Low-Complexity Massive MIMO Tensor Precoding

We present a novel and low-complexity massive multiple-input multiple-output (MIMO) precoding strategy based on novel findings concerning the subspace separability of Rician fading channels. Considering a uniform rectangular array at the base station, we show that the subspaces spanned by the channel vectors can be factorized as a tensor product between two lower dimensional subspaces. Based on this result, we formulate tensor maximum ratio transmit and zero-forcing precoders. We show that the proposed tensor precoders exhibit lower computational complexity and require less instantaneous channel state information than their linear counterparts. Finally, we present computer simulations that demonstrate the applicability of the proposed tensor precoders in practical communication scenarios.

I. Introduction

Massive multiple-input multiple-output (MIMO) is one of the main enabling technologies for 5G networks. It consists of employing many active antennas at the base station (BS) to serve multiple users on the same time-frequency resource [1]. It can provide high data throughput by spatial signal processing at the BS (precoding) to combat multi-user interference and to provide large beamforming gains. Maximum ratio transmission (MRT) and zero-forcing (ZF) precoding are known to perform well if accurate instantaneous channel state information (CSI) is available at the BS. However, in practice, CSI estimates are often noisy, since accurate estimation of the high-dimensional massive MIMO channels can be quite expensive in terms of power and time-frequency resources. Moreover, the ZF precoder is known to be computationally expensive due to the large number of computations it requires to invert the high-dimensional channel Gram matrix [2].