A Data-assisted Algorithm for Truly Grant-free Transmissions of Future mMTC

In truly grant-free (TGF) transmissions, pilot is crucial to fully exploit the user separation capability of spatial domain. Based on pilots, joint active user detection and channel estimation can be done. The state-of-art work suggests to use the recovered data to improve the channel estimation accuracy. In this paper, a novel data-assisted algorithm is proposed. This algorithm jointly utilizes the recovered and unrecovered data to further improve the performance. The constant modulus (CM) feature is used as the optimization target. To obtain an efficient convergence, a fixed modulus version of CM is employed, and multiple relatively good spatial combining vectors are normalized and used as the initial values of the quasi-Newton iterations. Furthermore, a target function of post signal to interference plus noise ratio is proposed to gain a better performance and faster convergence. The simulation results show that the proposed method achieves a tremendous performance gain, especially when more receiving antennas are employed.