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
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abstract
In this paper, we propose a novel joint caching and non-orthogonal multiple access (NOMA) scheme to facilitate advanced downlink transmission for next generation cellular networks. In addition to reaping the conventional advantages of caching and NOMA transmission, the proposed cache-aided NOMA scheme also exploits cached data for interference cancellation which is not possible with separate caching and NOMA transmission designs. Furthermore, as caching can help to reduce the residual interference power, several decoding orders are feasible at the receivers, and these decoding orders can be flexibly selected for performance optimization. We characterize the achievable rate region of cache-aided NOMA and investigate its benefits for minimizing the time required to complete video file delivery. Our simulation results reveal that, compared to several baseline schemes, the proposed cache-aided NOMA scheme significantly expands the achievable rate region for downlink transmission, which translates into substantially reduced file delivery times.
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eess.SP 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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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.