The traditional ground industrial internet of things (IIoT) cannot supply wireless interconnections anywhere due to the small-scale communication coverage. In this paper, a multibeam satellite IIoT in Ka-band is proposed to realize wide-area coverage and long-distance transmissions, which uses NOMA for the beam to improve transmission rate. To guarantee QoS for the satellite IIoT, the beam power is optimized to match the theoretical transmission rate with the service rate. The transmission rate for each beam is maximized by optimizing the power allocation proportion of each node subject to the constraints of the total power for the beam and the minimal transmission rate for each node. Satellite-ground integrated IIoT is proposed to use the ground cellular network to supplement the satellite coverage in the blocked areas. The power allocation and network selection for the integrated IIoT are proposed to decrease the transmission cost. Simulation results are provided to validate the superiority of employing NOMA in the satellite IIoT and show lower transmission cost for the integrated IIoT.

Unmanned Aerial Vehicles (UAVs) can be used as aerial base stations for data collection in next-generation wireless networks due to their high adaptability and maneuverability. This paper investigates the scenario where multiple UAVs cooperatively fly over heterogeneous ground users (GUs) and collect data without a central controller. With the consideration of signal-to-interference-and-noise ratio (SINR) and fairness among users, we jointly optimize the trajectories of UAVs and the GUs associations to maximize the total throughput and energy efficiency. We formulate the long-term optimization problem as a decentralized partially observed Markov decision processes (DEC-POMDP) and derive an approach combining the coalition formation game (CFG) and multi-agent deep reinforcement learning (MADRL). We first formulate the discrete association scheduling problem as a non-cooperative theoretical game and use the CFG algorithm to achieve a decentralized scheme converging to Nash equilibrium (NE). Then, a MARL-based technique is developed to optimize the trajectories and energy consumption continuously in a centralized-training but decentralized-execution manner. Simulation results demonstrate that the proposed algorithm outperforms the commonly used schemes in the literature, regarding the fair throughput and energy consumption in a distributed manner.

As an aerial service platform for Internet of Things (IoT), unmanned aerial vehicle (UAV) can provide integrated sensing, computing and communication (ISCAC) services for the IoT nodes. In this paper, a UAV-enabled ISCAC system is proposed for IoT to meet the evolving requirements of emerging services in 6G networks. This system has three functions: sensing user equipments (UEs) for acquiring radar sensing information, executing computing tasks, and offloading incomplete tasks to the access point (AP) for further processing. Through jointly optimizing UAV CPU frequency, UAV radar sensing power, transmit power of UEs, and UAV trajectory, the weighted total energy consumption of both the UAV and the UEs can be minimized. We present a three-layer iterative optimization algorithm to tackle the original non-convex optimization problem. Finally, the effectiveness of the algorithm and its superiority in energy consumption compared to other benchmark schemes are verified through simulation results.

This paper considers worst-case downlink secure communication from base station (BS) to mobile legitimate vehicle at the presence of an eavesdropper, where reconfigurable intelligent surface mounted unmanned ariel vehicle (RIS-UAV) acts as an ariel passive relay to assist the communication. Our aim is to achieve the maximum worst-case downlink secrecy rate via optimizing power allocation, RIS passive beamforming and UAV trajectory. The formulated optimization problem is non-convex and non-smoothness, which is quite hard to solve directly. Therefore, we divide the original optimization problem into three subproblems, power allocation optimization, passive beamforming optimization and UAV trajectory optimization, which can be solved by applying successive convex approximation (SCA), $\mathcal {S}$ -Procedure and semidefinite relaxtion (SDR). Finally, we propose an alternating iterative optimization algorithm to get suboptimal solutions by alternately optimizing the three subproblems. Numerical simulations show the effectiveness of the proposed scheme against the other benchmarks.

We study the feasibility of the total power minimization problem subject to power budget and Signal-to-Interference-plus-Noise Ratio (SINR) constraints in cognitive radio networks. As both the primary and the secondary users are allowed to transmit simultaneously on a shared spectrum, uncontrolled access of secondary users degrades the performance of primary users and can even lead to system infeasibility. To find the largest feasible set of secondary users (i.e., the system capacity) that can be supported in the network, we formulate a vector-cardinality optimization problem. This nonconvex problem is however hard to solve, and we propose a convex relaxation heuristic based on the sum-of-infeasibilities in optimization theory. Our methodology leads to the notion of admission price for spectrum access that can characterize the tradeoff between the total energy consumption and the system capacity. Price-driven algorithms for joint power and admission control are then proposed that quantify the benefits of energy-infeasibility balance. Numerical results are presented to show that our algorithms are theoretically sound and practically implementable.

The exponential increase of the intelligent connected devices and the dramatic growth of the wireless data traffic have motivated the development of the green wireless networks as well as the Internet of Things. In this paper, we study the minimization problem of the total power to satisfy the required rate constraints in Internet of Things, where the users simultaneously communicate through multiple independent channels. This problem is complicated due to the non-linear data rate function based on the Shannon capacity formula. To this end, we first transfer the initial problem in power domain to an equivalent problem in rate domain instead of direct approximation for the high data rate. Then, we approximate it to a convex problem with the spectral radius constraints by the use of the Neumann expansion and nonlinear Perron-Frobenius theorem. By doing so, we achieve the close upper bound for this total power minimization problem. Moreover, we obtain the lower bound by making use of the convex relaxation technique, and finally get the global optimal solution by leveraging the branch-and-bound method. Simulation results verify that our proposed algorithms have a good approximation to the global optimal value for the power and rate allocations.