Learning and deployment of adaptive wireless communications

1. 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 implements a respective encoding or decoding based on a corresponding encoder machine-learning network or a decoder machine-learning network that has been trained, by determining a rate of change of an objective function relative to variations in the encoder machine-learning network or the decoder machine-learning network upon processing one or more training signals, to respectively encode or decode information over a communication channel;

determining first information;

using the encoder to process the first information and generate a first radio frequency (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.

2. The method of claim 1 , 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.

3. The method of claim 2 , 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.

4. The method of claim 3 , wherein the plurality of channel modes corresponds to channel conditions including one or more of level of noise, signal-to-noise ratio (SNR), delay spread, or time scale of channel variations.

5. The method of claim 4 , wherein the time scale of channel variations is based on one or more of a characteristic of a channel environment, a speed of movement of environmental objects, or location of additional radio emitters in a neighborhood of the channel.

6. The method of claim 5 , wherein the characteristic of the channel environment includes whether the environment is rural, urban, or in a spacecraft, and

wherein the speed of movement of environmental objects corresponds to channel coherence time or correlation time of channel statistics.

7. The method of claim 3 , wherein determining the channel mode and updating at least one of the encoder or the decoder based on the channel mode comprises:

providing the feedback information to a transmission mode controller;

selecting, by the transmission mode controller, the channel mode from the plurality of channel modes based at least on the feedback information; and

updating the encoder or the decoder using a respective set of encoding or decoding techniques that correspond to the selected channel mode.

8. The method of claim 1 , 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.

9. The method of claim 1 , 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.

10. The method of claim 1 , wherein determining the rate of change of the objective function upon processing one or more training signals comprises:

determining a measure of distance between an input training signal and an output training signal that corresponds to a representation of the input training signal altered by transmission through a communication channel model; and

determining the objective function using the measure of distance.

11. The method of claim 1 , wherein processing the first information using the encoder to generate the first RF signal comprises:

accessing, by the encoder, a lookup table that corresponds to encoding mappings learned during training the encoder using the encoder machine-learning network; and

mapping the first information to the first RF signal using the lookup table.

12. 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 implements a respective encoding or decoding based on a corresponding encoder machine-learning network or a decoder machine-learning network that has been trained, by determining a rate of change of an objective function relative to variations in the encoder machine-learning network or the decoder machine-learning network upon processing one or more training signals, to respectively 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.

13. The system of claim 12 , 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.

14. The system of claim 13 , 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.

15. The system of claim 14 , wherein the plurality of channel modes corresponds to channel conditions including one or more of level of noise, signal-to-noise ratio (SNR), delay spread, or time scale of channel variations.

16. The system of claim 15 , wherein the time scale of channel variations is based on one or more of a characteristic of a channel environment, a speed of movement of environmental objects, or location of additional radio emitters in a neighborhood of the channel.

17. The system of claim 16 , wherein the characteristic of the channel environment includes whether the environment is rural, urban, or in a spacecraft, and

wherein the speed of movement of environmental objects corresponds to channel coherence time or correlation time of channel statistics.

18. The system of claim 14 , wherein determining the channel mode and updating at least one of the encoder or the decoder based on the channel mode comprises:

providing the feedback information to a transmission mode controller;

selecting, by the transmission mode controller, the channel mode from the plurality of channel modes based at least on the feedback information; and

updating the encoder or the decoder using a respective set of encoding or decoding techniques that correspond to the selected channel mode.

19. The system of claim 13 , 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.

20. The system of claim 13 , 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.

21. The system of claim 12 , wherein determining the rate of change of the objective function upon processing one or more training signals comprises:

determining a measure of distance between an input training signal and an output training signal that corresponds to a representation of the input training signal altered by transmission through a communication channel model; and

determining the objective function using the measure of distance.

22. The system of claim 12 , wherein processing the first information using the encoder to generate the first RF signal comprises:

accessing, by the encoder, a lookup table that corresponds to encoding mappings learned during training the encoder using the encoder machine-learning network; and

mapping the first information to the first RF signal using the lookup table.

23. At least one non-transitory computer-readable medium storing instructions that, when executed, cause at least one processor to perform operations comprising:

determining an encoder and a decoder, at least one of which is configured to implements a respective encoding or decoding that is based on at least one of an a corresponding encoder machine-learning network or a decoder machine-learning network that has been trained, by determining a rate of change of an objective function relative to variations in the encoder machine-learning network or the decoder machine-learning network upon processing one or more training signals, to respectively 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.