In order to solve the problem of high energy consumption of relay communication and high difficulty in the construction of 5G base stations, the research on Reconfigurable Intelligent Surface (RIS) technology was introduced in 6G mobile communication. Aiming at the problem of characteristic loss and instability of the truncated Hadamard matrix and Discrete Fourier Transform (DFT) matrix when constructing intelligent surfaces, a new RIS phase shift model design scheme of constructing unitary matrix based on Hankel matrix and Toeplitz matrix was proposed. The characteristics of the unitary matrix were used to minimize the channel error and improve the reliability of the communication channel. The simulation results show that compared with that of non-RIS-assisted communication, the user receiving rate of RIS-assisted communication can obtain a gain of 1 (bit·s ^-1)/Hz when the number of RIS units is 15. With the increase of the number of RIS units, the gain of the user receiving rate will be more and more significant. When the number of RIS units is 4, compared to the method of using DFT matrix to construct intelligent reflecting surfaces, the methods of using the two obtained unitary matrices to construct reflecting surfaces have higher reliability, and can obtain the performance gain of about 0.5 dB.

In NarrowBand Internet of Things (NB-IoT) systems, the Internet of Things (IoT) terminals should decode Downlink Control Information (DCI) quickly to receive resource allocation and scheduling information of the data channel correctly. Therefore, a low complexity Narrowband Physical Downlink Control Channel (NPDCCH) blind detection algorithm using correlation detection was proposed for NPDCCH with search space size being greater than or equal to 32. By employing two correlation judgments on the data in a possible minimum repetition transmission unit of NPDCCH, the invalid data in searching space was removed to reduce the computation complexity. Then, the repetition periods with the valid data were combined and decoded to improve the blind detection performance. Finally, theoretical and simulation analysis of two correlation thresholds used in correlation detection were carried out. Results show that compared with conventional exhaustive blind detection algorithm, the decoding complexity of the proposed algorithm is reduced by at least 75% and the detection performance gain is increased by 2.5 dB to 3.5 dB. The proposed algorithm is more beneficial for engineering practice.

User relevance measure is the foundation and core of heterogeneous information network research. The existing user relevance measure methods still have improvement space due to insufficient multi-dimensional analysis and link analysis. Aiming at the fact, a user relevance measure method combining Latent Dirichlet Allocation (LDA) and meta-path analysis was proposed. Firstly, the LDA was used to model the topic, and the relevance of nodes was analyzed by the node contents in the network. Secondly, the meta-path was introduced to describe the relationship type between nodes, and relevance measure was carried out for users in heterogeneous information network by relevance measure method (DPRel). Thirdly, the relevance of nodes was incorporated into the calculation of user relevance measure. Finally, the experiment was carried out on IMDB real movie dataset, and the proposed method was compared with the collaborative filtering recommendation method embedded in LDA topic model ULR-CF (Unifying LDA and Ratings Collaborative Filtering) and meta-path based similarity method (PathSim).The experimental results show that the proposed method can overcome the drawback of data sparsity and improve the accuracy of user relevance measure.

A risk assessment model based on Bayesian network was proposed. In this model, each risk event influencing the Trusted Platform Control Module (TPCM)'s trust was analyzed. According to the relation among risks, the Bayesian network evaluation model was built. According to the evaluation from expert, Bayesian network inferring method was used to evaluate the risk probability and its influence. The whole system's risk value and risk priority were determined. An example was given to verify the model's correctness and validation.

In order to guide commercial decisions for cross marketing, a new classification algorithm named CHC based on frequent closed itemsets and imprecise reasoning was proposed. The H-C algorithm for mining frequent closed itemsets based on hyperlinked data structure, H-Struct, was improved. The header table of H-Struct was adjusted by inserting the class label to prune the search space; the local relative support and maximum support were used to exclude meaningless patterns; the maximum length of patterns mined was applied to improve the usability of rules. The reasoning algorithm of EMYCIN was extended to handle the rules whose right is negative. The algorithm improved above traditional classification algorithm's limitations in deriving only class label. Furthermore, this algorithm obtained a value referring to the confidence of the classification result to facilitate and simplify the process of evaluating multiple cross marketing plans. The experimental results show that the enhanced algorithm is efficient in run time and classification precision.