Online learning for spectrum sensing and reconfigurable antenna control
Online learning for spectrum sensing and reconfigurable antenna control
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DOI：
10.4108/icst.crowncom.2014.255737
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发表时间：
2014-06
期刊：
影响因子：
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通讯作者：
Kevin Wanuga;Nikhil Gulati;H. Saarnisaari;K. Dandekar
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文献类型：
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作者：
Kevin Wanuga;Nikhil Gulati;H. Saarnisaari;K. Dandekar
高效的动态频谱接入（DSA）策略依赖于准确的频谱感知信息来最优地利用频谱白色空间。由于无线信号衰落和热噪声的存在，使得从本地频谱测量中获得准确的信道状态信息（CSI）变得困难，热噪声使测量信号失真并导致关于频谱资源占用的不确定性。电可重构天线系统（ERAS）为系统设计者提供了额外的自由度，以利用方向图和极化分集来提高本地频谱感知决策的准确性。我们提出了一种学习技术，利用ERAS提供的模式和极化分集，以提高频谱感知精度。虽然所提出的方法被设计成在可重构天线的独特设计约束内工作，但是该方法不是天线特定的，并且将与各种各样的可重构天线设计一起工作。通过在室内办公环境中使用无线开放接入研究平台（WARP）软件定义无线电（SDR）平台进行测量，验证了系统性能。
Efficient dynamic spectrum access (DSA) policies rely on accurate spectrum sensing information to exploit spectrum white space optimally. Obtaining accurate channel state information (CSI) from local spectrum measurements is made difficult by wireless signal fading and the presence of thermal noise which distorts measured signals and leads to uncertainty regarding the occupancy of spectrum resources. Electrically reconfigurable antenna systems (ERAS) offer the system designer an additional degree of freedom to exploit pattern and polarization diversity to improve the accuracy of local spectrum sensing decisions. We propose a learning technique to exploit pattern and polarization diversity offered by ERAS to improve spectrum sensing accuracy. While the proposed approach is designed to work within the unique design constraints of reconfigurable antennas, the approach is not antenna specific and will work with a wide variety of reconfigurable antenna designs. Validation of system performance is provided from measurements taken using the wireless open access research platform (WARP) software defined radio (SDR) platform in an indoor office environment.