Method for associating user equipment in a cellular network via multi-agent reinforcement learning

A method for associating user equipment with base stations of a cellular network, said association method implementing a multi-agent reinforcement learning method, an agent being associated with each user equipment, wherein:

• each user equipment uj determines a set Sj of base stations in the coverage of each of which it is located;
• each agent associated with a user equipment selects, on the basis of a partial observation (oj(t)) of its environment at a given time, an action (aj(t)) out of a plurality of possible actions, each possible action corresponding to a request to associate the user equipment with a base station (BSi∈Sj) of said set, said action being selected on the basis of a strategy (πj) aiming to maximize an action value calculated as the expected sum of a plurality of future rewards, updated at said given time, the user equipment transmitting to a central controller an association request corresponding to the action thus selected;
• each base station determines whether each base station can satisfy all of the association requests received from the user equipment given the transmission resources that each base station has available and in the negative case informs the central controller thereof;
• the central controller calculates a reward (r(t)), common to the actions of the agents, and broadcasts the reward to all the user equipment, said reward being zero if one of the base stations cannot satisfy all of the association requests received and equal to the value taken by a utility function of the network, representative of the quality of service provided to the user equipment, otherwise.