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Thanks to the inherent characteristics of flexible mobility and autonomous operation, Unmanned Aerial Vehicles (UAV) will inevitably be integrated into 5G/B5G cellular networks to assist remote sensing for real-time assessment and monitoring applications. Most existing UAV-assisted data collection schemes focus on optimizing energy consumption and data collection throughput, which overlook the temporal value of collected data. In this paper, we employ age of information (AoI) as a performance metric to quantify the temporal correlation among data packets consecutively sampled by the IoT devices, and investigate an AoI-energy-aware data collection scheme for UAV assisted IoT networks. We aim to minimize the weighted sum of expected average AoI, propulsion energy of UAV, and the transmission energy at IoT devices, by jointly optimizing the UAV flight speed, hovering locations, and bandwidth allocation for data collection. Considering the system dynamics, the optimization problem is modeled as a Markov Decision Process. To cope with the multi-dimensional action space, we develop a Twin Delayed Deep Deterministic policy gradient (TD3)-based UAV trajectory planning algorithm (TD3-AUTP) by introducing the deep neural network (DNN) for feature extraction. Through simulation results, we demonstrate that our proposed scheme outperforms the deep Q-network and Actor-Critic based algorithms in terms of achievable AoI and energy efficiency.

The complexity of reservoir conditions puts forward higher requirements for drilling fluid performance. The existing water-based drilling fluids (WBFDs) are difficult to meet the requirements at hi...

The integration of mobile-edge computing (MEC) and energy harvesting (EH) can potentially improve the network performances and prolong the battery life of the device. In this article, we study the resource management problem in the device-to-device (D2D)-aided wireless powered MEC networks where one device can forward or execute computation data for other devices with its resources. Our problem seeks to optimize the computation offloading strategy, transmission power, energy transmit power, as well as CPU speed to maximize the long-term utility energy efficiency (UEE). UEE is defined as the achieved computation data per unit energy. Since the formulated problem is in fractional form and hard to solve, we employ the Dinkelbach algorithm to transform the problem into a parametric subtractive form. Furthermore, considering that the formulated problem is time varying and stochastic due to the dynamic task arrival rate and battery level, we transform the long-term problem into deterministic drift-plus-penalty subproblems for each time slot by introducing virtual queues and adopting the Lyapunov optimization theory. The proposed scheme can balance the optimal UEE and stable data queue by introducing the control parameter $V$ . Theoretically, we reveal the tradeoff between the UEE and stable queue length for wireless powered MEC systems as $[O(1/V), O(V)]$ . Finally, the simulations illustrate the efficiency of the proposed scheme compared with the existed work in terms of the UEE, stable queue length, and battery level.

Currently, the computing capability of smart mobile devices has been extremely improved. Exploiting computing resources of mobile devices to assist the network through offloading computation tasks will promisingly boost the fifth-generation (5G) and beyond heterogeneous networks. We investigate the user-assisted multi-task offloading scheme based on the mobile edge computing (MEC)-Cloud architecture to reduce the end-to-end computing latency. The offloading strategy, computing resource allocation and spectrum allocation are jointly optimized to minimize the computation latency while guaranteeing the energy available to the users. The formulated optimizing problem is a large-scale mixed-integer nonlinear optimizing problem which is hard to solve within a rational time. To overcome this problem, a low-complexity distributed framework based on the alternating direction method of multipliers algorithm is proposed to minimize the computing latency for all tasks. Compared with the existing schemes, the proposed scheme can reduce the computing latency and improve the performance efficiently. Simulation results illustrate the effectiveness of the proposed scheme in respect of latency reduction with different parameters.

Training deep neural models in the presence of corrupted supervision is challenging as the corrupted data points may significantly impact the generalization performance. To alleviate this problem, we present an efficient robust algorithm that achieves strong guarantees without any assumption on the type of corruption and provides a unified framework for both classification and regression problems. Unlike many existing approaches that quantify the quality of the data points (e.g., based on their individual loss values), and filter them accordingly, the proposed algorithm focuses on controlling the collective impact of data points on the average gradient. Even when a corrupted data point failed to be excluded by our algorithm, the data point will have a very limited impact on the overall loss, as compared with state-of-the-art filtering methods based on loss values. Extensive experiments on multiple benchmark datasets have demonstrated the robustness of our algorithm under different types of corruption.

The millimeter wave (mmWave) band has been considered as an effective way to meet the increasing traffic demand by sufficient bandwidth and high spatial reuse. The harsh propagation experienced at such high frequencies requires a dense base station deployment, which is not feasible to provide wired backhaul due to the unavailability of fiber drops. To address this issue, the integrated access and backhaul (IAB) architecture is proposed by the third generation partnership project (3GPP). In this paper, we investigate the spectrum and power allocation for IAB mmWave enabled cellular network to maximize the network capacity with considering the data rate requirements. A novel resource allocation scheme based on the sequential convex programming approach (RASCPA) is proposed. The simulation results show that our proposed scheme is superior to other schemes. The proposed scheme can increase the network capacity while satisfying the user data rate requirements.

Both the low latency and high energy efficiency are expected in the 5th generation mobile communication system (5G). The content prediction is believed as a promising way to reduce the latency. However, pushing a large amount of content will result in low energy efficiency. In order to get higher energy efficiency, we propose the prediction based D2D-assisted pushing in Heterogenous Ultra-dense Networks (H-UDNs). The D2D relay-user equipment can assist the pushing process of user equipments who are occupied with other services at that time, which can benefit on energy efficiency and latency reduction. Moreover, a new metric of energy efficiency for prediction is proposed in this paper called the Ratio of Energy to Prediction Success Rate (REPSR). Specifically, it can describe the energy efficient characteristics of prediction. Therefore, we can improve the energy efficiency and reduce the latency through the energy efficient prediction based D2D-assisted pushing.

Perovskite solar cells, with an energy conversion rate of over 20% in just a few years after their emergence in 2009, have become the most promising solar photovoltaic cells, and are also an important research object in the solar energy direction of various countries. In this paper, the development of perovskite solar cells in recent years was studied and the preparation and improvement methods of perovskite films were introduced.

Caching popular contents at mobile devices can improve the system performance and relieve the traffic burden for the device-to-device (D2D) enabled mobile ultra dense network (UDN). In this paper, the D2D enabled mobile networks are formulated as a 3-layer architecture involving the link layer, social tie layer and preference layer. Based on the 3-layer architecture, a proactive content sharing scheme including content caching, content diffusion and resource allocation is proposed. For content caching, we propose a joint seed D2D user (DUE) and caching content selection algorithm (JSCSA) with graph theory and caching utility. With contents cached at seed DUEs, a content diffusion approach is proposed based on the social tie and D2D service priority, where seed DUEs can proactively deliver contents to other DUEs with strong social tie when seed DUEs have no requesters to serve. Furthermore, a joint subchannel allocation and power control algorithm (JSAPCA) is proposed to optimize the system throughput. Simulations are carried out to illustrate that the proposed proactive content sharing scheme has superior performances compared with the existing works.

UE-to-Network relay leverages the proximity communication between user equipments (UEs) and allows certain UEs to provide relay assistance for others, which can greatly improve system energy efficiency. In this paper, we consider the scenario where UEs suffering from bad channel condition and low battery level can communicate with the base station directly or via the help of other UEs in heterogeneous networks. The optimal power allocation and connectivity among UEs are studied, which aims at minimizing the system transmission energy while guaranteeing the minimum data rate requirement of each UE. An optimization framework is presented to formulate the system transmission energy minimization problem, which can be converted into a weighted one-to-one matching problem. And a practical joint transmission mode with relay selection and power allocation (JMRP) algorithm is developed to solve it. Simulation results show that the proposed algorithm outperforms the existing works in terms of system transmission energy and throughput.

Heterogeneous small cell network is a promising technique in the next generation mobile communications. Many works have been studied in small cells, including resource allocation and interference mitigation, but most studies didn't consider the quality-of-service (QoS) and power consumption. This paper focuses on the power allocation based on non-cooperative scheme to mitigate the interference and increase the energy efficiency in small cells. The delay constraint is introduced in small cells to guarantee the QoS. We reconsider the capacity according to Shannon' capacity formula and bring in the concept of effective capacity. We take the total power consumption of the small cells into account and employ energy efficiency metric to formulate the problem of power allocation. The power allocation problem is modeled as non-cooperative supermodular game, and it is shown to converge to Nash equilibrium, and then it is transformed into a convex optimization problem, which is solved by the multi-agent Q-learning algorithm based on conjecture. The effectiveness of the proposed supermodular game based power allocation is verified by the simulations.