MMSE precoder for massive MIMO using 1-bit quantization

Abstract

We propose a novel linear minimum-mean-squared-error (MMSE) precoder design for a downlink (DL) massive multiple-input-multiple-output (MIMO) scenario. For economical and computational efficiency reasons low resolution 1-bit digital-to-analog (DAC) and analog-to-digital (ADC) converters are used. This comes at the cost of performance gain that can be recovered by the large number of antennas deployed at the base station (BS) and an appropiate precoder design to mitigate the distortions due to the coarse quantization. The proposed precoder takes the quantization non-linearities into account and is split into a digital precoder and an analog precoder. We formulate the two-stage precoding problem such that the MSE of the users is minimized under the 1-bit constraint. In the simulations, we compare the new optimized precoding scheme with previously proposed linear precoders in terms of uncoded bit error ratio (BER).Comment: Presented in ICASSP 2016, 20-25 March 2016, Shanghai, Chin

Reconsidering Linear Transmit Signal Processing in 1-Bit Quantized Multi-User MISO Systems

“…4 how the superposition vector τ T = [2, 1] works; (i) the first symbol (solid points) is multiplied by a factor 2 and defines which quadrant the received symbol should lie in, (ii) the second symbol (hollow points) is added to the first and defines which 16-QAM symbol should be received. Since we assume QPSK input symbols and the specific superposition matrix defined in (27), the superimposed received symbols will be M-QAM, where M depends on the chosen number of streams per user, R k .…”

Construction of 1-Bit Transmit-Signal Vectors for Downlink MU-MISO Systems With PSK Signaling

“…Focusing on the impact of one-bit DACs in the transmit-side operations, it is assumed that the BS is equipped with one-bit DACs while each user (receiver) is with ideal analog-to-digital converters (ADCs) with infinite resolution. As in the closely related works [11], [12], [15], we also consider a normalized m-PSK constellation C = {c 0 , c 1 , ..., c m−1 }, each of which constellation point is defined as…”

“…In [10], various non-linear precoding techniques were proposed based on semidefinite relaxation (SDR), squared ∞ -norm relaxation, and sphere decoding. The authors proposed low-complexity quantized precoding methods as quantized ZF (QZF) [11] and quantized minimum-mean squared error (QMMSE) [12], which simply applied the one-bit quantization to the outputs of the conventional linear precoding methods. Also, a branchand-bound and a biconvex relaxation approaches were presented in [13] and [14], respectively.…”

Hybrid Analog-Digital Precoding for Interference Exploitation (Invited Paper)

“…The performance of 1-bit quantized ZF precoding is analysed in [9], while a 1-bit precoding based on the minimum-mean squared error (MMSE) criterion is considered in [10]. In [11]- [13], non-linear schemes that directly map the data symbols to the transmit signals are proposed, where the precoding method in [11] is based on the gradient descend method (GDM), an iterative approach based on biconvex relaxation is proposed in [12], and several complicated precoding methods based on semidefinite relaxation (SDR) and l ∞ -norm relaxation are proposed in [13]. Nevertheless, the above works may not be optimal as it ignores that interference can be exploited on an instantaneous basis [14]- [17].…”