Proceedings Article10.1109/ICSP.2016.7878018
Joint channel estimation algorithm based on structured compressed sensing for FDD multi-user massive MIMO
Ruoyu Zhang,Honglin Zhao,Shaobo Jia,Chengzhao Shan +3 more
- 01 Nov 2016
pp 1202-1207
5
TL;DR: A novel channel estimation algorithm for multi-user massive MIMO system employing structured compressed sensing (CS) theory and a structured joint subspace matching pursuit (SJSMP) algorithm is proposed to estimate channels with limited pilot jointly at the BS.
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Abstract: Accurate channel state information (CSI) at transmitter is of importance to sufficiently exploit the merits of massive multiple input multiple output (MIMO). Because of the large amount of antennas at base station (BS), the pilot overhead becomes unaffordable, especially in frequency-division duplexing (FDD) massive MIMO systems. To alleviate the overwhelming pilot overhead, a novel channel estimation algorithm for multi-user massive MIMO system employing structured compressed sensing (CS) theory is proposed. Firstly, the angular domain channel representation of massive MIMO is analyzed. Then, due to the practical scattering environment, the common sparsity and private sparsity structure of channel matrix exist in multi-user massive MIMO system. Finally, basing on the statistical information of multi-user channel matrix, a structured joint subspace matching pursuit (SJSMP) algorithm is proposed, which is to estimate channels with limited pilot jointly at the BS. Particularly, the common support and private support of multi-user channel matrix are separately estimated to reduce the pilot overhead with improved CSI estimation quality in terms of MSE.
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Citations
Distributed Compressed Sensing Aided Sparse Channel Estimation in FDD Massive MIMO System
TL;DR: A DCS-aided channel estimation algorithm, which combines least square method and DCS method, is proposed to estimate the two parts of channel vector in angular domain among different subcarriers and is capable to significantly reduce the training overhead for channel estimation.
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A comprehensive survey: Small cell meets massive MIMO
TL;DR: The state of the art of massive MIMO technique with small cell network with performance metrics and modeling tools for system analysis are studied and future challenges and research problems are highlighted.
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Adaptive training‐feedback scheme for FDD in massive MIMO systems
Yi Huang,Haiquan Wang,Danbei Gao,Zhijin Zhao +3 more
TL;DR: In this paper , an adaptive training-feedback scheme based on spatial reciprocity in frequency division duplex (FDD) massive MIMO systems was proposed to improve the estimation accuracy and to minimize the training consumption.
User Grouping based Structured Joint Sparse Channel Estimation for 3D MIMO System
Xudong Fang,Wuyang Zhou +1 more
- 01 Oct 2019
TL;DR: A user grouping based structured joint sparse channel estimation (UG-SJSCE) algorithm which can achieve significantly lower complexity and lower normalized mean squared error (NMSE) is proposed.
Spatial Information Aided Joint UL/DL Channel Tracking for Massive MIMO System in HST Environment
Yangliu Zhao,Kaihang Zheng,Yinglei Teng,An Liu,Vincent Lau +4 more
TL;DR: This paper proposes a dynamic grid-based joint UL/DL channel tracking scheme for massive MIMO HST systems, leveraging spatial reciprocity and fuzzy spatial information to improve tracking performance and estimate AoAs/AoDs using a block MM algorithm with SGFS.
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TL;DR: It is demonstrated theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal.
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TL;DR: In this paper, a greedy algorithm called Orthogonal Matching Pursuit (OMP) was proposed to recover a signal with m nonzero entries in dimension 1 given O(m n d) random linear measurements of that signal.
Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays
Fredrik Rusek,Daniel Persson,Buon Kiong Lau,Erik G. Larsson,Thomas L. Marzetta,Fredrik Tufvesson +5 more
TL;DR: The gains in multiuser systems are even more impressive, because such systems offer the possibility to transmit simultaneously to several users and the flexibility to select what users to schedule for reception at any given point in time.
Scaling up MIMO: Opportunities and Challenges with Very Large Arrays
Fredrik Rusek,Daniel Persson,Buon Kiong Lau,Erik G. Larsson,Thomas L. Marzetta,Ove Edfors,Fredrik Tufvesson +6 more
TL;DR: Very large MIMO as mentioned in this paper is a new research field both in communication theory, propagation, and electronics and represents a paradigm shift in the way of thinking both with regards to theory, systems and implementation.