Method and system for managing operational temperature of base station

A method for managing operational temperature conditions of a base station in a communication system, the method being executed in a local computing device in the communication system, the method comprising:

• acquiring from a central computing device, weights and model parameters of a central machine learning (ML) model;
• training a local ML model using the acquired weights and model parameters of the central ML model and base station specific data wherein, ML is utilized for training the local ML model and the base station specific data comprises property measurements of one or more properties affecting the operational temperature conditions of the base station;
• transmitting to the central computing device, weights and model parameters of the trained local ML model;
• transmitting to the central computing device, operational parameters generated based on the base station specific data;
• and
• determining optimized operational temperature conditions to be applied to the base station using the trained local ML model.

A central computing device in a communication system for managing operational temperature conditions of one or more base stations in the communication system, the central computing device comprising,

• processing circuitry comprising at least one processor;
• at least one memory connected to the at least one processor and storing program code that is executed by the at least one processor, whereby the central computing device is operable to:
• acquire for each local computing device of a first group of local computing devices associated with the one or more base stations, initial operational parameters;
• determine one or more clusters by grouping each local computing device of the first group of local computing devices under one of the determined clusters based on the acquired operational parameters;
• train a central machine learning (ML) model for each determined cluster;
• acquire, for each local computing device of a second group of local computing devices associated with the one or more base stations, updated operational parameters;
• compute an operational difference measure of the second group of local computing devices using the acquired updated operational parameters;
• and
• determine optimized operational temperature conditions to be transmitted to the second group of local computing devices by using distributed ML, in response to determining that the computed operational difference measure is less than a threshold value.

A local computing device in a communication system for managing operational temperature conditions of a base station in the communication system, the local computing device comprising:

• processing circuitry comprising at least one processor;
• at least one memory connected to the at least one processor and storing program code that is executed by the at least one processor, whereby the local computing device is operable to:
• acquire from a central computing device, weights and model parameters of a central machine learning (ML) model;
• train a local ML model using the acquired weights and model parameters of the central ML model and base station specific data wherein, ML is utilized for training the local ML model and the base station specific data comprises property measurements of one or more properties affecting the operational temperature conditions of the base station;
• transmit to the central computing device, weights and model parameters of the trained local ML model;
• transmit to the central computing device, operational parameters generated based on the base station specific data;
• and
• determine optimized operational temperature conditions to be applied to the base station using the trained local ML model.