论文标题
正交时间频率空间调制的贝叶斯神经网络检测器
Bayesian Neural Network Detector for an Orthogonal Time Frequency Space Modulation
论文作者
论文摘要
为5G无线系统提出了正交时频空间（OTFS）调制，以处理高移动性通信。现有的低复杂性OTF探测器在丰富的散射环境中表现出较差的性能，在这种环境中，有大量移动反射器反映了向接收器的传输信号。在本文中，我们提出了一个OTFS检测器，称为BPICNET OTFS检测器，该检测器集成了NN，贝叶斯推断和平行干扰取消概念。仿真结果表明，所提出的OTFS检测器的表现明显优于最先进的检测器。
The orthogonal time-frequency space (OTFS) modulation is proposed for beyond 5G wireless systems to deal with high mobility communications. The existing low complexity OTFS detectors exhibit poor performance in rich scattering environments where there are a large number of moving reflectors that reflect the transmitted signal towards the receiver. In this paper, we propose an OTFS detector, referred to as the BPICNet OTFS detector that integrates NN, Bayesian inference, and parallel interference cancellation concepts. Simulation results show that the proposed OTFS detector significantly outperforms the state-of-the-art.
