network to process the first RF signal and generate a compressed signal; calculating a measure of compression in the compressed signal; using a decoder machine-learning network to process the compressed signal and generate a second RF signal that represents a reconstruction of the first RF signal; calculating a measure of distance between the second RF signal and the first RF signal; and updating at least one of the encoder machine-learning network or the decoder machine-learning network based on (i) the measure of distance between the second RF signal and the first RF signal, and (ii) the measure of compression in the compressed signal.
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Citations
32 Claims
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1. A method of training at least one machine-learning network to learn compact representations of radio frequency (RF) signals, the method performed by at least one processor executing instructions stored on at least one computer memory coupled to the at least one processor, the method comprising:
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determining a first RF signal to be compressed; using an encoder machine-learning network to process the first RF signal and generate a compressed signal; using a decoder machine-learning network to process the compressed signal and generate a second RF signal that represents a reconstruction of the first RF signal; calculating a measure of distance between the second RF signal and the first RF signal; obtaining a measure of compression in the compressed signal; and updating at least one of the encoder machine-learning network or the decoder machine-learning network based on (i) the measure of distance between the second RF signal and the first RF signal, and (ii) the measure of compression in the compressed signal, wherein the updating comprises; determining an objective function that comprises at least;
(i) the measure of distance between the second RF signal and the first RF signal, and (ii) the measure of compression in the compressed signal,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 selected at least one of the first variation or the second variation. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 29, 31)
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10. A method of deploying at least one machine-learning network that has been trained to learn compact representations of radio frequency (RF) signals, the method performed by at least one processor executing instructions stored on at least one computer memory coupled to the at least one processor, the method comprising:
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determining an encoder machine-learning network and a decoder machine-learning network that have been trained to learn compact representations of RF signals; determining a first RF signal to be compressed; using the encoder machine-learning network to process the first RF signal and generate a first compressed signal; obtaining a second compressed signal that comprises the first compressed signal or an alteration thereof; and using the decoder machine-learning network to process the second compressed signal to generate a second RF signal as a reconstruction of the first RF signal, wherein at least one of the encoder machine-learning network or the decoder machine-learning network is trained based on (i) a measure of distance between a training RF signal and a reconstruction of the training RF signal, and (ii) a measure of compression in compressing the training RF signal, wherein the training comprises; determining an objective function that comprises at least;
(i) the measure of distance between the second RF signal and the first RF signal, and (ii) the measure of compression in the compressed signal,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 selected at least one of the first variation or the second variation. - View Dependent Claims (11, 12, 13, 14)
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15. A system comprising:
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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 a first RF signal to be compressed; using an encoder machine-learning network to process the first RF signal and generate a compressed signal; obtaining a measure of compression in the compressed signal; using a decoder machine-learning network to process the compressed signal and generate a second RF signal that represents a reconstruction of the first RF signal; calculating a measure of distance between the second RF signal and the first RF signal; and updating at least one of the encoder machine-learning network or the decoder machine-learning network based on (i) the measure of distance between the second RF signal and the first RF signal, and (ii) the measure of compression in the compressed signal, wherein the updating comprises; determining an objective function that comprises at least;
(i) the measure of distance between the second RF signal and the first RF signal, and (ii) the measure of compression in the compressed signal,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 selected at least one of the first variation or the second variation. - View Dependent Claims (16, 17, 18, 19, 20, 21, 22, 23, 30, 32)
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24. A system comprising:
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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 machine-learning network and a decoder machine-learning network that have been trained to compress RF signals; determining a first RF signal to be compressed; using the encoder machine-learning network to process the first RF signal and generate a first compressed signal; obtaining a second compressed signal that comprises the first compressed signal or an alteration thereof; and using the decoder machine-learning network to process the second compressed signal to generate a second RF signal as a reconstruction of the first RF signal, wherein at least one of the encoder machine-learning network or the decoder machine-learning network is trained based on (i) a measure of distance between a training RF signal and a reconstruction of the training RF signal, and (ii) a measure of compression in compressing the training RF signal, wherein the training comprises; determining an objective function that comprises at least;
(i) the measure of distance between the second RF signal and the first RF signal, and (ii) the measure of compression in the compressed signal,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 selected at least one of the first variation or the second variation. - View Dependent Claims (25, 26, 27, 28)
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Specification