The invention discloses an ultra-dense
heterogeneous network small
station coding cooperative caching method based on value
function approximation. The method includes the following steps: adopting areinforcement learning method based on value
function approximation, expressing a value function as a function of state and action, taking the operation of maximizing the number of file requests directly served by the average cumulative small stations as an optimization objective, continuously interacting with the environment to adapt to the dynamic changes of the environment, mining a potential file request
transfer mode, obtaining an approximate expression of the value function, and further obtaining a cooperative caching decision that matches the file request
transfer mode; and encoding thecooperative caching decision by a
macro base station, and transmitting encoded cooperative caching results to each small
station. The scheme of the invention formulates the caching decision by meansof the
transfer mode of the file requests in a real network mined by
reinforcement learning, does not need any assumptions about the prior distribution of data, and is more suitable for an actual
system; and moreover, through the real-time interaction with the environment, the time-varying file popularity can be tracked, the corresponding caching strategy is made, the process is simple and feasible, and the operation of solving NP-hard problems is not required.