Adaptive transmission and transmission path selection based on predicted channel state

A transmitter node for wireless communication, comprising:

• one or more memories;
• and
• one or more processors, coupled to the one or more memories, configured to:
• receive, from an assisting wireless node, information to configure a machine learning model for predicting a future state associated with a wireless channel at a future time instance, wherein the information configuring the machine learning model comprises one or more weights associated with the machine learning model and one or more architectural parameters for the machine learning model;
• receive, from the assisting wireless node, one or more parameters for detecting a machine learning outage event, wherein the one or more parameters for detecting the machine learning outage event include a mean channel prediction error over a time period or a number of transmissions that resulted in incorrect hybrid automatic repeat request feedback;
• transmit, to the assisting wireless node, a request to update the machine learning model based at least in part on detecting the machine learning outage event in accordance with the one or more parameters for detecting the machine learning outage event;
• receive, from the assisting wireless node, one or more updates to the one or more architectural parameters associated with the machine learning model;
• predict the future state using the machine learning model associated with the one or more updates to the one or more architectural parameters associated with the machine learning model, and based at least in part on one or more inputs to the machine learning model, wherein the one or more inputs comprise a current state associated with the wireless channel or one or more previous states associated with the wireless channel;
• select one or more parameters for a transmission to occur at the future time instance based at least in part on the future state associated with the wireless channel;
• and
• transmit the transmission using the one or more parameters.

A non-transitory computer-readable medium storing a set of instructions for wireless communication, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a transmitter node, cause the transmitter node to:
• receive, from an assisting wireless node, information to configure a machine learning model for predicting a future state associated with a wireless channel at a future time instance, wherein the information configuring the machine learning model comprises one or more weights associated with the machine learning model and one or more architectural parameters for the machine learning model;
• receive, from the assisting wireless node, one or more parameters for detecting a machine learning outage event, wherein the one or more parameters for detecting the machine learning outage event include a mean channel prediction error over a time period or a number of transmissions that resulted in incorrect hybrid automatic repeat request feedback;
• transmit, to the assisting wireless node, a request to update the machine learning model based at least in part on detecting the machine learning outage event in accordance with the one or more parameters for detecting the machine learning outage event;
• receive, from the assisting wireless node, one or more updates to the one or more architectural parameters associated with the machine learning model;
• predict the future state using the machine learning model associated with the one or more updates to the one or more architectural parameters associated with the machine learning model, and based at least in part on one or more inputs to the machine learning model, wherein the one or more inputs comprise a current state associated with the wireless channel or one or more previous states associated with the wireless channel;
• select one or more parameters for a transmission to occur at the future time instance based at least in part on the future state associated with the wireless channel;
• and
• transmit the transmission using the one or more parameters.

An apparatus for wireless communication, comprising:

• means for receiving, from an assisting wireless node, information configuring a machine learning model for predicting a future state associated with a wireless channel at a future time instance, wherein the information configuring the machine learning model comprises one or more weights associated with the machine learning model and one or more architectural parameters for the machine learning model;
• means for receiving, from the assisting wireless node, one or more parameters for detecting a machine learning outage event, wherein the one or more parameters for detecting the machine learning outage event include a mean channel prediction error over a time period or a number of transmissions that resulted in incorrect hybrid automatic repeat request feedback;
• means for transmitting, to the assisting wireless node, a request to update the machine learning model based at least in part on detecting the machine learning outage event in accordance with the one or more parameters for detecting the machine learning outage event;
• means for receiving, from the assisting wireless node, one or more updates to the one or more architectural parameters associated with the machine learning model;
• means for predicting the future state using the machine learning model associated with the one or more updates to the one or more architectural parameters associated with the machine learning model, and based at least in part on one or more inputs to the machine learning model, wherein the one or more inputs comprise a current state associated with the wireless channel or one or more previous states associated with the wireless channel;
• means for selecting one or more parameters for a transmission to occur at the future time instance based at least in part on the future state associated with the wireless channel;
• and
• means for transmitting the transmission using the one or more parameters.