Digital Communications and Networks最新文献

The prediction for Multivariate Time Series (MTS) explores the interrelationships among variables at historical moments, extracts their relevant characteristics, and is widely used in finance, weather, complex industries and other fields. Furthermore, it is important to construct a digital twin system. However, existing methods do not take full advantage of the potential properties of variables, which results in poor predicted accuracy. In this paper, we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network (AFSTGCN). First, to address the problem of the unknown spatial-temporal structure, we construct the Adaptive Fused Spatial-Temporal Graph (AFSTG) layer. Specifically, we fuse the spatial-temporal graph based on the interrelationship of spatial graphs. Simultaneously, we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods. Subsequently, to overcome the insufficient extraction of disordered correlation features, we construct the Adaptive Fused Spatial-Temporal Graph Convolutional (AFSTGC) module. The module forces the reordering of disordered temporal, spatial and spatial-temporal dependencies into rule-like data. AFSTGCN dynamically and synchronously acquires potential temporal, spatial and spatial-temporal correlations, thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy. Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

Benefiting from the development of Federated Learning (FL) and distributed communication systems, large-scale intelligent applications become possible. Distributed devices not only provide adequate training data, but also cause privacy leakage and energy consumption. How to optimize the energy consumption in distributed communication systems, while ensuring the privacy of users and model accuracy, has become an urgent challenge. In this paper, we define the FL as a 3-layer architecture including users, agents and server. In order to find a balance among model training accuracy, privacy-preserving effect, and energy consumption, we design the training process of FL as game models. We use an extensive game tree to analyze the key elements that influence the players’ decisions in the single game, and then find the incentive mechanism that meet the social norms through the repeated game. The experimental results show that the Nash equilibrium we obtained satisfies the laws of reality, and the proposed incentive mechanism can also promote users to submit high-quality data in FL. Following the multiple rounds of play, the incentive mechanism can help all players find the optimal strategies for energy, privacy, and accuracy of FL in distributed communication systems.

In this paper, we explore a distributed collaborative caching and computing model to support the distribution of adaptive bit rate video streaming. The aim is to reduce the average initial buffer delay and improve the quality of user experience. Considering the difference between global and local video popularities and the time-varying characteristics of video popularity, a two-stage caching scheme is proposed to push popular videos closer to users and minimize the average initial buffer delay. Based on both long-term content popularity and short-term content popularity, the proposed caching solution is decouple into the proactive cache stage and the cache update stage. In the proactive cache stage, we develop a proactive cache placement algorithm that can be executed in an off-peak period. In the cache update stage, we propose a reactive cache update algorithm to update the existing cache policy to minimize the buffer delay. Simulation results verify that the proposed caching algorithms can reduce the initial buffer delay efficiently.