Fifth-generation (5G) cellular networks are being developed to meet the ever-growing data traffic across mobile devices and their applications. The core of 5G cellular networks is leveraging wider and higher frequencies available at millimeter wave frequency (mmWave) bands, thus providing very high data rates for mobile devices. Multi-input multi-output (MIMO) is an essential technology for overcoming the high propagation loss at mmWave communications. In non-orthogonal multiple access (NOMA), multiple cellular user equipments (CUEs) communicate over the same time-frequency resources using a multiplexed power domain. In device-to-device (D2D) communications, two D2D user equipments (DUEs) communicate without passing through the base station. In the underlaying scenario, DUEs reuse the frequency resources allocated to CUEs for spectrum utilization but DUEs cause interferences for cellular and D2D communications. Integrating D2D communications with other 5G technologies has great potential for spectral efficiency improvement. Unfortunately, interference management and resource allocation are becoming increasingly challenging due to aggressive frequency reuse. In this paper, D2D communications at mmWave underlaying MIMO-NOMA cellular network system model is developed. Consequently, a novel resource allocation for D2D communications underlaying MIMO-NOMA cellular network is proposed. A resource allocation optimization problem is formulated for spectral efficiency maximization. To solve this NP-hard problem, the problem is decomposed into three subproblems: interference-aware graph-based user clustering, MIMO-NOMA beamforming design, and optimized power allocation based on particle swarm optimization. Simulation results demonstrate that the proposed algorithm for D2D communications at mmWave underlaying MIMO-NOMA cellular network delivers a greater spectral efficiency compared to the conventional D2D communications that operate underlay MIMO-orthogonal multiple access cellular networks.

In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view, including drone and non-drone targets. The analysis of the received signals allows multiple targets to be distinguished because of their different reflection patterns. The proposed framework consists of four processes: signal processing, cloud point clustering, target tracking, and target recognition. Signal processing translates the raw collected signals into spare cloud points. These points are merged into several clusters, each representing a single target in three-dimensional space. Target tracking estimates the new location of each detected target. A novel convolutional neural network model was designed to extract and recognize the features of drone and non-drone targets. For the performance evaluation, a dataset collected with an IWR6843ISK mmWave sensor by Texas Instruments was used for training and testing the convolutional neural network. The proposed recognition model achieved accuracies of 98.4% and 98.1% for one and two targets, respectively.

The growing number of connected devices in high-density environments poses serious challenges for accommodating and managing these devices across different network slicing services, such as ultra-reliable low-latency communication (URLLC) and massive machine-type communication (mMTC). Because every service has distinct quality of service (QoS) requirements, it is essential to ensure the seamless coexistence of these devices. The main difficulty is allocating network resources to maximize spectrum utilization while meeting mMTC’s massive connectivity demands and offering URLLC’s demand for ultra-reliable, low-latency communication. In this study, non-orthogonal multiple access (NOMA) network slicing is utilized to share radio resources among various services, thereby improving connectivity for large-scale device deployments. When these services exist in high-density NOMA environments characterized by high network congestion with radio resource sharing, the level of difficulty increases significantly. To address these issues, an optimization algorithm is proposed for subchannel assignment and power allocation in NOMA high-density networks for URLLC and mMTC devices. The solution adopts a Q-learning algorithm to optimize decision-making processes and ensure efficient resource sharing between URLLC and mMTC devices, while satisfying their distinct QoS requirements. Extensive simulations demonstrate that the proposed algorithm is flexible and scalable in dynamic scenarios, outperforming random and exhaustive search algorithms in high-density NOMA networks in terms of sum rates. The sum rate of the proposed algorithm increased by approximately 23.44% compared to that of the exhaustive search algorithm.

With the increasing complexity of cyber threats and the emergence of quantum computing, enhancing secure communication is essential. This study explores an effective hybrid quantum key distribution (QKD) protocol that integrates photonic and atomic systems to leverage their respective strengths. The concept of symmetry plays a crucial role in this context, as it underpins the principles of entanglement and the balance between key generation and error correction. The photonic system is used for the initial key generation, while the atomic system facilitates entanglement swapping, error correction, and privacy amplification. The comprehensive theoretical framework encompasses key components, detailed security proofs, performance metrics, and an analysis of system vulnerabilities, illustrating the resilience of the hybrid protocol against potential threats. Extensive experimental studies demonstrate that the hybrid QKD protocol seamlessly integrates photonic and atomic systems, enabling secure key distribution with minimal errors and loss rates over long distances. This combination of the two systems reveals exceptional resilience against eavesdropping, significantly improving both security and robustness compared with traditional QKD protocols. Consequently, this makes it a compelling solution for secure communication in the increasingly digital world.

In this paper, a framework for simultaneous tracking and recognizing drone targets using a low-cost and small-sized millimeter-wave radar is presented. The radar collects the reflected signals of multiple targets in the field of view including drone and non-drone targets. The analysis of the received signals allows multiple targets to be distinguished because of the different reflection patterns. The proposed framework consists of four processes including signal processing, cloud points clustering, target tracking, and target recognition. Signal processing translates the raw signals into spare cloud points. These points are merged into several clusters, each representing a single target. Target tracking estimates the new locations of each detected target. A novel convolutional neural network model is developed for drone and/or non-drone targets feature extraction and recognition. For performance evaluation, a dataset collected with an IWR6843ISK mmWave sensor by Texas Instruments is used for training and testing the convolutional neural network. The proposed recognition model achieves an accuracy of 98.4% for one target and 98.1% for two targets.

Device-to-device (D2D) communication is a key enabling technology for sixth-generation wireless networks due to its potential to improve spectral efficiency, extend network coverage, and reduce communication latency. However, the performance of multi-hop D2D communications is constrained by limited relay energy, dynamic wireless channels, and the complexity of relay selection and resource allocation. To address these challenges, this paper proposes a base station (BS)-assisted multi-hop D2D communication framework with radio frequency (RF) energy harvesting. Relay nodes harvest energy from dedicated BS transmissions and subsequently participate in decode-and-forward multi-hop forwarding. The energy-efficiency maximization problem is formulated as a mixed-integer nonlinear fractional programming (MINFP) problem that jointly optimizes relay selection, transmit power allocation, and transmission time scheduling under energy-causality and transmit-power constraints. To efficiently solve this problem, a Dinkelbach-based alternating optimization framework integrating utility-based relay selection and graph-based multi-hop routing is developed. Simulation results demonstrate that, under the considered simulation settings, the proposed framework improves energy efficiency by approximately 20–40% compared with the considered benchmark schemes.