196 research outputs found
Neural Network-Optimized Channel Estimator and Training Signal Design for MIMO Systems with Few-Bit ADCs
This paper is concerned with channel estimation in MIMO systems with few-bit
ADCs. In these systems, a linear minimum mean-squared error (MMSE) channel
estimator obtained in closed-form is not an optimal solution. We first consider
a deep neural network (DNN) and train it as a non-linear MMSE channel estimator
for few-bit MIMO systems. We then present a first attempt to use DNN in
optimizing the training signal and the MMSE channel estimator concurrently.
Specifically, we propose an autoencoder with a specialized first layer, whose
weights embed the training signal matrix. Consequently, the trained autoencoder
prompts a new training signal design that is customized for the MIMO channel
model under consideration.Comment: 5 pages, 3 figures, to appear in IEEE Signal Processing Letter
DNN-based Detectors for Massive MIMO Systems with Low-Resolution ADCs
Low-resolution analog-to-digital converters (ADCs) have been considered as a
practical and promising solution for reducing cost and power consumption in
massive Multiple-Input-Multiple-Output (MIMO) systems. Unfortunately,
low-resolution ADCs significantly distort the received signals, and thus make
data detection much more challenging. In this paper, we develop a new deep
neural network (DNN) framework for efficient and low-complexity data detection
in low-resolution massive MIMO systems. Based on reformulated maximum
likelihood detection problems, we propose two model-driven DNN-based detectors,
namely OBMNet and FBMNet, for one-bit and few-bit massive MIMO systems,
respectively. The proposed OBMNet and FBMNet detectors have unique and simple
structures designed for low-resolution MIMO receivers and thus can be
efficiently trained and implemented. Numerical results also show that OBMNet
and FBMNet significantly outperform existing detection methods.Comment: 6 pages, 8 figures, submitted for publication. arXiv admin note: text
overlap with arXiv:2008.0375
Linear and Deep Neural Network-based Receivers for Massive MIMO Systems with One-Bit ADCs
The use of one-bit analog-to-digital converters (ADCs) is a practical
solution for reducing cost and power consumption in massive
Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused
by one-bit ADCs makes the data detection task much more challenging. In this
paper, we propose a two-stage detection method for massive MIMO systems with
one-bit ADCs. In the first stage, we propose several linear receivers based on
the Bussgang decomposition, that show significant performance gain over
existing linear receivers. Next, we reformulate the maximum-likelihood (ML)
detection problem to address its non-robustness. Based on the reformulated ML
detection problem, we propose a model-driven deep neural network-based
(DNN-based) receiver, whose performance is comparable with an existing support
vector machine-based receiver, albeit with a much lower computational
complexity. A nearest-neighbor search method is then proposed for the second
stage to refine the first stage solution. Unlike existing search methods that
typically perform the search over a large candidate set, the proposed search
method generates a limited number of most likely candidates and thus limits the
search complexity. Numerical results confirm the low complexity, efficiency,
and robustness of the proposed two-stage detection method.Comment: 12 pages, 10 figure
System Energy-Efficient Hybrid Beamforming for mmWave Multi-user Systems
This paper develops energy-efficient hybrid beamforming designs for mmWave
multi-user systems where analog precoding is realized by switches and phase
shifters such that radio frequency (RF) chain to transmit antenna connections
can be switched off for energy saving. By explicitly considering the effect of
each connection on the required power for baseband and RF signal processing, we
describe the total power consumption in a sparsity form of the analog precoding
matrix. However, these sparsity terms and sparsity-modulus constraints of the
analog precoding make the system energy-efficiency maximization problem
non-convex and challenging to solve. To tackle this problem, we first transform
it into a subtractive-form weighted sum rate and power problem. A compressed
sensing-based re-weighted quadratic-form relaxation method is employed to deal
with the sparsity parts and the sparsity-modulus constraints. We then exploit
alternating minimization of the mean-squared error to solve the equivalent
problem where the digital precoding vectors and the analog precoding matrix are
updated sequentially. The energy efficiency upper bound and a heuristic
algorithm are also examined for comparison purposes. Numerical results confirm
the superior performances of the proposed algorithm over benchmark
energy-efficiency hybrid precoding algorithms and heuristic ones.Comment: submitted to TGC
Energy-Efficient Design for Downlink Cloud Radio Access Networks
This work aims to maximize the energy efficiency of a downlink cloud radio access network (C-RAN), where data is transferred from a baseband unit in the core network to several remote radio heads via a set of edge routers over capacity-limited fronthaul links. The remote radio heads then send the received signals to their users via radio access links. We formulate a new mixed-integer nonlinear problem in which the ratio of network throughput and total power consumption is maximized. This challenging problem formulation includes practical constraints on routing, predefined minimum data rates, fronthaul capacity and maximum RRH transmit power. By employing the successive convex quadratic programming framework, an iterative algorithm is proposed with guaranteed convergence to a Fritz John solution of the formulated problem. Significantly, each iteration of the proposed algorithm solves only one simple convex program. Numerical examples with practical parameters confirm that the proposed joint optimization design markedly improves the C-RAN's energy efficiency compared to benchmark schemes.This work is supported in part by an ECR-HDR scholarship
from The University of Newcastle, in part by the Australian
Research Council Discovery Project grants DP170100939 and
DP160101537, in part by Vietnam National Foundation for
Science and Technology Development under grant number
101.02-2016.11 and in part by a startup fund from San Diego
State University
A novel approach to estimate the evolution of fracture energy and tensile softening curve of concrete from very early age
Concrete fracture properties and their evolution over time are critical inputs for numerous engineering aspects. Despite substantial efforts invested, there exists a crucial need to establish a comprehensive model for reliable estimation of such evolution. In this paper, combining reliable experimental data and in-depth analysis, a novel approach for proper estimation of the evolution of fracture energy and tensile softening curve of concrete from early age is proposed. Fundamentally, the approach relies on three key criteria related to (i) tensile strength, (ii) tensile strength-fracture energy correlation and especially, (iii) centroid coordinates of the area under the stress-crack opening curve. The capability and reliability of the proposed approach are clearly demonstrated through a detailed assessment of these criteria and examples of practical applications
Miniaturized multisensor system with a thermal gradient: Performance beyond the calibration range
Two microchips, each with four identical microstructured sensors using SnO2 nanowires as sensing material (one chip decorated with Ag nanoparticles, the other with Pt nanoparticles), were used as a nano-electronic nose to distinguish five different gases and estimate their concentrations. This innovative approach uses identical sensors working at different operating temperatures thanks to the thermal gradient created by an integrated microheater. A system with in-house developed hardware and software was used to collect signals from the eight sensors and combine them into eight-dimensional data vectors. These vectors were processed with a support vector machine allowing for qualitative and quantitative discrimination of all gases after calibration. The system worked perfectly within the calibrated range (100% correct classification, 6.9% average error on concentration value). This work focuses on minimizing the number of points needed for calibration while maintaining good sensor performance, both for classification and error in estimating concentration. Therefore, the calibration range (in terms of gas concentration) was gradually reduced and further tests were performed with concentrations outside these new reduced limits. Although with only a few training points, down to just two per gas, the system performed well with 96% correct classifications and 31.7% average error for the gases at concentrations up to 25 times higher than its calibration range. At very low concentrations, down to 20 times lower than the calibration range, the system worked less well, with 93% correct classifications and 38.6% average error, probably due to proximity to the limit of detection of the sensors
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