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Table Of Integrals Series And Products

Matrix Factorization Techniques for Recommender Systems

In order to effectively provide ultra reliable low latency communications and pervasive connectivity for Internet of Things (IoT) devices, next-generation wireless networks can leverage intelligent, data-driven functions enabled by the integration of machine learning (ML) notions across the wireless core and edge infrastructure. In this context, this paper provides a comprehensive tutorial that overviews how artificial neural networks (ANNs)-based ML algorithms can be employed for solving various wireless networking problems. For this purpose, we first present a detailed overview of a number of key types of ANNs that include recurrent, spiking, and deep neural networks, that are pertinent to wireless networking applications. For each type of ANN, we present the basic architecture as well as specific examples that are particularly important and relevant wireless network design. Such ANN examples include echo state networks, liquid state machine, and long short term memory. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality applications over wireless networks as well as edge computing and caching. For each individual application, we present the main motivation for using ANNs along with the associated challenges while we also provide a detailed example for a use case scenario and outline future works that can be addressed using ANNs. In a nutshell, this paper constitutes the first holistic tutorial on the development of ANN-based ML techniques tailored to the needs of future wireless networks.

Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

1. Random Variables and Stochastic Processes

Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications. For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. For each type of neural network, we present the basic architecture and training procedure, as well as the associated challenges and opportunities. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality and edge caching.For each individual application, we present the main motivation for using ANNs along with the associated challenges while also providing a detailed example for a use case scenario and outlining future works that can be addressed using ANNs. In a nutshell, this article constitutes one of the first holistic tutorials on the development of machine learning techniques tailored to the needs of future wireless networks.

The outage probability (OP) performance of mobile device-to-device (D2D) cooperative networks with incremental amplify-and-forward (IAF) relaying and transmit antenna selection (TAS) is investigated in this paper. The exact closed-form OP expressions are derived for two TAS schemes. Based on the derived closed-form OP expressions, a power allocation minimization problem is formulated. Then through Monte Carlo simulations under different conditions, we have verified the accuracy of the derived OP expressions. The simulation results showed that the power allocation parameter has an important influence on the OP performance.

Performance analysis of IAF relaying mobile D2D cooperative networks

This paper presents an artificial neural network (ANN)-based detection algorithm for an unmanned aerial vehicle (UAV). The slope, kurtosis, and skewness of the signal received from the UAV are employed in this algorithm. The training of the three corresponding feature matrices is done using UAV, and non-UAV signals can be classified effectively for the UAV sensor network based on ANN. Outdoor data over a bridge in the Jimo District, Qingdao, and indoor data from a research laboratory are used for system training and evaluation. The results obtained show that the proposed detection algorithm based on an ANN outperforms methods based on the slope, kurtosis, and skewness of the received signal in terms of the error rate. The recognition rate with the proposed algorithm is greater than 82% within a distance of 3 km, which is better than other UAV detection methods such as active radar, acoustic, and visual recognition.

A UAV Detection Algorithm Based on an Artificial Neural Network

The filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) is being studied by many researchers as a key enabler for the fifth-generation air interface. In this paper, a hybrid peak-to-average power ratio (PAPR) reduction scheme is proposed for FBMC/OQAM signals by utilizing multi data block partial transmit sequence (PTS) and tone reservation (TR). In the hybrid PTS-TR scheme, the data blocks signal is divided into several segments, and the number of data blocks in each segment is determined by the overlapping factor. In each segment, we select the optimal data block to transmit and jointly consider the adjacent overlapped data block to achieve minimum signal power. Then, the peak reduction tones are utilized to cancel the peaks of the segment FBMC/OQAM signals. Simulation results and analysis show that the proposed hybrid PTS-TR scheme could provide better PAPR reduction than conventional PTS and TR schemes in FBMC/OQAM systems. Furthermore, we propose another multi data block hybrid PTS-TR scheme by exploiting the adjacent multi overlapped data blocks, called as the multi hybrid (M-hybrid) scheme. Simulation results show that the M-hybrid scheme can achieve about 0.2-dB PAPR performance better than the hybrid PTS-TR scheme.

Hybrid PAPR Reduction Scheme for FBMC/OQAM Systems Based on Multi Data Block PTS and TR Methods

Advances in sensor technology have accelerated the development of mobile internet applications and contributed to the tremendous growth of the Internet of Things (IoT). Mobile IoT networks typically operate in complex and highly variable urban wireless channel environments. This makes effective and reliable communications challenging. To ensure efficient and stable communications, mobile IoT networks must adapt to the complex environment. This can be achieved by predicting the outage probability (OP). This paper investigates OP analysis and prediction for these networks. Exact closed-form OP expressions are derived and Monte-Carlo simulation is used to verify the analysis and evaluate the OP performance. Then, an OP prediction approach based on an improved grey wolf optimization (IGWO) algorithm and Elman neural network (IGWO-Elman) is proposed. The IGWO algorithm employs improved opposition-based learning to optimize the population initialization and ensure sufficient population diversity. The Elman neural network uses the IGWO algorithm to obtain good network parameters. Simulation results are presented which demonstrate that the prediction accuracy of IGWO-Elman is better than SVM, ELM, and BP algorithms. In terms of prediction accuracy, the IGWO-Elman algorithm is increased by 44%.

Lingwei Xu

Papers

Performance analysis of IAF relaying mobile D2D cooperative networks

A UAV Detection Algorithm Based on an Artificial Neural Network

Hybrid PAPR Reduction Scheme for FBMC/OQAM Systems Based on Multi Data Block PTS and TR Methods

Intelligent Outage Probability Prediction for Mobile IoT Networks Based on an IGWO-Elman Neural Network

Design of optical-acoustic hybrid underwater wireless sensor network