Leveraging unmanned aerial vehicles (UAVs) as relays, the space-air-ground integrated Internet of Things (SAG-IoT) networks can provide wireless access services to the connected devices with high data rate, high flexibility, and wide coverage. However, besides bringing lots of benefits, the SAG-IoT systems also face many challenges due to the high mobility, unreliable satellite links, and limited resources. Especially, when multiple UAVs act relays for cross-tier communication to transfer the collected data from ground network tier to the satellite tier, how to select an appropriate number of UAVs as gateways to improve system spectral efficiency (SE) is an important issue. To the best of our knowledge, it is an entirely new problem since most existing works utilize all UAVs as relay nodes and neglect the spectrum allocation. Toward this end, we present this paper to discuss the joint optimization problem of gateway selection, bandwidth allocation, and UAV deployment with the objective of maximizing the system SE. A Dinkelbach method based iterative algorithm is proposed as our solution by alternately adopting simulated annealing and successively convex programming technologies. Experimental results show that our designed joint optimization schemes are able to significantly improve the system SE and can make optimal decisions of gateway selection, bandwidth allocation, as well as UAV deployment.

As an emerging network architecture and technology, mobile edge computing (MEC) can alleviate the tension between the computation-intensive applications and the resource-constrained mobile devices. However, most available studies on computation offloading in MEC assume that the edge severs host various applications and can cope with all kinds of computation tasks, ignoring limited computing resources and storage capacities of the MEC architecture. To make full use of the available resources deployed on the edge servers, in this paper, we study the cross-server computation offloading problem to realize the collaboration among multiple edge servers for multi-task mobile edge computing, and propose a greedy approximation algorithm as our solution to minimize the overall consumed energy. Numerical results validate that our proposed method can not only give near-optimal solutions with much higher computational efficiency, but also scale well with the growing number of mobile devices and tasks.

With the continuous development of fifth-generation technology, the number of mobile terminal Internet of Things devices has increased exponentially. How to effectively improve the throughput of fifth-generation systems has become a challenge. In the Internet of Things networks, ultra-dense networks and non-orthogonal multiple access technology have drawn extensive attention in recent years, because they can achieve multiplexing from the space domain and power domain. To improve the throughput of the system, this article combines non-orthogonal multiple access with ultra-dense networks technology and considers the orthogonal frequency division multiplexing non-orthogonal multiple access–based ultra-dense networks with multiple base stations and multiple Internet of Things devices. In particular, first, we build the network model and channel model. Second, we construct the downlink transmission rate maximizing problem subject to the total power. Then, to solve this problem, we divide it into three sub-problems: device grouping, inter-sub-band power allocation, and intra-sub-band power allocation problems. Solving these sub-problems, we obtain the optimal power allocation schemes by jointly employing channel-state sorting–pairing algorithm, water-filling algorithm, and convex optimization theory. Finally, numerical simulations are conducted to validate the performance of our proposed optimal downlink power allocation scheme. Experimental results show that the total throughput of the system has been significantly improved.

Intelligent Reconfigurable Surface (IRS) is an innovative and promising technology to achieve intelligent reconfigurable wireless environment, and thus, enables cost-effective and energy-efficient wireless communications. Due to the broadcasting nature of the wireless signals, the reflected signal in IRS-assisted wireless communications networks might suffer from eavesdropping. Thus, it is essential to tackle the secrecy aware problems in IRS-assisted wireless communications networks. In this paper, we consider an aerial intelligent reconfigurable surface (AIRS) assisted wireless relay network scenario, where IRS is mounted on the aerial platform. The artificial noise is added to interrupt the eavesdropping. A secrecy rate maximization problem is formulated subject to the total transmit power and reflecting phase shift constraints. To solve this problem, we first divide the secrecy maximization problem into three sub-problems, i.e., transmit power allocation, AIRS trajectory design and reflecting phase shift optimization. These three sub-problems are solved alternately until convergence to maximize the secrecy rate. Especially, for the AIRS trajectory design and reflecting phase shift optimzation, we employ the successive convex approximation (SCA) and positive Semidefinite Relaxation (SDR) technologies to convert the non-convex optimization problems into convex problems, respectively. The intercept probability of the proposed optimal schemes is derived and the theoretical analyses show that the intercept probablility can be reduced by increasing the numbers of IRS elements. Simulation results show that the joint optimzation of transmit power, AIRS trajectory and reflecting phase shift can effectively improve the secrecy rate.

In this paper, it is investigated a multi-user multipleinput single-output wireless communication scenario (MISO) in which a base station (BS) serves multiple users with the assistance of a reconfigurable intelligent surface (RIS). Specifically, the delay sensitivity QoS aware total effective capacity (TEC) is maximized. By employing Zero-Forcing (ZF) beamforming at the BS, the beamforming optimization is converted the power allocation. Then, we alternately optimize QoS provisioning power allocation schemes and RIS phase shifts. In particular, we derive a closed-form expression for the optimal power allocation scheme based on the Lagrangian method and the Karoosh-Koon-Tucker (KKT) condition. Meanwhile, the RIS phase shift strategy is optimized by employing the gradient descent method. Finally, the performance of the proposed joint optimal schemes for RIS-assisted wireless communication is verified by simulations. The simulation results demonstrate that the optimization scheme effectively guarantees the user's delay QoS requirements and the TEC significantly outperforms the random reflecting phase shift scheme

Energy harvesting, which offers a never-ending energy supply, has emerged as a prominent technology to prolong the lifetime and reduce costs for the battery-powered wireless sensor networks. However, how to improve the energy efficiency while guaranteeing the quality of service (QoS) for energy harvesting based wireless sensor networks is still an open problem. In this paper, we develop statistical delay-bounded QoS-driven power control policies to maximize the effective energy efficiency (EEE), which is defined as the spectrum efficiency under given specified QoS constraints per unit harvested energy, for energy harvesting based wireless sensor networks. For the battery-infinite wireless sensor networks, our developed QoS-driven power control policy converges to the Energy harvesting Water Filling (E-WF) scheme and the Energy harvesting Channel Inversion (E-CI) scheme under the very loose and stringent QoS constraints, respectively. For the battery-finite wireless sensor networks, our developed QoS-driven power control policy becomes the Truncated energy harvesting Water Filling (T-WF) scheme and the Truncated energy harvesting Channel Inversion (T-CI) scheme under the very loose and stringent QoS constraints, respectively. Furthermore, we evaluate the outage probabilities to theoretically analyze the performance of our developed QoS-driven power control policies. The obtained numerical results validate our analysis and show that our developed optimal power control policies can optimize the EEE over energy harvesting based wireless sensor networks.