With the increasing demand for real-time data processing in disaster relief and remote areas, autonomous aerial vehicles (AAVs)-assisted mobile-edge computing (MEC) systems have emerged as a promising solution by providing power and computing resources to user devices (UDs). This article introduces a multicluster fair service AAV-assisted wireless powered MEC system aimed at addressing the challenges between energy efficiency and fair service. We formulate a long-term multiobjective energy efficiency optimization problem (LMEEOP) to tradeoff UD energy consumption, AAV energy consumption, and offline rate. However, this problem is a mixed integer nonlinear programming problem and difficult to solve. Thus, we propose a trajectory limited online joint optimization approach (TLOJOA), which decomposes LMEEOP into three subproblems: 1) AAV flight trajectory design; 2) offloading decision generation; and 3) resource allocation. Specifically, a Self-Attention combined proximal policy optimization (PPO) algorithm captures long-range dependencies to balance energy consumption and offline rate. Additionally, a fast offloading strategy generation algorithm with offloading importance ranking is proposed to improve UD energy efficiency. Finally, the AAV flight trajectory and offloading decision are combined to achieve efficient resource allocation. The proposed approach provides a robust solution for improving multicluster fair service and energy efficiency optimization in AAV-assisted MEC systems, especially for mission-critical scenarios with constrained resources. Simulation results validate that the proposed approach can achieve outstanding system performance compared to other benchmark approaches, especially in fairness of service.

With the rapid popularity of mobile devices (MDs), mobile edge computing (MEC) networks and wireless power transmission (WPT) will receive more attention. Naturally, by integrating these two technologies, the inherent energy consumption during task execution can be effectively reduced, and the collected energy can be provided to charge the MD. In this article, our research focuses on extending the battery time of MDs by maximizing the harvested energy and minimizing the consumed energy in the MEC–WPT system, which is formulated as a residual energy maximization problem and also a non-convex optimization problem. On the basis of study on maximizing the residual energy under multi-users and multi-time blocks, we propose an effective jointly optimization method (i.e., jointly optimize the energy harvesting time, task-offloading time, task-offloading size and the MDs’ CPU frequency), which combines the convex optimization method and the augmented Lagrangian to solve the residual energy maximum problem. We leverage Time Division Multiple Access (TMDA) mode to coordinate computation offloading. Simulation results show that our scheme has better performance than the benchmark schemes on maximizing residual energy. In particular, our proposed scheme is outstanding in the failure rate of multiple MDs and can adapt to the task size to minimize the failure rate.

With increasing OTA channel instability and various network function-sliced application scenarios in 5G wireless communications. The conventional CQI-based Adaptative Modulation and Coding schemes that requires strictly precise SNR-to-CQI or SNR-to-MCS mapping begin to see their limitations. In this paper, a unified non-CQI-based adaptive modulation and coding scheme is proposed with low implementation complexity while maintaining precise target BLER adjustability. Comple-mentation from the Guassian filtered SNR metric enhances the algorithm convergence speed and performance. A SNR-to-MCS mapping lookup table based on intensive simulations is also given in this paper, and the influence of HARQ re-transmissions is discussed. The performances of the proposed AMC scheme are validated via simulations.

The combination of mobile edge computing (MEC) and wireless power transfer (WPT) is recognized as a promising technology to solve the problem of limited battery capacities and insufficient computation capabilities of mobile devices. This technology can transfer energy to users by radio frequency (RF) in wireless powered mobile edge computing. The user converts the harvested energy, stores it in the battery, and utilizes the harvested energy to execute corresponding local computing and offloading tasks. This paper adopts the Frequency Division Multiple Access (FDMA) technique to achieve task offloading from multiple mobile devices to the MEC server simultaneously. Our objective is to study multiuser dynamic joint optimization of computation and wireless resource allocation under multiple time blocks to solve the problem of maximizing residual energy. To this end, we formalize it as a nonconvex problem that jointly optimizes the number of offloaded bits, energy harvesting time, and transmission bandwidth. We adopt convex optimization technology, combine with Karush–Kuhn–Tucker (KKT) conditions, and finally transform the problem into a univariate constrained convex optimization problem. Furthermore, to solve the problem, we propose a combined method of Bisection method and sequential unconstrained minimization based on Reformulation-Linearization Technique (RLT). Numerical results demonstrate that the performance of our joint optimization method outperforms other benchmark schemes for the residual energy maximization problem. Besides, the algorithm can maximize the residual energy, reduce the computation complexity, and improve computation efficiency.

Unlike traditional vision-based camera tracking, human indoor localization and activity trajectory recognition also employ other methods such as infrared tracking, acoustic localization, and locators. These methods have significant environmental limitations or dependency on specialized equipment. Currently, WiFi-based human sensing is a novel and important method for human activity recognition. However, most WiFi-based activity recognition methods have limitations, such as using WiFi fingerprints to identify human activities. They either require extensive sample collection and training, are constrained by a fixed environmental layout, or rely on the precise positioning of transmitters (TXs) and receivers (RXs) within the space. If the positions are uncertain, or change, the sensing performance becomes unstable. To address the dependency of current WiFi indoor human activity trajectory reconstruction on the TX-RX position, we propose WiPIHT, a stable system for tracking indoor human activity trajectories using a small number of commercial WiFi devices. This system does not require additional hardware to be carried or locators to be attached, enabling passive, real-time, and accurate tracking and trajectory reconstruction of indoor human activities. WiPIHT is based on an innovative CSI channel analysis method, analyzing its autocorrelation function to extract location-independent real-time movement speed features of the human body. It also incorporates Fresnel zone and motion velocity direction decomposition to extract movement direction change patterns independent of the relative position between the TX-RX and the human body. By combining real-time speed and direction curve features, the system derives the shape of the human movement trajectory. Experiments demonstrate that, compared to existing methods, our system can accurately reconstruct activity trajectory shapes even without knowing the initial positions of the TX or the human body. Additionally, our system shows significant advantages in tracking accuracy, real-time performance, equipment, and cost.