This paper introduces a network architecture that integrates the space-air-ground integrated network with mobile edge computing (MEC) and orbital edge computing to advance sixth-generation communication systems. The proposed system employs unmanned aerial vehicles equipped with reconfigurable intelligent surfaces and satellite-based MEC to optimize resource management in complex, dynamic environments. By efficiently managing resources such as bandwidth and computational power at both base stations and low Earth orbit satellites, while making offloading decisions, the system aims to minimize utility costs while meeting stringent performance requirements. We utilize a long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) algorithm to solve the formulated nonlinear programming problem, enabling dynamic and adaptive resource management. The LSTM-enhanced DDPG improves convergence speed by 44.44% compared to conventional DDPG, significantly enhancing cost efficiency. Simulation results validate the robustness of the proposed method against state-of-the-art approaches. 

The integration of terrestrial and non-terrestrial networks with mobile edge computing (MEC) and orbital edge computing (OEC) technologies is essential for advancing 6G communication networks. This paper introduces a network architecture that combines terrestrial and non-terrestrial networks by integrating drones (also known as UAV)-carried reconfigurable intelligent surfaces (RIS) and satellite-based MEC to optimize resource allocation in intelligent autonomous transportation systems (IATS). The primary objective is to minimize total system utility costs through the optimal allocation of bandwidth, computational power at the base station and low Earth orbit (LEO) satellite, and offloading decisions, all while adhering to strict performance and delay constraints. We address the complex resource optimization challenge by formulating a nonlinear programming (NLP) problem. To solve this problem, we employ long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) and LSTM-enhanced twin delayed deep deterministic policy gradient (TD3) algorithms, which enable dynamic and adaptive resource management. These LSTM-enhanced algorithms improve convergence speed by 44.44% and 73.81%, respectively, compared to their conventional counterparts, while significantly enhancing cost efficiency. Our simulation results demonstrate substantial improvements in system performance, with effective resource allocation and minimal utility costs, providing a robust solution for ensuring high-quality, low-latency communication in diverse 6G IATS environments. 

This paper presents a novel low-complexity discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) scheme designed to reduce computational overhead while maintaining robust performance in wireless communication systems. The core idea is to modify the signal processing chain by merging the computations of multiple functional blocks, resulting in a significant reduction in the complexity of conventional DFT-s-OFDM implementations. A comparative analysis is conducted to evaluate the proposed scheme against traditional DFT-s-OFDM, using key metrics such as error vector magnitude (EVM) and computational complexity. Simulation results demonstrate that the proposed scheme achieves performance comparable to conventional DFT-s-OFDM in terms of EVM under various system configurations, while significantly reducing computational complexity. This makes the proposed scheme a promising candidate for next-generation wireless communication systems, particularly in uplink scenarios involving battery-powered devices. 

Unmanned aerial vehicles (UAVs) are foreseen to take on an important role in 6 G networks. UAVs can be used as aerial base stations (BSs) and aerial relays that offer flexibility in positioning, extended coverage, and reliable connectivity. A challenge in the deployment of UAVs is determining UAV placements and finding a suitable clustering of user equipment (UE) around the UAVs. In this paper, the K-means, K-means++, K-medians, and bisecting K-means algorithms are considered to obtain the UAV placements and the associated clustering of the UEs in non-orthogonal multiple access (NOMA) based cooperative UAV networks. Given a large set of UEs, the four clustering algorithms are executed for up to 1 0 0 different network topologies. Box plots are used to reveal the impact of clustering dispersion on UAV network performance. In addition, the performance supported by each of the four clustering algorithms in terms of outage probability and sum rate is assessed in the presence of Nakagamim fading. Simulation results are provided subject to the number of topologies, the BS transmit signal-to-noise ratio, the fading severity parameter m, and the number of clusters. The results illustrate the impact of the clustering dispersion associated with each clustering algorithm and system parameters on the outage probability and the sum rate of the considered NOMA-based cooperative UAV networks.

Flying ad hoc networks (FANETs) based on unmanned aerial vehicles (UAVs) can efficiently accomplish connectivity in post-disaster recovery scenarios. In this paper, a non-orthogonal multiple access based FANET is considered comprising of cooperative UAVs. The UAVs act as decode-and-forward relays serving a cluster of user equipments (UEs) in their coverage area. The partitioning of UEs around their nearest UAV is performed using a weighted K-means clustering approach. In particular, the induced weighting accounts for the feature that UAVs in different regions of a disaster scenario may operate with different transmit powers. The outage probability of the considered FANET in the presence of Nakagami-m fading is assessed. 

Unmanned aerial vehicles (UAVs) will constitute an important component of 6G mobile networks to improve coverage in scenarios where deployment of terrestrial infrastructure is challenging or economically infeasible. In this paper, we study a non-orthogonal multiple access (NOMA)-based cooperative UAV network in generic scattering environments. In this system, NOMA improves overall throughput and cooperative communication using decode-and-forward relaying at the UAVs improves coverage. A semi-analytical model of the Rician K-factor is utilized to assess the impact of different scattering environments on the outage probability of the considered UAV network as a function of frequency. Numerical results are provided to reveal the effect of frequency, number of scatterers, and radius of the scattering sphere on the outage probability. 

6G networks are foreseen to improve global coverage and offer seamless connectivity toward a 6G-based Internet of Things through satellite-air-ground integrated networks (SA-GINs). Flying ad hoc networks (FANETs), based on unmanned aerial vehicles (UAVs), are an important part of SAGINs in scenarios where the deployment of conventional terrestrial networks is challenging. In this paper, we study a non-orthogonal multiple access (NOMA)-based cooperative FANET (C-FANET) in the context of propagation environments that can be represented by a semi-analytical model of the Rician K-factor. NOMA at the base station improves overall throughput while cooperative communication with UAVs acting as decode-and-forward relays improves coverage. The semi-analytical model of the Rician K-factor allows to assess the impact of frequency, number of scatterers, and radius of the scattering sphere on the outage probability and sum rate of the NOMA-based C-FANET. Numerical results are provided for a range of generic scattering environments showing options for improving system performance through the choice of frequency, transmit signal-to-noise ratio at the BS and UAVs, and receiver antenna directivity. 

Given the nature of broadcasting systems having limited feedback to the transmitter, impairments induced during transmission need to be coped with at the receiver. In this paper, we focus on digital multimedia broadcasting (DMB) which is deployed in South Korea. An extensive measurement campaign conducted in a live DMB system resulted in a set of videos containing a wide range of syntax, freeze, and texture impairments. Subjective tests were conducted with participants from East Asia which rated the quality of DMB video clips with syntax, freeze, and texture impairments and the related repaired clips. The analysis of the obtained quality ratings has revealed the potential of quality of experience (QoE) improvement of each of the three impairment types. The results support the conjecture that syntax repair offers the largest QoE improvement followed by freeze repair and texture repair. This finding can assist system developers of digital broadcasting systems in deciding about the range of repair techniques to be engaged subject to, e.g., QoE, complexity, and latency constraints. 

6G mobile networks are foreseen to integrate non-terrestrial networks (NTNs), such as unmanned aerial vehicle (UAV) networks, with conventional terrestrial networks to improve global coverage. UAVs offer flexible deployment and strong line-of-sight propagation links when placed at favorable altitudes. In this paper, we propose a hybrid amplify-and-forward (AF) decode-and-foward (DF) relaying based non-orthogonal multiple access (NOMA) UAV system. In this system, the base station uses NOMA to simultaneously communicate on the downlink with a number of hybrid AF-DF UAV relays which improves spectral efficiency. The UAVs select the AF or DF relaying mode such that the highest signal-to-interference-plus-noise ratio at the user equipments is obtained. Hence, either the low signal processing complexity in AF relaying mode or the potentially lower power dissipation in DF relaying mode is utilized. Analytical expressions of the outage probability and sum rate of the hybrid AF-DF relaying based NOMA UAV system are derived. Numerical results are provided revealing the performance gains offered by the proposed system in the presence of Nakagami-m fading and the system's benefits over AF and DF based systems. 

Non-terrestrial networks based on unmanned aerial vehicles (UAVs) are considered for integration with 6G mobile networks to provide ubiquitous communication through heterogeneous networks. UAVs offer benefits such as flexible deployment, mobility control, and strong line-of-sight links. In this paper, orthogonal multiple access (OMA) and nonorthogonal multiple access (NOMA) are combined to form a hybrid OMA-NOMA cognitive radio (CR) assisted cooperative UAV system. The selection of either the OMA or NOMA mode is carried out such that the best link quality is offered to the user equipments (UEs) compared to the individual modes. In this system, the base station uses the NOMA mode for the transmission to the UAVs which act as decode-and-forward relays each serving a cluster of UEs. The CR paradigm is used for the NOMA mode of the hybrid system to reduce the interference caused by the UAVs to adjacent clusters of UEs. Analytical expressions of the outage probability and sum rate of the OMA, NOMA, and hybrid OMA-NOMA systems are derived. Numerical results are provided to reveal the performance advantages of the proposed hybrid OMA-NOMA CR-assisted cooperative UAV system over Nakagami-m fading. Further, design options and tradeoffs between the OMA, NOMA, and hybrid OMA-NOMA systems are illustrated. © 2024 IEEE.