Multi-User Detection Based on Sparsity Adaptive Matching Pursuit Compressive Sensing for Uplink Grant-free Non-Orthogonal Multiple Access
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摘要: 免授权非正交多址接入技术(NOMA)结合多用户检测技术(MUD)，能够满足大规模机器通信(mMTC)场景中的大连接量、低信令开销和低时延传输等需求。在基于压缩感知(CS)的MUD算法中，活跃用户数往往作为已知信息，而实际通信系统中很难准确估计。基于此，该文提出一种改进稀疏度自适应匹配的多用户算法(MSAMP-MUD)。该算法首先利用广义Dice系数匹配准则选择与残差最匹配的原子，更新用户支撑集；当残差能量接近噪声能量时，终止迭代，从而获得最终支持集；否则，采取上述准则更新用户支撑集，提高支撑集中活跃用户数估计精度。在迭代过程中，根据最近两次残差能量之比，选取不同的迭代步长，以降低检测迭代次数。仿真结果表明，所提算法与传统基于CS的MUD算法相比，误码率降低约9%，迭代次数减少约10%。Abstract: Grant-free Non-Orthogonal Multiple Access (NOMA) combined with Multi-User Detection (MUD) technology can meet the requirements of large connection volume, low signaling overhead and low latency transmission in massive Machine Type Communications (mMTC) scenarios. In the MUD algorithm based on Compressed Sensing (CS), the number of active users is often used as known information, but it is difficult to accurately estimate in the actual communication system. Based on this, this paper proposes a multi-user algorithm (Modified Sparsity Adaptive Matching Pursuit MUD, MSAMP-MUP) to improve the adaptive matching of sparsity. Firstly, the algorithm uses the generalized Dice coefficient matching criterion to select the atom that best matches the residual, and updates the user support set. When the residual energy is close to the noise energy, the iteration is terminated to obtain the final support set; Otherwise, the above criteria are used to update the user support set, and the estimation accuracy of the active users in the support set is improved. In the iteration process, different iteration steps are selected according to the ratio of the last two residual energies, so as to reduce the number of detection iterations. The simulation results show that, compared with the traditional CS-based MUD algorithm, the proposed algorithm reduces the bit error rate by about 9% and the number of iterations by about 10%.
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表 1 系统仿真主要参数
参数 参数值 系统用户数$K$ 200 子载波数$N$ 100 时隙数$J$ 7 阈值${\varepsilon _1}$ 1.2 调制方式 QPSK 过载率$\lambda $ 200% 扩频矩阵 Toeplitz矩阵 
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