150 research outputs found
Minimum BER Precoding in 1-Bit Massive MIMO Systems
1-bit digital-to-analog (DACs) and analog-to-digital converters (ADCs) are
gaining more interest in massive MIMO systems for economical and computational
efficiency. We present a new precoding technique to mitigate the
inter-user-interference (IUI) and the channel distortions in a 1-bit downlink
MUMISO system with QPSK symbols. The transmit signal vector is optimized taking
into account the 1-bit quantization. We develop a sort of mapping based on a
look-up table (LUT) between the input signal and the transmit signal. The LUT
is updated for each channel realization. Simulation results show a significant
gain in terms of the uncoded bit-error-ratio (BER) compared to the existing
linear precoding techniques.Comment: Presented in IEEE SAM 2016, 10th-13th July 2016, Rio De Janeiro,
Brazi
Measurement-driven Quality Assessment of Nonlinear Systems by Exponential Replacement
We discuss the problem how to determine the quality of a nonlinear system
with respect to a measurement task. Due to amplification, filtering,
quantization and internal noise sources physical measurement equipment in
general exhibits a nonlinear and random input-to-output behaviour. This usually
makes it impossible to accurately describe the underlying statistical system
model. When the individual operations are all known and deterministic, one can
resort to approximations of the input-to-output function. The problem becomes
challenging when the processing chain is not exactly known or contains
nonlinear random effects. Then one has to approximate the output distribution
in an empirical way. Here we show that by measuring the first two sample
moments of an arbitrary set of output transformations in a calibrated setup,
the output distribution of the actual system can be approximated by an
equivalent exponential family distribution. This method has the property that
the resulting approximation of the statistical system model is guaranteed to be
pessimistic in an estimation theoretic sense. We show this by proving that an
equivalent exponential family distribution in general exhibits a lower Fisher
information measure than the original system model. With various examples and a
model matching step we demonstrate how this estimation theoretic aspect can be
exploited in practice in order to obtain a conservative measurement-driven
quality assessment method for nonlinear measurement systems.Comment: IEEE International Instrumentation and Measurement Technology
Conference (I2MTC), Taipei, Taiwan, 201
Multiple Parameter Estimation With Quantized Channel Output
We present a general problem formulation for optimal parameter estimation
based on quantized observations, with application to antenna array
communication and processing (channel estimation, time-of-arrival (TOA) and
direction-of-arrival (DOA) estimation). The work is of interest in the case
when low resolution A/D-converters (ADCs) have to be used to enable higher
sampling rate and to simplify the hardware. An Expectation-Maximization (EM)
based algorithm is proposed for solving this problem in a general setting.
Besides, we derive the Cramer-Rao Bound (CRB) and discuss the effects of
quantization and the optimal choice of the ADC characteristic. Numerical and
analytical analysis reveals that reliable estimation may still be possible even
when the quantization is very coarse.Comment: 9 pages, 9 figures, International ITG Workshop on Smart Antennas -
WSA 2010, Bremen, German
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