论文标题
深度多发射极频谱占用映射对传感器数量，噪声和阈值的稳定性
Deep Multi-Emitter Spectrum Occupancy Mapping that is Robust to the Number of Sensors, Noise and Threshold
论文作者
论文摘要
频谱占用映射中的主要目标之一是创建一个对传感器数量，占用阈值（以DBM），传感器噪声，发射器数量和传播环境的假设的鲁棒性的系统。我们表明，这种系统可以使用聚合过程使用神经网络设计，以允许在训练和测试过程中使用可变数量的传感器。该过程将可变数量的测量数转换为近似对数可能比率（LLRS），后者被作为固定分辨率图像馈送到神经网络中。 LLR的使用为噪声和占用阈值的影响提供了鲁棒性。换句话说，可以对系统进行标称数量的传感器，阈值和噪声水平的培训，并且在没有重新训练的情况下仍能在其他各个级别上运行良好。我们的系统在不了解发射器数量的情况下运行，并且没有明确试图估计其数量或权力。接收器的操作曲线具有使用地形图和商业网络设计工具的逼真的繁殖环境，显示了神经网络的性能如何随环境而变化。在该系统中使用非常低分辨率的传感器仍然可以产生良好的性能。
One of the primary goals in spectrum occupancy mapping is to create a system that is robust to assumptions about the number of sensors, occupancy threshold (in dBm), sensor noise, number of emitters and the propagation environment. We show that such a system may be designed with neural networks using a process of aggregation to allow a variable number of sensors during training and testing. This process transforms the variable number of measurements into approximate log-likelihood ratios (LLRs), which are fed as a fixed-resolution image into a neural network. The use of LLR's provides robustness to the effects of noise and occupancy threshold. In other words, a system may be trained for a nominal number of sensors, threshold and noise levels, and still operate well at various other levels without retraining. Our system operates without knowledge of the number of emitters and does not explicitly attempt to estimate their number or power. Receiver operating curves with realistic propagation environments using topographic maps with commercial network design tools show how performance of the neural network varies with the environment. The use of very low-resolution sensors in this system can still yield good performance.
