The emergence of new applications has increased the demand of mobile users for edge computing. Therefore, the optimization of base station load balancing and task offloading is particularly important. In this paper, a two-tier heterogeneous mobile edge computing (MEC) system based on Stackelberg Game (SG) is studied. The introduction of SG can improve user association flexibility while reducing base station load. The introduction of network slicing can classify users according to different types, which increases the universality of the proposed framework. To solve the above problem, we map an optimization problem of efficiency, computation delay and computation energy consumption. Due to the non-convexity of the optimization problem, the Multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm based on SG is proposed in this paper. The gradient descent strategy can converge faster and obtain the optimal solution of user association policy and unload policy. Through simulation, it is verified that the proposed scheme can obtain lower computation delay and computation energy consumption on the basis of solving the problem of base station overload.

The evolution from fifth-generation mobile communications (5G) to beyond 5G (B5G) will lead to more ubiquitous and smarter paradigms in the Internet of Things (IoT). Communication will shift from the classical information theory to a semantic communication (SemCom) paradigm driven by artificial intelligence (AI) to enhance the capacity and optimize resources. Image SemCom (ISC) will empower IoT applications, such as drone image acquisition. However, ISC requires suitable devices and sufficient computing resources to support complex neural network models, posing a significant challenge. To address this, we propose a federated semantic feature distillation (FedSFD) architecture to improve the global performance of ISC by combining federated learning (FL) and feature distillation (FD) for the feature knowledge transfer. First, lightweight IoT device models in the edge group and the powerful server model alternately minimize losses to update parameters. After learning middle-layer features of all the edge models, the server can guide the individual device models. Second, we incorporate the information bottleneck (IB) concept into the design of loss functions to balance compression and reconstruction. Third, focusing on the tradeoff between the local training and knowledge interaction, FedSFD achieves image semantic reconstruction without sharing the private data, ensuring personalization and privacy protection in the FL framework. Finally, compared to the baseline, the simulation experiments show that the proposed approach achieves better ISC reconstruction and noise robustness during group ISC.

In recent years, there has been significant attention and development in the Tactile Internet (TI) as a new type of network that supports typical applications such as remote healthcare and virtual reality. However, the demand for ultra-low latency and the inflexibility of traditional network architecture have presented serious challenges. In this paper, we propose a utility function for TI based on 5G network function virtualization (NFV) technology to evaluate network performance. The utility function considers the transmission of haptic and video data flows, as well as their latency and cost. We then formulate a network optimization problem with the utility function as the objective, strictly constraining latency and cost. To address this problem, we design two algorithms: the VNF deployment and routing of haptic and video flows (DRHV) algorithm tackles the VNF deployment and path selection issue, while the deep deterministic policy gradient(DDPG)-based traffic scheduling (DTS) algorithm allocates traffic resources and saves costs. Simulation results demonstrate that our algorithms achieve excellent optimization performance under the given constraints, and parameter changes align with the expected relationships and trends.

Nowadays, Industrial Internet of Things (IIoT) has been considered as a promising technology to provide data acquisition for Industry 4.0. However, packet compression in IIoT still exists multi-protocol heterogeneity, lack of robustness, compression ratio to be improved and some other issues. Inspired by the above reasons, in this paper we investigate a novel deep packet compression for IIoT in order to reduce redundant data while maintaining high robustness. For one thing, facing to protocol heterogeneity, we propose Universal Dynamic Ethernet Header Compression (UDEHC) to embrace five major protocols. For another, we provide Generalized Deduplication using Static Dictionary (GDSD) scheme to obtain 16-20% compression improvement and high reliability. Extensive simulation results are provided to validate the theoretical analysis and demonstrate the effectiveness in wireless circumstance.