Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals

By Mehrzad Biguesh, Alex B. Gershman. IEEE Transactions on Signal Processing, 2006

1,014 citations recorded by the source corpus.

Subfields: Computational Mechanics, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/541e5bbfbff7f0a4b8900174e70753f094c2d77f

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In Computational Mechanics

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Citations in the corpus

A Unified Transmission Strategy for TDD/FDD Massive MIMO Systems With Spatial Basis Expansion Model (2017, background): “The last error term comes from the noise and is proportional to τ(2), which is consistent with the result in the conventional τ × 1 MISO DL system [33].”
Intelligent Reflecting Surface-Assisted Multi-User MISO Communication: Channel Estimation and Beamforming Design (2020, methodology): “MMSE estimator signiﬁcantly outperforms the LS estimator since it is based on the Bayesian estimation technique which achieves the minimum MSE between the true and estimated channel by exploiting prior knowledge of the channel’s large scale fading statistics [37].”
Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues (2016, background): “Channel estimation as a fundamental problem in wireless communication systems has been extensively investigated i n the past decades [85].”
MIMO radar waveform design based on mutual information and minimum mean-square error estimation (2007, background): “For example, the mathematical optimization problems formulated in [34][37] can be seen to be special cases of that treated in Theorem 1.”
Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems (2014, methodology): “There is one important observation for massive MIMO systems, which can help to address the above issues.”
Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead (2019, methodology): “Conventionally, this information is obtained by exploiting pilot signals sent to the radar by the communication system, where classic methods such as least-squares (LS) and minimum mean squared error (MMSE) channel estimation [62] could be readily applied.”
Millimeter Wave Communications for Future Mobile Networks (2017, methodology): “In the second step, the effective channel matrix W H A HF A in the analog domain is estimated by classical algorithms, such as least squares (LS) [139].”
A Comprehensive Survey of Pilot Contamination in Massive MIMO—5G System (2016, methodology): “The different CE schemes for conventional MIMO [68] and massive MIMO have been studied [25], [65], [67], [69]–[72] and a summary of this is presented in Table II.”
On channel estimation and optimal training design for amplify and forward relay networks (2008, methodology): “This is a consistent phenomenon as in the traditional SISO or MISO channel estimation [19].”
Millimeter-Wave Massive MIMO Communication for Future Wireless Systems: A Survey (2018, background): “Research efforts have identified increased training period as important for improved and accurate CSI acquisition [103], and the optimization of the training duration can be formulated as a concave maximization problem with global optimal solution obtainable via convex”
Hybrid MIMO Architectures for Millimeter Wave Communications: Phase Shifters or Switches? (2015, methodology): “In traditional training-based methods considering architectures with one RF chain per antenna, it is well known that any sequence of training symbols with orthogonal rows of the same norm is optimal in the least-squares sense [88].”