With the widespread adoption of various mobile applications, the amount of traffic in wireless networks is growing at an exponential rate, which exerts a great burden on mobile core networks and backhaul links. Mobile edge caching, which enables mobile edges with cache storages, is a promising solution to alleviate this problem. In this paper, we aim to review the state of the art of mobile edge caching. We first present an overview of mobile edge caching and its advantages. We then discuss the locations where mobile edge caching can be realized in the network. We also analyze different caching criteria and their respective effects on the caching performances. Moreover, we compare several caching schemes and discuss their pros and cons. We further present a detailed and in-depth discussion on the caching process, which can be delineated into four phases including content request, exploration, delivery and update. For each phase, we identify different issues and review related works in addressing these issues. Finally, we present a number of challenges faced by current mobile edge caching architectures and techniques for further studies.

Predicting traffic flow plays an important role in reducing traffic congestion and improving transportation efficiency for smart cities. Traffic Flow Prediction (TFP) in the smart city requires efficient models, highly reliable networks, and data privacy. As traffic data, traffic trajectory can be transformed into a graph representation, so as to mine the spatio-temporal information of the graph for TFP. However, most existing work adopt a central training mode where the privacy problem brought by the distributed traffic data is not considered. In this paper, we propose a Federated Deep Learning based on the Spatial-Temporal Long and Short-Term Networks (FedSTN) algorithm to predict traffic flow by utilizing observed historical traffic data. In FedSTN, each local TFP model deployed in an edge computing server includes three main components, namely Recurrent Long-term Capture Network (RLCN) module, Attentive Mechanism Federated Network (AMFN) module, and Semantic Capture Network (SCN) module. RLCN can capture the long-term spatial-temporal information in each area. AMFN shares short-term spatio-temporal hidden information when it trains its local TFP model by the additive homomorphic encryption approach based on Vertical Federated Learning (VFL). We employ SCN to capture semantic features such as irregular non-Euclidean connections and Point of Interest (POI). Compared with existing baselines, several simulations are conducted on practical data sets and the results prove the effectiveness of our algorithm.

With emergence of Internet of Things (IoTs), wireless traffic has grown dramatically, posing severe strain on core network and backhaul bandwidth. Proactive caching in mobile edge computing systems can not only efficiently mitigate the traffic congestion and relieve burden of backhaul, but also can reduce the service latency for end devices. However, proactive caching heavily relies on the prediction accuracy of content popularity, which is typically unknown and change over time. In this work, we propose an online proactive caching scheme based on Bidirectional Deep Recurrent Neural Network (BRNN) model to predict time-series content requests and update edge caching accordingly. Specifically, on the first layer, a one-dimensional Convolution Neural Network (CNN) is devised to reduce the computational costs. Then, BRNN is employed to predict time-varying requests from users. Afterwards, a fully connected neural network (FCNN) is harnessed to learn and sample predicts from the BRNN. Finally, we conduct experiments based on real datasets, which demonstrate that the proposed approach can achieve considerably high prediction accuracy and significantly improve content hit rate of end devices.

The rapid emergence of satellite systems introduces unprecedented interference challenges to both existing satellite networks and Terrestrial Networks (TNs), necessitating innovative strategies to efficiently manage spectrum resources amid heightened competition. Traditional interference management methods fail to address the unique challenges facing satellite systems. These challenges include higher propagation delays caused by the high altitude of Low Earth Orbit (LEO) satellites, increased Doppler shifts due to their high speeds, atmospheric attenuations affecting LEO satellite-TN links, and limited processing capacity in satellite systems. This article provides a comprehensive exploration of interference in LEO satellite-Integrated Terrestrial Networks (LITNets), encompassing various types of interference, including Inter-Beam Interference (IBI), which occurs between different beams of the same satellites; Inter-Satellite Interference (ISI), which arises between different satellites; and LEO satellite-Terrestrial infrastructure Interference (LTI). Moreover, it outlines strategies for interference management and reviews current mitigation methods. Finally, the article concludes by discussing the research challenges and proposing future directions for enhancing spectrum efficiency and interference management in LITNets.

With proliferation of smart devices and wireless applications, the recent few years have witnessed data surge. These massive data needs to be stored, transmitted, and processed in time to exploit their value for decision making. Conventional cloud computing requires transmission of massive amount of data in and out of core network, which can lead to longer service latency and potential traffic congestion. As a new platform, mobile edge computing (MEC) moves computation and storage resources to edge network in proximity to the data source. With MEC, data can be processed locally, and thus mitigate issues of latency and congestion. However, it is very challenging to reap the benefits of MEC everywhere due to geographic constraints, expensive deployment cost, and immoveable base stations. Because of easy deployment and high mobility of unmanned aerial vehicles (UAVs), air-ground integrated mobile edge networks (AGMEN) is proposed, where UAVs are employed to assist the MEC network. Such an AGMEN expects to provide MEC services ubiquitously and reliably. In this article, we first introduce the characteristics and components of UAV. Then, we will review the applications, key challenges, and current research technologies of AGMEN, from perspectives of communication, computation, and caching, respectively. Finally, we will discuss some essential research directions for AGMEN.

Network slicing is one of the emerging technologies allowing resource sharing among different network entities in 5G networks. It enables delivering smart, critical, and multi-services with distinctive requirements transiting from network as an infrastructure to network as a service setup. Although its advantages, it is facing several challenges raised from isolation and resource sharing among services leading to security issues. Security is a critical problem for network slicing as slices serving customized services with different requirements may also have different security levels and policies. Thus, considering the impact of these security issues on network slices is required when defining and designing security protocols. Addressing these challenges is necessary to protect users’ security and privacy while maintaining the required performance and QoS. Most of the existing works covered only one or more aspects of the network slicing including, architecture, taxonomy, challenges, security issues, attacks classification, possible solutions, and future scope. In this paper, we extensively investigated all these aspects and others, we analyzed how the security can be ensured inside and outside of the network slices with resource isolation, machine learning, and cryptography with an E2E security. We presented a deep review of the security issues threatening the network slicing and how to mitigate them over a multi-domain infrastructure in 5G networks. we evaluated the performance of some of these solutions in preventing malicious attacks through experiments using Open Air Interface.