115 research outputs found
Linear Precoding with Low-Resolution DACs for Massive MU-MIMO-OFDM Downlink
We consider the downlink of a massive multiuser (MU) multiple-input
multiple-output (MIMO) system in which the base station (BS) is equipped with
low-resolution digital-to-analog converters (DACs). In contrast to most
existing results, we assume that the system operates over a frequency-selective
wideband channel and uses orthogonal frequency division multiplexing (OFDM) to
simplify equalization at the user equipments (UEs). Furthermore, we consider
the practically relevant case of oversampling DACs. We theoretically analyze
the uncoded bit error rate (BER) performance with linear precoders (e.g., zero
forcing) and quadrature phase-shift keying using Bussgang's theorem. We also
develop a lower bound on the information-theoretic sum-rate throughput
achievable with Gaussian inputs, which can be evaluated in closed form for the
case of 1-bit DACs. For the case of multi-bit DACs, we derive approximate, yet
accurate, expressions for the distortion caused by low-precision DACs, which
can be used to establish lower bounds on the corresponding sum-rate throughput.
Our results demonstrate that, for a massive MU-MIMO-OFDM system with a
128-antenna BS serving 16 UEs, only 3--4 DAC bits are required to achieve an
uncoded BER of 10^-4 with a negligible performance loss compared to the
infinite-resolution case at the cost of additional out-of-band emissions.
Furthermore, our results highlight the importance of taking into account the
inherent spatial and temporal correlations caused by low-precision DACs
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
On Out-of-Band Emissions of Quantized Precoding in Massive MU-MIMO-OFDM
We analyze out-of-band (OOB) emissions in the massive multi-user (MU)
multiple-input multiple-output (MIMO) downlink. We focus on systems in which
the base station (BS) is equipped with low-resolution digital-to-analog
converters (DACs) and orthogonal frequency-division multiplexing (OFDM) is used
to communicate to the user equipments (UEs) over frequency-selective channels.
We demonstrate that analog filtering in combination with simple
frequency-domain digital predistortion (DPD) at the BS enables a significant
reduction of OOB emissions, but degrades the
signal-to-interference-noise-and-distortion ratio (SINDR) at the UEs and
increases the peak-to-average power ratio (PAR) at the BS. We use Bussgang's
theorem to characterize the tradeoffs between OOB emissions, SINDR, and PAR,
and to study the impact of analog filters and DPD on the error-rate performance
of the massive MU-MIMO-OFDM downlink. Our results show that by carefully tuning
the parameters of the analog filters, one can achieve a significant reduction
in OOB emissions with only a moderate degradation of error-rate performance and
PAR.Comment: Presented at the 2017 Asilomar Conference on Signals, Systems, and
Computers, 6 page
One-Bit Massive MIMO: Channel Estimation and High-Order Modulations
We investigate the information-theoretic throughout achievable on a fading
communication link when the receiver is equipped with one-bit analog-to-digital
converters (ADCs). The analysis is conducted for the setting where neither the
transmitter nor the receiver have a priori information on the realization of
the fading channels. This means that channel-state information needs to be
acquired at the receiver on the basis of the one-bit quantized channel outputs.
We show that least-squares (LS) channel estimation combined with joint pilot
and data processing is capacity achieving in the single-user,
single-receive-antenna case.
We also investigate the achievable uplink throughput in a massive
multiple-input multiple-output system where each element of the antenna array
at the receiver base-station feeds a one-bit ADC. We show that LS channel
estimation and maximum-ratio combining are sufficient to support both multiuser
operation and the use of high-order constellations. This holds in spite of the
severe nonlinearity introduced by the one-bit ADCs
Massive Multi-Antenna Communications with Low-Resolution Data Converters
Massive multi-user (MU) multiple-input multiple-output (MIMO) will be a core technology in future cellular communication systems. In massive MU-MIMO systems, the number of antennas at the base station (BS) is scaled up by several orders of magnitude compared to traditional multi-antenna systems with the goals of enabling large gains in capacity and energy efficiency. However, scaling up the number of active antenna elements at the BS will lead to significant increases in power consumption and system costs unless power-efficient and low-cost hardware components are used. In this thesis, we investigate the performance of massive MU-MIMO systems for the case when the BS is equipped with low-resolution data converters.First, we consider the massive MU-MIMO uplink for the case when the BS uses low-resolution analog-to-digital converters (ADCs) to convert the received signal into the digital domain. Our focus is on the case where neither the transmitter nor the receiver have any a priori channel state information (CSI), which implies that the channel realizations have to be learned through pilot transmission followed by BS-side channel estimation, based on coarsely quantized observations. We derive a low-complexity channel estimator and present lower bounds and closed-form approximations for the information-theoretic rates achievable with the proposed channel estimator together with conventional linear detection algorithms. Second, we consider the massive MU-MIMO downlink for the case when the BS uses low-resolution digital-to-analog converters (DACs) to generate the transmit signal. We derive lower bounds and closed-form approximations for the achievable rates with linear precoding under the assumption that the BS has access to perfect CSI. We also propose novel nonlinear precoding algorithms that are shown to significantly outperform linear precoding for the extreme case of 1-bit DACs. Specifically, for the case of symbol-rate 1-bit DACs and frequency-flat channels, we develop a multitude of nonlinear precoders that trade between performance and complexity. We then extend the most promising nonlinear precoders to the case of oversampling 1-bit DACs and orthogonal frequency-division multiplexing for operation over frequency-selective channels.Third, we extend our analysis to take into account other hardware imperfections such as nonlinear amplifiers and local oscillators with phase noise.The results in this thesis suggest that the resolution of the ADCs and DACs in massive MU-MIMO systems can be reduced significantly compared to what is used in today\u27s state-of-the-art communication systems, without significantly reducing the overall system performance
MSE-optimal 1-bit Precoding for Multiuser MIMO via Branch and Bound
In this paper, we solve the sum mean-squared error (MSE)-optimal 1-bit
quantized precoding problem exactly for small-to-moderate sized multiuser
multiple-input multiple-output (MU-MIMO) systems via branch and bound. To this
end, we reformulate the original NP-hard precoding problem as a tree search and
deploy a number of strategies that improve the pruning efficiency without
sacrificing optimality. We evaluate the error-rate performance and the
complexity of the resulting 1-bit branch-and-bound (BB-1) precoder, and compare
its efficacy to that of existing, suboptimal algorithms for 1-bit precoding in
MU-MIMO systems
Massive MU-MIMO-OFDM Uplink with Hardware Impairments: Modeling and Analysis
We study the impact of hardware impairments at the base station (BS) of an
orthogonal frequency-division multiplexing (OFDM)-based massive multiuser (MU)
multiple-input multiple-output (MIMO) uplink system. We leverage Bussgang's
theorem to develop accurate models for the distortions caused by nonlinear
low-noise amplifiers, local oscillators with phase noise, and oversampling
finite-resolution analog-to-digital converters. By combining the individual
effects of these hardware models, we obtain a composite model for the BS-side
distortion caused by nonideal hardware that takes into account its inherent
correlation in time, frequency, and across antennas. We use this composite
model to analyze the impact of BS-side hardware impairments on the performance
of realistic massive MU-MIMO-OFDM uplink systems
Hardware-friendly two-stage spatial equalization for all-digital mm-wave massive MU-MIMO
Next generation wireless communication systems are expected to combine millimeter-wave communication with massive multi-user multiple-input multiple-output technology. All-digital base-station implementations for such systems need to process high-dimensional data at extremely high rates, which results in excessively high power consumption. In this paper, we propose two-stage spatial equalizers that first reduce the problem dimension by means of a hardware-friendly, low-resolution linear transform followed by spatial equalization on a lower-dimensional signal. We consider adaptive and non-adaptive dimensionality reduction strategies and demonstrate that the proposed two-stage spatial equalizers are able to approach the performance of conventional linear spatial equalizers that directly operate on high-dimensional data, while offering the potential to reduce the power consumption of spatial equalization
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
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