111 research outputs found
Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO with Low-Precision ADCs
This paper considers a multiple-input multiple-output (MIMO) receiver with
very low-precision analog-to-digital convertors (ADCs) with the goal of
developing massive MIMO antenna systems that require minimal cost and power.
Previous studies demonstrated that the training duration should be {\em
relatively long} to obtain acceptable channel state information. To address
this requirement, we adopt a joint channel-and-data (JCD) estimation method
based on Bayes-optimal inference. This method yields minimal mean square errors
with respect to the channels and payload data. We develop a Bayes-optimal JCD
estimator using a recent technique based on approximate message passing. We
then present an analytical framework to study the theoretical performance of
the estimator in the large-system limit. Simulation results confirm our
analytical results, which allow the efficient evaluation of the performance of
quantized massive MIMO systems and provide insights into effective system
design.Comment: accepted in IEEE Transactions on Signal Processin
On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems
Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output
(MIMO) systems is a favorable candidate for the fifth generation (5G) cellular
systems. However, a key challenge is the high power consumption imposed by its
numerous radio frequency (RF) chains, which may be mitigated by opting for
low-resolution analog-to-digital converters (ADCs), whilst tolerating a
moderate performance loss. In this article, we discuss several important issues
based on the most recent research on mmWave massive MIMO systems relying on
low-resolution ADCs. We discuss the key transceiver design challenges including
channel estimation, signal detector, channel information feedback and transmit
precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative
technique of improving the overall system performance. Finally, the associated
challenges and potential implementations of the practical 5G mmWave massive
MIMO system {with ADC quantizers} are discussed.Comment: to appear in IEEE Communications Magazin
Joint Channel-and-Data Estimation for Large-MIMO Systems with Low-Precision ADCs
The use of low precision (e.g., 1-3 bits) analog-to-digital convenors (ADCs)
in very large multiple-input multiple-output (MIMO) systems is a technique to
reduce cost and power consumption. In this context, nevertheless, it has been
shown that the training duration is required to be {\em very large} just to
obtain an acceptable channel state information (CSI) at the receiver. A
possible solution to the quantized MIMO systems is joint channel-and-data (JCD)
estimation. This paper first develops an analytical framework for studying the
quantized MIMO system using JCD estimation. In particular, we use the
Bayes-optimal inference for the JCD estimation and realize this estimator
utilizing a recent technique based on approximate message passing. Large-system
analysis based on the replica method is then adopted to derive the asymptotic
performances of the JCD estimator. Results from simulations confirm our
theoretical findings and reveal that the JCD estimator can provide a
significant gain over conventional pilot-only schemes in the quantized MIMO
system.Comment: 7 pages, 4 figure
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
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