Unmanned aerial vehicles (UAVs) applications have increased in popularity in recent years because of their ability to incorporate a wide variety of sensors while retaining cheap operating costs, easy deployment, and excellent mobility. However, controlling UAVs remotely in complex environments limits the capability of the UAVs and decreases the efficiency of the whole system. Therefore, many researchers are working on autonomous UAV navigation where UAVs can move and perform the assigned tasks based on their surroundings. With recent technological advancements, the application of artificial intelligence (AI) has proliferated. Autonomous UAV navigation is an example of an application in which AI plays a critical role in providing fundamental human control characteristics. Thus, many researchers have adopted different AI approaches to make autonomous UAV navigation more efficient. This paper comprehensively surveys and categorizes several AI approaches for autonomous UAV navigation implicated by several researchers. Different AI approaches comprise mathematical-based optimization and model-based learning approaches. The fundamentals, working principles, and main features of the different optimization-based and learning-based approaches are discussed in this paper. In addition, the characteristics, types, navigation models, and applications of UAVs are highlighted to make AI implementation understandable. Finally, the open research directions are discussed to provide researchers with clear and direct insights for further research.

Next-generation wireless networks must handle a growing density of mobile users while accommodating a rapid increase in mobile data traffic flow and a wide variety of services and applications. High-frequency waves will perform an essential role in future networks, but these signals are easily obstructed by objects and diminish over long distances. Reconfigurable intelligent surfaces (RISs) have attracted considerable interest because of their potential to improve wireless network capacity and coverage by intelligently changing the wireless propagation environment. Consequently, RISs possess potential technology for the sixth generation of communication networks. Machine learning (ML) is an effective method for maximizing the possible advantages of RIS-assisted communication systems, particularly when the computational complexity of operating and deploying RIS increases rapidly as the number of interactions between the user and the infrastructure starts to grow. Since ML is a promising strategy for improving a network and its performance, the application of ML in RISs is expected to open new avenues for interdisciplinary studies as well as practical applications. In this paper, we extensively investigate the ML algorithms used in RISs. We provide a brief overview of RISs, a summary of ML methods with RIS architecture, and a comparison of the available methodologies to explain the combination of these two technologies. Moreover, the significance of open research topics is emphasized to provide sound research directions.

The rapid advancements in wireless technologies have led to numerous research studies that provide evidence of the successful utilization of wireless signals, particularly, WiFi signals for human activity sensing in the indoor environment. As a promising technology, WiFi-based human sensing can be utilized for a variety of applications such as smart healthcare, smart homes, security, industry, office indoor environments etc., due to the availability of rich infrastructure. Furthermore, compared to other radio frequency (RF) based systems such as radio detection and ranging (RADAR) and radio frequency identification (RFID), WiFi is non-invasive, has low-cost, and provides ubiquitous coverage in the indoor setup. However, due to the limited accuracy and high complexity of the model-based approaches for human sensing, the systems empowered by the deep learning (DL) techniques have achieved remarkable performance gains and showed more robustness in dealing with complicated human sensing tasks. The article explores the physical layer parameters used in WiFi sensing such as received signal strength indicator (RSSI) and channel state information (CSI), the estimated parameters such as angle-of-arrival (AoA) and Doppler shift (DS) along with frequency modulated continuous wave (FMCW) RADAR technology. Moreover, the preliminary signal processing stages that are applied to the received WiFi signals in the DL assisted systems are discussed. This article provides a comprehensive literature survey on the recent advances in DL-empowered WiFi sensing focusing on human activity recognition and movement tracking followed by fall detection, single task-multi task classification, crowd monitoring and sensing, indoor localization, gaits recognition, and pose estimation. Furthermore, the paper highlight the challenges in the existing literature and discusses the possible future research directions in WiFi-based human sensing assisted by DL techniques.

In this paper, we propose a deep learning (DL) model to estimate the channel matrix for a massive multi-input multi-output (MIMO) system with a one-bit analog-to-digital converter (ADC). In the literature, various DL methods were proposed to estimate the channel matrix; however, they were limited to a small range of noise levels. We are driven to break the data appropriately so that the estimation performance improves with the signal-to-noise ratio (SNR) because the primary premise is that the DL model should perform better in the case of higher SNR. We suggested breaking the received signal into sequences to consider the entire system as multiple sub-systems, where each antenna serves the same number of users considered as a sub-system. Consequently, the model trained the data received by each sub-system sequentially. However, the conventional back-propagation with extensive training, because of the large number of base station (BS) antennas, causes a vanishing gradient, leading to training failure. Hence, we combine long short-term memory (LSTM) with gated recurrent units (GRU) to accelerate the training speed of the network under the hypothesis that LSTM effectively processes MIMO data and overcomes the vanishing gradient problem, whereas GRU accelerates the convergence rate. The results show that our model outperforms other methods at different SNRs and various numbers of BS antennas.

Flying ad-hoc networks (FANET) are one of the most important branches of wireless ad-hoc networks, consisting of multiple unmanned air vehicles (UAVs) performing assigned tasks and communicating with each other. Nowadays FANETs are being used for commercial and civilian applications such as handling traffic congestion, remote data collection, remote sensing, network relaying, and delivering products. However, there are some major challenges, such as adaptive routing protocols, flight trajectory selection, energy limitations, charging, and autonomous deployment that need to be addressed in FANETs. Several researchers have been working for the last few years to resolve these problems. The main obstacles are the high mobility and unpredictable changes in the topology of FANETs. Hence, many researchers have introduced reinforcement learning (RL) algorithms in FANETs to overcome these shortcomings. In this study, we comprehensively surveyed and qualitatively compared the applications of RL in different scenarios of FANETs such as routing protocol, flight trajectory selection, relaying, and charging. We also discuss open research issues that can provide researchers with clear and direct insights for further research.

Cloud radio access network (C-RAN) is a promising mobile wireless sensor network architecture to address the challenges of ever-increasing mobile data traffic and network costs. C-RAN is a practical solution to the strict energy-constrained wireless sensor nodes, often found in Internet of Things (IoT) applications. Although this architecture can provide energy efficiency and reduce cost, it is a challenging task in C-RAN to utilize the resources efficiently, considering the dynamic real-time environment. Several research works have proposed different methodologies for effective resource management in C-RAN. This study performs a comprehensive survey on the state-of-the-art resource management techniques that have been proposed recently for this architecture. The resource management techniques are categorized into computational resource management (CRM) and radio resource management (RRM) techniques. Then both of the techniques are further classified and analyzed based on the strategies used in the studies. Remote radio head (RRH) clustering schemes used in CRM techniques are discussed extensively. In this research work, the investigated performance metrics and their validation techniques are critically analyzed. Moreover, other important challenges and open research issues for efficient resource management in C-RAN are highlighted to provide future research direction.