Age of Information Updates in Non-Orthogonal Multiple Access-mobile Edge Computing System Based on Reinforcement Learning
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摘要: 物联网发展对信息时效性的需求越来越高，信息新鲜度变得至关重要。为了维持信息新鲜度，在非正交多址接入(NOMA)和移动边缘计算(MEC)的联合系统中，对多设备单边缘计算服务器的传输场景进行了研究。在该场景中，如何分配卸载任务量和卸载功率以最小化平均更新代价是一个具有挑战性的问题。该文考虑到现实中的信道状态变化情况，基于多代理深度确定性策略梯度(MADDPG)算法，考虑信息新鲜度影响，建立了最小化平均更新代价的优化问题，提出一种寻找最优的卸载因子和卸载功率决策。仿真结果表明，采用部分卸载的方式可以有效地降低平均更新代价，利用MADDPG算法可以进一步优化卸载功率，经比较，MADDPG算法在降低平均更新代价方面优于其他方案，并且适当地减少设备数量在降低平均更新代价方面效果更好。
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关键词:
- 非正交多址接入 /
- 移动边缘计算 /
- 信息年龄 /
- 多代理深度确定性策略梯度
Abstract: With the development of the Internet of Things, the demand for timeliness of information is increasing, and the freshness of information is becoming crucial. In order to maintain the freshness of information, the transmission scenario of multiple devices and single Mobile Edge Computing (MEC) server is studied in the joint system of Non-Orthogonal Multiple Access (NOMA) and MEC. In this scenario, how to allocate the amount of unload tasks and unload power to minimize the average update cost is a challenging problem. Considering the channel state variation in reality, an optimal unloading factor and unloading power decision are proposed based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. Simulation results show that partial unloading can effectively reduce the average update cost, and MADDPG algorithm can further optimize the unloading power. By comparison, MADDPG algorithm is better than other schemes in reducing the average update cost, and the appropriate reduction of the number of equipment is better in reducing the average update cost. -
表 1 仿真参数设置
CPU周期 信道带宽 计算容量 CPU频率 干扰功率的代价 总干扰功率 $ 2 \times {10^3} $cycle/bit 2 MHz 10 GHz 0.2 GHz 0.1 20 W 
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