Power control method and communication device thereof
A power control method, for a first communication device, includes applying Bayesian Optimization, Causal Bayesian Optimization, or Dynamic Causal Bayesian Optimization to at least one data so as to determine a transmission power control value, and outputting the transmission power control value. The at least one data is extracted from at least one signal at least from a second communication device. The transmission power control value is configured to instruct the second communication device how to set the transmission power of the second communication device. Even if the second communication device moves fast, the second communication device is able to adjust its transmission power according to the optimized transmission power control value, thereby minimizing the power consumption of the second communication device.
1. A power control method, for a first communication device, comprising:
applying Bayesian Optimization, Causal Bayesian Optimization, or Dynamic Causal Bayesian Optimization to at least one data to search for a plurality of independent variable optimal values together from at least one independent variable so as to determine a transmission power control (TPC) value, wherein the at least one data is extracted from at least one signal at least from a second communication device, wherein the plurality of independent variable optimal values comprises the TPC value; and
outputting the TPC value, wherein the TPC value is configured to instruct the second communication device how to set transmission power of the second communication device;
wherein a causal structure of a causal graph, which is configured for a power consumption of the second communication device and the at least one independent variable, and a plurality of causal variables of the causal graph are determined together.
2. The power control method of claim 1 , further comprising:
outputting a signal-to-interference-plus-noise ratio (SINR) target value, wherein the Bayesian Optimization, the Causal Bayesian Optimization, or the Dynamic Causal Bayesian Optimization is applied to the at least one data to determine the TPC value and the SINR target value together.
3. The power control method of claim 1 , wherein a constraint based on fixed throughput is added to the Bayesian Optimization, the Causal Bayesian Optimization, or the Dynamic Causal Bayesian Optimization.
4. The power control method of claim 1 , wherein the plurality of independent variable optimal values comprises the TPC value, an SINR target value, and at least one first independent variable optimal value.
5. The power control method of claim 1 , wherein the causal graph is generated based on maximum a posteriori and point estimation.
6. The power control method of claim 1 , wherein a plurality of subdata in a grounding data is mapped to the plurality of causal variables of the causal graph by using a plurality of observation functions, to generate the causal graph from the grounding data based on maximum a posteriori and point estimation.
7. The power control method of claim 6 , wherein the plurality of observation functions are obtained based on a causal semantic generative model.
8. The power control method of claim 1 , wherein second power consumption corresponding to the second communication device at a second time instant is a function of the at least one independent variable at the second time instant, the at least one independent variable at a first time instant, or a first power consumption corresponding to the second communication device at the first time instant.
9. The power control method of claim 1 , wherein the first communication device is one of a distribution unit and a user equipment, and the second communication device is the other of the distribution unit and the user equipment.
10. A communication device, comprising:
a storage circuit, configured to store instructions of:
applying Bayesian Optimization, Causal Bayesian Optimization, or Dynamic Causal Bayesian Optimization to at least one data to search for a plurality of independent variable optimal values together from at least one independent variable so as to determine a transmission power control (TPC) value, wherein the at least one data is extracted from at least one signal at least from a second communication device, wherein the plurality of independent variable optimal values comprises the TPC value; and
outputting the TPC value, wherein the TPC value is configured to instruct the second communication device how to set transmission power of the second communication device; and
a processing circuit, coupled to the storage circuit, configured to execute the instructions stored in the storage circuit;
wherein a causal structure of a causal graph, which is configured for a power consumption of the second communication device and the at least one independent variable, and a plurality of causal variables of the causal graph are determined together.
11. The communication device of claim 10 , wherein the instructions further comprises:
outputting a signal-to-interference-plus-noise ratio (SINR) target value, wherein the Bayesian Optimization, the Causal Bayesian Optimization, or the Dynamic Causal Bayesian Optimization is applied to the at least one data to determine the TPC value and the SINR target value together.
12. The communication device of claim 10 , wherein a constraint based on fixed throughput is added to the Bayesian Optimization, the Causal Bayesian Optimization, or the Dynamic Causal Bayesian Optimization.
13. The communication device of claim 10 , wherein the plurality of independent variable optimal values comprises the TPC value, an SINR target value, and at least one first independent variable optimal value.
14. The communication device of claim 10 , wherein the causal graph is generated based on maximum a posteriori and point estimation.
15. The communication device of claim 10 , wherein a plurality of subdata in a grounding data is mapped to the plurality of causal variables of the causal graph by using a plurality of observation functions, to generate the causal graph from the grounding data based on maximum a posteriori and point estimation.
16. The communication device of claim 15 , wherein the plurality of observation functions are obtained based on a causal semantic generative model.
17. The communication device of claim 10 , wherein a second power consumption corresponding to the second communication device at a second time instant is a function of the at least one independent variable at the second time instant, the at least one independent variable at a first time instant, or a first power consumption corresponding to the second communication device at the first time instant.
18. The communication device of claim 10 , wherein the first communication device is one of a distribution unit and a user equipment, and the second communication device is the other of the distribution unit and the user equipment.