Complex Gaussian belief propagation algorithms for distributed multicell multiuser MIMO detection
Paper
Paper/Presentation Title | Complex Gaussian belief propagation algorithms for distributed multicell multiuser MIMO detection |
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Presentation Type | Paper |
Authors | Yue, Ziqi (Author), Guo, Qing (Author) and Xiang, Wei (Author) |
Journal or Proceedings Title | Proceedings of the IEEE Global Communications Conference (GLOBECOM 2014) |
ERA Conference ID | 42915 |
Number of Pages | 6 |
Year | 2015 |
Place of Publication | Piscataway, NJ. United States |
ISBN | 9781479935116 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/GLOCOM.2014.7037421 |
Conference/Event | IEEE Global Communications Conference (GLOBECOM 2014) |
IEEE Global Telecommunications Conference | |
Event Details | IEEE Global Telecommunications Conference IEEE GLOBECOM Rank B B |
Event Details | IEEE Global Communications Conference (GLOBECOM 2014) Parent IEEE Global Telecommunications Conference Delivery In person Event Date 08 to end of 12 Dec 2014 Event Location Austin, United States |
Abstract | In this paper, we considered a practical system where the number of base station antennas serving tens users is large but finite. The signal must be collected before detection, and the optimal maximum a posteriori (MAP) detector has high computational complexity that grows exponentially with the number of users. Even the suboptimal MMSE-SIC (soft interference cancellation) requires complexity proportional to the cube of the number of the antenna units. In this paper, we proposed a distributed detection scheme done at each antenna unit separately, termed complex Gaussian belief propagation algorithm (CGaBP), for multicell multi-user detection. The multiuser detection problem is reduced to a sequence of scalar estimations, and detecting each individual user using CGaBP is asymptotically equivalent to detecting the same user through a scalar additive Gaussian channel with some degradation in the signal-to-noise ratio (SNR) of the desired user due to the collective impact of interfering users. The degradation is determined by the unique fixed-point of state evolution equations. Numerical results show that CGaBP has low complexity and overhead, and achieves optimal data estimates for Gaussian symbols, and is better than MMSE-SIC for finite-alphabet symbols. |
Keywords | MIMO systems; antennas; detection; signal to noise ratio |
ANZSRC Field of Research 2020 | 400607. Signal processing |
400601. Antennas and propagation | |
400908. Microelectronics | |
Public Notes | © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Byline Affiliations | Harbin Institute of Technology, China |
School of Mechanical and Electrical Engineering | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q2y50/complex-gaussian-belief-propagation-algorithms-for-distributed-multicell-multiuser-mimo-detection
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