| With the exponential growth of user access and the increasing complexity of terrestrial wireless network environments,airborne networks based on intelligent unmanned aerial vehicles(UAVs)are regarded as an important component of the sixth generation(6G)communications and networks.UAV networks can achieve high line-of-sight(Lo S)link probability,and thus can be used as flying base stations,air relays,and emergency network carriers,thereby assisting the terrestrial network.However,UAVs often have weak communication capability and limited battery capacity,and their broadcast nature of wireless channels also leads to a high risk of being eavesdropped.Therefore,how to effectively improve the communication performance,energy efficiency and security of UAV networks has become a cutting-edge scientific issue in the field of UAV networks.In this case,collaborative beamforming(CB)can effectively improve the transmission performance and energy efficiency of distributed communication systems.Thus,it is a potentially effective method to realize long-distance,low-cost,and secure UAV networks.However,the random distribution of UAV locations and the complexity and variability of service demands weaken the communication gain achieved by CB,and also increase the flight energy consumption for controlling UAVs.Thus,this thesis introduces CB to achieve energy-efficient low-cost remote UAV communications and energy-efficient physical layer secure communications.To this end,this thesis adopts multi-objective optimization theory to characterize the conflicting relationship between key communication performance indicators and energy efficiency in UAV CB networks,and then proposes various enhanced swarm intelligence algorithms to optimize the key decisionmaking variables in such UAV CB systems.The main contributions and innovations of this thesis are outlined as follows:1.CB-based low-cost remote UAV communication approach.This approach aims to use the high gain provided by CB to the UAV swarm to expand the transmission range and rate,thereby reducing the energy caused by frequent flying of the UAVs.This approach focuses on UAV air-to-ground communication and UAV-assisted Internet-ofthings(Io Ts)as two major scenarios,and proposes scene-matching optimization objectives and strategies.The innovations are outlined as:(1)Time and energy efficient UAV CB-based communication method: First,considering a remote communication scenario with a UAV swarm and several terrestrial base stations,a time and energy minimization multi-objective optimization problem is formulated to simultaneously optimize the total transmission time,virtual antenna array performing time,and total energy consumption.Following this,the optimal expressions of the relationships between the virtual antenna array performing time,UAV speeds,and energy consumption are derived.Then,an improved swarm intelligence method is proposed to solve the problem.Finally,simulation results show that the proposed method can effectively reduce the time and energy consumption of UAVs,and the proposed algorithm outperforms various comparison algorithms and strategies.(2)Air-ground jointly CB-based data harvesting and dissemination method in UAVassisted Io Ts: First,CB is introduced into both UAVs and Io T devices,thereby enhancing the transmission capability of UAVs and Io T sensors without changing their original communication devices.Following this,a time-and energy-efficient and secure multi-objective optimization problem is formulated by adopting multi-objective optimization theory.This problem is proved to be NP-hard,large-scale,and mixed-variable.Then,an enhanced swarm intelligence optimization algorithm is proposed to solve the problem.Finally,simulation results show that the proposed algorithm outperforms various baseline algorithms and strategies,and also has certain robustness.2.CB-based physical layer secure communication methods.(1)Energy-efficient physical layer secure communication in UAV CB-based networks:First,considering the scenarios with a known eavesdropper and multiple unknown eavesdroppers,a multi-objective optimization problem is formulated to achieve physical layer security and improve the energy-efficiency of UAVs.This problem is NP-hard,and thus a novel improved swarm intelligence algorithm is proposed to solve it.Following this,the proposed algorithm is extended to a parallel-distributed version to address the time-sensitive characteristics of some scenarios,thereby obtaining feasible solutions with a faster response time.Finally,simulation results show that the proposed algorithm achieves the best overall performance and outperforms the various networking strategies.(2)UAV CB-based secure relay: First,considering the UAV relaying scenario with the presence of multiple known and unknown eavesdroppers,a secure and energy-saving multiobjective optimization problem is formulated to maximize the confidentiality and energy efficiency of the relay system.Following this,a novel multi-objective swarm intelligence algorithm is proposed for solving the problem,which contains improved operators to make it more suitable for solving the formulated problem.Finally,simulation results show that the proposed algorithm can achieve high confidentiality and energy efficiency under different network sizes.(3)Two-way secure communication method for UAV swarms under eavesdropper collusion: This work proposes a new paradigm of two-way aerial secure communication for UAVs,which is based on CB and can overcome eavesdropper collusion issues.Specifically,considering the worst case in which the eavesdroppers collude based on signal detection,a signal processing method based on CB is used to circumvent the eavesdroppers.Accordingly,a multiobjective optimization problem of eavesdropper collusion is formulated and a swarm intelligence algorithm is enhanced by the problem characteristics.Simulation results show that the proposed method outperforms various state-of-the-art benchmarks and has a certain robustness. |
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