Licensed and Unlicensed Spectrum Management for Cognitive M2M: A Context-Aware Learning Approach

作者：Haijun Liao, Xinyi Chen, Zhenyu Zhou, Nian Liu, Bo Ai · 发表于：IEEE Transactions on Cognitive Communications and Networking · 年份：2020 · DOI：10.1109/tccn.2020.3006268 · 被引用次数：35 · 研究领域：Advanced Bandit Algorithms Research、Age of Information Optimization、Cognitive Radio Networks and Spectrum Sensing

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 C2-EXP3. C2-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 C2-EXP3 can achieve a bounded deviation from the optimal performance with global state information. Four existing algorithms are compared with C2-EXP3 to demonstrate its effectiveness and reliability under various simulation settings.