This paper investigates the UAV-assisted wireless powered IoT system, where a UAV takes off from a data center, flies to each of the ground sensor nodes (SNs) in order to transfer energy and collect data form the SNs, and then returns to the data center. For such a system, an optimization problem is formulated to minimize the average age of information (AoI) of the data collected from all ground SNs. Since the average AoI depends on the UAV’s trajectory, the time required for energy harvesting (EH) and data collection for each SN, these factors need to be optimized jointly. Moreover, instead of the traditional linear EH model, we employ a non-linear model because the behavior of the EH circuits are non-linear by nature. To solve this non-convex problem, we propose to decompose it into two sub-problems, i.e., a joint energy transfer and data collection time allocation problem and a UAV’s trajectory planning problem. For the first sub-problem, we prove that it is convex and give an optimal solution by using Karush-Kuhn-Tucker (KKT) conditions. This solution is used as the input for the second sub-problem, and we solve optimally it by designing dynamic programming (DP) and ant colony (AC) heuristic algorithms. Simulation results show that the DP-based algorithm obtains the minimal average AoI of the system, and the AC-based heuristic finds solutions with near-optimal average AoI. The results also reveal that the average AoI increases as the flying altitude of the UAV increases and linearly with the size of the collected data at each ground SN.

This article investigates the unmanned-aerial-vehicle (UAV)-enabled wireless powered cooperative mobile edge computing (MEC) system, where a UAV installed with an energy transmitter (ET) and an MEC server provides both energy and computing services to sensor devices (SDs). The active SDs desire to complete their computing tasks with the assistance of the UAV and their neighboring idle SDs that have no computing task. An optimization problem is formulated to minimize the total required energy of UAV by jointly optimizing the CPU frequencies, the offloading amount, the transmit power, and the UAV’s trajectory. To tackle the nonconvex problem, a successive convex approximation (SCA)-based algorithm is designed. Since it may be with relatively high computational complexity, as an alternative, a decomposition and iteration (DAI)-based algorithm is also proposed. The simulation results show that both proposed algorithms converge within several iterations, and the DAI-based algorithm achieve the similar minimal required energy and optimized trajectory with the SCA-based one. Moreover, for a relatively large amount of data, the SCA-based algorithm should be adopted to find an optimal solution, while for a relatively small amount of data, the DAI-based algorithm is a better choice to achieve smaller computing energy consumption. It also shows that the trajectory optimization plays a dominant factor in minimizing the total required energy of the system and optimizing acceleration has a great effect on the required energy of the UAV. Additionally, by jointly optimizing the UAV’s CPU frequencies and the amount of bits offloaded to UAV, the minimal required energy for computing can be greatly reduced compared to other schemes and by leveraging the computing resources of idle SDs, the UAV’s computing energy can also be greatly reduced.

Two emerging technologies toward 5G wireless networks, namely non-orthogonal multiple access (NOMA) and cognitive radio (CR), will provide more efficient utilization of wireless spectrum in the future. In this article, we investigate the integration of NOMA with CR into a holistic system, namely a cognitive NOMA network, for more intelligent spectrum sharing. Design principles of cognitive NOMA networks are perfectly aligned to functionality requirements of 5G wireless networks, such as high spectrum efficiency, massive connectivity, low latency, and better fairness. Three different cognitive NOMA architectures are presented, including underlay NOMA networks, overlay NOMA networks, and CR-inspired NOMA networks. To address inter-network and intra-network interference, which largely degrade the performance of cognitive NOMA networks, cooperative relaying strategies are proposed. For each cognitive NOMA architecture, our proposed cooperative relaying strategy shows its potential to significantly lower outage probabilities. We discuss open challenges and future research directions on implementation of cognitive NOMA networks.

IEEE 802.11n is an ongoing next-generation wireless LAN standard that supports a very highspeed connection with more than 100 Mb/s data throughput measured at the medium access control layer. This article investigates the key MAC enhancements that help 802.11n achieve high throughput and high efficiency. A detailed description is given for various frame aggregation mechanisms proposed in the latest 802.11n draft standard. Our simulation results confirm that A-MSDU, A-MPDU, and a combination of these methods improve extensively the channel efficiency and data throughput. We analyze the performance of each frame aggregation scheme in distinct scenarios, and we conclude that overall, the two-level aggregation is the most efficacious.

Wireless networks comprising unmanned aerial vehicles can offer limited connectivity in a cost-effective manner to disaster-struck regions where terrestrial infrastructure might have been damaged. While these drones offer advantages such as rapid deployment to far-flung areas, their operations may be rendered ineffective by the absence of an adequate energy management strategy. This article considers the multi-faceted applications of these platforms and the challenges thereof in the networks of the future. In addition to providing an overview of the work done by researchers in determining the features of the air-to-ground channel, the article explores the use of drones in fields as diverse as military surveillance and network rehabilitation for disaster-struck areas. It also presents a case study that envisages a scenario in which drones operate alongside conventional wireless infrastructure, thereby allowing a greater number of users to establish a line-of-sight link for communication. This study investigates a power allocation strategy for the microwave base station and the small base stations operating at 28 GHz frequency band. The self-adaptive power control strategy for drones is dependent on the maximum allowable interference threshold and minimum data rate requirements. This study highlights the importance of incorporating the drones in the multi-tier heterogeneous network to extend the network coverage and capacity.