A novel unified framework of geometry-based stochastic models (GBSMs) for the fifth generation (5G) wireless communication systems is proposed in this paper. The proposed general 5G channel model aims at capturing small-scale fading channel characteristics of key 5G communication scenarios, such as massive multiple-input multiple-output (MIMO), high-speed train (HST), vehicle-to-vehicle (V2V), and millimeter wave (mmWave) communication scenarios. It is a three-dimensional (3D) non-stationary channel model based on the WINNER II and Saleh-Valenzuela (SV) channel models considering array-time cluster evolution. Moreover, it can easily be reduced to various simplified channel models by properly adjusting model parameters. Statistical properties of the proposed general 5G small-scale fading channel model are investigated to demonstrate its capability of capturing channel characteristics of various scenarios, with excellent fitting to some corresponding channel measurements.

This paper proposes a novel theoretical non-stationary three dimensional (3-D) wideband twin-cluster channel model for massive multiple-input multiple-output (MIMO) communication systems with carrier frequencies on the order of gigahertz (GHz). As the dimension of antenna arrays cannot be ignored for massive MIMO, near field effects instead of far field effects are considered in the proposed model. These include the spherical wavefront assumption and a birth-death process to model non-stationary properties of clusters such as cluster appearance and disappearance on both the array and time axes. Their impacts on massive MIMO channels are investigated via statistical properties including correlation functions, condition numbers, and angular power spectra. Additionally, the impact of elevation angles on correlation functions is discussed. A corresponding simulation model for the theoretical model is also proposed. Finally, numerical analysis shows that the proposed channel models are able to serve as a design framework for massive MIMO channel modeling.

This paper proposes a novel non-stationary wideband multi-confocal ellipse two dimensional (2-D) channel model for massive multiple-input multiple-output (MIMO) communication systems. Spherical wavefront is assumed in the proposed channel model, instead of the plane wavefront assumption used in conventional MIMO channel models. In addition, the birth-death process is incorporated into the proposed model to capture the dynamic properties of clusters on both the array and time axes. Statistical properties of the channel model such as the space-time-frequency correlation function and power imbalance on the antenna array are studied. The impact of the spherical wavefront assumption on the statistical properties of the channel model is investigated. Furthermore, numerical analysis shows that the proposed channel model is able to capture specific characteristics of massive MIMO channel as observed in measurements.

Actual vehicle-to-vehicle (V2V) channel measurements have shown that the wide-sense stationary (WSS) modeling assumption is valid only for very short time intervals. This fact motivates us to develop non-WSS V2V channel models. In this paper, we propose a novel three-dimensional (3D) theoretical non-WSS regular-shaped geometry-based stochastic model (RS-GBSM) and the corresponding sum-of-sinusoids (SoS) simulation model for non-isotropic scattering wideband multiple-input multiple-output (MIMO) V2V fading channels. The movements of the transmitter (Tx), scatterers, and receiver (Rx) result in the time-varying angles of departure (AoDs) and angles of arrival (AoAs) that make our models non-stationary. The proposed RS-GBSMs, combining line-of-sight (LoS) components, a two-sphere model, and multiple confocal elliptic-cylinder models, have the ability to study the impacts of vehicular traffic density (VTD) and non-stationarity on channel statistics, and jointly consider the azimuth and elevation angles by using the von Mises Fisher (VMF) distribution. The proposed RS-GBSMs are sufficiently generic and adaptable to model various V2V scenarios. Based on the proposed 3D non-WSS RS-GBSMs, important local channel statistical properties are derived and thoroughly investigated. The impacts of VTD and non-stationarity on these channel statistical properties are investigated by comparing them with those of the corresponding WSS model. The proposed non-WSS RS-GBSMs are validated by measurements in terms of the channel stationary time. Finally, numerical and simulation results demonstrate that the 3D non-WSS model is more practical to characterize real V2V channels.

Alzheimer’s disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.

This paper studies generalized spatial modulation (Gen-SM) schemes in multicell multiuser massive multiple-input multiple-output (MIMO) systems as a promising high-throughput and energy-efficient technique for fifth-generation (5G) wireless networks. A detection algorithm for such systems is proposed based on linear processing techniques. By applying the concept of order statistics, a general framework for approximating the achievable sum rates with linear detection is also given. The probability of detecting antenna combinations is analyzed and is used to approximate the sum-rate performance with practical channel conditions, such as antenna correlation, imperfect channel information, and pilot contamination. The fundamental trade-off between spectral efficiency (SE) and energy efficiency (EE) is also investigated. Despite offering less SE, spatial modulation (SM) with a single active antenna per user is shown to be the most energy-efficient transmission mode among the Gen-SM class. Within the operating range of SM (i.e., in the low-to-moderate SE regime), SM is demonstrated to achieve better EE compared with conventional massive MIMO schemes. The performance in terms of economic efficiency, indicating economic profitability (in monetary unit per second), is also analyzed and is shown to serve as a complementary performance metric, enabling an implicit trade-off between SE and EE.