153 research outputs found
A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels
In this paper, we establish a general framework on the reduced dimensional
channel state information (CSI) estimation and pre-beamformer design for
frequency-selective massive multiple-input multiple-output MIMO systems
employing single-carrier (SC) modulation in time division duplex (TDD) mode by
exploiting the joint angle-delay domain channel sparsity in millimeter (mm)
wave frequencies. First, based on a generic subspace projection taking the
joint angle-delay power profile and user-grouping into account, the reduced
rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived
for spatially correlated wideband MIMO channels. Second, the statistical
pre-beamformer design is considered for frequency-selective SC massive MIMO
channels. We examine the dimension reduction problem and subspace (beamspace)
construction on which the RR-MMSE estimation can be realized as accurately as
possible. Finally, a spatio-temporal domain correlator type reduced rank
channel estimator, as an approximation of the RR-MMSE estimate, is obtained by
carrying out least square (LS) estimation in a proper reduced dimensional
beamspace. It is observed that the proposed techniques show remarkable
robustness to the pilot interference (or contamination) with a significant
reduction in pilot overhead
Distributed Compressive CSIT Estimation and Feedback for FDD Multi-user Massive MIMO Systems
To fully utilize the spatial multiplexing gains or array gains of massive
MIMO, the channel state information must be obtained at the transmitter side
(CSIT). However, conventional CSIT estimation approaches are not suitable for
FDD massive MIMO systems because of the overwhelming training and feedback
overhead. In this paper, we consider multi-user massive MIMO systems and deploy
the compressive sensing (CS) technique to reduce the training as well as the
feedback overhead in the CSIT estimation. The multi-user massive MIMO systems
exhibits a hidden joint sparsity structure in the user channel matrices due to
the shared local scatterers in the physical propagation environment. As such,
instead of naively applying the conventional CS to the CSIT estimation, we
propose a distributed compressive CSIT estimation scheme so that the compressed
measurements are observed at the users locally, while the CSIT recovery is
performed at the base station jointly. A joint orthogonal matching pursuit
recovery algorithm is proposed to perform the CSIT recovery, with the
capability of exploiting the hidden joint sparsity in the user channel
matrices. We analyze the obtained CSIT quality in terms of the normalized mean
absolute error, and through the closed-form expressions, we obtain simple
insights into how the joint channel sparsity can be exploited to improve the
CSIT recovery performance.Comment: 16 double-column pages, accepted for publication in IEEE Transactions
on Signal Processin
Performance Analysis of Channel Extrapolation in FDD Massive MIMO Systems
Channel estimation for the downlink of frequency division duplex (FDD)
massive MIMO systems is well known to generate a large overhead as the amount
of training generally scales with the number of transmit antennas in a MIMO
system. In this paper, we consider the solution of extrapolating the channel
frequency response from uplink pilot estimates to the downlink frequency band,
which completely removes the training overhead. We first show that conventional
estimators fail to achieve reasonable accuracy. We propose instead to use
high-resolution channel estimation. We derive theoretical lower bounds (LB) for
the mean squared error (MSE) of the extrapolated channel. Assuming that the
paths are well separated, the LB is simplified in an expression that gives
considerable physical insight. It is then shown that the MSE is inversely
proportional to the number of receive antennas while the extrapolation
performance penalty scales with the square of the ratio of the frequency offset
and the training bandwidth. The channel extrapolation performance is validated
through numeric simulations and experimental measurements taken in an anechoic
chamber. Our main conclusion is that channel extrapolation is a viable solution
for FDD massive MIMO systems if accurate system calibration is performed and
favorable propagation conditions are present.Comment: arXiv admin note: substantial text overlap with arXiv:1902.0684
Improving the Performance of R17 Type-II Codebook with Deep Learning
The Type-II codebook in Release 17 (R17) exploits the angular-delay-domain
partial reciprocity between uplink and downlink channels to select part of
angular-delay-domain ports for measuring and feeding back the downlink channel
state information (CSI), where the performance of existing deep learning
enhanced CSI feedback methods is limited due to the deficiency of sparse
structures. To address this issue, we propose two new perspectives of adopting
deep learning to improve the R17 Type-II codebook. Firstly, considering the low
signal-to-noise ratio of uplink channels, deep learning is utilized to
accurately select the dominant angular-delay-domain ports, where the focal loss
is harnessed to solve the class imbalance problem. Secondly, we propose to
adopt deep learning to reconstruct the downlink CSI based on the feedback of
the R17 Type-II codebook at the base station, where the information of sparse
structures can be effectively leveraged. Besides, a weighted shortcut module is
designed to facilitate the accurate reconstruction. Simulation results
demonstrate that our proposed methods could improve the sum rate performance
compared with its traditional R17 Type-II codebook and deep learning
benchmarks.Comment: Accepted by IEEE GLOBECOM 2023, conference version of
Arxiv:2305.0808
Massive MIMO ์์คํ ์ ์ํ ์ฑ๋ ์ถ์ ๋ฐ ํผ๋๋ฐฑ ๊ธฐ๋ฒ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ์ ๊ธฐยท์ปดํจํฐ๊ณตํ๋ถ, 2017. 2. ์ด์ ์ฐ.To meet the demand of high throughput in next generation wireless systems, various directions for physical layer evolution are being explored. Massive multiple-input multiple-output (MIMO) systems, characterized by a large number of antennas at the transmitter, are expected to become a key enabler for spectral efficiency improvement. In massive MIMO systems, thanks to the orthogonality between different users' channels, high spectral and energy efficiency can be achieved through simple signal processing techniques. However, to get such advantages, accurate channel state information (CSI) needs to be available, and acquiring CSI in massive MIMO systems is challenging due to the increased channel dimension. In frequency division duplexing (FDD) systems, where CSI at the transmitter is achieved through downlink training and uplink feedback, the overhead for the training and feedback increases proportionally to the number of antennas, and the resource for data transmission becomes scarce in massive MIMO systems. In time division duplexing (TDD) systems, where the channel reciprocity holds and the downlink CSI can be obtained through uplink training, pilot contamination due to correlated pilots becomes a performance bottleneck when the number of antennas increases.
In this dissertation, I propose efficient CSI acquisition techniques for various massive MIMO systems. First, I develop a downlink training technique for FDD massive MIMO systems, which estimates the downlink channel with small overhead. To this end, compressed sensing tools are utilized, and the training overhead can be highly reduced by exploiting the previous channel information. Next, a limited feedback scheme is developed for FDD massive MIMO systems. The proposed scheme reduces the feedback overhead using a dimension reduction technique that exploits spatial and temporal correlation of the channel. Lastly, I analyze the effect of pilot contamination, which has been regarded as a performance bottleneck in multi-cell massive MIMO systems, and propose two uplink training strategies. An iterative pilot design scheme is developed for small networks, and a scalable training framework is also proposed for networks with many cells.1 Introduction 1
1.1 Massive MIMO 1
1.2 CSI Acquisition in Massive MIMO Systems 3
1.3 Contributions and Organization 6
1.4 Notations 7
2 Compressed Sensing-Aided Downlink Training 9
2.1 Introduction 10
2.2 System Model 13
2.2.1 Channel Model 13
2.2.2 Downlink Channel Estimation 16
2.3 CS-Aided Channel Training 19
2.3.1 Training Sequence Design 20
2.3.2 Channel Estimation 21
2.3.3 Estimation Error 23
2.4 Discussions 26
2.4.1 Design of Measurement Matrix 26
2.4.2 Extension to MIMO Systems 27
2.4.3 Comparison to CS with Partial Support Information 28
2.5 Simulation Results 29
2.6 Conclusion 37
3 Projection-Based Differential Feedback 39
3.1 Introduction 40
3.2 System Model 44
3.2.1 Multi-User Beamforming with Limited Feedback 45
3.2.2 Massive MIMO Channel 47
3.3 Projection-Based Differential Feedback 48
3.3.1 Projection-Based Differential Feedback Framework 48
3.3.2 Projection for PBDF Framework 51
3.3.3 Efficient Algorithm 57
3.4 Discussions 58
3.4.1 Projection with Imperfect CSIR 58
3.4.2 Acquisition of Channel Statistics 61
3.5 Simulation Results 62
3.6 Conclusion 69
4 Mitigating Pilot Contamination via Pilot Design 71
4.1 Introduction 72
4.2 System Model 73
4.2.1 Multi-cell Massive MIMO Systems 74
4.2.2 Uplink Channel Training 75
4.2.3 Data Transmission 77
4.3 Iterative Pilot Design Algorithm 78
4.3.1 Algorithm 79
4.3.2 Proof of Convergence 81
4.4 Generalized Pilot Reuse 81
4.4.1 Concept of Pilot Reuse Schemes 81
4.4.2 Pilot Design based on Grassmannian Subspace Packing 82
4.5 Simulation Results 85
4.5.1 Iterative Pilot Design 85
4.5.2 Generalized Pilot Reuse 87
4.6 Conclusion 89
5 Conclusion 91
5.1 Summary 91
5.2 Future Directions 93
Bibliography 96
Abstract (In Korean) 109Docto
Hybrid Message Passing Algorithm for Downlink FDD Massive MIMO-OFDM Channel Estimation
The design of message passing algorithms on factor graphs has been proven to
be an effective manner to implement channel estimation in wireless
communication systems. In Bayesian approaches, a prior probability model that
accurately matches the channel characteristics can effectively improve
estimation performance. In this work, we study the channel estimation problem
in a frequency division duplexing (FDD) downlink massive multiple-input
multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)
system. As the prior probability, we propose the Markov chain two-state
Gaussian mixture with large variance difference (TSGM-LVD) model to exploit the
structured sparsity in the angle-frequency domain of the massive MIMO-OFDM
channel. In addition, we present a new method to derive the hybrid message
passing (HMP) rule, which can calculate the message with mixed linear and
non-linear model. To the best of the authors' knowledge, we are the first to
apply the HMP rule to practical communication systems, designing the
HMP-TSGM-LVD algorithm under the structured turbo-compressed sensing (STCS)
framework. Simulation results demonstrate that the proposed HMP-TSGM-LVD
algorithm converges faster and outperforms its counterparts under a wide range
of simulation settings
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