3,150 research outputs found
Channel Estimation for Spatially/Temporally Correlated Massive MIMO Systems with One-Bit ADCs
This paper considers the channel estimation problem for massive
multiple-input multiple-output (MIMO) systems that use one-bit
analog-to-digital converters (ADCs). Previous channel estimation techniques for
massive MIMO using one-bit ADCs are all based on single-shot estimation without
exploiting the inherent temporal correlation in wireless channels. In this
paper, we propose an adaptive channel estimation technique taking the spatial
and temporal correlations into account for massive MIMO with one-bit ADCs. We
first use the Bussgang decomposition to linearize the one-bit quantized
received signals. Then, we adopt the Kalman filter to estimate the spatially
and temporally correlated channels. Since the quantization noise is not
Gaussian, we assume the effective noise as a Gaussian noise with the same
statistics to apply the Kalman filtering. We also implement the truncated
polynomial expansion-based low complexity channel estimator with negligible
performance loss. Numerical results reveal that the proposed channel estimators
can improve the estimation accuracy significantly by using the spatial and
temporal correlations of channels.Comment: Accepted to EURASIP Journal on Wireless Communications and Networkin
Beamspace Aware Adaptive Channel Estimation for Single-Carrier Time-varying Massive MIMO Channels
In this paper, the problem of sequential beam construction and adaptive
channel estimation based on reduced rank (RR) Kalman filtering for
frequency-selective massive multiple-input multiple-output (MIMO) systems
employing single-carrier (SC) in time division duplex (TDD) mode are
considered. In two-stage beamforming, a new algorithm for statistical
pre-beamformer design is proposed for spatially correlated time-varying
wideband MIMO channels under the assumption that the channel is a stationary
Gauss-Markov random process. The proposed algorithm yields a nearly optimal
pre-beamformer whose beam pattern is designed sequentially with low complexity
by taking the user-grouping into account, and exploiting the properties of
Kalman filtering and associated prediction error covariance matrices. The
resulting design, based on the second order statistical properties of the
channel, generates beamspace on which the RR Kalman estimator can be realized
as accurately as possible. It is observed that the adaptive channel estimation
technique together with the proposed sequential beamspace construction shows
remarkable robustness to the pilot interference. This comes with significant
reduction in both pilot overhead and dimension of the pre-beamformer lowering
both hardware complexity and power consumption.Comment: 7 pages, 3 figures, accepted by IEEE ICC 2017 Wireless Communications
Symposiu
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
A Novel Antenna Selection Scheme for Spatially Correlated Massive MIMO Uplinks with Imperfect Channel Estimation
We propose a new antenna selection scheme for a massive MIMO system with a
single user terminal and a base station with a large number of antennas. We
consider a practical scenario where there is a realistic correlation among the
antennas and imperfect channel estimation at the receiver side. The proposed
scheme exploits the sparsity of the channel matrix for the effective selection
of a limited number of antennas. To this end, we compute a sparse channel
matrix by minimising the mean squared error. This optimisation problem is then
solved by the well-known orthogonal matching pursuit algorithm. Widely used
models for spatial correlation among the antennas and channel estimation errors
are considered in this work. Simulation results demonstrate that when the
impacts of spatial correlation and imperfect channel estimation introduced, the
proposed scheme in the paper can significantly reduce complexity of the
receiver, without degrading the system performance compared to the maximum
ratio combining.Comment: in Proc. IEEE 81st Vehicular Technology Conference (VTC), May 2015, 6
pages, 5 figure
Low-Complexity and Robust Quantized Hybrid Beamforming and Channel Estimation
Hybrid beamforming with phase shifters and switches has been identified as a low-cost and energy-efficient approach to harness the benefits of massive multiple-input multiple-output (MIMO) systems. In this paper, three subconnected hybrid beamforming structures with different combinations of phase shifters and switches will be considered. Firstly we assume that perfect channel state information (CSI) is available and the wireless channel follows uncorrelated Rayleigh fading model. Then, we derive the closed-form expressions of the low-complexity beamformers and their asymptotic achievable sum-rates. Based on the proposed beamformers, we develop quantized hybrid beamforming and channel estimation techniques for correlated Rayleigh fading channels. These methods rely on designing novel RF codebooks and they can be used in both CSI acquisition and data transmission phases. The proposed methods benefit from low computational complexity, low signaling overhead and robustness to estimation errors. Moreover, they are applicable to both frequency and time division duplex systems
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