Edge computing has emerged as a promising solution for relieving the tension between resource-limited machine type devices (MTDs) and computational-intensive tasks. To realize successful task offloading with limited spectrum, we focus on the cognitive machine-to-machine (CM2M) paradigm which enables a massive number of MTDs to either opportunistically use the licensed spectrum that is temporarily available, or to exploit the under-utilized unlicensed spectrum. We formulate the channel selection problem with both licensed and unlicensed spectrum as an adversarial multi-armed bandit (MAB) problem, and combine the exponential-weight algorithm for exploration and exploitation (EXP3) and Lyapunov optimization to develop a context-aware channel selection algorithm named $\mathrm {C}^{2}$ -EXP3. $\mathrm {C}^{2}$ -EXP3 can learn the long-term optimal channel selection strategy based on only local information, while dynamically achieving service reliability awareness, energy awareness, and backlog awareness. Specifically, we provide a rigorous theoretical analysis and prove that $\mathrm {C}^{2}$ -EXP3 can achieve a bounded deviation from the optimal performance with global state information. Four existing algorithms are compared with $\mathrm {C}^{2}$ -EXP3 to demonstrate its effectiveness and reliability under various simulation settings.

中文翻译：

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认知 M2M 的许可和非许可频谱管理：一种情境感知学习方法

边缘计算已成为缓解资源受限机器类型设备 (MTD) 与计算密集型任务之间紧张关系的有前途的解决方案。为了实现有限频谱的成功任务卸载，我们专注于认知机器对机器 (CM2M) 范式，该范式使大量 MTD 能够机会性地使用暂时可用的许可频谱，或者利用未充分利用的未许可频谱光谱。我们将带有许可和未许可频谱的信道选择问题表述为对抗性多臂强盗 (MAB) 问题，并结合探索和开发的指数权重算法 (EXP3) 和 Lyapunov 优化来开发上下文感知信道选择算法命名 $\mathrm {C}^{2}$ -EXP3。 $\mathrm {C}^{2}$ -EXP3可以仅根据本地信息学习长期最优信道选择策略，同时动态实现服务可靠性感知、能量感知、积压感知。具体来说，我们提供了严格的理论分析并证明 $\mathrm {C}^{2}$ -EXP3 可以使用全局状态信息实现与最佳性能的有界偏差。四种现有算法与 $\mathrm {C}^{2}$ -EXP3 以证明其在各种模拟设置下的有效性和可靠性。