Iterative Channel Estimation for Large Scale MIMO with Highly Quantized Measurements in 5G

Large-scale MIMO systems offer high spectral efficiency with excellent error performance at low power so long as accurate channel estimates are available. When channel estimation is performed using only pilot signals, undesirably long pilot sequences are needed to achieve the required accuracy. This paper describes an iterative receiver algorithm where detected/decoded data symbols extend the pilot sequences as virtual pilot signals. By using extrinsic feedback, where only information on how the error correction code decoder modifies a posteriori bit probabilities from the detector output is fed back to the channel estimation and detection system, the errors made by the detector and channel estimator do not lead to instability. The proposed system is able to estimate time domain multipath channels with high accuracy. Communications with this system only requires 0.5 dB more power than the system using ideal channel state information, and about 2.5 dB less power than the system that estimates the channel using only the pilot signal. The receiver is also able to operate with coarsely quantized measurements so that low cost receivers can be used at each antenna.