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As a novel paradigm, Vehicular Edge Computing (VEC) can effectively support computation-intensive or delay-sensitive applications in the Internet of Vehicles era. Computation offloading and resource management strategies are key technologies that directly determine the system cost in VEC networks. However, due to vehicle mobility and stochastic arrival computation tasks, designing an optimal offloading and resource allocation policy is extremely challenging. To solve this issue, a deep reinforcement learning-based intelligent offloading and power allocation scheme is proposed for minimizing the total delay cost and energy consumption in dynamic heterogeneous VEC networks. Specifically, we first construct an end-edge-cloud offloading model in a bidirectional road scenario, taking into account stochastic task arrival, time-varying channel conditions, and vehicle mobility. With the objective of minimizing the long-term total cost composed of the energy consumption and task delay, the Markov Decision Process (MDP) can be employed to solve such optimization problems. Moreover, considering the high-dimensional continuity of the action space and the dynamics of task generation, we propose a deep deterministic policy gradient-based adaptive computation offloading and power allocation (DDPG-ACOPA) algorithm to solve the formulated MDP problem. Extensive simulation results demonstrate that the proposed DDPG-ACOPA algorithm performs better in the dynamic heterogeneous VEC environment, significantly outperforming the other four baseline schemes.

Channel estimation is considered a fundamental task in achieving reconfigurable intelligent surface (RIS)-aided communication systems. However, the location distribution of users in a cell is nonuniform, which leads to worse channel estimation performance for a single neural network. In this letter, we propose two federated learning (FL)-based hierarchical networks to improve channel estimation performance. Specifically, a hierarchical neural network (HNet) is proposed to enhance the channel estimation accuracy, which can perform different channel feature extraction and mapping tasks for users in different regions. Furthermore, a hierarchical residual neural network (HReNet) is presented to reduce the communication overhead during model training. Simulation results reveal that the FL-based HNet and HReNet schemes yield better channel estimation performance when users are nonuniformly distributed.

Dense deployment of small base stations (SBSs) powered by on-grid and harvested renewable energy has been spotlighted as a promising solution to implement grid energy conservation in a hybrid-energy heterogeneous cellular network (HCN), which caters for the rapidly increasing demand of multimedia communication services. However, energy harvesting is inherently opportunistic, and battery leakage may deteriorate the network availability. In this article, we propose a power-aware user association and renewable energy configuration scheme for optimizing on-grid energy in HCN powered by hybrid energy with battery leakage. In this scheme, we present a power-aware user association rule that can get dynamically an availability set of SBS by predicting transmit power required by other users in each time slot (TS). Meanwhile, we exploit the Lyapunov optimization technique to control energy consumption of each user and design the heuristic algorithms to balance energy utilization for renewable energy configuration in different TSs. Numerical simulation results are provided to demonstrate the feasibility of the proposed scheme. It is shown that the proposed scheme outperforms the existing schemes in optimizing the on-grid energy consumption.

Pt/CeO2catalysts were modified with non-thermal plasma (dielectric barrier discharge) with different discharge power to investigate the correlation of discharge power and catalyst performance for toluene oxidation. Two volcano peaks (Pt/CeO2-P2 (4.31 W) and Pt/CeO2-P6 (9.46 W)) appeared in the activity evaluation curve, whose catalytic activities were dramatically improved after plasma treatment. The results of XRD, BET, HRTEM, XPS and H2-TPR showed that there were obvious effects on the Pt particle size and CeO2-rod length, oxygen vacancy concentration and Pt/CeO2reducibility by plasma treatment. The Pt atom (slope = 3.0309, R2= 0.8301) was more impacted by the plasma than oxygen vacancies (slope = 2.5225, R2= 0.9130) when the discharge power was between 3.39 and 10.18 W. In addition, the Pt/CeO2-P6 catalyst (with plasma treatment) still maintained good activity and stability and excellent water resistance. Plasma discharge power could be one of the important factors for catalyst performance optimization of plasma applied in catalyst modification. Therefore, the optimization of the Pt/CeO2catalyst with the highest catalytic activity would be modulated by discharge power.