Transmission space reproduction method and transmission space reproduction device
A transmission space reproduction method according to an embodiment includes: a propagation characteristics calculation step of performing simulation while sequentially changing a parameter in a reverberation chamber used to reproduce propagation characteristics so as to calculate propagation characteristics in the reverberation chamber, a machine learning step of forming a learning model for a calculated parameter by machine learning using actually measured propagation characteristics and parameters, a parameter generation step of generating a parameter corresponding to propagation characteristics to be reproduced using the formed learning model, and a reproduction execution step of controlling a channel emulator based on the generated parameter so as to perform processing of forming a transmission space having the propagation characteristics to be reproduced in the reverberation chamber.
1 . A transmission space reproduction method comprising:
simulating while sequentially changing a parameter in a reverberation chamber used to reproduce propagation characteristics so as to calculate propagation characteristics in the reverberation chamber;
forming a learning model for a calculated parameter by machine learning using actually measured propagation characteristics and parameters;
generating a parameter corresponding to propagation characteristics to be reproduced using the formed learning model; and
controlling a channel emulator based on the generated parameter so as to perform processing of forming a transmission space having the propagation characteristics to be reproduced in the reverberation chamber.
2 . The transmission space reproduction method according to claim 1 , wherein in simulating, the simulation is performed while further changing a parameter of a dynamic reflector that is provided in the reverberation chamber and is capable of controlling phases of arrival waves when reflecting radio waves so as to calculate the propagation characteristics in the reverberation chamber.
3 . The transmission space reproduction method according to claim 1 , wherein in the simulating, the parameter that is sequentially changed includes at least one of a shape, a size, or a material of the reverberation chamber, a transmission location, a transmission signal, or a transmission beam direction by a transmission antenna, or a reception location or a reception signal by a measurement object.
4 . The transmission space reproduction method according to claim 1 , wherein the simulating is performed using a ray tracing method or a finite-difference time-domain method.
5 . The transmission space reproduction method according to claim 1 , wherein the forming the learning model is performed by machine learning using the calculated propagation characteristics, the calculated parameter, and the actually measured propagation characteristics and parameters.
6 . The transmission space reproduction method according to claim 1 , wherein the generating the parameter corresponding to the propagation characteristics to be reproduced comprises inputting the propagation characteristics to be reproduced into the formed learning model.
7 . The transmission space reproduction method according to claim 1 , wherein the propagation characteristics include at least one of reception power, a polarization ratio of an incident field, delay time, arrival direction, delay spread, angular spread, or a number of clusters.
8 . A transmission space reproduction device comprising:
a propagation characteristics calculation circuitry configured to perform simulation while sequentially changing a parameter in a reverberation chamber used to reproduce propagation characteristics so as to calculate propagation characteristics in the reverberation chamber;
a machine learning circuitry configured to form a learning model for a parameter calculated by the propagation characteristics calculation circuitry by machine learning using actually measured propagation characteristics and parameters;
a parameter generation circuitry configured to generate a parameter corresponding to propagation characteristics to be reproduced using the learning model formed by the machine learning circuitry; and
a reproduction execution circuitry configured to control a channel emulator based on the parameter generated by the parameter generation circuitry so as to perform processing of forming a transmission space having the propagation characteristics to be reproduced in the reverberation chamber.
9 . The transmission space reproduction device according to claim 8 , wherein the propagation characteristics calculation circuitry performs the simulation while further changing a parameter of a dynamic reflector that is provided in the reverberation chamber and is capable of controlling phases of arrival waves when reflecting radio waves so as to calculate the propagation characteristics in the reverberation chamber.
10 . The transmission space reproduction device according to claim 8 , wherein the parameter that is sequentially changed by the propagation characteristics calculation circuitry includes at least one of a shape, a size, or a material of the reverberation chamber, a transmission location, a transmission signal, or a transmission beam direction by a transmission antenna, or a reception location or a reception signal by a measurement object.
11 . The transmission space reproduction device according to claim 8 , wherein the propagation characteristics calculation circuitry performs the simulation using a ray tracing method or a finite-difference time-domain method.
12 . The transmission space reproduction device according to claim 8 , wherein the machine learning circuitry forms the learning model by machine learning using the calculated propagation characteristics, the calculated parameter, and the actually measured propagation characteristics and parameters.
13 . The transmission space reproduction device according to claim 8 , wherein the parameter generation circuitry generates the parameter corresponding to the propagation characteristics to be reproduced by inputting the propagation characteristics to be reproduced into the learning model formed by the machine learning circuitry.
14 . The transmission space reproduction device according to claim 8 , wherein the propagation characteristics include at least one of reception power, a polarization ratio of an incident field, delay time, arrival direction, delay spread, angular spread, or a number of clusters.