Energy Efficient Uplink Transmission in Cooperative mmWave NOMA Networks with Wireless Power Transfer

In 5G wireless networks, cooperative non-orthogonal multiple access (NOMA) and wireless power transfer (WPT) are efficient ways to improve the spectral efficiency (SE) and energy efficiency (EE). In this paper, a new cooperative NOMA scheme with WPT is proposed, where EE optimization with a constrained maximum transmit power and minimum required SE is considered for the user grouping and transmit power allocation of users. We obtain a sub-optimal solution by decoupling the original problem in two sub-problems: an iterative algorithm is considered for the user grouping, while, in addition, we utilize the Bat Algorithm (BA) for solving the power allocation problem, where BA was proved to be able to achieve a higher accuracy and efficiency with respect to other meta-heuristic algorithms. Furthermore, to validate the performance of the proposed system, analytical expressions for the energy outage probability and outage probability of users are derived, confirming the effectiveness of the simulation results. It is demonstrated that the proposed cooperative NOMA with WPT offers a considerable improvement in terms of SE and EE of the network compared to other methods. Finally, the effectiveness of BA in solving the EE optimization problem is demonstrated through a high convergence speed by comparing it with other methods

A computationally efficient crack detection approach based on deep learning assisted by stockwell transform and linear discriminant analysis

This paper presents SpeedyNet, a computationally efficient crack detection method. Rather than using a computationally demanding convolutional neural network (CNN), this approach made use of a simple neural network with a shallow architecture augmented by a 2D Stockwell transform for feature transformation and linear discriminant analysis for feature reduction. The approach was employed to classify images with minute cracks under three simulated noisy conditions. Using time–frequency image transformation, feature conditioning and a fast deep learning-based classifier, this method performed better in terms of speed, accuracy and robustness compared to other image classifiers. The performance of SpeedyNet was compared to that of two popular pre-trained CNN models, Xception and GoogleNet, and the results demonstrated that SpeedyNet was superior in both classification accuracy and computational speed. A synthetic efficiency index was then defined for further assessment. Compared to GoogleNet and the Xception models, SpeedyNet enhanced classification efficiency at least sevenfold. Furthermore, SpeedyNet's reliability was demonstrated by its robustness and stability when faced with network parameter and input image uncertainties including batch size, repeatability, data size and image dimensions

Resource allocation and user association for load balancing in NOMA-based cellular heterogeneous networks

Abstract Small cells, specially femtocells, are deployed in the coverage of macrocells and form underlay heterogeneous networks (HetNets) to increase the capacity of cellular networks. Load balancing is necessary to increase the resource utilization in HetNets. Non-orthogonal multiple access (NOMA) technique has been proposed to increase the spectral efficiency in the fifth-generation (5G) of cellular networks. The aim of this paper is to present a user association and resource block (RB) allocation scheme in NOMA-based cellular HetNet for maximization of Jain’s fairness index and spectral efficiency. The maximization problem is decomposed into two sub-problems. At first, the number of RBs that each small cell base station (SBS) can allocate for connection requests, is obtained. Then, the NOMA groups and RB allocation are optimized to increase the spectral efficiency. The results indicate the efficiency of the proposed scheme in enhancing the fairness index and spectral efficiency in HetNets compared to the conventional scheme