论文标题
阻止基于广义空间调制的基于神经网络的深神信号检测器
Block Deep Neural Network-Based Signal Detector for Generalized Spatial Modulation
论文作者
论文摘要
对于未来的高容量和节能网络，正在考虑广义空间调制（GSM）。但是，由于渠道间干扰而引起的信号检测是GSM系统中的一个挑战，是该字母的重点。具体而言，我们探讨了在GSM中使用深神网络（DNN）进行信号检测的可行性。特别是，我们提出了一个基于块DNN（B-DNN）架构，其中有源天线及其传输星座符号由较小的sub-dnn检测到。在N-Forrinary DNN检测后，实现了基于欧几里得的软星座算法。拟议的B-DNN检测器实现了BER性能，其性能优于传统块零强度（B-ZF）和块最小均时误差（B-MMSE）检测方案，并且与经典最大似然（ML）检测器相似。此外，所提出的方法需要较少的计算时间，并且比替代传统数值方法更准确。
Generalized Spatial Modulation (GSM) is being considered for high capacity and energy-efficient networks of the future. However, signal detection due to inter-channel interference among the active antennas is a challenge in GSM systems and is the focus of this letter. Specifically, we explore the feasibility of using deep neural networks (DNN) for signal detection in GSM. In particular, we propose a block DNN (B-DNN) based architecture, where the active antennas and their transmitted constellation symbols are detected by smaller sub-DNNs. After N-ordinary DNN detection, the Euclidean distance-based soft constellation algorithm is implemented. The proposed B-DNN detector achieves a BER performance that is superior to traditional block zero-forcing (B-ZF) and block minimum mean-squared error (B-MMSE) detection schemes and similar to that of classical maximum likelihood (ML) detector. Further, the proposed method requires less computation time and is more accurate than alternative conventional numerical methods.
