论文标题
低复杂度分类方法的快速（FTN）信号检测速度
Low Complexity Classification Approach for Faster-than-Nyquist (FTN) Signalling Detection
论文作者
论文摘要
比尼奎斯特（FTN）信号更快可以提高光谱效率（SE）；但是，以高计算复杂性为代价，以消除引入的符合板干扰（ISI）。由ML在物理层（PHY）问题中取得成功的动机，在本文中，我们研究了ML在降低FTN信号传导的检测复杂性方面的使用。特别是，我们将FTN信号检测问题视为一项分类任务，其中接收的信号被视为属于所有可能的类样本集的未标记类样本。如果我们使用卸货分类器，则所有可能的类样本的集合属于$ n $维空间，其中$ n $是传输块长度，具有巨大的计算复杂性。我们提出了一个低复杂分类器（LCC），该分类器（LCC）利用FTN信号的ISI结构来执行$ n_p \ ll n $ dimension Space中的分类任务。拟议的LCC由两个阶段组成：1）离线预先分类，该截面构造了$ N_P $二维空间中标记的类样本和2）在线分类，其中发生了接收样品的检测。提出的LCC也会扩展以产生软输出。仿真结果表明了拟议的LCC在平衡性能和复杂性方面的有效性。
Faster-than-Nyquist (FTN) signaling can improve the spectral efficiency (SE); however, at the expense of high computational complexity to remove the introduced intersymbol interference (ISI). Motivated by the recent success of ML in physical layer (PHY) problems, in this paper we investigate the use of ML in reducing the detection complexity of FTN signaling. In particular, we view the FTN signaling detection problem as a classification task, where the received signal is considered as an unlabeled class sample that belongs to a set of all possible classes samples. If we use an off-shelf classifier, then the set of all possible classes samples belongs to an $N$-dimensional space, where $N$ is the transmission block length, which has a huge computational complexity. We propose a low-complexity classifier (LCC) that exploits the ISI structure of FTN signaling to perform the classification task in $N_p \ll N$-dimension space. The proposed LCC consists of two stages: 1) offline pre-classification that constructs the labeled classes samples in the $N_p$-dimensional space and 2) online classification where the detection of the received samples occurs. The proposed LCC is extended to produce soft-outputs as well. Simulation results show the effectiveness of the proposed LCC in balancing performance and complexity.
