Reconfigurable intelligent surface (RIS) is a technology that can intelligently control the signal transmission link to enhance the signal transmission quality. Rate splitting multiple access (RSMA) technology can effectively restrain the same channel interference between users and achieve better system performance by flexibly controlling the rate allocation strategy and beamforming strategy. In this article, we combine RSMA with RIS and propose an energy efficiency optimization algorithm for 6G multicell communication system. We first formulate an optimization problem which is to maximize energy efficiency of the system by jointly optimizing beamforming vectors of base stations (BSs), phase shifts matrix of RISs, and rate allocation matrix of users. Then, in order to solve this nonconvex optimization problem, we decouple the original problem into two subproblems: 1) beamforming optimization and 2) phase shifts optimization. For the former, we use the successive convex approximation (SCA) method to get the solution, and for the latter, we adopt semidefinite relaxation and exterior point methods to solve. In addition, we analyze the complexity of the proposed algorithm. Simulation result shows that compared with space division multiple access (SDMA) and nonorthogonal multiple access (NOMA), the proposed mechanism in this article can significantly improve system energy efficiency.

Low Earth orbit (LEO) satellite networks are characterized by rapid topological changes, numerous network nodes and varying states of node resource constraints, which have resulted in traditional routing algorithms no longer being suitable for LEO satellite network routing. Therefore, this paper proposes an inductive learning architecture based on Graph Sample and Aggregate (GraphSAGE), which can significantly reduce the number of topology nodes to be trained, thereby reducing the computational complexity of the nodes. Then deep reinforcement learning (DRL) is employed for the continuous learning optimization of routing algorithms, and its generalization is improved by selecting GraphSAGE to construct the DRL agent. In the proposed graph neural-network-based routing optimization algorithm for LEO satellite networks, each Deep Q-Network (DQN) agent independently generates the hidden states of the nodes through the GraphSAGE model and uses them as inputs to the DRL model to make routing decisions. After a simulation and comparison, the proposed algorithm not only improves the overall network throughput, but also reduces the average end-to-end delay. The average throughput of the proposed algorithm increases by 29.47% and 18.42% compared to that of Dijkstra and the DQN, respectively. The average end-to-end delay is reduced by 39.76% and 15.29%, respectively, and can also adapt to changing topologies.

Sharing is undoubtedly one of the most important features of 6G networks, and blockchain can provide an extended trust-as-a-service (TAAS) distributed sharing solution for 6G networks. However, as a special distributed technology system, blockchain naturally needs to face the “impossible triangle” problem: in the case of ensuring security and decentralization, scalability will inevitably become the Achilles heel of the blockchain system. This article will design a high-throughput blockchain system for 6G networks to achieve trusted sharing and efficient scheduling of network infrastructure, assist future networks in integrating an open Internet architecture, and realize the combination of openness and distributed control. We adopt a shard blockchain design and incorporate digital twins and federated learning; formulate an optimization model for maximizing the throughput of the blockchain network by getting the optimal number of shards, also analyzing other factors affecting the throughput such as service distribution; propose a security performance analysis model for the blockchain network to describe the measures taken against the Byzantine attacks on the network. Analysis and simulation results show that the transaction throughput of the proposed method can reach more than 30 times larger than that of a nonsharding scheme. They also show that when one-third of the nodes in the system are attacked, the consensus of the system is hardly affected; even if the number of nodes being attacked at the same time reaches half of the total number of nodes, the probability of the occurrence of failed shards is still less than $10^{-4}$ , and the system still has good survivability.

In smart factories, Automated Guided Vehicles (AGVs) are usually used for patrol service, which can carry out such tasks as detecting abnormal device status, transporting material and simultaneously making their own path plan. Hence, these tasks require intensive computational power and real-time response and it is difficult to complete them by a single node. Therefore, in this paper, we propose a joint optimization method of task offloading and resource scheduling of multiple AGVs for smart factory patrol service. The goal is to minimize the overall energy consumption of the AGVs, by jointly using MEC and D2D offloading, while meeting the constraints of the delay, power, available computing capacity and bandwidth of AGVs. We first analyze the impact of information collection and cooperation tasks on transmission delay and formulate an energy consumption minimization problem. Then, we propose a two-step algorithm to get the solutions of the optimization problem. For resource allocation sub-problem, we design an efficient algorithm for power and bandwidth allocation by using the Karush-Kuhn-Tucker (KKT) condition based on the properties of convex bandwidth. To solve the nonlinear task offloading problem, we transform it into a linear mixed integer problem by introducing slack variables and use Gurobi for the solution. Simulation results show that compared with other methods, the proposed algorithm has good performances on convergence speed and energy conservation of AGVs in the process of patrol tasks under different scenarios.

Wireless mesh networks (WMN) promise to be an effective way to solve the “last mile” access problem on the Internet of Things (IoT) and the key to next-generation wireless networks. The current routing algorithms of WMN are difficult to adapt to complex environments and guarantee the reliable transmission of services. Therefore, this paper proposes a reliable routing algorithm that combines the improved breadth-first search and a graph neural network, namely GraphSAGE. The algorithm consists of two parts: (1) A multi-path routing algorithm based on the improved breadth-first search. This algorithm can continuously iterate link information based on network topology and output all shortest paths. (2) A GraphSAGE-based performance optimization algorithm. This algorithm creates a method to generate network labels for supervised training of GraphSAGE. Then, the network labels and GraphSAGE are used to learn graph features to obtain the value of network performance for each shortest path. Finally, the path with the best network performance is selected for data transmission. Simulation results show that in the face of complex environments, the proposed algorithm can effectively alleviate network congestion, improve throughput, and reduce end-to-end delay and packet loss rate compared with the traditional shortest-path routing algorithm and the Equal-Cost Multi-Path routing (ECMP).