This paper proposes an optimization framework for power and time resource allocation during time sharing non-orthogonal multiple access (TS-NOMA) transmissions performed by an unmanned aerial vehicle (UAV) in the context of a large-scale scenario. The objective of the proposed UAV-TS-NOMA system and optimization framework is to jointly maximize the energy efficiency (EE) and the downlink throughput fairness among users within the UAV communication range. The idea behind is to propose a communication system that: i) merges the advantages of UAV communications with the ones offered by the TS-NOMA paradigm and ii) maximizes the EE and the downlink fairness among users. The resulting model finds applicability in performing energy efficient and throughput fair transmissions into power-constrained communication scenarios. Performance investigations regarding the proposed framework in finding the optimal set of resources which maximizes jointly the above mentioned network metrics, have shown the advantage of the proposed two-step optimization framework in finding the optimal configuration of both power and time resources, respecting both the power constraints at the transmitter and the quality-of-service requirement of the users. In addition, it is shown how under particular conditions the proposed framework jointly optimizes the aforementioned network metrics in only one step.

To capture the advantages of unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) technologies, we propose the use of multiple passive aerial RISs in a massive multiple-input multiple-output (MIMO) network. Each aerial RIS is comprised of a RIS panel attached to a UAV, the intention being to support in extending network coverage from the massive MIMO base station. Compared with stationary RISs, our proposed aerial RISs (termed as UAV-RISs) have the ability to reach more users thanks to the line-of-sight links. Our aim is to maximise the total network throughput by finding the optimal power control coefficients at the base station and the phase shifts of the multiple RISs used in the system. This is jointly solved subject to the power consumption constraints, UAV-RIS deployment, and quality-of-service required at the users. We apply zero-forcing precoding for the beamforming design at the base station, and develop an iterative algorithm based on first-order approximation, block coordinate descent, and alternating optimisation technique. Numerical results demonstrate that our proposed method exhibits low computational-complexity and outperforms benchmark schemes in terms of the total network throughput achieved and improvement for the users with worst-case throughput.

Wireless backhaul is a cost-effective and flexible alternative to wired backhaul, yet it suffers from unreliability. This paper studies the secrecy performance of a relay network with such unreliable wireless backhaul. Bernoulli process is adopted to model the wireless backhaul reliability. In the network, a relay employing time-switching-based radio frequency harvesting technique aids in forwarding the signal from the best transmitter. An eavesdropper, which is able to wiretap signals from both the transmitter and the relay, uses selection combining to maximize its received signal-to-noise ratio. Analytical expressions for secrecy outage probability, ergodic secrecy rate and non-zero achievable secrecy rate are derived, which can reveal the effect of the number of transmitters and backhaul reliability on the system performance. Furthermore, for the first time the impact of energy harvesting time fraction upon secrecy performances under unreliable backhaul is presented.

In this paper, we consider an Internet-of-Things network supported by several satellites and multiple cache-assisted unmanned aerial vehicles (UAVs). Due to the long-distance transmission and detrimental effects from the transmission environment, the latency can be extremely high, especially in the presence of backhaul congestion. Therefore, we formulate an optimisation problem with the aim of minimising the total network latency. To reduce the complexity of the original problem, it is divided into three sub-problems, namely, clustering ground users associated with UAVs, cache placement in UAVs (to support the network in avoiding backhaul congestion), and power allocation for satellites and UAVs. We propose a distributed optimisation method consisting of: a non-cooperative game is designed to obtain the solution to the clustering problem; a genetic algorithm, which is powerful in the scenario of many variables, is employed to obtain the optimal solution to the high-complexity caching problem; and a quick estimation technique is used for power allocation. Additionally, a centralised optimisation method is presented as a benchmark. Simulation results show that although the distributed method leads to network latency of approximately 30% higher than the centralised method, it takes significantly less time to execute and is suitable for systems requiring strict real-time computing constraints. Furthermore, the numerical results prove the efficiency of our methods compared with other conventional ones.

In this paper, we deal with an end-to-end (e2e) latency minimisation problem of digital twin-aided offloading in edge networks with the support of unmanned aerial vehicle (UAV) employed by multiple antennas under the stringent constraints of ultra-reliable low-latency communications (URLLC) links. The minimised latency is obtained by jointly optimising the transmit power, offloading factors, and the processing rate of Internet of Things (IoT) devices and edge servers (ES). An alternative optimisation approach combined with appropriate inner approximations is effectively exploited to tackle this challenging problem. Numerical results of various simulations are presented to validate the proposed digital twin solution to support IoT applications.

By exploring the intelligent cooperation of unmanned aerial vehicle (UAV) swarms, the formed flying ad-hoc networks (FANETs) can support a variety of collaborative operations with real-time communications in emergency scenarios. To reduce the prohibitively high routing overhead with the connectivity guaranteed of multi-hop links, UAV swarms can construct a virtual backbone network (VBN) based on the graph-theoretical $d$ -hop connected dominating set ( $d$ -CDS), where each UAV outside VBN can send collected data to VBN within $d$ -hop distance. However, due to the high dynamics of FANETs in emergency scenarios, the optimal solution may not match the current status, which results in frequently intermittent connectivity. Besides, recomputing the solution from scratch will lead to significant maintance costs. Therefore, it is crucial to adapt the minimal $d$ -CDS to topology changes. To this end, we propose an $ \mathcal {O}(d\log (N))$ -approximation algorithm (i.e., $ N$ denotes the maximal number of nodes) with expected $ \mathcal {\widetilde{O}}(d\Delta ^2)$ (i.e., $ \Delta$ denotes the maximal degree of a vertex over the sequence of updates) time per update. The simulation results demonstrate that our adaptive solution can strike a better trade-off among the routing overhead, response time, and maintance costs per topology update compared with state-of-the-art schemes in emergency scenarios.