74 research outputs found
Spectral Efficiency of One-Bit Sigma-Delta Massive MIMO
We examine the uplink spectral efficiency of a massive MIMO base station employing a one-bit Sigma-Delta ( \Sigma \Delta ) sampling scheme implemented in the spatial rather than the temporal domain. Using spatial rather than temporal oversampling, and feedback of the quantization error between adjacent antennas, the method shapes the spatial spectrum of the quantization noise away from an angular sector where the signals of interest are assumed to lie. It is shown that, while a direct Bussgang analysis of the \Sigma \Delta approach is not suitable, an alternative equivalent linear model can be formulated to facilitate an analysis of the system performance. The theoretical properties of the spatial quantization noise power spectrum are derived for the \Sigma \Delta array, as well as an expression for the spectral efficiency of maximum ratio combining (MRC). Simulations verify the theoretical results and illustrate the significant performance gains offered by the \Sigma \Delta approach for both MRC and zero-forcing receivers
SVM-Based Channel Estimation and Data Detection for One-Bit Massive MIMO systems
The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much more challenging. In this paper, we show how Support Vector Machine (SVM), a well-known supervised-learning technique in machine learning, can be exploited to provide efficient and robust channel estimation and data detection in massive MIMO systems with one-bit ADCs. First, the problem of channel estimation for uncorrelated channels is formulated as a conventional SVM problem. The objective function of this SVM problem is then modified for estimating spatially correlated channels. Next, a two-stage detection algorithm is proposed where SVM is further exploited in the first stage. The performance of the proposed data detection method is very close to that of Maximum-Likelihood (ML) data detection when the channel is perfectly known. We also propose an SVM-based joint Channel Estimation and Data Detection (CE-DD) method, which makes use of both the to-be-decoded data vectors and the pilot data vectors to improve the estimation and detection performance. Finally, an extension of the proposed methods to OFDM systems with frequency-selective fading channels is presented. Simulation results show that the proposed methods are efficient and robust, and also outperform existing ones
On the Effect of Mutual Coupling in One-Bit Spatial Sigma-Delta Massive MIMO Systems
The one-bit spatial Sigma-Delta concept has recently been proposed as an
approach for achieving low distortion and low power consumption for massive
multi-input multi-output systems. The approach exploits users located in known
angular sectors or spatial oversampling to shape the quantization noise away
from desired directions of arrival. While reducing the antenna spacing
alleviates the adverse impact of quantization noise, it can potentially
deteriorate the performance of the massive array due to excessive mutual
coupling. In this paper, we analyze the impact of mutual coupling on the uplink
spectral efficiency of a spatial one-bit Sigma-Delta massive MIMO architecture,
and compare the resulting performance degradation to standard one-bit
quantization as well as the ideal case with infinite precision. Our simulations
show that the one-bit Sigma-Delta array is particularly advantageous in
space-constrained scenarios, can still provide significant gains even in the
presence of mutual coupling when the antennas are closely spaced.Comment: Presented in SPAWC 202
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
Asymptotic Signal Detection Rates with 1-bit Array Measurements
This work considers detecting the presence of a band-limited random radio
source using an antenna array featuring a low-complexity digitization process
with single-bit output resolution. In contrast to high-resolution
analog-to-digital conversion, such a direct transformation of the analog radio
measurements to a binary representation can be implemented hardware and
energy-efficient. However, the probabilistic model of the binary receive data
becomes challenging. Therefore, we first consider the Neyman-Pearson test
within generic exponential families and derive the associated analytic
detection rate expressions. Then we use a specific replacement model for the
binary likelihood and study the achievable detection performance with 1- bit
radio array measurements. As an application, we explore the capability of a
low-complexity GPS spectrum monitoring system with different numbers of
antennas and different observation intervals. Results show that with a moderate
amount of binary sensors it is possible to reliably perform the monitoring
task
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