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Real-time Online Learning for Pattern Reconfigurable Antenna State Selection

机译：实时在线学习模式可重构天线状态选择

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摘要

Pattern reconfigurable antennas (PRAs) can dynamically change their radiation pattern and provide diversity and directional gain. These properties allow them to adapt to channel variations by steering directional beams toward desired transmissions and away from interference sources, thus enhancing the overall performance of a wireless communication system. To fully exploit the benefits of a PRA, the key challenge is being able to optimally select the antenna state in real time. Current literature on this topic, to the best of our knowledge, focuses on the design of algorithms to optimally select the best antenna mode with evaluation performed in simulation or postprocessing. In this study, we have not only designed a real-time online antenna state selection framework for SISO wireless links but we have also implemented it in an experimental software defined radio testbed. We benchmarked the multi-armed bandit algorithm against other antenna state selection algorithms and show how it can improve system performance by mitigating the effects of interference taking advantage of the directionality PRAs provide. We also show that when the optimal state changes over time the bandit approach does not work very well. For such a scenario, we show how the Adaptive Pursuit algorithm works well and can be a great solution. We also discuss what changes could be done to the bandit algorithm to work better in this case.
机译：模式可重新配置天线（PRA）可以动态地改变其辐射模式并提供分集和方向增益。这些性质允许它们通过转向定向光束对所需的传输并远离干扰源来适应通道变化，从而提高无线通信系统的整体性能。为了充分利用PRA的益处，关键挑战是能够实时最佳地选择天线状态。本主题的当前文献，据我们所知，专注于算法的设计，以最佳地选择具有在模拟或后处理中执行的评估的最佳天线模式。在这项研究中，我们不仅为SISO无线链路设计了实时的在线天线状态选择框架，但我们还在实验软件定义的无线电测试中实现了它。我们对基准其它天线状态选择算法，并展示它如何通过减少的方向性的PRA提供的干扰趁着效果提高系统性能的多武装土匪算法。我们还表明，当最佳状态随着时间的推移而变化时，强盗方法并不能很好地工作。对于这样的场景，我们展示了自适应追求算法如何运行良好，可以是一个很好的解决方案。我们还讨论在这种情况下可以更好地完成Bainit算法的更改。

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