Superimposed Pilots Are Superior for Mitigating Pilot Contamination in Massive MIMO

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • University of Sheffield

Abstract

In this paper, superimposed pilots are introduced as an alternative to time-multiplexed pilot and data symbols for mitigating pilot contamination in massive multiple-input multiple-output (MIMO) systems. We propose a non-iterative scheme for uplink channel estimation based on superimposed pilots and derive an expression for the uplink signal-to-interference-plus-noise ratio (SINR) at the output of a matched filter employing this channel estimate. Based on this expression, we observe that power control is essential when superimposed pilots are employed. Moreover, the quality of the channel estimate can be improved by reducing the interference that results from transmitting data alongside the pilots, and an intuitive iterative data-aided scheme that reduces this component of interference is also proposed. Approximate expressions for the uplink SINR are provided for the iterative data-aided method as well. In addition, we show that a hybrid system with users utilizing both time-multiplexed and superimposed pilots is superior to an optimally designed system that employs only time-multiplexed pilots, even when the non-iterative channel estimate is used to build the detector and precoder. We also describe a simple approach to implement this hybrid system by minimizing the overall inter- and intracell interference. Numerical simulations demonstrating the performance of the proposed channel estimation schemes and the superiority of the hybrid system are also provided.

Details

Original languageEnglish
Article number7865983
Pages (from-to)2917-2932
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume65
Issue number11
Publication statusPublished - 1 Jun 2017
MoE publication typeA1 Journal article-refereed

    Research areas

  • Massive MIMO, pilot contamination, superimposed pilots

ID: 12960176