15 research outputs found
BER Performance of Uplink Massive MIMO With Low-Resolution ADCs
Massive multiple-input multiple-output (MIMO) is a promising technology for
next generation wireless communication systems (5G). In this technology, Base
Station (BS) is equipped with a large number of antennas. Employing high
resolution analog-to-digital converters (ADCs) for all antennas may cause high
costs and high power consumption for the BS. By performing numerical results,
we evaluate the use of low-resolution ADCs for uplink massive MIMO by analyzing
Bit Error Rate (BER) performance for different detection techniques (MMSE, ZF)
and different modulations (QPSK, 16-QAM) to find an optimal quantization
resolution. Our results reveal that the BER performance of uplink massive MIMO
systems with a few-bit resolution ADCs is comparable to the case of having full
precision ADCs. We found that the optimum choice of quantization level (number
of bits in ADCs) depends on the modulation technique and the number of antennas
at the BS.Comment: 4 pages, 9 figures; accepted for publication in iccke 201
Spectral shaping with low resolution signals
We aim at investigating the impact of low resolution digital-to-analog
converters (DACs) at the transmitter and low resolution analog-to-digital
converters (ADCs) at the receiver on the required bandwidth and the required
signalto- noise ratio (SNR). In particular, we consider the extreme case of
only 1-bit resolution (with oversampling), where we propose a single carrier
system architecture for minimizing the spectral occupation and the required SNR
of 1-bit signals. In addition, the receiver is optimized to take into account
the effects of quantization at both ends. Through simulations, we show that
despite of the coarse quantization, sufficient spectral confinement is still
achievable.Comment: Presented in Asilomar Conference on Signals, Systems, and Computers
2015, Pacific Grove, C
Low-Complexity Multiuser QAM Detection for Uplink 1-bit Massive MIMO
This work studies multiuser detection for one-bit massive multiple-input
multiple-output (MIMO) systems in order to diminish the power consumption at
the base station (BS). A low-complexity near-maximum-likelihood (nML) multiuser
detection algorithm is designed, assuming that each BS antenna port is
connected with a pair of single-bit resolution analog-to-digital converters
(ADCs) and each user equipment (UE) transmits symbols from a quadrature
amplitude modulation (QAM) constellation. First, a novel convex program is
formulated as a convex surrogate of the ML detector and subsequently solved
through an accelerated first-order method. Then, the solution of the convex
optimization problem is harnessed to solve a refined combinatorial problem with
reduced search space, requiring non-exponential complexity on the number of the
UEs. Judicious simulation study corroborates the efficacy of the resulting
two-phase detection algorithm. The proposed two-phase algorithm can achieve
symbol error rate (SER) performance close to the ML detector, with
significantly reduced computation cost compared to the nML detection schemes in
prior art
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
Divide and Conquer: One-Bit MIMO-OFDM Detection by Inexact Expectation Maximization
Adopting one-bit analog-to-digital convertors (ADCs) for massive
multiple-input multiple-output (MIMO) implementations has great potential in
reducing the hardware cost and power consumption. However, distortions caused
by quantization raise great challenges. In MIMO orthogonal frequency-division
modulation (OFDM) detection, coarse quantization renders the orthogonal
separation among subcarriers inapplicable, forcing us to deal with a problem
that has a very large problem size. In this paper we study the
expectation-maximization (EM) approach for one-bit MIMO-OFDM detection. The
idea is to iteratively decouple the MIMO-OFDM detection problem among
subcarriers. Using the perspective of block coordinate descent, we describe
inexact variants of the classical EM method for providing more flexible and
computationally efficient designs. Simulation results are provided to
illustrate the potential of the divide-and-conquer strategy enabled by EM
Near Maximum-Likelihood Detector and Channel Estimator for Uplink Multiuser Massive MIMO Systems with One-Bit ADCs
In massive multiple-input multiple-output (MIMO) systems, it may not be power
efficient to have a high-resolution analog-to-digital converter (ADC) for each
antenna element. In this paper, a near maximum likelihood (nML) detector for
uplink multiuser massive MIMO systems is proposed where each antenna is
connected to a pair of one-bit ADCs, i.e., one for each real and imaginary
component of the baseband signal. The exhaustive search over all the possible
transmitted vectors required in the original maximum likelihood (ML) detection
problem is relaxed to formulate an ML estimation problem. Then, the ML
estimation problem is converted into a convex optimization problem which can be
efficiently solved. Using the solution, the base station can perform simple
symbol-by-symbol detection for the transmitted signals from multiple users. To
further improve detection performance, we also develop a two-stage nML detector
that exploits the structures of both the original ML and the proposed
(one-stage) nML detectors. Numerical results show that the proposed nML
detectors are efficient enough to simultaneously support multiple uplink users
adopting higher-order constellations, e.g., 16 quadrature amplitude modulation.
Since our detectors exploit the channel state information as part of the
detection, an ML channel estimation technique with one-bit ADCs that shares the
same structure with our proposed nML detector is also developed. The proposed
detectors and channel estimator provide a complete low power solution for the
uplink of a massive MIMO system.Comment: 13 pages, 8 figures, 2 tables, submitted to IEEE Transactions on
Communication
Reliable OFDM Receiver with Ultra-Low Resolution ADC
The use of low-resolution analog-to-digital converters (ADCs) can
significantly reduce power consumption and hardware cost. However, their
resulting severe nonlinear distortion makes reliable data transmission
challenging. For orthogonal frequency division multiplexing (OFDM)
transmission, the orthogonality among subcarriers is destroyed. This
invalidates conventional OFDM receivers relying heavily on this orthogonality.
In this study, we move on to quantized OFDM (Q-OFDM) prototyping implementation
based on our previous achievement in optimal Q-OFDM detection. First, we
propose a novel Q-OFDM channel estimator by extending the generalized Turbo
(GTurbo) framework formerly applied for optimal detection. Specifically, we
integrate a type of robust linear OFDM channel estimator into the original
GTurbo framework and derive its corresponding extrinsic information to
guarantee its convergence. We also propose feasible schemes for automatic gain
control, noise power estimation, and synchronization. Combined with the
proposed inference algorithms, we develop an efficient Q-OFDM receiver
architecture. Furthermore, we construct a proof-of-concept prototyping system
and conduct over-the-air (OTA) experiments to examine its feasibility and
reliability. This is the first work that focuses on both algorithm design and
system implementation in the field of low-resolution quantization
communication. The results of the numerical simulation and OTA experiment
demonstrate that reliable data transmission can be achieved.Comment: 14 pages, 17 figures; accepted by IEEE Transactions on Communication
Mixed-ADC Massive MIMO Uplink in Frequency-Selective Channels
The aim of this paper is to investigate the recently developed mixed-ADC
architecture for frequency-selective channels. Multi-carrier techniques such as
orthogonal frequency division multiplexing (OFDM) are employed to handle
inter-symbol interference (ISI). A frequency-domain equalizer is designed for
mitigating the inter-carrier interference (ICI) introduced by the nonlinearity
of one-bit quantization. For static single-input-multiple-output (SIMO)
channels, a closed-form expression of the generalized mutual information (GMI)
is derived, and based on which the linear frequency-domain equalizer is
optimized. The analysis is then extended to ergodic time-varying SIMO channels
with estimated channel state information (CSI), where numerically tight lower
and upper bounds of the GMI are derived. The analytical framework is naturally
applicable to the multi-user scenario, for both static and time-varying
channels. Extensive numerical studies reveal that the mixed-ADC architecture
with a small proportion of high-resolution ADCs does achieve a dominant portion
of the achievable rate of ideal conventional architecture, and that it
remarkably improves the performance as compared with one-bit massive MIMO.Comment: 14 pages, 10 figures, to appear in IEEE Transactions on
Communication
Joint Channel-Estimation/Decoding with Frequency-Selective Channels and Few-Bit ADCs
We propose a fast and near-optimal approach to joint channel-estimation,
equalization, and decoding of coded single-carrier (SC) transmissions over
frequency-selective channels with few-bit analog-to-digital converters (ADCs).
Our approach leverages parametric bilinear generalized approximate message
passing (PBiGAMP) to reduce the implementation complexity of joint channel
estimation and (soft) symbol decoding to that of a few fast Fourier transforms
(FFTs). Furthermore, it learns and exploits sparsity in the channel impulse
response. Our work is motivated by millimeter-wave systems with bandwidths on
the order of Gsamples/sec, where few-bit ADCs, SC transmissions, and fast
processing all lead to significant reductions in power consumption and
implementation cost. We numerically demonstrate our approach using signals and
channels generated according to the IEEE 802.11ad wireless local area network
(LAN) standard, in the case that the receiver uses analog beamforming and a
single ADC
Low SNR Asymptotic Rates of Vector Channels with One-Bit Outputs
We analyze the performance of multiple-input multiple-output (MIMO) links
with one-bit output quantization in terms of achievable rates and characterize
their performance loss compared to unquantized systems for general channel
statistical models and general channel state information (CSI) at the receiver.
One-bit ADCs are particularly suitable for large-scale millimeter wave MIMO
Communications (massive MIMO) to reduce the hardware complexity. In such
applications, the signal-to-noise ratio per antenna is rather low due to the
propagation loss. Thus, it is crucial to analyze the performance of MIMO
systems in this regime by means of information theoretical methods. Since an
exact and general information-theoretic analysis is not possible, we resort to
the derivation of a general asymptotic expression for the mutual information in
terms of a second order expansion around zero SNR. We show that up to second
order in the SNR, the mutual information of a system with two-level (sign)
output signals incorporates only a power penalty factor of pi/2 (1.96 dB)
compared to system with infinite resolution for all channels of practical
interest with perfect or statistical CSI. An essential aspect of the derivation
is that we do not rely on the common pseudo-quantization noise model