Currently, the most widely used protocol for the transportation layer of computer networks for reliable transportation is the Transmission Control Protocol (TCP). However, TCP has some problems such as high handshake delay, head-of-line (HOL) blocking, and so on. To solve these problems, Google proposed the Quick User Datagram Protocol Internet Connection (QUIC) protocol, which supports 0-1 round-trip time (RTT) handshake, a congestion control algorithm configuration in user mode. So far, the QUIC protocol has been integrated with traditional congestion control algorithms, which are not efficient in numerous scenarios. To solve this problem, we propose an efficient congestion control mechanism on the basis of deep reinforcement learning (DRL), i.e., proximal bandwidth-delay quick optimization (PBQ) for QUIC, which combines traditional bottleneck bandwidth and round-trip propagation time (BBR) with proximal policy optimization (PPO). In PBQ, the PPO agent outputs the congestion window (CWnd) and improves itself according to network state, and the BBR specifies the pacing rate of the client. Then, we apply the presented PBQ to QUIC and form a new version of QUIC, i.e., PBQ-enhanced QUIC. The experimental results show that the proposed PBQ-enhanced QUIC achieves much better performance in both throughput and RTT than existing popular versions of QUIC, such as QUIC with Cubic and QUIC with BBR.

This paper focuses on the unmanned aerial vehicles (UAVs)-aided mobile networks, where multiple ground mobile users (GMUs) desire to upload data to a UAV. In order to maximize the total amount of data that can be uploaded, we formulate an optimization problem to maximize the uplink throughput by optimizing the UAV’s trajectory, under the constraints of the available energy of the UAV and the quality of service (QoS) of GMUs. To solve the non-convex problem, we propose a deep Q-network (DQN)-based method, in which we employ the iterative updating process and the Experience Relay (ER) method to reduce the negative effects sequence correlation on the training results, and the $\epsilon$-greedy method is applied to balance the exploration and exploitation, for achieving the better estimations of the environment and also taking better actions. Different from previous works, the mobility of the GMUs is taken into account in this work, which is more general and closer to practice. Simulation results show that the proposed DQN-based method outperforms a traditional Q-Learning-based one in terms of both convergence and network throughput. Moreover, the larger battery capacity the UAV has, the higher uplink throughput can be achieved.

This paper investigates the outage performance of simultaneous wireless information and power transfer (SWIPT)-enabled relay networks with the decode-and-forward relaying protocol, where the effect of the energy triggering threshold at the relay on the system performance is considered. The closed-form expressions of the system outage probability and throughput are derived in Rician channel fading. Monte Carlo Simulation method is used to verify the accuracy of the derived closed-form expressions. The effects of some system parameters on the system performances are discussed via simulations, which show that the system outage probability increases with the increase of the minimum transmission rate required by the users and also decreases with the increase of the energy conversion efficiency. Besides, the system throughput increaseswith the increment of the transmit power of the source node, as well as the energy conversion efficiency. Additionally, the outage performance of the system with the equal two-hop distance is better than that of the system with unequal two-hop distance.

With the widespread deployment of various emerging intelligent applications, information timeliness is crucial for intelligent decision-making in vehicular networks, where vehicular edge computing (VEC) has become an important paradigm to enhance computing capabilities by offloading tasks to edge nodes. To promote the information timeliness in VEC, an optimization problem is formulated to minimize the age of information (AoI) by jointly optimizing task offloading and subcarrier allocation. Due to the time-varying channel and the coupling of the continuous and discrete optimization variables, the problem exhibits non-convexity, which is difficult to solve using traditional mathematical optimization methods. To efficiently tackle this challenge, we employ a hybrid proximal policy optimization (HPPO)-based deep reinforcement learning (DRL) method by designing the mixed action space involving both continuous and discrete variables. Moreover, an action masking mechanism is designed to filter out invalid actions in the action space caused by limitations in the effective communication distance between vehicles. As a result, a mask-assisted HPPO (MHPPO) method is proposed by integrating the action masking mechanism into the HPPO. Simulation results show that the proposed MHPPO method achieves an approximately 28.9% reduction in AoI compared with the HPPO method and about a 23% reduction compared with the mask-assisted deep deterministic policy gradient (MDDPG).

Accurate prediction of cloud resource utilization is critical. It helps improve service quality while avoiding resource waste and shortages. However, the time series of resource usage in cloud computing systems often exhibit multidimensionality, nonlinearity, and high volatility, making the high-precision prediction of resource utilization a complex and challenging task. At present, cloud computing resource prediction methods include traditional statistical models, hybrid approaches combining machine learning and classical models, and deep learning techniques. Traditional statistical methods struggle with nonlinear predictions, hybrid methods face challenges in feature extraction and long-term dependencies, and deep learning methods incur high computational costs. The above methods are insufficient to achieve high-precision resource prediction in cloud computing systems. Therefore, we propose a new time series prediction model, called SDVformer, which is based on the Informer model by integrating the Savitzky-Golay (SG) filters, a novel Discrete-Variation Self-Attention (DVSA) mechanism, and a type-aware mixture of experts (T-MOE) framework. The SG filter is designed to reduce noise and enhance the feature representation of input data. The DVSA mechanism is proposed to optimize the selection of critical features to reduce computational complexity. The T-MOE framework is designed to adjust the model structure based on different resource characteristics, thereby improving prediction accuracy and adaptability. Experimental results show that our proposed SDVformer significantly outperforms baseline models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Informer in terms of prediction precision, on both the Alibaba public dataset and the dataset collected by Beijing Jiaotong University (BJTU). Particularly compared with the Informer model, the average Mean Squared Error (MSE) of SDVformer decreases by about 80%, fully demonstrating its advantages in complex time series prediction tasks in cloud computing systems.

This article investigates a relay-assisted wireless powered communication network (WPCN), where the access point (AP) inspires the auxiliary nodes to participate together in charging the sensor, and then the sensor uses its harvested energy to send status update packets to the AP. An incentive mechanism is designed to overcome the selfishness of the auxiliary node. In order to further improve the system performance, we establish a Stackelberg game to model the efficient cooperation between the AP–sensor pair and auxiliary node. Specifically, we formulate two utility functions for the AP–sensor pair and the auxiliary node, and then formulate two maximization problems respectively. As the former problem is non-convex, we transform it into a convex problem by introducing an extra slack variable, and then by using the Lagrangian method, we obtain the optimal solution with closed-form expressions. Numerical experiments show that the larger the transmit power of the AP, the smaller the age of information (AoI) of the AP–sensor pair and the less the influence of the location of the auxiliary node on AoI. In addition, when the distance between the AP and the sensor node exceeds a certain threshold, employing the relay can achieve better AoI performance than non-relaying systems.