Deep Neural Network-Based Blind Multiple User Detection for Grant-free Multi-User Shared Access

Abstract

Multi-user shared access (MUSA) is introduced as advanced code domain non-orthogonal complex spreading sequences to support a massive number of machine-type communications (MTC) devices. In this paper, we propose a novel deep neural network (DNN)-based multiple user detection (MUD) for grant-free MUSA systems. The DNN-based MUD model determines the structure of the sensing matrix, randomly distributed noise, and inter-device interference during the training phase of the model by several hidden nodes, neuron activation units, and a fit loss function. The thoroughly learned DNN model is capable of distinguishing the active devices of the received signal without any a priori knowledge of the device sparsity level and the channel state information. Our numerical evaluation shows that with a higher percentage of active devices, the DNN-MUD achieves a significantly increased probability of detection compared to the conventional approaches.

Enumeration and Identification of Active Users for Grant-Free NOMA Using Deep Neural Networks

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Multi-User Detection Based on Improved Cheetah Optimization Algorithm

“…In recent years, significant progress has been made in multi-user detection techniques covering a wide range of areas, including deep learning-based methods that utilize deep neural network structures to learn complex signal features and interference patterns [1][2][3], multitask learning techniques that model the multi-user detection problem as a unified multitask learning problem [4], information sharing between multiple receiver nodes for collaborative signal processing techniques [5][6][7], nonconvex optimization methods that improve the local search capability and convergence speed of the system by introducing nonconvex constraints and optimization methods [8], biomimetic optimization algorithms that solve the optimization problem by simulating the behavior of groups of living organisms in nature [9][10][11][12][13], and modeling the multi-user detection using methods such as graph-based models that model signal transmission relationships and interference relationships for analysis and optimization [14]. These techniques play an important role in improving system performance, reducing complexity, and enhancing real-time performance.…”

Terahertz Sensing using Deep Neural Network for Material Identification

“…Extracting entangled features of the transmission spectroscopy creates many opportunities to detect the materials and their potential use cases. Recent advancements in artificial intelligence (AI) and machine learning (ML) show [8] the prospect of investigating detection [9], estimation, and prediction in wireless communication using realistic data-driven approaches [10]- [12]. In this regard, joint feature extraction and detection of noisy spectroscopic measurements are required to identify the materials uniquely.…”