2 research outputs found
BER Performance Analysis of Coarse Quantized Uplink Massive MIMO
Having lower quantization resolution, has been introduced in the literature,
as a solution to reduce the power consumption of massive MIMO and millimeter
wave MIMO systems. In this paper, we analyze bit error rate (BER) performance
of quantized uplink massive MIMO employing a few-bit resolution ADCs.
Considering Zero-Forcing (ZF) detection, we derive a closed-form quantized
signal-to-interference-plus-noise ratio (SINR) to achieve an analytical BER
approximation for coarse quantized M-QAM massive MIMO systems, by using a
linear quantization model. The proposed expression is a function of
quantization resolution in bits. We further numerically investigate the effects
of different quantization levels, from 1-bit to 4-bits, on the BER of three
modulation types of QPSK, 16-QAM, and 64-QAM. Uniform and non-uniform
quantizers are employed in our simulation.
Monte Carlo simulation results reveal that our approximate formula gives a
tight upper bound for the BER performance of -bit resolution quantized
systems using non-uniform quantizers, whereas the use of uniform quantizers
cause a lower performance for the same systems. We also found a small BER
performance degradation in coarse quantized systems, for example 2-3 bits QPSK
and 3-4 bits 16-QAM, compared to the full-precision (unquantized) case.
However, this performance degradation can be compensated by increasing the
number of antennas at the BS.Comment: 9 pages, 7 figures, submitted to the IEEE Journal
Unifying Message Passing Algorithms Under the Framework of Constrained Bethe Free Energy Minimization
Variational message passing (VMP), belief propagation (BP) and expectation
propagation (EP) have found their wide applications in complex statistical
signal processing problems. In addition to viewing them as a class of
algorithms operating on graphical models, this paper unifies them under an
optimization framework, namely, Bethe free energy minimization with differently
and appropriately imposed constraints. This new perspective in terms of
constraint manipulation can offer additional insights on the connection between
different message passing algorithms and is valid for a generic statistical
model. It also founds a theoretical framework to systematically derive message
passing variants. Taking the sparse signal recovery (SSR) problem as an
example, a low-complexity EP variant can be obtained by simple constraint
reformulation, delivering better estimation performance with lower complexity
than the standard EP algorithm. Furthermore, we can resort to the framework for
the systematic derivation of hybrid message passing for complex inference
tasks. Notably, a hybrid message passing algorithm is exemplarily derived for
joint SSR and statistical model learning with near-optimal inference
performance and scalable complexity