Chirp Index Modulated Pilots-Aided Joint Activity Detection and Channel Estimation for Asynchronous Massive Access

In the massive machine-type communications (mMTC) supported by grant-free access with massive MIMO, the BS faces the urgent task of the joint activity detection and channel estimation (JADCE). But the random mobility and low cost of the devices yield time delay and frequency offset between the devices and the BS, which will significantly reduce the performance of the JADCE. Towards this end, we design a chirp index modulation-based pilot sequence (CIM-PS), and analyze its auto-correlation and cross-correlation features under time delay and frequency offset. In order to fulfill the JADCE for the asynchronous massive access under time delay and frequency offset, we formulate the JADCE as a maximum likelihood estimation (MLE) problem with the help of the CIM-PS, and then propose a covariance-based coordinate descent algorithm with the least squares channel estimation (CD-LS) to solve it. Simulation results and analysis show that the designed CIM-PS is more robust to time delay and frequency offset than the random Gaussian sequences. Compared with the benchmark schemes, the proposed CD-LS algorithm can achieve the same DEP performance with less antennas in the presence of only time delay and a lower computation complexity of searching frequency offset. In scenarios where both time delay and frequency offset exist, CD-LS can still achieve a good DEP and channel estimation performance.