Transactions on Emerging Telecommunications Technologies最新文献

In today's wireless communication systems, the integration of 5G millimeter-wave (mmWave) Massive Multiple Input–Multiple Output (M-MIMO) technology offers significant advancements and capabilities that address the growing demand for higher data rates, increased capacity, and improved user experiences. In three-dimensional (3D) environments, the design of beamforming for uplink multi-user M-MIMO relies on accurate uplink channel state information (CSI) at the transmitter/receiver. In fact, it is difficult for the Base Station (BS) and the User Equipment (UE) to obtain beam patterns due to computational complexity, multiple 3D beams, and regulating the weight of antenna elements, which leads to significantly low sum-rate. Hence, a robust, deep adaptive learning framework in the case of 3D beamforming is needed. This paper proposes a deep adaptive learning-based beam combining framework using User-Wise Attention-Assisted Deep Adaptive Neural Network (UWA-DANN) for mmWave-3DM-MIMO systems. In this, a UWA mechanism learns a set of beam features and corresponding weights for each user. This mechanism allows the system to focus on different users independently and adapt the beamforming process accordingly. Also, it allows the network to dynamically focus on relevant information from the input channels and user constraints. To this end, a dynamic beam pattern is adapted using the DANN model to learn user positions, channel measurements, and beamforming weights. This approach learns to map input parameters such as user positions, channel measurements, and corresponding beam patterns to extract relevant features for beam pattern adaptation. Thus, the UWA-DANN approach provides higher data rates, low complexity, and improved link stability for users. Experimental results show that the proposed UWA-DANN model obtains robust performance over existing schemes in terms of achievable rate and sum-rate under field trial sites in urban scenarios.

Existing research in the field of reconfigurable intelligent surface (RIS)-aided physical layer security assumed Gaussian signal inputs, which is inapplicable to practical communication systems, where finite-alphabet inputs are used. This paper considers an RIS-aided secure multiple-input multiple-output wireless communication system with finite-alphabet inputs, where artificial noise (AN) is invoked at the transmitter to enhance the secure performance. In order to maximize the secrecy rate (SR), the data precoder, the AN precoder, and RIS's reflection coefficients are jointly optimized subject to the constraints of the maximum transmit power and the finite resolution of the phase shifts of RIS. Particularly, due to the finite-alphabet input, the exact expression of the SR involves multiple integrals and lacks a closed-form expression. To tackle this, a closed-form lower bound of the SR is derived as the objective function, which is theoretically proved to be equal to the SR in the high signal-to-noise ratio region. Numerical results show that the RIS can significantly improve the secure performance, and the maximum possible SR (due to the finite-alphabet inputs) can be achieved by increasing the number of the RIS's elements or by increasing the transmit power, which shows the performance advantage of the proposed optimization algorithm.

Rapid growth and technological improvement in wireless communication, driven by engineers from various disciplines, have reached significant milestones. Unmanned Aerial vehicles (UAVs) and flying ad hoc networks (FANET) have undergone one of the biggest innovations. UAVs have drawn a lot of attention from research institutions. They are increasingly employed in various application fields, such as real-time monitoring, precision farming, wireless coverage, military surveillance, climate monitoring, disaster surveillance, and monitoring and rescue operations. The primary characteristics of disasters are their unpredictability and the scarcity of resources in the affected areas. To reduce the loss of lives and livelihoods, disaster management has received much attention. Numerous methodologies and technologies have been developed to predict and handle disasters. UAVs are increasingly being used in disaster management. Additionally, artificial intelligence and collaborative machine learning techniques are gaining prominence among researchers, who are investigating the possibility of their use in disaster management tasks to better cope with the severe and frequently devastating effects of natural catastrophes. This paper provides a review of the relevant FANET research activities in disaster management and emerging artificial intelligence techniques, along with several observations and research challenges. The papers are categorized based on the disaster scenario-related problems and their proposed solutions. FANET problems are receiving less attention from the research community, and the main challenges with FANETs are also highlighted. Finally, significant insights are presented that can aid in improving research related to the application of FANETs in disaster management.

Mobile edge computing (MEC) has achieved significant attention due to the availability of computational tasks in specific scenarios such as emergency applications like forest fire and earthquake remedies. The computationally demanding policy and user offloading policy are challenging problems to address in the energy constrained unmanned aerial vehicle (UAV) network. In this work, the computational task offloading, and power management is solved by using the multi-agent deterministic power management algorithm (MADPM) based on deep reinforcement learning. Every UAV works together as a team to understand the actor critic environment and to make decisions that will help achieve the goals. This involves transferring computational tasks from UAVs to more powerful ground stations or other UAVs to save energy and enhance performance. It requires intelligent decision-making to determine which tasks to offload and when. The joint optimization problem is verified with the simulation results and the proposed work is enabled with MEC in achieving the emergence of UAV related applications. Our simulations show that the MADPM algorithm, as suggested, enhances task offloading efficiency by 35% and reduces power consumption by 25% when compared with current methods. These findings underscore the ability of our method to greatly improve the UAV operational capacities.

In this paper, a full-duplex (FD) space-air ground integrated network (SAGIN) system with passive and active eavesdroppers (PE/AE) and a friendly jammer (FJ) is investigated. The shadowing side information (SSI)-based unmanned aerial vehicle relay node (URN) selection strategy is considered to improve signal-to-interference plus noise power ratio (SINR) at the ground destination unit. To quantify the secrecy performance of the considered scenario, outage probability (OP), interception probability (IP), and transmission secrecy outage probability (TSOP) are investigated in the presence of FJ and PE/AE. The results have shown that aerial AE is an important threat since it can severely degrade the OP of the main transmission link. Furthermore, the FJ can decrease the IP of the eavesdropper by causing interference with the cost of power consumption of URNs. Simulations are performed to verify the theoretical findings.

The security principles of the fifth generation (5G) are anticipated to include robust cryptography models, information security models, and machine learning (ML) powered Intrusion Detection Systems (IDS) specifically designed for Internet of Things (IoT) based wireless sensor networks (WSNs). Nevertheless, the existing security models fall short in addressing the dynamic network characteristics of WSNs. In this context, the suggested system introduces a secure and collaborative multi-watchdog system through the implementation of deep convolutional neural network (DCNN) and distributed particle filtering evaluation scheme (DPFES). The proposed system utilizes deep learning (DL) techniques to create a dynamic multi-watchdog system that safeguards each sensor node by monitoring its transmissions. Furthermore, the proposed approach includes secure data-centric and node-centric evaluation methods that are crucial for enhancing the security of 5G-based IoT-WSN networks. The network evaluation processes based on DL facilitate the creation of a secure multi-watchdog system within dense IoT-WSN environments. This system enables the deployment of active watchdog IDS agents as needed. The proposed approach includes various components such as a system dynamics model, cooperative watchdog model, Dual Line Minimum Connected Dominating Set (DL-MCDS), and DL-based event analysis procedures. From a technical perspective, the system is driven by the implementation of DPFES, which utilizes particle filtering frameworks to analyze network events and establish a secure 5G environment. The system has been successfully implemented, and its results have been compared with those of other similar works. The performance of the proposed cooperative multi-watchdog system demonstrates a significant improvement of $$ and $$ compared to other techniques.

Moving target detection in a heavy clutter area is a challenging problem in the field of radar automatic target detection. Within the framework of track posture hypothesis testing, the radar-blips-oriented target detection algorithms named ErgSad and RanSaD are proposed for single target and formation targets detection, respectively in this paper. With the assumption that target motion is a linear Gaussian process in a short time, the algorithms use dual blips in different scans to generate the track hypothesis and test the hypothesis with residual blips in other scans to determine the existence of moving targets. To reduce time consumption and minimize the error of model estimation, the sampling rules and the supporting domain related to timestamps of track hypothesis models were designed. Simulation data experiments show that the proposed algorithms have more superior performance to detect targets than the state-of-the-art algorithms in the serious clutter environment.