Channel Estimation for Generalized Superimposed Cell-free Massive MIMO-OFDM Systems

This paper proposes a generalized superimposed channel estimation scheme for uplink cell-free massive multiple-input multiple-output orthogonal frequency-division multiplexing (mMIMO-OFDM) system. We consider that some subcarriers transmit superimposed pilots and information symbols; others only carry information symbols, which is different from the standard OFDM system and reduce the pilot reused and enhanced spectral efficiency. The estimated channels are used to detect the data streams, and consequently, bit error rate (BER) and sum-rate performances are evaluated. This work considers two levels of receiver cooperations (centralized processing and local processing). We show that centralized processing provides much lower normalized mean-squared error (NMSE) and BER than that for local processing, and it is also shown that the generalized superimposed training scheme gives better performance in channel estimation compared with the conventional superimposed training (ST) and the regular pilots (RP) scheme.