40 research outputs found
Reconsidering Linear Transmit Signal Processing in 1-Bit Quantized Multi-User MISO Systems
In this contribution, we investigate a coarsely quantized Multi-User
(MU)-Multiple Input Single Output (MISO) downlink communication system, where
we assume 1-Bit Digital-to-Analog Converters (DACs) at the Base Station (BS)
antennas. First, we analyze the achievable sum rate lower-bound using the
Bussgang decomposition. In the presence of the non-linear quanization, our
analysis indicates the potential merit of reconsidering traditional signal
processing techniques in coarsely quantized systems, i.e., reconsidering
transmit covariance matrices whose rank is equal to the rank of the channel.
Furthermore, in the second part of this paper, we propose a linear precoder
design which achieves the predicted increase in performance compared with a
state of the art linear precoder design. Moreover, our linear signal processing
algorithm allows for higher-order modulation schemes to be employed
Spatial Sigma-Delta Modulation for Coarsely Quantized Massive MIMO Downlink: Flexible Designs by Convex Optimization
This paper considers the context of multiuser massive MIMO downlink precoding
with low-resolution digital-to-analog converters (DACs) at the transmitter.
This subject is motivated by the consideration that it is expensive to employ
high-resolution DACs for practical massive MIMO implementations. The challenge
with using low-resolution DACs is to overcome the detrimental quantization
error effects. Recently, spatial Sigma-Delta modulation has arisen as a viable
way to put quantization errors under control. This approach takes insight from
temporal Sigma-Delta modulation in classical DAC studies. Assuming a 1D uniform
linear transmit antenna array, the principle is to shape the quantization
errors in space such that the shaped quantization errors are pushed away from
the user-serving angle sector. In the previous studies, spatial Sigma-Delta
modulation was performed by direct application of the basic first- and
second-order modulators from the Sigma-Delta literature. In this paper, we
develop a general Sigma-Delta modulator design framework for any given order,
for any given number of quantization levels, and for any given angle sector. We
formulate our design as a problem of maximizing the
signal-to-quantization-and-noise ratios experienced by the users. The
formulated problem is convex and can be efficiently solved by available
solvers. Our proposed framework offers the alternative option of focused
quantization error suppression in accordance with channel state information.
Our framework can also be extended to 2D planar transmit antenna arrays. We
perform numerical study under different operating conditions, and the numerical
results suggest that, given a moderate number of quantization levels, say, 5 to
7 levels, our optimization-based Sigma-Delta modulation schemes can lead to bit
error rate performance close to that of the unquantized counterpart
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