237 research outputs found
SVM-Based Channel Estimation and Data Detection for One-Bit Massive MIMO systems
The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much more challenging. In this paper, we show how Support Vector Machine (SVM), a well-known supervised-learning technique in machine learning, can be exploited to provide efficient and robust channel estimation and data detection in massive MIMO systems with one-bit ADCs. First, the problem of channel estimation for uncorrelated channels is formulated as a conventional SVM problem. The objective function of this SVM problem is then modified for estimating spatially correlated channels. Next, a two-stage detection algorithm is proposed where SVM is further exploited in the first stage. The performance of the proposed data detection method is very close to that of Maximum-Likelihood (ML) data detection when the channel is perfectly known. We also propose an SVM-based joint Channel Estimation and Data Detection (CE-DD) method, which makes use of both the to-be-decoded data vectors and the pilot data vectors to improve the estimation and detection performance. Finally, an extension of the proposed methods to OFDM systems with frequency-selective fading channels is presented. Simulation results show that the proposed methods are efficient and robust, and also outperform existing ones
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
- …