With the rapid development of Internet of Things (IoT) technology, billions of Mobile Devices (MDs) are putting a massive burden on limited radio resources. Mobile edge computing (MEC) can save MDs’ energy consumption and relieve network pressure by offloading their tasks to edge servers. Compared with cloud servers, edge servers are closer to the users but have less storage capacity. The heterogeneous edge-cloud computing paradigm recently developed combines the advantages of both. In this architecture, edge servers provide powerful computing power, while the cloud provides sufficient storage capacity. Since many IoT devices in such a scenario are mobile, it is more practical to consider user mobility when optimizing the network. Besides, properly utilizing the mobility context can be beneficial for improving network performance as well. We focused on the edge-cloud collaborative computing scheme, as well as the joint optimization of power control, transmission scheduling, and offloading decisions among MDs and edge servers so as to minimize the total energy consumption of all MDs while considering user mobility. The problem was modeled as a Mixed-Integer Programming (MIP) optimization problem that provided the optimal solution. We also proposed a low-complexity heuristic algorithm. Simulations showed that the proposed edge-cloud collaborative scheme could significantly reduce the energy consumption of MDs compared with other schemes and demonstrated the importance of considering mobility awareness.

With the advent of sixth-generation mobile communication technology (6G) and the emergence of future application scenarios such as Metaverse and digital twin (DT), the existing traditional wireless communication technology based on Shannon’s information theory has not been able to meet the increasing demand for data transmission. Semantic communications (SemCom), which greatly reduces the amount of information transmitted and alleviates the burden of communication by transmitting the meaning behind the information, has been considered a promising 6G enabler. SemCom’s resource allocation is critical to the system’s reliability and effectiveness. Compared to traditional wireless communication systems, the system architecture and performance metrics of SemCom have undergone significant changes, making it difficult for traditional resource allocation strategies to adapt well to this new architecture. However, the issue remains unresolved and inadequately researched. In order to provide researchers with valuable insight to promote follow-up research, this paper reviews the latest research results in recent years and presents an overview of research progress in the field of resource allocation in wireless SemCom.

As the Internet of Things (IoT) expands globally, the challenge of signal transmission in remote regions without traditional communication infrastructure becomes prominent. An effective solution involves integrating aerial, terrestrial, and space components to form a Space–Air–Ground Integrated Network (SAGIN). This paper discusses an uplink signal scenario in which various types of data collection sensors as IoT devices use Unmanned Aerial Vehicles (UAVs) as relays to forward signals to low-Earth-orbit satellites. Considering the fairness of resource allocation among IoT devices of the same category, our goal is to maximize the minimum uplink channel capacity for each category of IoT devices, which is a multi-objective optimization problem. Specifically, the variables include the deployment locations of UAVs, bandwidth allocation ratios, and the association between UAVs and IoT devices. To address this problem, we propose a multi-objective evolutionary algorithm that ensures fair resource distribution among multiple parties. The algorithm is validated in eight different scenario settings and compared with various traditional multi-objective optimization algorithms. The experimental results demonstrate that the proposed algorithm can achieve higher-quality Pareto fronts (PFs) and better convergence, indicating more equitable resource allocation and improved algorithmic effectiveness in addressing this issue. Moreover, these pre-prepared, high-quality solutions from PFs provide adaptability to varying requirements in signal collection scenarios.

Mobility prediction is a powerful tool for network operators to optimize network performance. From cell level, if network operators know the cells to which the users will be connected in advance, wireless resources can be pre-allocated to improve network performance and better user experience can be provided in location-based services. Many next-cell prediction models and methods have been suggested and implemented. This article is devoted to next-cell prediction (cell level mobility prediction) in cellular networks, and provides a thorough survey of the prediction schemes and applications. Particularly, a two-level classification methodology was proposed and applied. We first divided the prediction schemes into three categories based on the mobility data used for prediction, i.e. Current Movement State based Approaches (CMSA), Historical Movement Pattern based Approaches (HMPA), and HybriD Approaches (HDA). Prediction schemes in each category were further classified based on the used prediction methods. The typical application scenarios were introduced as well, including handover management, resource allocation, etc. Finally, current challenges and potential trends in the near future were further discussed.