Abstract Unmanned aerial vehicles (UAVs) are envisioned to provide a variety of sensing applications in the cellular systems, which is known as the cellular Internet of UAVs. This paper considers a cellular Internet of UAVs, in which the sensory data collected by UAVs can be transmitted to the base station by cellular communications, or to the corresponding mobile devices directly by overlaying UAV‐to‐device communications. The authors first propose a joint sensing and transmission protocol to schedule UAV sensing and transmission, and then investigate the energy utility (EU) maximisation problem by jointly optimising the trajectory, speed, and transmit power of UAVs in the system. As this problem is non‐convex and difficult to be solved, the authors decouple it into three subproblems , i.e. trajectory optimisation subproblem, speed optimisation subproblem, and transmit power optimisation subproblem. Then, the authors propose a joint trajectory, speed, and transmit power optimisation algorithm to obtain the suboptimal solution of the original problem. Simulation results show that the authors' proposed algorithm can efficiently improve the EU in the system as compared with other benchmark schemes.

In recent years, unmanned aerial vehicles (UAVs) have unlocked numerous sensing applications, which are expected to add billions of dollars to the world economy in the next decade. To further improve the Quality-of-Service in these applications, the 3rd Generation Partnership Project has considered the use of terrestrial cellular networks to support UAV sensing services, also known as the cellular Internet of UAVs. In this paper, we consider a cellular Internet of UAVs, where the sensory data can be transmitted either to the base station via cellular links, or to the mobile devices by underlay UAV-to-Device (U2D) communications. To evaluate the freshness of the sensory data, the concept of age of information (AoI) is adopted, in which a lower AoI implies fresher data. Since UAVs' AoIs are determined by their trajectories during sensing and transmission, we investigate the AoI minimization problem for UAVs by designing their trajectories. This problem is a Markov decision problem with an infinite state-action space, and thus we utilize multi-agent deep reinforcement learning to approximate the state-action space. Then, we propose a multi-UAV trajectory design algorithm to solve this problem. Simulation results show that our proposed algorithm can achieve a lower AoI than a greedy algorithm, policy gradient algorithm, and overlay U2D scheme.

In this paper, we consider a cellular Internet of UAVs, where the sensory data can be transmitted either to the base station via cellular links, or to the mobile devices by underlay UAV-to-Device communications. To evaluate the freshness of the sensory data, the age of information (AoI) is adopted, in which a lower AoI implies fresher data. Since UAVs' AoIs are determined by their trajectories during sensing and transmission, we aim to minimize the AoIs of UAVs by designing their trajectories. This problem is a Markov decision problem with an infinite state-action space, and thus, we propose a multi-UAV trajectory design algorithm by leveraging multi-agent deep reinforcement learning to solve it. Simulation results show that our proposed algorithm outperforms both a greedy algorithm and a policy gradient algorithm.

In the current unmanned aircraft systems (UASs) for sensing services, unmanned aerial vehicles (UAVs) transmit their sensory data to terrestrial mobile devices over the unlicensed spectrum. However, the interference from surrounding terminals is uncontrollable due to the opportunistic channel access. In this paper, we consider a cellular Internet of UAVs to guarantee the Quality-of-Service (QoS), where the sensory data can be transmitted to the mobile devices either by UAV-to-Device (U2D) communications over cellular networks, or directly through the base station (BS). Since UAVs' sensing and transmission may influence their trajectories, we study the trajectory design problem for UAVs in consideration of their sensing and transmission. This is a Markov decision problem (MDP) with a large state-action space, and thus, we utilize multi-agent deep reinforcement learning (DRL) to approximate the state-action space, and then propose a multi-UAV trajectory design algorithm to solve this problem. Simulation results show that our proposed algorithm can achieve a higher total utility than policy gradient algorithm and single-agent algorithm.

In this paper, we consider a cellular Internet of unmanned aerial vehicles (UAVs) where the sensory data can be transmitted to the mobile devices directly by overlaying UAV-to-Device (U2D) communications, or through the base station (BS) by cellular communications. Since the transmission modes of UAVs may influence their trajectories, we study the trajectory design problem for UAVs aiming to maximize the total utility in consideration of their transmission modes. This problem is a Markov decision problem (MDP) with a large state-action space, and thus, we propose a multi-UAV trajectory design algorithm using multi- agent deep reinforcement learning (DRL) to solve this problem. Simulation results show that our proposed algorithm can achieve a higher total utility than the single-agent method.

In this paper, we consider a device-to-device (D2D) communications underlaying cellular network where Long Term Evolution (LTE) and D2D users are allowed to communicate over both licensed and unlicensed bands for spectrum efficiency improvement. LTE users utilize spectrum orthogonally and share it with D2D users. To maximize the total throughput of this D2D system, we leverage stochastic geometry to derive the throughput for each kind of users by modeling the deployment of users as Poisson point processes (PPPs), and investigate the mode selection problem for D2D users. Since the problem is NP-hard, we propose a sequential quadratic programming (SQP) based algorithm to obtain the corresponding suboptimal solutions. Theoretically, we evaluate the system performance by analyzing the throughput regions. Simulation results validate the accuracy of the geometric analysis and verify the effectiveness of the proposed algorithm.

In this paper, we consider device-to-device (D2D) communications as an underlay to cellular networks over both licensed and unlicensed spectrums, where long-term evolution (LTE) users utilize the spectrum orthogonally while D2D users share the spectrum with LTE users. In the system, each LTE and D2D user can access the licensed or unlicensed band for communications. To maximize the total throughput of the system, we leverage stochastic geometry to derive the throughput for each kind of user by modeling the deployment of users as Poisson point processes (PPPs), and investigate the spectrum access problem for these users. Since the problem is NP-hard, we propose a sequential quadratic programming (SQP)-based algorithm to obtain the corresponding suboptimal solutions. Theoretically, we evaluate the system performance by analyzing the throughput regions. Simulation results validate the accuracy of the geometric analysis and verify the effectiveness of the proposed algorithm.