论文标题
基于深度学习的列表球体对比尼奎斯特（FTN）信号检测更快的球体解码
Deep Learning-based List Sphere Decoding for Faster-than-Nyquist (FTN) Signaling Detection
论文作者
论文摘要
比尼奎斯特（FTN）信号更快，是一种候选非正规传输技术，可提高未来通信系统的光谱效率（SE）。但是，SE的这种改进是以其他计算复杂性为代价的，以消除有意引入的隔膜间干扰。在本文中，我们研究了深度学习（DL）以降低FTN信号传导的检测复杂性。为了消除在接收器上具有噪声增白过滤器的需求，我们首先基于使用一组正态基础函数并识别其操作区域的等效FTN信号模型。其次，我们提出了一个基于DL的列表Sphere Decoding（DL-LSD）算法，该算法选择并更新原始LSD的初始半径，以保证超级球内lattice点的预定义数字$ n _ {\ text {l}} $。这是通过训练神经网络来输出近似初始半径来实现的，该半径包括$ n _ {\ text {l}} $晶格点。在测试阶段，如果超过$ n _ {\ text {l}} $晶格点，我们将$ n _ {\ text {l}} $保持与接收到的FTN信号相对应的点的最接近点；但是，如果超晶体的小于$ n _ {\ text {l}} $点，我们将大约初始半径增加一个值，该值取决于输出半径从训练阶段的标准偏差。然后，基于获得的$ n _ {\ text {l}} $ points计算日志样比率（LLR）的近似值。仿真结果表明，所提出的DL-LSD的计算复杂性低于其按数量级的原始LSD的对应物。
Faster-than-Nyquist (FTN) signaling is a candidate non-orthonormal transmission technique to improve the spectral efficiency (SE) of future communication systems. However, such improvements of the SE are at the cost of additional computational complexity to remove the intentionally introduced intersymbol interference. In this paper, we investigate the use of deep learning (DL) to reduce the detection complexity of FTN signaling. To eliminate the need of having a noise whitening filter at the receiver, we first present an equivalent FTN signaling model based on using a set of orthonormal basis functions and identify its operation region. Second, we propose a DL-based list sphere decoding (DL-LSD) algorithm that selects and updates the initial radius of the original LSD to guarantee a pre-defined number $N_{\text{L}}$ of lattice points inside the hypersphere. This is achieved by training a neural network to output an approximate initial radius that includes $N_{\text{L}}$ lattice points. At the testing phase, if the hypersphere has more than $N_{\text{L}}$ lattice points, we keep the $N_{\text{L}}$ closest points to the point corresponding to the received FTN signal; however, if the hypersphere has less than $N_{\text{L}}$ points, we increase the approximate initial radius by a value that depends on the standard deviation of the distribution of the output radii from the training phase. Then, the approximate value of the log-likelihood ratio (LLR) is calculated based on the obtained $N_{\text{L}}$ points. Simulation results show that the computational complexity of the proposed DL-LSD is lower than its counterpart of the original LSD by orders of magnitude.
