Massive-MIMO Iterative Channel Estimation and Decoding (MICED) in the Uplink

TL;DR

This paper introduces MICED, an iterative algorithm for Massive MIMO uplink that improves channel estimation and spectral efficiency by leveraging decoded data as side-information, especially effective with superimposed pilots.

Contribution

The paper presents a novel iterative channel estimation and decoding algorithm that enhances spectral efficiency in Massive MIMO systems using superimposed pilots.

Findings

MICED increases spectral efficiency compared to conventional methods.

The algorithm reduces block-error-rate with both regular and superimposed pilots.

Superimposed pilots combined with MICED perform best in high mobility scenarios.

Abstract

Massive MIMO uses a large number of antennas to increase the spectral efficiency (SE) through spatial multiplexing of users, which requires accurate channel state information. It is often assumed that regular pilots (RP), where a fraction of the time-frequency resources is reserved for pilots, suffices to provide high SE. However, the SE is limited by the pilot overhead and pilot contamination. An alternative is superimposed pilots (SP) where all resources are used for pilots and data. This removes the pilot overhead and reduces pilot contamination by using longer pilots. However, SP suffers from data interference that reduces the SE gains. This paper proposes the Massive-MIMO Iterative Channel Estimation and Decoding (MICED) algorithm where partially decoded data is used as side-information to improve the channel estimation and increase SE. We show that users with precise data estimates can help users with poor data estimates to decode. Numerical results with QPSK modulation and LDPC codes show that the MICED algorithm increases the SE and reduces the block-error-rate with RP and SP compared to conventional methods. The MICED algorithm with SP delivers the highest SE and it is especially effective in scenarios with short coherence blocks like high mobility or high frequencies.