The explosive growth of wireless devices and data rate demands makes spectrum scarcity a serious problem. A promising solution is to employ OSA, which enables SUs to seek and opportunistically exploit the underutilized spectrum without interrupting the data transmission of PUs. However, the real-world implementation of OSA still faces several critical challenges including lack of global information, the dilemma of exploration and exploitation, and channel access competition. In this article, we propose a machine-learning-based OSA framework by integrating MAB and matching theory. First, we start from the single-SU scenario without global information while considering the volatility of channel availability. We propose an occurrence-aware OSA (OA-OSA) framework based on the UCB algorithm, which can achieve long-term optimal network throughput performance and a well-balanced trade-off between exploration and exploitation based on only local information. Then we extend OA-OSA to the multi- SU scenario with channel access competitions, and derive an OCA-OSA framework by integrating OA-OSA and the Gale-Shapley algorithm. Simulation results demonstrate that the proposed frameworks achieve superior performance in network throughput and less deviation from optimal performance with global information.

中文翻译：

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认知无线电网络中基于机器学习的机会频谱访问

无线设备的爆炸性增长和数据速率需求使频谱稀缺成为一个严重的问题。一种有前途的解决方案是采用OSA，它使SU能够寻找并机会利用未充分利用的频谱而不会中断PU的数据传输。但是，OSA的实际实现仍然面临一些关键挑战，包括缺乏全球信息，勘探和开发的困境以及渠道访问竞争。在本文中，我们通过结合MAB和匹配理论，提出了一种基于机器学习的OSA框架。首先，我们从没有全局信息的单SU场景开始，同时考虑信道可用性的波动性。我们提出了一种基于UCB算法的事件感知OSA（OA-OSA）框架，它可以实现长期最佳的网络吞吐性能，并且仅基于本地信息就可以在勘探与开发之间取得平衡。然后我们将OA-OSA扩展到具有信道访问竞争的多SU场景，并通过集成OA-OSA和Gale-Shapley算法来推导OCA-OSA框架。仿真结果表明，所提出的框架在网络吞吐量方面具有出色的性能，并且与全局信息相比，与最佳性能的偏差较小。