24 research outputs found
Massive MIMO Downlink 1-Bit Precoding with Linear Programming for PSK Signaling
Quantized massive multiple-input-multiple-output (MIMO) systems are gaining
more interest due to their power efficiency. We present a new precoding
technique to mitigate the multi-user interference and the quantization
distortions in a downlink multi-user (MU) multiple-input-single-output (MISO)
system with 1-bit quantization at the transmitter. This work is restricted to
PSK modulation schemes. The transmit signal vector is optimized for every
desired received vector taking into account the 1-bit quantization. The
optimization is based on maximizing the safety margin to the decision
thresholds of the PSK modulation. Simulation results show a significant gain in
terms of the uncoded bit-error-ratio (BER) compared to the existing linear
precoding techniques.Comment: Submitted to SPAWC 201
Massive MU-MIMO-OFDM Downlink with One-Bit DACs and Linear Precoding
Massive multiuser (MU) multiple-input multiple- output (MIMO) is foreseen to
be a key technology in future wireless communication systems. In this paper, we
analyze the downlink performance of an orthogonal frequency division
multiplexing (OFDM)-based massive MU-MIMO system in which the base station (BS)
is equipped with 1-bit digital-to-analog converters (DACs). Using Bussgang's
theorem, we characterize the performance achievable with linear precoders (such
as maximal-ratio transmission and zero forcing) in terms of bit error rate
(BER). Our analysis accounts for the possibility of oversampling the
time-domain transmit signal before the DACs. We further develop a lower bound
on the information-theoretic sum-rate throughput achievable with Gaussian
inputs.
Our results suggest that the performance achievable with 1-bit DACs in a
massive MU-MIMO-OFDM downlink are satisfactory provided that the number of BS
antennas is sufficiently large
Detection of 2x2 MIMO signals
In this paper, we investigate synchronization and equalization of 2 x 2 MIMO signals. We make a step further than that is described in our patent. In the patent, 3 PLLs and a four-channel adaptive filter was needed. Here we decrease the number of PLLs to two and use an adaptive filter of only four channels. In addition to that, we shortly introduce the filter method and the FFT method as well, for synchronization. False detection cancellation is also mentioned. The so-called 1-bit technique has been compared to our method. After briefly introducing the ideas, detailed Matlab or AWR analyses follow. Input data are real measurements, so the analyses serve also as experimental verifications. We take a glimpse on higher order MIMO and higher order modulations as well
Neural-Network Optimized 1-bit Precoding for Massive MU-MIMO
Base station (BS) architectures for massive multi-user (MU) multiple-input
multiple-output (MIMO) wireless systems are equipped with hundreds of antennas
to serve tens of users on the same time-frequency channel. The immense number
of BS antennas incurs high system costs, power, and interconnect bandwidth. To
circumvent these obstacles, sophisticated MU precoding algorithms that enable
the use of 1-bit DACs have been proposed. Many of these precoders feature
parameters that are, traditionally, tuned manually to optimize their
performance. We propose to use deep-learning tools to automatically tune such
1-bit precoders. Specifically, we optimize the biConvex 1-bit PrecOding (C2PO)
algorithm using neural networks. Compared to the original C2PO algorithm, our
neural-network optimized (NNO-)C2PO achieves the same error-rate performance at
lower complexity. Moreover, by training NNO-C2PO for
different channel models, we show that 1-bit precoding can be made robust to
vastly changing propagation conditions
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