INFORMATION TRANSCEIVING METHOD AND APPARATUS
An information transceiving apparatus, applicable to a network device, includes: a first transmitter configured to transmit configuration parameters for monitoring performances of one or more AI models to a terminal equipment; and a first receiver configured to receive performance monitoring results of the one or more AI models transmitted by the terminal equipment.
1 . An information transceiving apparatus, applicable to a network device, the apparatus comprising:
a first transmitter configured to transmit configuration parameters for monitoring performances of one or more AI models to a terminal equipment; and
a first receiver configured to receive performance monitoring results of the one or more AI models transmitted by the terminal equipment.
2 . The apparatus according to claim 1 , wherein the configuration parameters comprise a threshold for performance metric and/or a filter coefficient for performance metric and/or a counter for counting a monitoring result of a performance metric.
3 . The apparatus according to claim 2 , wherein the performance metric comprises a prediction error, and/or a prediction accuracy, and/or a throughput, and/or a frame error rate.
4 . The apparatus according to claim 3 , wherein the prediction error comprises a difference between a predicted result of an optimal beam (pair) outputted by an AI model and an actual measured result of the optimal beam (pair), or a difference between a predicted result of a first optimal beam (pair) outputted by an AI model and an actually measured result of a second optimal beam (pair), or an average value of differences between predicted results of multiple beams (pairs) outputted by an AI model and actually measured results of the multiple beams (pairs), or an average value of differences between predicted results of multiple first beams (pairs) outputted by an AI model and actually measured results of a second optimal beams (pairs).
5 . The apparatus according to claim 3 , wherein the prediction accuracy comprises a probability of whether a first optimal beam (pair) outputted by an AI model is identical to an actually measured second optimal beam (pair), or a probability that a first number of first beams (pairs) outputted by an AI model contain actually measured optimal beams (pairs), or a probability that optimal beams (pairs) outputted by an AI model are contained in a second number of actually measured second beams (pairs).
6 . The apparatus according to claim 4 , wherein the measured result and/or predicted result comprises L1-RSRP or SINR.
7 . The apparatus according to claim 4 , wherein the actual measured result is determined according to training data of an AI model or according to actual measured results when an AI model is not applied.
8 . The apparatus according to claim 1 , the apparatus further comprising:
first processor circuitry configured to activate or deactivate or select or switch an AI model according to the performance monitoring results.
9 . The apparatus according to claim 8 , wherein the configuration parameters comprise a first configuration parameter for determining to activate an AI model, and/or a second configuration parameter for determining to deactivate an AI model, and/or a third configuration parameter for selecting an AI model and/or switching an AI model.
10 . The apparatus according to claim 9 , wherein the first configuration parameter, the second configuration parameter and the third configuration parameter are identical or different.
11 . The apparatus according to claim 1 , wherein configuration parameters for monitoring performances of different AI models are identical or different.
12 . The apparatus according to claim 1 , wherein when the first transmitter transmits the configuration parameters for monitoring performances of multiple AI models, the first transmitter is further configured to transmit identifiers of AI models to which the configuration parameters correspond, and the performance monitoring results further include identifiers of corresponding AI models.
13 . The apparatus according to claim 1 , wherein the configuration parameters are carried by RRC or an MAC CE or DCI, and the performance monitoring results are carried by UCI.
14 . The apparatus according to claim 1 , wherein the AI model is deployed at a side of the terminal equipment.
15 . An information transceiving apparatus, applicable to a terminal equipment, the apparatus comprising:
a second receiver configured to receive configuration parameters transmitted by a network device for monitoring performances of one or more AI models; and
a second transmitter configured to transmit performance monitoring results of the one or more AI models to the network device.
16 . The apparatus according to claim 15 , wherein the performance monitoring results comprise AI model performance indication information, and/or a value of an AI model performance metric, and/or identifier of an AI model.
17 . The apparatus according to claim 15 , the apparatus further comprising:
a first calculator configured to calculate a value of the AI model performance metric.
18 . The apparatus according to claim 17 , wherein the first calculator calculates the value of the performance metric according to the configuration parameters, and the performance monitoring results include the value of the performance metric.
19 . The apparatus according to claim 17 , the apparatus further comprising:
second processor circuitry configured to perform filtering processing on the value of the performance metric according to the configuration parameters (filter coefficient and/or counter) and/or compare the value of the performance metric with the configuration parameters (threshold), and generate the performance monitoring result containing AI model performance indication information according to a result of the filtering processing and/or a result of comparison.
20 . The apparatus according to claim 19 , wherein the second processor circuitry processes the value of the performance metric according to the configured filter coefficient for the performance metric to generate a value of a filtered performance metric, compares the value of filtered performance metric with a configured threshold for the performance metric, and when the value of the filtered performance metric is greater than the configured threshold for the performance metric, generates the performance monitoring result containing the AI model performance indication information, or when the value of the filtered performance metric is less than the configured threshold for the performance metric, generates the performance monitoring result containing the AI model performance indication information; or,
the second processor circuitry compares the value of the performance metric with the configured threshold for the performance metric, and when the value of the performance metric is greater than the configured threshold for the performance metric, a counter for counting the monitoring results of the performance metric is increased by 1, when the value of the performance metric is less than the configured threshold for the performance metric, the counter for counting the monitoring results of the performance metric is decreased by 1, and when a value of the counter reaches a maximum count value, the performance monitoring result containing the AI model performance indication information is generated; or when the value of the performance metric is less than the configured threshold for the performance metric, the counter for counting the monitoring results of the performance metric is increased by 1, when the value of the performance metric is greater than the configured threshold for the performance metric, the counter for counting the monitoring results of the performance metric is decreased by 1, and when a value of the counter reaches a maximum count value, the performance monitoring result containing the AI model performance indication information is generated.