The low-altitude economy (LAE), as a new economic paradigm, plays an indispensable role in cargo transportation, healthcare, infrastructure inspection, and especially post-disaster communications. Specifically, unmanned aerial vehicles (UAVs), as one of the core technologies of the LAE, can be deployed to provide communication coverage, facilitate data collection, and relay data for trapped users, thereby significantly enhancing the efficiency of post-disaster response efforts. However, conventional UAV self-organizing networks exhibit low reliability in long-range cases due to their limited onboard energy and transmit ability. Therefore, in this paper, we design an efficient and robust UAV-swarm enabled collaborative self-organizing network to facilitate post-disaster communications. Specifically, a ground device transmits data to UAV swarms, which then use collaborative beamforming (CB) technique to form virtual antenna arrays and relay the data to a remote access point (AP) efficiently. Then, we formulate a rescue-oriented post-disaster transmission rate maximization optimization problem (RPTRMOP), aimed at maximizing the transmission rate of the whole network. Given the challenges of solving the formulated RPTRMOP by using traditional algorithms, we propose a two-stage optimization approach to address it.In the first stage, the optimal multi-path traffic routing and the theoretical upper bound on the transmission rate of the network are derived.In the second stage, we transform the formulated RPTRMOP into a variant named V-RPTRMOP based on the obtained optimal multi-path traffic routing, aimed at rendering the actual transmission rate closely approaches its theoretical upper bound by optimizing the excitation current weight and the placement of each participating UAV via a diffusion model-enabled particle swarm optimization (DM-PSO) algorithm. Simulation results show the effectiveness of the proposed two-stage optimization approach in improving the transmission rate of the constructed network, which demonstrates the great potential for post-disaster communications. Moreover, the robustness of the constructed network is also validated via evaluating the impact of three unexpected situations on the system transmission rate.

Emerging 6G technologies, such as terahertz communication and ultra-massive multiple-input multiple-output, offer exciting prospects but face challenges like limited range and multipath interference. In this paper, we seek to use an unmanned aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) to assist the terrestrial mmWave networks. Specifically, we consider a typical urban scenario where a UAV-carried IRS rebuilds the line of sight (LoS) channel between a mobile user and a base station under the existence of obstacles. Then, we formulate an optimization problem to maximize the transmission rates and minimize the UAV energy consumption, by jointly optimizing the UAV trajectory and the phase shifts of IRS. The problem is non-convex and with high dynamic, and thus we propose a deep reinforcement learning (DRL)-based algorithm with neural episodic control, long short-term memory (LSTM), and a phase control method to solve the problem, thereby enhancing the stability and accelerate convergence speed. Simulation results demonstrate that the proposed algorithm effectively addresses the problem and outperforms other benchmark algorithms.

Recent developments in unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) have provided users with flexible and resilient computing services. However, meeting the computing-intensive and latency-sensitive demands of users poses a significant challenge due to the limited energy resources of UAVs. To address this challenge, we present a joint optimization approach for multi-UAV-assisted MEC systems. First, we formulate a problem aimed at minimizing the total task completion delay, reducing the total UAV energy consumption, and maximizing the total amount of offloaded tasks by jointly optimizing task offloading and UAV trajectory control. Since the problem is a mixed-integer non-linear programming (MINLP) problem, we propose a joint task offloading and UAV trajectory control (JTOUTC) algorithm. Specifically, the original problem is divided into the subproblems of task offloading and UAV trajectory control, which are resolved alternately by adopting block alternate descent and successive convex approximation methods. Simulation results show that the proposed JTOUTC has superior system performance compared to other benchmark methods.

Maritime wireless communications as the promising applications gradually spur people's interest. However, they are suffering from critical security issues due to the feature of open channels. This paper propose to utilize a group of unmanned aerial vehicles (UAVs) as a jammer to achieve physical layer security during the maritime wireless communications. Specifically, these UAVs form a maritime UAV-enabled virtual antenna array (UVAA), which can be allowed to transmit jamming signals to the eavesdropper. In the designed system, we formulate a maritime wireless communication multi-objective optimization problem (MWCMOP) to simultaneously achieve the maximization of the signal-to-interference-plus-noise ratio (SINR) of the legal vessel, minimization of the SINR of the eavesdropping vessel, and minimization of the total flying energy cost of UAVs. It is challenging to solve the formulated MWCMOP since it is complex and NP-hard. Thus, we present a novel improved evolutionary algorithm to deal with the problem. Simulation results show that the presented algorithm is superior to other peer algorithms and it is capable of searching a transmission-maximizing flight scheme.

Unmanned aerial vehicles (UAVs) are playing a pivotal role in wireless networks due to their high mobility and on-demand deployment advantages. However, the UAV-enabled communications are susceptible to be wiretapped by eavesdroppers due to the strong line-of-sight (LoS) dominated air-ground channel. In this paper, we consider a UAV-enabled secure communication scenario, in which a group of UAVs form a UAV-enabled virtual antenna array (UVAA) to transmit information towards the remote base stations (BSs) via collaborative beamforming (CB), while multiple known and unknown eavesdroppers aiming to wiretap the information. Specifically, a secure communication multi-objective optimization problem (SCMOP) is formulated to achieve the maximization of the worst-case secrecy rate, the minimization of the maximum sidelobe level (SLL) as well as the minimization of the flight energy consumption of UAVs by obtaining optimal locations and excitation current weights concerning the UAVs as well as determining an optimal receiver BS that can achieve superior communication performance. To solve the formulated SCMOP which is demonstrated to be non-convex and NP-hard, an improved multi-objective salp swarm algorithm (IMSSA) with several specific operating factors is proposed. Simulations results demonstrate that the proposed IMSSA can deal with the formulated SCMOP effectively and outperforms other benchmark strategies. Moreover, the multi-hop relay is introduced to verify the reasonability of the UVAA system, and two benchmark schemes of the formulated SCMOP are introduced to demonstrate the necessity of the formulated SCMOP. In addition, the performance of the UVAA system under certain unexpected circumstances is estimated. Finally, experimental implementation is conducted by using a Raspberry Pi and the results demonstrate the practicality of the proposed CB-based secure communication approach in real-world scenarios.

Unmanned aerial vehicles (UAVs) have been demonstrated to be a prominent component for wireless communications. In this work, we consider an emergency communication scenario wherein a UAV-based relay system collects data from ground users, and then uses different UAV-enabled virtual antenna arrays (UVAAs) to transmit the collected data to several remote base stations (BSs) via collaborative beamforming (CB). However, several adjacent aerial users (AUs) are carrying out other missions at the same time, which may be interfered by the signal transmitted by the UVAAs. Thus, we formulate a reliable and energy-efficient communication multi-objective optimization problem (RECMOP) to jointly maximize the minimum receiving signal-to-noise ratio (SNR) of the BSs, minimize the maximum average receiving SNR of the AUs, and minimize the propulsion power consumption of the UAVs, so that diminishing the energy cost while enhancing the system performance. The formulated RECMOP is intricate since it is proven to be NP-hard and non-convex. Therefore, an improved multi-objective gravitational search algorithm (IMOGSA) with several specific designs is proposed to handle the formulated problem. Simulation results manifest that the proposed IMOGSA can effectively solve the formulated RECMOP, and it outperforms other benchmarks in both smaller and larger scale UAV networks. Moreover, extended simulation demonstrates the robustness of the proposed CB-based approach under several unexpected circumstances.

Unmanned aerial vehicles (UAVs) as the aerial relay become a highly desired scheme to assist terrestrial network. In this work, we intend to utilize the UAV swarm to assist the communication between the base station (BS) equipped with the planar array antenna (PAA) and the IoT devices by collaborative beamforming (CB). Specifically, we formulate an average achievable rate and energy bi-objective optimization problem (AREBOP) to improve the average achievable rate of IoT terminal devices and energy consumption of UAV swarm by jointly optimize the excitation current weights of BS and UAVs, the position of UAVs and user association order of IoT terminal devices. Moreover, the formulated AREBOP is proved to be NP-hard. Thus, we proposed an multi-objective grasshopper algorithm with specific initialization (MOGOASI) to solve this problem. Simulation results show the effectiveness of MOGOASI and illustrate that the performance of MOGOASI is superior compared to some benchmarks.

Unmanned aerial vehicles (UAVs) have the advantages of low-cost and flexible deployment. It can be used as the aerial transmitter in forests scenarios. However, forest terrain has severe multi-path reflections and may have several eavesdroppers, which degrade the performance of the communications. In this paper, we consider a collaborative UAV secure communication model under a forest channel, where multiple UAVs form a virtual antenna array (VAA) to communicate with a ground base station (BS) and circumvent the influence of the eavesdropper. Moreover, we formulate a multi-objective optimization problem (MOP) to simultaneously optimize the secure performance and energy efficiency of the considered system. Then, we propose an enhanced multi-objective particle swarm optimization algorithm (EMOPSO) which is based on chaos theory, linear weight reduction method and optimal set-based mutation approach to solve the formulated MOP. Simulation results show that the proposed method is effective and the proposed EMOPSO outperforms other benchmark algorithms.

Unmanned aerial vehicles (UAVs) have found enor-mous applications and are expected to bring tremendous op-portunities in the forthcoming 5G/6G wireless communications. However, there exist some challenges that need to be tackled for achieving reliable communications in UAV networks due to the open channels. In this work, we propose to perform a UAV-enabled virtual antenna array (UVAA) and adopt collaborative beamforming (CB) to transmit data towards different base stations (BSs), while an aerial user (AU) that locates near the UVAA is carrying out another task. In the considered scenario, we formulate a reliable communication multi-objective optimization problem (RCMOP) to simultaneously maximize the total receiving signal-to-noise ratio (SNR) of the BSs, minimize the total receiving SNR of the AU and minimize the flying energy consumptions of UAVs, which is achieved by cooperatively optimizing the positions and excitation current weights of UAVs and the order of transmitting data towards different BSs. The formulated RCMOP is sophisticated so that we propose an improved multi-objective salp swarm algorithm (IMSSA) to solve the problem. Simulation results verify that the proposed IMSSA can effectively solve the formulated RCMOP and it outperforms some other traditional strategies,

Automated guided vehicle (AGV) communications and networks have attracted extensive attention and have broad prospects in the field of wireless transmission. Unmanned aerial vehicle (UAV) can be used as flight base station (BS) to receive information from the ground AGVs. In this paper, we study an AGV-UAV communication scenario, in which a group of AGVs form an AGV-based virtual antenna array (AVAA) and communicate with different UAVs based on distributed collaborative beamforming (DCB). We formulate an AGV-UAV communication multi-objective optimization problem (AUCMOP) to simultaneously maximize the total transmission rate, minimize the total repositioning time of AGVs and minimize the total motion energy consumptions of AGVs by optimizing the positions, excitation current weights and moving speeds of AGVs, as well as the sequence for communicating with different UAVs. The formulated AUCMOP is complicated, and thus we propose an improved multi-objective ant lion optimization algorithm with Chebyshev chaos-opposition based learning solution initialization and hybrid solution update method (IMOALOCH). Simulation results show that the proposed IMOALOCH can solve the formulated AUCMOP effectively and it is superior to other comparison algorithms.

Due to the flexibility and high line-of-sight (LoS) probability, unmanned aerial vehicles (UAVs) can play an important role in 5G/6G networks. In this work, we study a novel air-to-air (A2A) communication scheme, in which two UAV swarms perform two UAV-enabled virtual antenna arrays (UVAAs) for exchanging data by using collaborative beamforming (CB). In order to improve the transmission efficiency and save the energy consumptions of the UAVs, we formulate an A2A communication multi-objective optimization problem (A2ACMOP) to simultaneously enhance the duplex transmission rates and reduce the total energy consumptions of the UAV swarms by deploying the UAVs and adjusting their excitation current weights. Due to the complexity and NP-hardness of the formulated A2ACMOP, we propose an improved non-dominated sorting genetic algorithm-III (INSGA-III) with opposition-based learning solution initialization and hybrid solution update operators to solve the problem. Simulation results verify that the proposed INSGA-III can effectively solve the formulated A2ACMOP and it has better performance than some other benchmark strategies.