An Overview of Low-Rank Channel Estimation for Massive MIMO Systems
Hongxiang Xie,Feifei Gao,Shi Jin +2 more
TLDR
A general overview of the current low-rank channel estimation approaches is provided, including their basic assumptions, key results, as well as pros and cons on addressing the aforementioned tricky challenges.Abstract:
Massive multiple-input multiple-output is a promising physical layer technology for 5G wireless communications due to its capability of high spectrum and energy efficiency, high spatial resolution, and simple transceiver design. To embrace its potential gains, the acquisition of channel state information is crucial, which unfortunately faces a number of challenges, such as the uplink pilot contamination, the overhead of downlink training and feedback, and the computational complexity. In order to reduce the effective channel dimensions, researchers have been investigating the low-rank (sparse) properties of channel environments from different viewpoints. This paper then provides a general overview of the current low-rank channel estimation approaches, including their basic assumptions, key results, as well as pros and cons on addressing the aforementioned tricky challenges. Comparisons among all these methods are provided for better understanding and some future research prospects for these low-rank approaches are also forecasted.read more
Citations
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Proceedings ArticleDOI
Error Bounds for FDD Massive MIMO Channel Covariance Conversion with Set-Theoretic Methods
TL;DR: In this paper, the performance of a simple algorithm that requires only a simple matrix-vector multiplication cannot be improved significantly in some practical scenarios, especially if coarse information about the support of the angular power spectrum is available.
Journal ArticleDOI
Non-Iterative Downlink Training Sequence Design Based on Sum Rate Maximization in FDD Massive MIMO Systems
TL;DR: The analysis of the complexity results demonstrates that more than four orders ofmagnitude reduction in the computational complexity is achieved using the superposition training design, which signifies the feasibility of this approach for practical implementations compared with state-of-the-art iterative algorithms for DL training designs.
Proceedings ArticleDOI
Scalable and Distributed MMSE Algorithms for Uplink Receive Combining in Cell-Free Massive MIMO Systems
Robbe Van Rompaey,Marc Moonen +1 more
TL;DR: In this article, the problem of optimal uplink receive combining is tackled by providing an efficient distributed MMSE algorithm, with a minimal number of exchanged parameters between the APs and the network center.
Journal ArticleDOI
Randomized Approximate Channel Estimator in Massive-MIMO Communication
TL;DR: A rank-restrained low-complexity MMSE channel estimator for massive MIMO communications is proposed, leveraging a novel concept of randomized low-rank approximation, which significantly reduces the time complexity as well as the energy consumption in CSI acquisition, which yet attains the near-optimal estimation accuracy.
Journal ArticleDOI
SHAFA: sparse hybrid adaptive filtering algorithm to estimate channels in various SNR environments
TL;DR: The authors construct a cost function that uses the statistical error term and sparse penalty term and devise a non-uniform step size in the proposed algorithm to further balance the convergence speed and estimation error.
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