This paper studies improving the detector performance which considers the activity state (AS) temporal correlation of the user equipments (UEs) in the time domain under the uplink grant-free non-orthogonal multiple access (GF-NOMA) system. The Bernoulli Gaussian-Markov chain (BG-MC) probability model is used for exploiting both the sparsity and slow change characteristic of the AS of the UE. The GAMP Bernoulli Gaussian-Markov chain (GAMP-BG-MC) algorithm is proposed to improve the detector performance, which can utilize the bidirectional message passing between the neighboring time slots to fully exploit the temporally-correlated AS of the UE. Furthermore, the parameters of the BG-MC model can be updated adaptively during the estimation procedure with unknown system statistics. Simulation results show that the proposed algorithm can improve the detection accuracy compared with the existing methods while keeping the same order complexity.

翻译：本文研究在上行免授权非正交多址系统中利用用户设备活动状态在时域上的时间相关性来提高检测器性能。采用伯努利高斯-马尔可夫链概率模型以同时利用用户设备活动状态的稀疏性和慢变特性。提出GAMP伯努利高斯-马尔可夫链算法来提升检测器性能，该算法通过相邻时隙间的双向消息传递充分挖掘用户设备活动状态的时间相关性。此外，在系统统计特性未知的情况下，BG-MC模型参数可在估计过程中自适应更新。仿真结果表明，与现有方法相比，所提算法在保持相同阶复杂度的情况下提高了检测精度。