A Deep Learning-Aided Detection Method for FTN-Based NOMA

Abstract

The rapid booming of future smart city applications and Internet of things (IoT) has raised higher demands on the next-generation radio access technologies with respect to connection density, spectral efficiency (SE), transmission accuracy, and detection latency. Recently, faster-than-Nyquist (FTN) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies to achieve higher SE and massive connections, respectively. In this paper, we aim to exploit the joint benefits of FTN and NOMA by superimposing multiple FTN-based transmission signals on the same physical recourses. Considering the complicated intra- and interuser interferences introduced by the proposed transmission scheme, the conventional detection methods suffer from high computational complexity. To this end, we develop a novel sliding-window detection method by incorporating the state-of-the-art deep learning (DL) technology. The data-driven offline training is first applied to derive a near-optimal receiver for FTN-based NOMA, which is deployed online to achieve high detection accuracy as well as low latency. Monte Carlo simulation results validate that the proposed detector achieves higher detection accuracy than minimum mean squared error-frequency domain equalization (MMSE-FDE) and can even approach the performance of the maximum likelihood-based receiver with greatly reduced computational complexity, which is suitable for IoT applications in smart city with low latency and high reliability requirements.

Deep Neural Network (DNN) for Efficient User Clustering and Power Allocation in Downlink Non-Orthogonal Multiple Access (NOMA) 5G Networks

“…However, the maximum number of users considered in [28] is limited to four. In [29], a deep learning assisted receiver was developed for the uplink of the Faster than Nyquist (FTN) based NOMA system. However, the robustness of the technique with highorder modulations has not been investigated in [29].…”

“…In [29], a deep learning assisted receiver was developed for the uplink of the Faster than Nyquist (FTN) based NOMA system. However, the robustness of the technique with highorder modulations has not been investigated in [29]. To enhance the sum-rate of the NOMA system under the condition of imperfect SIC, a resource management framework based on DNN was designed in [30].…”

Faster-than-Nyquist Signaling for MIMO Communications

“…For example, in [21], a blind symbol acceleration factor estimation using deep learning is studied. A deep learning assisted detection method for FTN based non-orthogonal multiple access is proposed in [22].…”

Some New Perspectives on the Multi-User Detection in Uplink Grant-Free Noma Using Deep Neural Network

“…In the context of UL grant-free NOMA systems, the deep learning based algorithms have been proposed primarily for the constellation design, resource allocation, and throughput optimization [21]- [23]. However, several applications of deep learning based MUD can be found in the literature [24]- [28]. In [25] the authors considered a composite traffic model where the BS, which has perfect channel information, serves the UEs with two types of activation pattern: random and periodic.…”

“…We do not employ any l 2 regularization since both batchnormalization and dropout act as the regularizers [35], [39]. Several stochastic gradient descent based optimizers are available in the literature [40], [41] for minimizing the left hand side of the equality (28). We detail the choice of the optimizer in a later section.…”