ID	原文	译文
54067	提出多用户的区域划分方法，将用户分为区内用户和干扰较小的区外用户，并推导出误码率计算公式;	Firstly, an evenly division method is proposed divide users into inner partition users and outer partition users which respectively contributes to performance improvement of spectral efficiency and bit error rate, and the formula of bit error rate is derived.
54068	提出分区向量计算方法，引入CAN算法，提高分区向量的自相关性;	Then, the partition vector calculation method is proposed which introduce CAN algorithm to improve the auto-correlation of the partition vector and further reduce bit error rate.
54069	从频谱效率和高斯信道下的误码率等角度，分析该方法的总体性能，并给出分区向量最优长度的计算方法。	Last, the overall performance of the method is analyzed from the aspects of spectral efficiency and bit error rate under Gaussian channel, and the optimal length of the partition vector is given.
54070	仿真结果表明，该方法能较大程度地扩充用户数，保证误码率在一定范围以内，并且频谱效率较高。	Simulation results show that as the number of users increases, this method can ensure the bit error rate within a certain range and the spectrum efficiency stable. Therefore, this method can effectively increase the number of users of TDCS.
54071	迭代软阈值学习算法(Learned Iterative Soft-Thresholding Algorithm，LISTA)将迭代软阈值算法(Iterative Soft-Thresholding Algorithm，ISTA)展开为递归前馈神经网络优化稀疏恢复的求解。	Learned iterative soft-thresholding algorithm(LISTA) expands the iterative soft-thresholding algorithm(ISTA) into a recursive feedforward neural network to optimize the solution of sparse recovery.
54072	针对LISTA单次迭代只依赖于前一迭代点限制算法收敛速率的问题，本文提出了一种引入多状态记忆机制的迭代软阈值学习算法(Learned Iterative Soft-Thresholding Algorithm with Multi-state Memory Mechanism，LISTA-MM)。	To address the problem that each iteration in LISTA only relies on the previous iteration point resulting in the limitation of convergence rate, this paper proposes a learned soft-thresholding algorithm with multi-state memory mechanism(LISTA-MM). This method improves LISTA based on the first-order iterative fixed-step algorithm and sets the state connection degree.
54073	该算法基于一阶迭代固定步长算法对LISTA进行改进，设置状态连接度数，选择性地组合多个先前迭代点的稀疏信息，确保了迭代过程中信息被正确传递并充分利用，进而加快了算法的收敛速度。	This algorithm selectively combines the sparse information of several previous iteration points, ensures that the sparse information is transferred correctly and fully utilized during the iteration, and thus speeds up the convergence speed of the algorithm.
54074	实验结果表明，LISTA-MM在保证稀疏恢复精度的同时有效提高了收敛速度。	The experimental results show that LISTA-MM not only ensures the precision of sparse recovery, but also effectively improves the convergence rate of sparse recovery.
54075	此外，本文将LISTA-MM扩展为卷积形式，并探索其在图像超分辨率中的应用，	In addition, this paper extends LISTA-MM into a convolution manner and explores its application in image super-resolution.
54076	实验结果表明，基于LISTA-MM的网络在图像质量评价指标和可视化效果上均优于其他网络，重构图像具有与原始图像相近的清晰细节纹理。	The experimental results show that the LISTA-MM based network is superior to other networks in both image quality evaluation and visualization effect, and the reconstructed images have clear and detail texture closing to the original image.