  An optimization problem of content placement in cooperative caching is
formulated, with the aim of maximizing sum mean opinion score (MOS) of mobile
users. Firstly, a supervised feed-forward back-propagation connectionist model
based neural network (SFBC-NN) is invoked for user mobility and content
popularity prediction. More particularly, practical data collected from
GPS-tracker app on smartphones is tackled to test the accuracy of mobility
prediction. Then, a learning automata-based Q-learning (LAQL) algorithm for
cooperative caching is proposed, in which learning automata (LA) is invoked for
Q-learning to obtain an optimal action selection in a random and stationary
environment. It is proven that the LA-based action selection scheme is capable
of enabling every state to select the optimal action with arbitrarily high
probability if Q-learning is able to converge to the optimal Q value
eventually. To characterize the performance of the proposed algorithms, the sum
MOS of users is applied to define the reward function. Extensive simulations
reveal that: 1) The prediction error of SFBC-NN lessen with the increase of
iterations and nodes; 2) the proposed LAQL achieves significant performance
improvement against traditional Q-learning; 3) the cooperative caching scheme
is capable of outperforming non-cooperative caching and random caching of 3%
and 4%.
