No Downlink Pilots are Needed in TDD Massive MIMO

TL;DR

This paper introduces a blind algorithm for estimating effective channel gain in TDD Massive MIMO systems that eliminates the need for downlink pilots, especially benefiting environments lacking channel hardening.

Contribution

It proposes a novel blind estimation method for effective channel gain in TDD Massive MIMO, removing the reliance on downlink pilots and improving performance in non-hardening channels.

Findings

Blind algorithm outperforms traditional methods in non-hardening environments.

Capacity lower bound derived for the proposed scheme.

Significant performance gains over no-pilot and pilot-based methods.

Abstract

We consider the Massive Multiple-Input Multiple-Output (MIMO) downlink with maximum-ratio and zero-forcing processing and time-division duplex (TDD) operation. To decode, the terminals must know their instantaneous effective channel gain. Conventionally, it is assumed that by virtue of channel hardening, this instantaneous gain is close to its average and hence that terminals can rely on knowledge of that average (also known as statistical channel information). However, in some propagation environments, such as keyhole channels, channel hardening does not hold. We propose a blind algorithm to estimate the effective channel gain at each user, that does not require any downlink pilots. We derive a capacity lower bound of each user for our proposed scheme, applicable to any propagation channel. Compared to the case of no downlink pilots (relying on channel hardening), and compared to training-based estimation using downlink pilots, our blind algorithm performs significantly better. The difference is especially pronounced in environments that do not offer channel hardening.