With the rapid development of low-altitude intelligent networks (LAINs), the growing demand for data services poses significant challenges for the existing networks. At the same time, the airspace becomes increasingly complex due to the escalating count of low-altitude users. Although unmanned aerial vehicles (UAVs) carrying mobile base stations to provide communication services can effectively alleviate pressure on existing network infrastructure, they unfortunately face the dual challenge of sustaining reliable data transmission and guaranteeing UAV flight safety. Therefore, in this paper, we propose a collision-free UAVs communication model specifically designed for the hybrid low-altitude environment, incorporating both static and dynamic, as well as known and unknown obstacles. To efficiently support safe flight operations of UAVs, an artificial potential field (APF)-based collision probability map is constructed, enabling the UAVs to dynamically evaluate and avoid obstacles while maintaining high communication performance constrained by limited energy resources. To maximize energy efficiency in low-altitude environments with hybrid obstacles, an adaptive association multi-agent deep deterministic policy gradient (AA-MADDPG) algorithm is proposed to enable collaborative trajectory planning among multiple UAVs. Simulation results confirm that the proposed strategy enhances energy efficiency by 58.06% and reduces collision probability by 86.18%, achieving significant improvements in both communication performance and flight safety.

With the development of unmanned aerial vehicles (UAVs) and the diversification of low-altitude applications, the cooperation among UAVs with different capabilities and objectives offers an exciting prospect for achieving efficient and ubiquitous communication coverage. However, coordinating the cooperation and competition among heterogeneous UAVs is an intractable challenge. In this paper, we propose a novel centralized-distributed heterogeneous-UAVs intelligent communication network system, which addresses the cooperation-competition issue among heterogeneous UAVs through reasonable task allocation. Specifically, a hub UAV makes ground users (GUs) scheduling decisions based on global information and provides backhaul link support through trajectory optimization. Meanwhile, high-mobility distributed UAVs cooperate to ensure fair, efficient communication for assigned GUs. Although centralized user scheduling offers greater flexibility and better performance, it also faces the serious problems which includes time-varying local observation spaces, hybrid action spaces, heterogeneous state spaces, and reward discrepancies. To solve these problems, we propose a convolutional neural network-assisted heterogeneous-UAVs proximal policy optimization algorithm, which aims to jointly optimize UAV trajectories and user scheduling, maximizing the system’s total fair energy efficiency. The simulation results demonstrate that the proposed CNN-HUPPO algorithm outperforms the four multi-agent deep reinforcement learning (MADRL) benchmark algorithms and two baseline algorithms in terms of fairness and accumulative fair energy efficiency.

In the next-generation emergency communication networks, unmanned aerial vehicles (UAVs), serving as aerial base stations, have attracted increasing attention recently due to their high mobility and low cost. Therefore, this paper conceives a heterogeneous UAVs assisted intelligent communication network system, in which tethered UAVs (T-UAV) make ground users (GUs) scheduling decisions to avoid resource competition, while other UAVs cooperate to provide communication services. However, the limited onboard resources of UAVs and cooperativecompetitive problems among heterogeneous UAVs becomes key challenges in UAV-assisted intelligent emergency communication networks. In order to tackle these issues, we propose a heterogeneous multi-agent approximate policy optimization (HMAPPO) algorithm to maximize the total fair energy efficiency by jointly optimizing UAV trajectories and user scheduling. Simulation results demonstrate that HMAPPO outperforms other baseline algorithms in terms of energy efficiency of the system and fairness of GUs. Furthermore, benefit to the partial parameter sharing mechanism, the proposed HMAPPO significantly accelerates the training process compared to other benchmark algorithms.

Benefit to the advantages of low cost, strong security, flexibility and high line-of-sight (LoS), UAVs constitute a promising platform to accomplish data collection for the IoT networks. However, it has to be admitted that the constrained energy consumption of UAVs has become the main challenge for the UAV-assisted IoT data collection. The existed works are mainly based on the assumption that the amount of data uploaded by different devices are the same, which makes the energy consumption of UAVs deviating from the real situation. Therefore, in this paper, for the sake of practical consideration, the amount of data uploaded varies from different devices in a multi-UAV-assisted IoT data collection scenario. To solve the problem of minimizing the total energy consumption of UAVs, we decouple it into two subproblems, devices clustering and UAVs trajectory planning, and propose an iterative optimization algorithm with energy and data volume constraints. Numerical results show that the performance of the proposed method outperforms the comparison schemes significantly in terms of saving total UAVs energy consumption and reducing the standard deviation of UAVs energy consumption.

In optical wireless communications (OWC), hybrid optical orthogonal frequency division multiplexing (O-OFDM) schemes, such as hybrid asymmetrically clipped O-OFDM (HACO-OFDM) and layered asymmetrically clipped O-OFDM (LACO-OFDM), enjoy both high power and spectral efficiency. However, due to the non-orthogonal transmission of signal components induced by the clipping distortion, successive interference cancellation (SIC) is required in these hybrid O-OFDM schemes, leading to notably increased complexity. In this paper, we conceive novel hybrid O-OFDM schemes to offer both high spectral and power efficiency, whilst possessing low complexity. By elaborately designing a time-domain (TD) distortion elimination methodology at the transmitter, a novel distortion-elimination hybrid O-OFDM (DEHO-OFDM) is first proposed, which combines asymmetrically clipped O-OFDM (ACO-OFDM) and pulse-amplitude-modulated discrete multitone (PAM-DMT) in an interference-free manner. In order to further improve the spectral efficiency, an enhance DEHO-OFDM (EDEHO-OFDM) is designed by activating the remaining subcarrier resources. Both DEHO-OFDM and EDEHO-OFDM can be realized through a single-IFFT transmitter and standard OFDM receiver, leading to much lower complexity than the existing hybrid O-OFDM schemes. Simulation results have demonstrated the superiorities of the proposed schemes over HACO-OFDM and LACO-OFDM in terms of peak-to-average-power ratio (PAPR) and bit error rate (BER).

Small-cell aided ultra-dense networks (UDNs) constitute an efficient solution to the ever-increasing thirst for more data. Thanks to the vast untapped high-frequency spectrum of visible light, visible light communications (VLCs) are a natural candidate for UDN. In this paper, layered asymmetrically clipped optical OFDM (LACO-OFDM) aided ultra-dense VLC (UD-VLC) is investigated in terms of its user association, multiple access (MA), and resource allocation. To handle the severe inter-cell interference (ICI) amongst the densely deployed access points, we propose a novel overlapped clustering technique relying on a hybrid non-orthogonal MA and orthogonal MA scheme for enhancing the performance, with the aid of our dynamic resource allocation conceived. Our simulations show that the proposed LACO-OFDM aided UD-VLC using our hybrid MA scheme is more robust against the ICI, at a price of modestly decreasing the sum throughput.

The concept of amorphous-boundary-based user-centric cells invoked for visible light communication (VLC) was shown to offer extra throughput benefits over the conventional network-centric VLC. However, this improvement was quantified based on a number of idealized simplifying assumptions, such as operating exactly at the Shannon capacity. Also, the light emitting diode in VLC was assumed to have no non-linear distortion and no clipping distortion. Furthermore, greedily supporting all the user equipments in the system may in fact reduce the achievable throughput, when the transmit power is restricted. To provide more practical performance estimate, in this paper, the dynamic throughput maximization of user-centric VLC (UC-VLC) systems is investigated under a range of practical considerations, where the number of served UEs, the modulation-mode assignment and the power allocation strategy are all dynamically decided by our proposed heuristic dynamic-programming-based algorithm. Our simulations indicate that both the achievable throughput and the outage probability of the proposed UC-VLC system is better than that of the conventional NC-VLC system, under a range of practical constraints.