28 research outputs found
Hardware-friendly two-stage spatial equalization for all-digital mm-wave massive MU-MIMO
Next generation wireless communication systems are expected to combine millimeter-wave communication with massive multi-user multiple-input multiple-output technology. All-digital base-station implementations for such systems need to process high-dimensional data at extremely high rates, which results in excessively high power consumption. In this paper, we propose two-stage spatial equalizers that first reduce the problem dimension by means of a hardware-friendly, low-resolution linear transform followed by spatial equalization on a lower-dimensional signal. We consider adaptive and non-adaptive dimensionality reduction strategies and demonstrate that the proposed two-stage spatial equalizers are able to approach the performance of conventional linear spatial equalizers that directly operate on high-dimensional data, while offering the potential to reduce the power consumption of spatial equalization
Finite-Alphabet Wiener Filter Precoding for mmWave Massive MU-MIMO Systems
Power consumption of multi-user (MU) precoding is a major concern in
all-digital massive MU multiple-input multiple-output (MIMO) base-stations with
hundreds of antenna elements operating at millimeter-wave (mmWave) frequencies.
We propose to replace part of the linear Wiener filter (WF) precoding matrix by
a finite-alphabet WF precoding (FAWP) matrix, which enables the use of
low-precision hardware that consumes low power and area. To minimize the
performance loss of our approach, we present methods that efficiently compute
FAWP matrices that best mimic the WF precoder. Our results show that FAWP
matrices approach infinite-precision error-rate and error-vector magnitude
performance with only 3-bit precoding weights, even when operating in realistic
mmWave channels. Hence, FAWP is a promising approach to substantially reduce
power consumption and silicon area in all-digital mmWave massive MU-MIMO
systems.Comment: Presented at the Asilomar Conference on Signals, Systems, and
Computers, 201
Capacity Bounds for Communication Systems with Quantization and Spectral Constraints
Low-resolution digital-to-analog and analog-to-digital converters (DACs and
ADCs) have attracted considerable attention in efforts to reduce power
consumption in millimeter wave (mmWave) and massive MIMO systems. This paper
presents an information-theoretic analysis with capacity bounds for classes of
linear transceivers with quantization. The transmitter modulates symbols via a
unitary transform followed by a DAC and the receiver employs an ADC followed by
the inverse unitary transform. If the unitary transform is set to an FFT
matrix, the model naturally captures filtering and spectral constraints which
are essential to model in any practical transceiver. In particular, this model
allows studying the impact of quantization on out-of-band emission constraints.
In the limit of a large random unitary transform, it is shown that the effect
of quantization can be precisely described via an additive Gaussian noise
model. This model in turn leads to simple and intuitive expressions for the
power spectrum of the transmitted signal and a lower bound to the capacity with
quantization. Comparison with non-quantized capacity and a capacity upper bound
that does not make linearity assumptions suggests that while low resolution
quantization has minimal impact on the achievable rate at typical parameters in
5G systems today, satisfying out-of-band emissions are potentially much more of
a challenge.Comment: Appears in the Proceedings of IEEE International Symposium on
Information Theory (ISIT) 202