We study robust low-tubal-rank tensor sensing from corrupted linear measurements. We propose a preconditioned subgradient descent (PSGD) method for a factorized$\ell _{1}$-loss formulation, which avoids repeated t-SVD computations during the iterative updates. Under the$\ell _{1}$-RIP assumption, PSGD enjoys a linear convergence rate independent of the tensor condition number. Experiments on both synthetic and real datasets demonstrate significant acceleration and robustness over the standard subgradient-based method.

The rapid development of Internet of Things (IoT) applications has imposed stringent demands on low-latency and energy-efficient task offloading. Although mobile edge computing (MEC) provides nearby computing capabilities, conventional data-driven offloading approaches suffer from redundant transmission and limited scalability under constrained wireless and computational resources. To address this, we propose a general semantic-aware offloading framework that integrates semantic communication, Non-Orthogonal Multiple Access (NOMA), MEC, and user mobility prediction in edge-enabled IoT networks. The offloading user first performs local semantic extraction based on a tunable semantic factor that influences both the local computation cost and the transmitted data volume, and theoretical modeling focuses on adjustable granularity to derive an approximate optimal theoretical bound. The resulting semantic information is then transmitted to the MEC server for task execution. We formulate a joint optimization problem to minimize total user-side energy consumption by optimizing offloading decisions, semantic factors, transmit power, and time-slot scheduling. To solve this, we develop a Mixed-Integer Linear Programming (MILP)-based solution via linearization and piecewise approximation of the original non-convex problem, and we further propose a low-complexity heuristic algorithm for practical feasibility.

The integration of Digital Twin (DT) technology with Mobile Edge Computing (MEC) offers a promising solution for real-time system monitoring and optimization in smart industrial environments. To fully exploit the advantages of their integration, the allocation of MEC resources should carefully consider the application requirements of DT scenarios. This paper focuses on MEC-assisted DT scenarios and proposes a framework to model the dynamic interaction between device states, synchronization demands, and resource allocation, considering the varying synchronization frequency required by different device operational states. Under constraints of communication, computation, and storage resources, we perform joint optimization of the synchronization frequency and task offloading strategies. From both theoretical analysis and practical application perspectives, an optimal strategy based on solving an Integer Linear Programming (ILP) problem and a low-complexity heuristic algorithm are proposed. Extensive simulations in a smart factory scenario demonstrate significant advantages in improving the value of the utility function. This paper provides new insights into the deep integration of MEC and DT technologies and offers valuable references for applications in resource-constrained industrial environments.

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.

1) In[1], the pseudocode ofAlgorithm 1(mobility-aware heuristic (MAH) algorithm) in Section IV-B on page 2814 was inadvertently left out of the paper. The pseudocode ofAlgorithm 1is described below.

As an indispensable part of the future 6G communication system, SIoT (Satellite Internet of Things) plays a vital role in global communication. However, the communication requirements of users, the manufacturing and launching costs of satellites, and the performance coverage of constellations pose significant challenges to the construction of SIoT. To facilitate the evolution of the 5G network into the 6G space-ground integrated network, this paper proposes a multi-area on-demand classification (MOC) constellation design based on CubeSats. Firstly, the area of interest is classified into two categories: the area with cellular base stations and the area without cellular base stations. Secondly, three coverage, communication quality, and cost models are established. The coverage model establishes the initial configuration of the constellation and determines the value for evaluating coverage. The communication model determines the evaluation values for communication quality. The cost model determines the evaluation values for cost. Finally, the multi-objective Manta ray foraging optimization algorithm (MOMRFO) optimizes the three evaluation values to obtain the Pareto front for the best configuration constellation. Based on actual data from the cellular base station, the simulation results have demonstrated the MOC constellation scheme’s feasibility and effectiveness for future SIoT deployment.

Edge caching is a widely adopted technique for improving network performance and user experience. To optimize its benefits, researchers are exploring the use of recommendation systems, which can leverage advanced algorithms to determine which content should be cached at the edge, leading to lower latency, reduced bandwidth usage, and ultimately better network and service management. The majority of existing works, on the other hand, are based on independently and identically distributed request patterns. In this work, the problem of joint caching and recommendation policy for long-term user request behavior was studied, which is more realistic. Specifically, the continuous request behavior of users was regarded as being relevant and was modeled as an Absorbing Markov Chain. The goal was to minimize the expected content delivery cost over multiple viewing sessions while meeting the requirements for expected value on quality of recommendation. The optimization problem was formulated and transformed into a mixed-integer programming (MIP) problem, which provides an optimal solution (JCRP). Meanwhile, a heuristic algorithm with low computational complexity that is more friendly to network resources has been proposed (LvJCR). Simulation results show that the proposed algorithms have advantages in cost minimization over long sessions.

With the rapid development of 5G and the Internet of Things (IoT), the cloud-based access mode is applied more and more in IoT scenarios. The emerging IoT applications put forward higher requirements on energy consumption and processing capacity of the network, as well as more strict privacy restrictions. The emergence of mobile-edge computing (MEC) solves the aforementioned user experience problems, which greatly improves execution efficiency and saves a lot of energy. However, existing studies pay more attention to issues, such as energy consumption and delay, but ignore the privacy and imbalanced server load problems that MEC may cause. This article made bandwidth resource allocation and task offloading decisions for multiple users via joint optimization, provided the privacy requirements of IoT terminals and load balancing requirements are satisfied. The objective was to minimize the total energy consumption of all terminal devices. By modeling the problem as an integer linear programming (ILP) problem, the optimal solution is obtained. Considering the high computational complexity of solving ILP problems, a more practical heuristic algorithm was proposed. The simulation results showed the effectiveness of the proposed optimal and heuristic schemes in reducing energy consumption and demonstrated the importance of privacy protection.

With the widely use of smart devices and rapid development of communication technologies, it becomes easier for base stations to obtain the context information of users. The context information can be utilized to optimize system resource allocation. This paper focuses on context-aware unicast cellular networks. Assuming that some channel state information (CSI) can be predicted based on user context such as user location and moving pattern, joint power control and transmission scheduling is applied to minimize the transmission energy consumption. By reducing the energy minimization problem to a semi-assignment problem, our proposed algorithm can find the optimal solution in polynomial time. Simulation results show that the proposed context-aware scheme outperforms the traditional round-robin scheduler and opportunistic scheduler, which do not consider the feature of context-awareness.

This paper investigates the linear network coding problem for erasure broadcast channel with user feedback. An innovative linear network code is shown to be uniformly optimal for the system. In general, determining the existence of innovative packets is proved to be NP-complete. When the finite field size is larger than the number of users, innovative packets always exist and the problem of finding an innovative encoding vector with smallest Hamming weight is considered. The corresponding decision problem is shown to be NP-complete. Optimal and approximate network coding algorithms for maximizing the sparsity of encoding vectors are designed.

Energy saving is important for many wireless devices. In a multi-hop wireless network with multiple sessions, XOR network coding can be applied to opposite traffic flows so as to reduce the number of packet transmissions, which in turn reduce transmission energy. Such a change in packet forwarding, however, impacts the design of traffic routing. Traditional routing algorithms, which typically aim at finding shortest paths between source and destination nodes, may no longer work well. In this paper, an iterative routing algorithm is proposed, which favors paths that can provide more pair-wise XOR network coding opportunities. Simulation results show that this algorithm integrates well with the XOR forwarding method and can reduce energy cost significantly when compared with traditional shortest-path routing, with and without network coding.