Proposes NOMA cache-aided MEC with LSTM task popularity prediction and BLA-based multi-agent Q-learning for joint optimization of offloading, caching and resources, claiming outperformance over benchmarks and optimality of the action selection scheme.
Cache-aided non-orthogonal multiple access: The two-user case,
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Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach
Proposes NOMA cache-aided MEC with LSTM task popularity prediction and BLA-based multi-agent Q-learning for joint optimization of offloading, caching and resources, claiming outperformance over benchmarks and optimality of the action selection scheme.