Coordinated load balancing in mobile edge computing network

A system comprising:

• a memory storing instructions;
• and
• a processor configured to execute the instructions to:
• obtain at least one policy parameter of a neural network corresponding to a load balancing policy;
• receive trajectories for each mobile device in a plurality of mobile devices of a mobile edge computing (MEC) network, each trajectory corresponding to a sequence of states, actions and rewards for the actions of a respective mobile device, the actions indicating a base station to which the mobile device should connect for a request and the rewards being received after the actions guide a learning process, wherein the sequence of states is generated based on a continuous interaction of an existing policy of the respective mobile device with the MEC network;
• estimate advantage functions for each mobile device in the plurality of mobile devices based on the trajectories for each respective mobile device;
• and
• update the at least one policy parameter based on the estimated advantage functions such that the load balancing policy is determined based on states of each mobile device in the plurality of mobile devices,
• wherein the advantage functions are determined based on a difference between a cost-to-go function and a value function, and
• wherein the sequence of states of each trajectory corresponds to states over a predetermined number of time steps for each mobile device of the plurality of mobile devices.

A non-transitory computer-readable storage medium storing instructions that, when executed, cause at least one processor to:

• obtain at least one policy parameter of a neural network corresponding to a load balancing policy;
• receive trajectories for each mobile device in a plurality of mobile devices of a mobile edge computing (MEC) network, each trajectory corresponding to a sequence of states, actions and rewards for the actions of a respective mobile device, the actions indicating a base station to which the mobile device should connect for a request and the rewards being received after the actions guide a learning process, wherein the sequence of states is generated based on a continuous interaction of an existing policy of the respective mobile device with the MEC network;
• estimate advantage functions for each mobile device in the plurality of mobile devices based on the trajectories for each respective mobile device;
• and
• update the at least one policy parameter based on the estimated advantage functions such that the load balancing policy is determined based on states of each mobile device in the plurality of mobile devices,
• wherein the advantage functions are determined based on a difference between a cost-to-go function and a value function, and
• wherein the sequence of states of each trajectory corresponds to states over a predetermined number of time steps for each mobile device of the plurality of mobile devices.