Abstract: This application provides a beam training method and apparatus. The method includes: generating and sending a set up frame, where the set up frame includes M pieces of time information, each piece of time information corresponds to one receiving device in the M receiving devices, each piece of time information is used to indicate a feedback information sending moment of the corresponding receiving device, each piece of time information is further used to indicate an acknowledgment information receiving moment of the corresponding receiving device; receiving a sector sweep SSW frame, generating feedback information based on the SSW frame; sending the feedback information at a feedback information sending moment indicated by corresponding time information; receiving, at an acknowledgment information receiving moment indicated by the corresponding time information, acknowledgment information. Therefore, a training time can be shortened and signaling overheads can be reduced.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of collecting training attribute values for a set of attributes; generating a probability model using the training attribute values, the probability model providing predicted outcomes for at least one attribute in the set of attributes; identifying, using the probability model, a first predicted outcome corresponding to a subsequent combination of attribute values collected by a collection module, wherein the predicted outcomes can comprise the first predicted outcome; coordinating a first display of an ad content version of a subsequent webpage when the first predicted outcome satisfies an objective of the subsequent webpage, wherein the first display can comprise: (a) an ad at a first location on the subsequent webpage and a webpage content in a first format at a second location on the subsequent we

Abstract: This application provides a beam training method and apparatus. The method includes: generating and sending a set up frame, where the set up frame includes M pieces of time information, each piece of time information corresponds to one receiving device in the M receiving devices, each piece of time information is used to indicate a feedback information sending moment of the corresponding receiving device, each piece of time information is further used to indicate an acknowledgment information receiving moment of the corresponding receiving device, receiving a sector sweep SSW frame, generating feedback information based on the SSW frame, sending the feedback information at a feedback information sending moment indicated by corresponding time information, receiving, at an acknowledgment information receiving moment indicated by the corresponding time information, acknowledgment information. Therefore, a training time can be shortened, and signaling overheads can be reduced.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of collecting training attribute values for a set of attributes; generating a probability model using the training attribute values, the probability model providing predicted outcomes for at least one attribute in the set of attributes; identifying, using the probability model, a first predicted outcome corresponding to a subsequent combination of attribute values collected by a collection module, wherein the predicted outcomes can comprise the first predicted outcome; coordinating a first display of an ad content version of a subsequent webpage when the first predicted outcome satisfies an objective of the subsequent webpage, wherein the first display can comprise: (a) an ad at a first location on the subsequent webpage and a webpage content in a first format at a second location on the subsequent we

Abstract: Systems and methods are disclosed for managing the display of online ad content consistent with one or more e-commerce objectives. A collection module may be operable to collect attribute values for a set of attributes characterizing user visits to a set of training webpages and subsequent attribute values for a subsequent user visit to a subsequent webpage. A model-generation module may be operable to train a probability model with the attribute values that predicts outcomes for at least one performance metric. A display module may be operable to determine whether to display ad content on the subsequent webpage for the subsequent user visit depending on whether a predicted outcome from the probability model that is relevant to the subsequent attribute values is consistent with one or more e-commerce objectives. The probability model may be a decision tree with different predicted outcomes for different combinations of attribute values.

Abstract: Systems and methods are disclosed for managing the display of online ad content consistent with one or more e-commerce objectives. A collection module may be operable to collect attribute values for a set of attributes characterizing user visits to a set of training webpages and subsequent attribute values for a subsequent user visit to a subsequent webpage. A model-generation module may be operable to train a probability model with the attribute values that predicts outcomes for at least one performance metric. A display module may be operable to determine whether to display ad content on the subsequent webpage for the subsequent user visit depending on whether a predicted outcome from the probability model that is relevant to the subsequent attribute values is consistent with one or more e-commerce objectives. The probability model may be a decision tree with different predicted outcomes for different combinations of attribute values.