Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches.

An intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic waves propagation by changing the electric and magnetic properties of its surface. Therefore, IRS is considered a smart technology for the sixth generation (6G) of communication networks. In addition, machine learning (ML) techniques are now widely adopted in wireless communication as the computation power of devices has increased. As it is an emerging topic, we provide a comprehensive overview of the state-of-the-art on ML, especially on deep learning (DL)-based IRS-enhanced communication. We focus on their operating principles, channel estimation (CE), and the applications of machine learning to IRS-enhanced wireless networks. In addition, we systematically survey existing designs for IRS-enhanced wireless networks. Furthermore, we identify major issues and research opportunities associated with the integration of IRS and other emerging technologies for applications to next-generation wireless communication.

Visible light communication (VLC) has contributed new unused spectrum in addition to the traditional radio frequency communication and can play a significant role in wireless communication. The adaptation of VLC technology enhances wireless connectivity both in indoor and outdoor environments. Multiple-input multiple-output (MIMO) communication has been an efficient technique for increasing wireless communications system capacity and performance. With the advantages of MIMO techniques, VLC can achieve an additional degree of freedom. In this paper, we systematically perform a survey of the existing work based on MIMO VLC. We categorize the types of different MIMO techniques, and a brief description is given. Different problem-solving approaches are given in the subsequent sections. In addition, machine learning approaches are also discussed in sufficient detail. Finally, we identify the future study direction for MIMO-based communication in VLC.

Reconfigurable intelligent surface (RIS) consists of cost-effective passive elements which can be utilized in different scenarios in next-generation wireless communication. Deep learning (DL) algorithm plays a vital role in channel estimation (CE) due to the learning capability of DL tools to tackle the CE challenge. Bi-directional long-short term memory (Bi-LSTM) model collects data from both past (backward) and future (forward) simultaneously to improve prediction accuracy and provide an additional feature extraction capability. To take advantage of the Bi-LSTM model, in this paper, we proposed a Bi-LSTM model based CE and signal detection for RIS empowered multi-user multiple input single output downlink orthogonal frequency division multiplexing systems. To measure the performance of the proposed model, it is trained by two different deep neural network (DNN) optimization algorithms. Additionally, the proposed model is compared with four existing DNN models. The least square and minimum mean square error estimators are used to investigate CE and signal detection for performance comparison. The proposed Bi-LSTM model based RIS CE is capable of learning and generalizing rapidly and outperforms the comparable estimators when different pilots and cyclic prefix values are available. Simulation results confirm the effectiveness of the proposed model for CE and signal detection.

Deep learning (DL) techniques can significantly improve successive interference cancellation (SIC) performance for the non-orthogonal multiple access (NOMA) system. The NOMA-orthogonal frequency division multiplexing (OFDM) system is considered in this paper to develop a hybrid deep neural network (HyDNN) model for multiuser uplink channel estimation (CE) and signal detection (SD). The proposed HyDNN uses a combination of a bi-directional long short-term memory (BiLSTM) network and a one-dimensional convolutional neural network (1D-CNN) to optimize errors in the system. The extraction of input signal characteristics from OFDM is carried out using the 1D-CNN model and fed into the time series BiLSTM network to infer the signal at the receiver terminal. The HyDNN model learns through the simulated channel data during offline training. To optimize the loss during learning the model the Adam optimizer is utilized. After successful training, the transmitted symbols in the online deployment are instantly recovered with optimal prediction rates by using the proposed HyDNN model. In comparison to the traditional CE and SD method for the NOMA scheme and other existing DL models, the proposed technique demonstrates satisfactory performance enhancements. In addition, the simulation outcomes show robustness with different training parameters such as minibatch sizes and learning rates.

Index modulation (IM) leads to a decrease in power consumption and transmitter complexity compared to classical orthogonal frequency division multiplexing (OFDM) systems. The overall bit-error rate (BER) performance of the OFDM with IM (OFDM-IM) system is significantly influenced by the accuracy of index-bit detection. To take advantage of IM, in this paper, we propose a recurrent neural network-based signal detection scheme for OFDM-IM. In addition, we introduce a more effective long-short-term memory (LSTM)-based detection technique to improve the BER performance of the OFDM-IM system. The Adam optimization algorithm is utilized to reduce the total system loss. Before entering the network, the received signal and channel matrix are pre-processed based on domain knowledge to enhance the performance of the proposed system. At first, the model is trained in offline to minimize BER using the simulation dataset, and then the trained model is employed in the online phase to detect the OFDM-IM signal. We compare the performance of the proposed LSTM-based detector with traditional detectors and other deep learning (DL) detectors. The simulation outcomes show that our proposed detector outperforms conventional detectors and other DL detectors under perfect and imperfect channel conditions.