Non-orthogonal multiple access (NOMA) is regarded as a promising technology in achieving high capacity and massive connectivity. In this paper, the reliable transmission scheme of downlink NOMA systems is investigated. In particular, we divide the disc covered by the base station into several annular areas, where the receivers are randomly located following a uniform distribution. In this way, NOMA pairing is performed by randomly selecting receivers from two different areas. Firstly, we derive the closed-form expressions of bit error rate (BER) with quadrature phase-shift keying (QPSK) modulation, where the channel is modeled as small-scale Rayleigh fading and large-scale path loss. To achieve reliable communications, then, the BER performance of the receiver with the worst channel gain in each area is studied. Finally, an optimal power allocation algorithm is proposed, which obtains the minimum transmission power and optimal power allocation factor with a given BER constraint of all receivers. Extensive simulations demonstrate the accuracy of obtained BER expressions and the effectiveness of the proposed algorithm. These results provide valuable insight into realizing on reliable transmission of NOMA with randomly deployed receivers.

As user equipments (UEs) and infrastructure evolve, the network architecture becomes increasingly heterogeneous, presenting challenges in managing diverse communication technologies and varied network demands. Recognizing these challenges, the integration of unmanned aerial vehicles (UAVs) and device-to-device (D2D) communication emerges as an effective strategy to enhance network capacity and address the complexities of a heterogeneous network. In this context, mobile edge computing (MEC), facilitated by servers at the network edge, significantly reduces data processing latency and alleviates backhaul congestion. This paper proposes a UAV and D2D-enabled heterogeneous MEC framework that supports two offloading modes for UEs: the first is to offload tasks to UAVs equipped with computing servers, and the second is to offload tasks to nearby idle UEs via D2D links. Unlike existing schemes, the selection of offloading modes is determined by the network load, which improves resource utilization. To further reduce computing latency, we formulate an optimization problem that jointly considers UAV trajectory planning, resource management, and task offloading policy. Since the problem is non-convex and challenging to solve in polynomial time, we decompose it into a joint resource optimization and an offload scheme optimization. Based on this, an optimization algorithm based on block descent and potential game is proposed to obtain the optimal solution. Simulation results indicate that compared to typical UAV-based MEC without D2D, our solution can reduce latency by about 20%. In comparison to equivalent resource allocation, the proposed algorithm can reduce latency by about 25%.

Unmanned aerial vehicles (UAVs) are widely adopted as aerial relays assisting seamless coverage due to flexible deployment and high maneuverability, but their service time is considered as the bottleneck due to the constrained energy. The energy-efficient intelligent reflecting surface (IRS) is adopted in the UAV system for long-term service. In this paper, the cooperative relay-transmission in UAV-IRS based terahertz (THz) networks is studied, where UAVs with IRSs as relays facilitate long-term seamless coverage through THz transmission, and the maximum transmission capacity based on sum rate and energy consumption is formulated as a maximization problem to optimize UAV altitude, relay selection and user association. In the single-user-single-UAV scenario, how relay selection is affected by the IRS element number and UAV transmitting power is analyzed. In the multiple-user-multiple-UAV scenario, the maximization problem is decomposed into three sub-problems and solved by an alternating optimization method: optimizing UAV height using gradient descent and interior point algorithms, solving relay selection as a linear programming problem by continuous variable relaxation, and optimizing user association as a knapsack problem through association matrix transformation. Simulation results demonstrate the effectiveness of the proposed method and indicate that the cooperative relay-transmission scheme achieves quasi-optimal performance when compared with existing schemes.

With the ongoing expansion of user numbers and the diversity of business types, wireless networks are progressing toward broader spatial dimensions and increased functionality. With respect to coverage, the space–air–ground integrated network (SAGIN) is considered a promising solution for providing seamless global connectivity to emerging services. In terms of functionality, wireless networks are expected to offer not only communication services but also sensing and computing services, which are crucial for certain businesses. However, within SAGIN there is a significant variance in performance among different types of radio access networks (RANs). This necessitates a reconsideration of system architecture and handover selection schemes to meet the quality of service (QoS) requirements of various business applications. The main focus of this article is to explore the integration of sensing and mobile edge computing (MEC) with SAGIN and also to examine the system architecture, selection schemes, and handover procedures. We first provide an overview of SAGIN’s capabilities and highlight its integrated architecture. Subsequently, we introduce several handover selection schemes, followed by an elaboration on the redesigned handover procedure for SAGIN. Finally, we present some simulation results and discuss challenges that need to be addressed in practical implementation.

Modeling and control for a novel coaxial ducted fan aerial robot in-ground-effect is presented in this article. Based on experiments using the ducted fan bench test, the fitting curve of the ground effect thrust of the ducted fan aerial robot at different heights is obtained. In addition, the flow field simulation results of the prototype with ground effect at different heights can be obtained using computational fluid dynamics software. A simplified model of the prototype for control can be designed based on several reasonable hypotheses that are established using blade element and momentum theory. To compensate for the disturbance associated with ground effect, a nonlinear disturbance observer is designed to estimate the disturbance, and control structure of the closed-loop system is composed of a nonlinear disturbance observer combined with a double-loop proportion–integration–differentiation controller. The results of several numerical simulations and experiments demonstrate the effectiveness of this controller structure. The performances of tracking trajectory and system stability are improved significantly, compared to the situation that the ground effect is not compensated for.

To meet the requirements of ubiquitous connectivity, the sixth-generation is expected to support higher frequencies such as millimeter waves and terahertz, as well as high-mobility scenarios like unmanned aerial vehicles and vehicular networks. However, communication scenarios under these conditions may suffer from beam misalignment due to high mobility, especially in high-frequency channels with narrow beam characteristics. Integrated sensing and communications (ISAC) enable hardware and spectrum resource sharing between radar sensing and wireless communication, and exhibit great potential in sensing-assisted communication. One pressing problem is that performance metrics for sensing and communication are often contradictory when they share the same hardware or wireless resources. Therefore, how to optimize the performance metrics between both is the key to fulfilling specific requirements. This article focuses on ISAC enabled predictive beamforming, and investigates the technical characteristics, system architecture, and optimization schemes involved in the performance trade-offs. We first provide an overview of the unique characteristics of ISAC and predictive beamforming and highlight the benefits of combining them. We then investigate the design architectures and propose an optimization scheme that can achieve a performance trade-off between sensing and communication to maximize throughput. Finally, we highlight some challenging issues that need to be addressed in practical implementations.