The radio stripe (RS) is an emerging architecture for cell-free massive multiple-input-multiple-output (CFm-MIMO) communication systems, where access points (APs) are sequentially connected via one optical-fiber link. The traditional precoded signal vector quantization approach is designed for star-topology MIMO architecture with a large demand for fronthaul capacity thereby it is not suitable for RS architecture. To address this challenge, a joint precoding matrix optimization and pruning approach is proposed to reduce the fronthaul traffic in the RS architecture. Central to our strategy is optimizing and pruning the precoding coefficient matrix (PCM), which is decomposed from the stationary point of the weighted sum-rate maximization problem. An iterative algorithm with closed-form iteration expression is developed for the joint optimization and pruning of PCMs. Note that the size of PCM only depends on the number of data streams rather than the antenna number. In contrast to transmitting different precoding matrices to different APs, the required PCMs for different APs are proved to be the same, which illustrates that only one PCM needs to be transmitted in the RS. Simulation results demonstrate that the proposed approach can reduce the fronthaul link traffic by 739% compared to the weighted minimum mean square error (WMMSE) precoding algorithm. When each signal symbol is quantized with 10 bits, the proposed approach can improve the sum-rate by 284% and 544% compared to the zero-forcing and maximum ratio transmission algorithms, respectively.

As a new trusted data sharing pattern with privacy protection, the integration mechanism of blockchain and Federated Learning has attracted extensive attention. Generally, this mechanism uses blockchain technology to supervise the original data and calculation results, which ignores the supervision of the Federated Learning model and computing process. Therefore, we introduce the concepts of the sandbox and state channel to construct a new data privacy sharing paradigm via Blockchain and Federated Learning. Under this paradigm, we use state channel to connect Blockchain and Federated Learning. And state channel is used to create a “trusted sandbox” to instantiate Federated Learning tasks in the trustless edge computing environment. Meanwhile, we also mainly solve problems about data privacy sharing in Federated Learning and system performance degradation caused by data quality. The simulation results show that the proposed method has better performance and efficiency than the traditional data sharing method.

In order to solve the problem of low accuracy of fault diagnosis algorithms caused by increased network dynamics, this paper proposes a dynamic network fault diagnosis algorithm under 5G network slices. By calculating the failure credibility of each link, a clustering algorithm is used to cluster the symptoms to correct the false symptoms. When constructing the fault propagation model, the time slice t is added to the fault propagation model, thereby depicting different network models in different time slices. Finally, locate the fault by solving the original state and current state of the faulty node. The experimental part compares the algorithm of this paper with the existing algorithm, and verifies that the algorithm of this paper improves the performance of the fault diagnosis algorithm.