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Ultra-dense unmanned aerial vehicle (UAV) plays an important role in the field of communications due to its flexibility and low-cost feature. Ultra-dense unnamed aerial vehicle base station (UAV-BS) can improve communication quality by providing temporary and cost-effective wireless communication services for hotspots. In this paper, a multiple UAV-BSs assisted downlink network is investigated to maximize the system throughput. It is still a challenging problem to jointly optimize the power allocation and the 3D deployment of multiple UAV-BSs. Therefore, in this paper, for effective interference management, the power allocation problem is first formulated as a non-cooperative game with a pricing mechanism to imitate the interactions among users served by UAV-BSs. Then, based on the combination of deep reinforcement learning (DRL) and the game theory, the power allocation and the 3D deployment of UAV-BSs are transformed into a Markov decision problem. Finally, a novel price-based proximal policy optimization (3PO) algorithm is proposed to explore the optimal policy to maximize the system throughput. Simulation results reveal that the proposed 3PO algorithm can significantly improve system throughput and energy efficiency compared to other baselines by jointly optimizing power allocation and 3D deployment for UAV-BSs.

Integrated sensing and communication (ISAC) becomes prevailing in wireless communications since it fully exploits the spectrum resources by incorporating data transmission and potential sensing functionalities of radio networks. With large bandwidth and directional communication, millimeter wave has the potentials for high data-rate communications and favorable time and spatial domain resolution, which can provide extensive sensing functionalities if properly utilized. Since self-backhauling at mmWave bands is considered a promising technology to enable high-throughput networks, we investigate how to embed ISAC functions into mmWave network by jointly considering high-speed data transmissions and high accuracy localization. To maximize the utilization of mmWave BSs for sensing and communication, we study the problem of optimal sensing task allocation taking into account target location, the requirements of different sensing tasks, user distribution, link scheduling, and data routing. With ISAC operations in mind, we analyze the time needed to complete each sensing task to facilitate problem formulation. To overcome the computational complexity in solution finding, we propose a sensing-oriented column generation (SOCG) scheme, which is shown to achieve near optimal performance via extensive performance evaluation. Furthermore, evaluation results demonstrate that the obtained sensing task allocation provides good throughput performance while ensuring the requirements of sensing tasks are satisfied.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent strategy where a UAV equipped with an MEC server is deployed to serve terminal devices. This paper considers a multi-UAV assisted network in which multiple UAVs and a terrestrial base station (BS) are deployed to provide MEC services to mobile users. The objective is to minimize an energy and latency-based cost function by jointly optimizing task offloading and MEC server selection decision, transmission power, UAV trajectory, and CPU frequency allocation. An alternating iterative approach based on the block descent method is proposed to solve this problem. In the first layer, task offloading and server selection decision subproblem is solved using a game theoretic approach. The second layer handles offloading and downloading transmission power allocations by utilizing a simplistic geometric waterfilling (GWF) technique, and the UAV trajectory by successive convex approximation (SCA). Whereas, the third layer solves the computation resource subproblem by performing CPU frequency allocation using a gradient descent method. The proposed method uses a segment-by-segment approach, which divides the entire UAV flight trajectory into shorter timeframe segments to reduce the computation time. Simulation results are presented to show that the proposed approach outperforms various benchmark schemes.

The positioning accuracy determines the scope of the application of an unmanned aerial vehicle (UAV). In view of the existing UAV cooperative localization methods that normally require prior information and the assistance of external systems, such as the global positioning system (GPS), this study aims to adopt range radios to measure the time-of-arrival (TOA) information among UAVs and then perform clock synchronization and cooperative localization based on ranging measurements. We propose a framework to jointly estimate the clock error and relative distance, adjust the onboard clock, and perform relative positioning. To achieve autonomous clock synchronization and ranging, a practical approach based on peer-to-peer pseudorange measurements is proposed in this study. We modeled the synchronous two-way ranging (STWR) process using a discretetime state-space model, according to which a linear parameter estimation method and clock steering method are presented. Finally, a closed loop consisting of STWR, parameter estimation, and clock tuning is constructed to improve the ranging accuracy, which leads to improved localization accuracy. Simulation results show that the proposed approach outperforms existing methods and can achieve sub-nanosecond-level time synchronization and meter-level cooperative localization.