Interaction-aware multiple-user joint spatial-temporal beam management
The technology described herein is directed towards a joint spatial/temporal domain beam management including spatial domain beam management on the observation window and temporal beam management on the prediction window. Incorporating the spatial and temporal dependencies increases the prediction interval and makes the predictions more accurate because the spatial beam management is performed on the observation interval. Also described is interaction-aware multi-user equipment (UE) beam management technology that models multiple user interactions in the beam management procedure, to predict future trajectory data and to predict the best future beam and/or future best subset of beams per UE. A single user class or multiple classes of users (e.g., vehicle class users, pedestrian class users) can be considered for the predicted future trajectory data and or beam prediction data.
1 . Network equipment, comprising:
at least one processor; and
at least one memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations, the operations comprising:
conducting a spatial beam management observation phase over a first number of first time intervals of a spatial observation period, comprising:
performing synchronization signal block beam sweeping to determine, from a group of beams, a high performing subgroup of beams with respect to received power data, corresponding to the synchronization signal block beam sweeping, returned from a user equipment, wherein the high performing subgroup of beams is higher performing than other beams of the group of beams,
determining a spatial beam pair that satisfies at least a first threshold high performance, comprising performing first channel state information-reference signal beam sweeping using the high performing subgroup of beams to obtain, from the user equipment, a first channel state information-reference signal identifier representing the spatial beam pair;
conducting a temporal beam management prediction phase over a second number of second time intervals of a temporal prediction period, comprising:
determining a temporal beam pair that satisfies at least a second threshold high performance, comprising performing second channel state information-reference signal beam sweeping based on the spatial beam pair to obtain, from the user equipment, a second channel state information-reference signal identifier representing the temporal beam pair; and
estimating trajectory data of the user equipment based on the spatial beam pair and the temporal beam pair.
2 . The network equipment of claim 1 , wherein the group of beams is a sparse group of beams, and wherein the performing of the synchronization signal block beam sweeping comprises obtaining the sparse group of beams from a model, and using the sparse group of beams during the performing of the synchronization signal block beam sweeping.
3 . The network equipment of claim 1 , wherein the performing of the second channel state information-reference signal beam sweeping based on the spatial beam pair comprises using at least some of the high performing subgroup of beams that comprises the spatial beam pair.
4 . The network equipment of claim 1 , wherein the trajectory data of the user equipment comprises first trajectory data of a first user equipment, wherein the operations further comprise estimating second trajectory data of a second user equipment, and predicting future optimal beam data for the first user equipment based on the first trajectory data and the second trajectory data, and wherein the future optimal beam data is determined to be optimal based on satisfying a defined performance criterion.
5 . The network equipment of claim 4 , wherein the estimating of the trajectory data and the predicting of the future optimal beam data for the first user equipment comprises inputting, to a model, a first dataset comprising historical beam measurement data, historical beam indication data, location data of the first user equipment, and speed data of the first user equipment, inputting, to the model, a second dataset comprising cross-user equipment trajectory dependency data based on the first trajectory data and the second trajectory data, and, in response to the inputting of the first dataset and the second dataset to the model, obtaining, from the model, an estimate of the first trajectory data and a prediction of the future optimal beam data.
6 . The network equipment of claim 5 , wherein the future optimal beam data for the first user equipment comprises first future optimal beam data, and wherein the operations further comprise predicting second future optimal beam data for the second user equipment based on the first trajectory data and the second trajectory data.
7 . The network equipment of claim 5 , wherein the network equipment comprises a distributed equipment corresponding to a communications neighborhood, wherein the model is a first model of the distributed equipment, wherein the determining of the spatial beam pair is performed using a second model of the distributed equipment comprising a spatial inference model, and wherein the determining of the temporal beam pair is performed using a third model of the distributed equipment comprising a temporal inference model.
8 . The network equipment of claim 5 , wherein the historical beam measurement data comprises first historical reference signal received power measurement data corresponding to the synchronization signal block beam sweeping and second historical reference signal received power measurement data corresponding to at least one of: the first channel state information-reference signal beam sweeping, or the second channel state information-reference signal beam sweeping.
9 . The network equipment of claim 1 , wherein the trajectory data of the user equipment comprises first trajectory data of a first user equipment, wherein the operations further comprise estimating respective other trajectory data of respective other user equipment of a group of user equipment that comprises the first user equipment and the respective other user equipment, predicting future optimal beam data for the first user equipment based on the first trajectory data and the other trajectory data, and predicting respective other future optimal beam data for the respective other user equipment based on the first trajectory data and the respective other trajectory data, and wherein the future optimal beam data is determined to be optimal based on satisfying a defined performance criterion.
10 . The network equipment of claim 9 , wherein the group of user equipment that comprises the first user equipment and the respective other user equipment is classified into a class of user equipment.
11 . The network equipment of claim 10 , wherein the operations further comprise classifying the group of user equipment into the class based on the first trajectory data and the respective other trajectory data.
12 . The network equipment of claim 10 , wherein the operations further comprise classifying the group of user equipment into the class based on first historical state data of the first user equipment and respective other historical state data of the respective other user equipment.
13 . The network equipment of claim 1 , wherein the trajectory data of the user equipment comprises first trajectory data of a first user equipment, wherein the first user equipment and respective first other user equipment are classified into a first classification group, wherein the operations further comprise estimating respective first other trajectory data of the respective first other user equipment, estimating respective second other trajectory data of respective second user equipment of a second classification group that comprises the respective second user equipment, predicting a future optimal beam for the first user equipment based on the first trajectory data, the first other trajectory data, and the second other trajectory data, and predicting respective other first future optimal beams for the respective first other user equipment based on the first trajectory data, the first other trajectory data, and the second other trajectory data, and wherein the future optimal beam is determined to be optimal based on satisfying a defined performance criterion.
14 . The network equipment of claim 13 , wherein the operations further comprise predicting respective other second future optimal beams for the respective second other user equipment based on the first trajectory data, the first other trajectory data, and the second other trajectory data.
15 . A method, comprising:
determining, by a system comprising at least one processor, for a group of respective user equipment in a communications neighborhood, respective trajectory data based on respective joint spatial and temporal beam data obtained for the respective user equipment of the group of respective user equipment comprising first user equipment and second user equipment;
based on the respective trajectory data, inputting, by the system to a social recurrent neural network model, first trajectory data of the first user equipment, second trajectory data of the second user equipment, and trajectory cross-dependency data; and
in response to the inputting, obtaining, by the system from the social recurrent neural network model, first predicted trajectory data and first predicted future optimal beam data for the first user equipment, and second predicted trajectory data and second predicted future optimal beam data for the second user equipment, wherein the first predicted future optimal beam data and the second predicted future optimal beam data are determined to be optimal based on satisfying a defined performance criterion.
16 . The method of claim 15 , wherein the first trajectory data is part of a dataset, the dataset further comprising historical beam measurement data and historical beam indication data, and wherein the inputting of the first trajectory data comprises inputting the dataset to the social recurrent neural network model.
17 . The method of claim 15 , further comprising determining, by the system, the respective joint spatial and temporal beam data, comprising conducting respective spatial beam management observation phases to obtain respective spatial beam data, and conducting respective temporal beam management prediction phases based on the respective spatial beam data.
18 . A non-transitory machine-readable medium, comprising executable instructions that, when executed by at least one processor, facilitate performance of operations, the operations comprising:
determining respective joint spatial and temporal beam data, comprising conducting respective spatial beam management observation phases to obtain respective spatial beam data, and conducting respective temporal beam management prediction phases based on the respective spatial beam data;
determining, for respective user equipment, respective trajectory data based on the respective joint spatial and temporal beam data, wherein the respective user equipment comprise first user equipment and second user equipment;
obtaining cross-dependency trajectory data based on the respective trajectory data;
inputting, to a model, the cross-dependency trajectory data, first trajectory data of the first user equipment, and second trajectory data of the second user equipment; and
in response to the inputting, obtaining, from the model, first predicted trajectory data and first predicted future optimal beam data for the first user equipment, and second predicted trajectory data and second predicted future optimal beam data for the second user equipment, wherein the first predicted future optimal beam data and the second predicted future optimal beam data are determined to be optimal based on satisfying a defined performance criterion.
19 . The non-transitory machine-readable medium of claim 18 , wherein, based on the first trajectory data, the second trajectory data, or the cross-dependency trajectory data, the model infers the first predicted trajectory data, the second predicted trajectory data, the first predicted future optimal beam data, or the second predicted future optimal beam data.
20 . The non-transitory machine-readable medium of claim 18 , wherein the conducting of the respective spatial beam management observation phases comprises:
performing respective synchronization signal block beam sweeping to determine respective high performing subgroups of beams based on respective received power data corresponding to the synchronization signal block beam sweeping, and
determining the respective optimal spatial beam data, comprising performing respective first channel state information-reference signal beam sweeping using the respective high performing subgroups of beams to obtain from the respective user equipment respective first channel state information-reference signal identifiers representing the respective optimal spatial beam data; and
wherein the conducting of the respective temporal beam management prediction phases comprises:
determining the respective optimal temporal beam data, comprising performing respective second channel state information-reference signal beam sweeping based on the respective spatial beam data to obtain from the respective user equipment respective second channel state information-reference signal identifiers representing the respective temporal beam data.