Polyanskiy [2] proposed a framework for the MAC problem where users employ a common codebook in the finite blocklength regime. In this work, we extend [2] to the case when each user generates a packet independently and asynchronously according to identical renewal processes. Each packet must be decoded with a delay no greater than d, which is a strict delay constraint. We derive a general converse bound for our system model before conducting a comparative analysis of several achievable transmission schemes. First, we consider a transmission scheme where each user transmits a packet as soon as it is generated and suffers interference from other users. We treat interference as noise (TIN). Then, we investigate block transmission where users transmit jointly in fixed slots. Finally, we explore packet splitting, allowing users to divide each packet into two parts with different blocklengths. Using optimized information bit allocation, we discuss two analytical approaches: dependence testing and Gaussian ball. Our numerical results indicate that, when dealing with large delays, TIN performs well compared to block transmission. Conversely, block transmission has the advantage as the system spectral efficiency increases. Packet splitting outperforms other schemes consistently.

The phenomenon of information cascades in communication networks has been widely studied, with earlier research primarily using statistical models for analysis. Recently, machine learning methods combined with big data analysis have proven to be powerful tools for understanding network behavior. However, previous research efforts have largely overlooked the relationships among various influential factors. In this paper, we extend machine learning based data analysis methods by introducing the concept of causality, termed the Causality-based Big Data Analysis (CBDA) method, which incorporates factors such as fake agents, welfare effects, and trusted versus untrusted prior knowledge to examine their impact on the outcomes of erroneous information cascades. Numerical results demonstrate that each factor significantly influences the outcome, generally increasing the likelihood of its preferred result. This is achieved by clarifying how the weight assigned to each influential factor affects the behavior of subsequent agents. The results of our proposed CBDA method show that trusted prior knowledge significantly reduces the probability of erroneous information cascades, offering superior explainability and effectiveness compared to existing methods.

As neural network became one of the indispensable technologies in many fields, a large number of data processing methods emerged one after another. Convolutional neural network (CNN) soon became a useful tool to classify images. However, the drawback of CNN is quite obviously, which is the training time would be tremendous long if the network running on CPU. To tackle this issue, this paper improves the BP algorithm based on the weight freezing method and entropy weight data processing method, and uses the experiment to verify the effectiveness of this approach. With MNIST handwritten database, CIFAR-10 database and PUBFIG83 and LFW database as background, the original LeNet-5 model has been optimized and the back propagation (BP) algorithm used in the original network training is improved. The results show that the improved algorithm can effectively reduce the complexity of the algorithm, and the training time reduces to 8%.