Artificial intelligence-enabled link adaptation

1 . A method in a first device in a wireless communication network, the method comprising:

obtaining channel state information characterizing a wireless communication channel between the first device and a second device in the wireless communication network;

training a machine learning (ML) module of the first device using the channel state information as an ML module input and one or more modulation and coding scheme (MCS) parameters as an ML module output to satisfy a training target, the one or more MCS parameters including a predicted modulation order, a predicted coding rate or both, wherein the ML module of the first device and a further ML module of the second device are trained to predict the same one or more MCS parameters.

2 . The method of claim 1 , wherein obtaining the channel state information comprises:

receiving, from the second device, a reference signal; and

determining the channel state information based on the reference signal.

3 . The method of claim 2 , wherein the first device is a user equipment (UE) and the second device is a network device, the method further comprising:

receiving, from the network device, training data corresponding to the reference signal, the training data comprising information indicating one or more MCS parameters predicted by the network device, the one or more MCS parameters including a predicted modulation order, a predicted coding rate or both;

wherein training an ML module of the first device to satisfy a training target comprises training the ML module of the UE to satisfy a training target of matching the one or more MCS parameters of the ML module output of the ML module of the UE to the one or more MCS parameters predicted by the network device.

4 . The method of claim 1 , wherein the first device is a user equipment (UE) and the second device is a network device, the method further comprising:

transmitting, from the UE, a reference signal,

wherein obtaining the channel state information comprises receiving, from the network device, a channel state information report that includes information indicating uplink channel state information determined by the network device based on the reference signal transmitted by the UE, and

wherein using the channel state information as an ML module input comprises using the uplink channel state information as the ML module input.

5 . The method of claim 1 , wherein the first device is a network device and the second device is a user equipment (UE), the method further comprising:

transmitting, from the network device, a reference signal,

wherein obtaining the channel state information comprises receiving, from the UE, a channel state information report that includes information indicating downlink channel state information determined by the UE based on the reference signal transmitted by the network device, and

wherein using the channel state information as an ML module input comprises using the downlink channel state information as the ML module input.

6 . The method of claim 2 , wherein:

the first device is a network device and the second device is a user equipment (UE);

receiving, from the second device, a reference signal comprises receiving a reference signal from the UE;

determining the channel state information based on the reference signal comprises determining uplink channel state information based on the reference signal received from the UE; and

using the channel state information as an ML module input comprises using the uplink channel state information as the ML module input.

7 . A method in a first device in a wireless communication network, the method comprising:

obtaining channel state information characterizing a wireless communication channel between the first device and a second device in the wireless communication network; and

obtaining, based on the channel state information as an input to a machine learning (ML) module of the first device that has been trained using channel state information characterizing a wireless communication channel between the first device and the second device in the wireless communication network as an ML module input and one or more modulation and coding scheme (MCS) parameters as an ML module output, one or more MCS parameters for communication between the first device and the second device, the one or more MCS parameters including a predicted modulation order, a predicted coding rate or both; and

transmitting to or receiving from the second device over the wireless communication channel using the one or more MCS parameters, wherein the ML module of the first device and a further ML module of the second device are trained to predict the same one or more MCS parameters.

8 . The method of claim 7 , wherein obtaining the channel state information comprises:

receiving, from the second device, a reference signal; and

determining the channel state information based on the reference signal.

9 . The method of claim 8 , wherein the first device is a user equipment (UE), the second device is a network device, the ML module used to obtain the one or more MCS parameters is trained to satisfy a training target of matching the one or more MCS parameters of the ML module output of the ML module of the UE to one or more MCS parameters predicted by the network device.

10 . The method of claim 8 , wherein the first device is a user equipment (UE), the second device is a network device, the method further comprising:

obtaining, based on the channel state information as an input to an ML encoder module of the UE, compressed channel state information; and

transmitting a channel state information report to the network device, the channel state information report comprising the compressed channel state information.

11 . The method of claim 7 , wherein the first device is a user equipment (UE) and the second device is a network device, the method further comprising:

transmitting, from the UE, a reference signal,

wherein obtaining the channel state information comprises receiving, from the network device, a channel state information report that includes information indicating uplink channel state information determined by the network device based on the reference signal transmitted by the UE, and

wherein using the channel state information as an ML module input comprises using the uplink channel state information as the ML module input.

12 . The method of claim 7 , wherein the first device is a network device and the second device is a user equipment (UE), the method further comprising:

transmitting, from the network device, a reference signal,

wherein obtaining the channel state information comprises receiving, from the UE, a channel state information report that includes information indicating downlink channel state information determined by the UE based on the reference signal transmitted by the network device, and

wherein using the channel state information as an ML module input comprises using the downlink channel state information as the ML module input.

13 . The method of claim 8 , wherein:

the first device is a network device and the second device is a user equipment (UE);

receiving, from the second device, a reference signal comprises receiving a reference signal from the UE;

determining the channel state information based on the reference signal comprises determining uplink channel state information based on the reference signal received from the UE; and

using the channel state information as an ML module input comprises using the uplink channel state information as the ML module input.

14 . A device comprising:

a memory storing processor-executable instructions; and

a processor for executing the processor-executable instructions to cause the device to:

obtain channel state information characterizing a wireless communication channel between the device and a second device in a wireless communication network; and

obtain, based on the channel state information as an input to a machine learning (ML) module of the device that has been trained using channel state information characterizing a wireless communication channel between the device and the second device in the wireless communication network as an ML module input and one or more modulation and coding scheme (MCS) parameters as an ML module output, one or more MCS parameters for communication between the device and the second device, the one or more MCS parameters including a predicted modulation order, a predicted coding rate or both; and

transmit to or receive from the second device over the wireless communication channel using the one or more MCS parameters, wherein the ML module of the device and a further ML module of the second device are trained to predict the same one or more MCS parameters.

15 . The device of claim 14 , wherein obtaining the channel state information comprises:

receiving, from the second device, a reference signal; and

determining the channel state information based on the reference signal.

16 . The device of claim 15 , wherein the device is a user equipment (UE), the second device is a network device, the ML module used to obtain the one or more MCS parameters is trained to satisfy a training target of matching the one or more MCS parameters of the ML module output of the ML module of the UE to one or more MCS parameters predicted by the network device.

17 . The device of claim 15 , wherein the device is a user equipment (UE), the second device is a network device, wherein the processor-executable instructions, when executed, further cause the processor to:

obtain, based on the channel state information as an input to an ML encoder module of the UE, compressed channel state information; and

transmit a channel state information report to the network device, the channel state information report comprising the compressed channel state information.

18 . The device of claim 14 , wherein the device is a user equipment (UE) and the second device is a network device, wherein the processor-executable instructions, when executed, further cause the processor to:

transmit, from the UE, a reference signal,

wherein obtaining the channel state information comprises receiving, from the network device, a channel state information report that includes information indicating uplink channel state information determined by the network device based on the reference signal transmitted by the UE, and

wherein using the channel state information as an ML module input comprises using the uplink channel state information as the ML module input.

19 . The device of claim 14 , wherein the device is a network device and the second device is a user equipment (UE), wherein the processor-executable instructions, when executed, further cause the processor to:

transmit, from the network device, a reference signal,

wherein obtaining the channel state information comprises receiving, from the UE, a channel state information report that includes information indicating downlink channel state information determined by the UE based on the reference signal transmitted by the network device, and

wherein using the channel state information as an ML module input comprises using the downlink channel state information as the ML module input.

20 . The device of claim 15 , wherein:

the device is a network device and the second device is a user equipment (UE);

receiving, from the second device, a reference signal comprises receiving a reference signal from the UE;

determining the channel state information based on the reference signal comprises determining uplink channel state information based on the reference signal received from the UE; and

using the channel state information as an ML module input comprises using the uplink channel state information as the ML module input.