  A novel non-orthogonal multiple access (NOMA) based cache-aided mobile edge
computing (MEC) framework is proposed. For the purpose of efficiently
allocating communication and computation resources to users' computation tasks
requests, we propose a long-short-term memory (LSTM) network to predict the
task popularity. Based on the predicted task popularity, a long-term reward
maximization problem is formulated that involves a joint optimization of the
task offloading decisions, computation resource allocation, and caching
decisions. To tackle this challenging problem, a single-agent Q-learning
(SAQ-learning) algorithm is invoked to learn a long-term resource allocation
strategy. Furthermore, a Bayesian learning automata (BLA) based multi-agent
Q-learning (MAQ-learning) algorithm is proposed for task offloading decisions.
More specifically, a BLA based action select scheme is proposed for the agents
in MAQ-learning to select the optimal action in every state. We prove that the
BLA based action selection scheme is instantaneously self-correcting and the
selected action is an optimal solution for each state. Extensive simulation
results demonstrate that: 1) The prediction error of the proposed LSTMs based
task popularity prediction decreases with increasing learning rate. 2) The
proposed framework significantly outperforms the benchmarks like all local
computing, all offloading computing, and non-cache computing. 3) The proposed
BLA based MAQ-learning achieves an improved performance compared to
conventional reinforcement learning algorithms.
