Beamforming prediction device, method and program
The present disclosure is to perform beamforming corresponding to the influence of a dynamic environment in which a user moves. The present disclosure relates to a beamforming prediction device that includes: a storage unit that stores a dictionary D obtained by learning fingerprints based on trajectories, and a fingerprint database based on trajectories; a trajectory prediction unit that calculates a trajectory of a mobile terminal, using location information about the mobile terminal; a fingerprint estimation unit that applies the trajectory of the mobile terminal to an input of the dictionary D, and calculates the sparse coefficient X corresponding to the trajectory of the mobile terminal; and a beamforming calculation unit that calculates beamforming of the mobile terminal, using the sparse coefficient X calculated by the fingerprint estimation unit and the fingerprint database.
1 . A beamforming prediction device comprising:
a storage unit that stores a fingerprint database and a dictionary, where the fingerprint database stores a plurality of fingerprints, each fingerprint includes the trajectory for a mobile terminal, a received signal strength for the mobile terminal, and a pair of beam arrival angle and beam departure angles for the mobile terminal dictionary, and the dictionary stores a set of feature vectors extracted from the fingerprint database;
a trajectory prediction unit that calculates a trajectory of a mobile terminal, using location information about the mobile terminal;
a fingerprint estimation unit that calculates a sparse coefficient corresponding to the trajectory of the given mobile terminal using sparse coding and the dictionary, where the sparse coefficient represents weights assigned to different trajectories; and
a beamforming calculation unit that calculates beamforming of the mobile terminal, where beamforming is calculated by optimizing transmission rate of the given mobile terminal using the sparse coefficient and trajectories stored in the fingerprint database.
2 . The beamforming prediction device according to claim 1 , further comprising:
a fingerprint accumulation unit that acquires and accumulates a fingerprint corresponding to a trajectory of the mobile terminal from a base station; and
a dictionary updating unit that learns the dictionary D, when a new fingerprint is accumulated in the fingerprint accumulation unit, using the new fingerprint and updates the dictionary D and the fingerprint database stored in the storage unit.
3 . The beamforming prediction device according to claim 1 , wherein
each of the fingerprints includes a trajectory of the mobile terminal, a base station that communicates with the mobile terminal, and a parameter of beamforming for performing communication with the base station, and
the beamforming calculation unit calculates the base station to which the mobile terminal is to be connected, and the parameter of beamforming for performing communication with the base station, using the sparse coefficient X and the fingerprint database.
4 . A beamforming prediction method comprising:
calculating, by a trajectory prediction unit, a trajectory of a given mobile terminal using location information about the mobile terminal;
providing a fingerprint database that stores a plurality of fingerprints, each fingerprint includes the trajectory for a mobile terminal, a received signal strength for the mobile terminal, and a pair of beam arrival angle and beam departure angles for the mobile terminal;
calculating, by a fingerprint estimation unit, a sparse coefficient corresponding to the trajectory of the given mobile terminal using sparse coding and a dictionary, where the sparse coefficient represents weights assigned to different trajectories, and the dictionary stores a set of feature vectors extracted from the fingerprint database;
calculating, by a beamforming calculation unit, beamforming for the given mobile terminal, where beamforming is calculated by optimizing transmission rate of the given mobile terminal using the sparse coefficient and trajectories stored in the fingerprint database.
5 . A non-transitory computer-readable medium having computer-executable instructions that, upon execution of the instructions by a processor of a computer, cause the computer to:
calculating a trajectory of a given mobile terminal using location information about the mobile terminal;
storing a plurality of fingerprints in a fingerprint database, each fingerprint includes the trajectory for a mobile terminal, a received signal strength for the mobile terminal, and a pair of beam arrival angle and beam departure angles for the mobile terminal;
calculating a sparse coefficient corresponding to the trajectory of the given mobile terminal using sparse coding and a dictionary, where the sparse coefficient represents weights assigned to different trajectories, and the dictionary stores a set of feature vectors extracted from the fingerprint database;
calculating beamforming for the given mobile terminal, where beamforming is calculated by optimizing transmission rate of the given mobile terminal using the sparse coefficient and trajectories stored in the fingerprint database.