Semi-blind channel estimation based on modified CMA and unitary scrambling for massive MIMO systems

Abstract

Pilot contamination is one of the main impairments in multi-cell massive Multiple-Input Multiple-Output systems. In order to improve the channel estimation in this context, we propose to use a semi-blind channel estimator based on the constant modulus algorithm (CMA). We consider an enhanced version of the CMA namely the Modified CMA which modifies the cost function of the CMA algorithm to the sum of cost functions for real and imaginary parts. Due to pilot contamination, the channel estimator may estimate the channel of a contaminating user instead of that of the user of interest (the user for which the Base Station wants to estimate the channel and then the data). To avoid this, we propose to scramble the users sequences before transmission. We consider different methods to perform unitary scrambling based on rotating the transmitted symbols (one Dimensional (1-D) scrambling) and using unitary matrices (two-Dimensional (2-D) scrambling). At the base station, the received sequence of the user of interest is descrambled leading to a better convergence of the channel estimator. We also consider the case where the Automatic Repeat reQuest protocol is used. In this case, using scrambling leads to a significant gain in terms of BLock Error Rate due to the change of the contaminating users data from one transmission to another induced by scrambling.