This paper explores the transformative integration of deep learning applications in the
deployment of Wireless Sensor Networks (WSNs). As WSNs continue to play a pivotal role in …

A canonical wireless communication system consists of a transmitter and a receiver. The
information bit stream is transmitted after coding, modulation, and pulse shaping. Due to the …

Timely and accurate cellular traffic prediction is difficult to achieve due to the complex spatial-
temporal characteristics of cellular traffic. The latest approaches mainly aim to model local …

The convenience of availing quality services at affordable costs anytime and anywhere
makes mobile technology very popular among users. Due to this popularity, there has been …

With the rapid development of mobile cellular technologies and the increasing popularity of
mobile and Internet of Things (IoT) devices, timely mobile traffic forecasting with high …

Network slicing promises to provision diversified services with distinct requirements in one
infrastructure. Deep reinforcement learning (eg, deep Q-learning, DQL) is assumed to be an …

Benefiting from the widely deployed LTE infrastructures, 5G wireless networks are becoming
a critical enabler for the emerging V2X communications. However, existing LTE networks …

Despite remarkable advances, current augmented and virtual reality (AR/VR) applications
are a largely individual and local experience. Interconnected AR/VR, where participants can …

In this work, we consider the use of model-driven deep learning techniques for massive
multiple-input multiple-output (MIMO) detection. Compared with conventional MIMO …

摘要第5 代移动通信(5G) 技术将为移动互联网的快速发展提供无所不在的基础性业务能力,
在满足未来10 年移动互联网流量增加1000 倍发展需求的同时, 为全行业 …