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Multi-user multiple-input multiple-output (MU-MIMO) technology can significantly improve the spectral and energy efficiencies of wireless networks. In the uplink MU-MIMO systems, the optimal precoder design at the base station utilizes the Lagrange multipliers method and the centralized iterative algorithm to minimize the mean squared error (MSE) of all users under the power constraint. The precoding matrices need to be fed back to the user equipment to explore the potential benefits of the joint transceiver design. We propose a CNN-based compression network named PCQNet to minimize the feedback overhead. We first illustrate the effect of the trainable compression ratios and feedback bits on the MSE between the original precoding matrices and the recovered ones. We then evaluate the block error rates as the performance measure of the centralized implementation with an optimal minimum mean-squared error (MMSE) transceiver. Numerical results show that the proposed PCQNet achieves near-optimal performance compared with other quantized feedback schemes and significantly reduces the feedback overhead with negligible performance degradation.

The year 2021 is not only a turning year for the development of China's internet industry, but also an eventful year. Under the situation of strong government supervision, the micro-ecology of China's internet industry is undergoing subtle changes. Internet enterprises are completely different from manufacturing in terms of profit model and industry ecology. Although the financial statements composition of internet enterprises are simple, the risk factors are more complex and secret, the matching principle is more difficult to be compatible, and the future trend is more difficult to grasp. With the aid of flexible financial management methods and contingency theory research, flexible dynamic indicators are increased. Using the improved BP neural network to study the fitting relationship between financial income, financial cost and non-financial indicators, to the strong correlation analysis with non-financial indicators such as UV, conversion rate, per capita access depth, advertisement click rate, DC UV, etc., let the Internet The analysis of corporate financial report data is more realistic and reasonable.

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A dynamic multi-symbol (DM) flipping scheme is proposed for various symbol flipping decodings of non-binary low-density parity-check (NB-LDPC) codes. This new approach divides the whole decoding process into multiple stages according to the number of iterations and allows a different maximum number of symbols to be flipped in each stage. Numerical analysis reveals that the proposed multi-symbol flipping scheme can yield a higher probability of correct flipping with the dynamic flipping threshold. The proposed DM scheme can be highly parallelized, extensively improving the decoder throughput over existing symbol flipping decoding algorithms based on prediction (SFDP). Numerical results show that the DM scheme can significantly enhance the error correction capability and achieve faster convergence speed with little increase in average computational complexity.INDEX TERMS NB-LDPC codes, multi-symbol flipping decoding, hard reliability.