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Unified bit-based probabilistic data association aided MIMO detection for high-order QAM

Unified bit-based probabilistic data association aided MIMO detection for high-order QAM
Unified bit-based probabilistic data association aided MIMO detection for high-order QAM
A unified Bit-based Probabilistic Data Association (B-PDA) detection approach is proposed for Multiple-Input Multiple-Output (MIMO) systems employing high-order Quadrature Amplitude Modulation (QAM). The new approach transforms the symbol detection process of QAM to a bit-based process by introducing a Unified Matrix Representation (UMR) of QAM. Both linear natural and nonlinear Gray bit-to-symbol mapping schemes are considered. Our analytical and simulation results demonstrate that the linear natural mapping based B-PDA approach attains an improved detection performance, despite dramatically reducing the computational complexity in contrast to the conventional symbol-based PDA aided MIMO detector. Furthermore, it is shown that the linear natural mapping based B-PDA method is capable of approaching the lower bound performance provided by the nonlinear Gray mapping based B-PDA MIMO detector. Since the linear natural mapping based scheme is simpler and more applicable in practice than its nonlinear Gray mapping based counterpart, we conclude that in the context of the uncoded B-PDA MIMO detector it is preferable to use the linear natural bit-to-symbol mapping, rather than the nonlinear Gray mapping.
probabilistic data association, pda, unified matrix representation, high-order qam, low complexity detection
1629-1634
Yang, Shaoshi
df1e6c38-ff3b-473e-b36b-4820db908e60
Lv, Tiejun
fb465673-1068-4cae-bb94-93ab1dd63f4d
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Yang, Shaoshi
df1e6c38-ff3b-473e-b36b-4820db908e60
Lv, Tiejun
fb465673-1068-4cae-bb94-93ab1dd63f4d
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Yang, Shaoshi, Lv, Tiejun and Hanzo, Lajos (2011) Unified bit-based probabilistic data association aided MIMO detection for high-order QAM. 12th IEEE Wireless Communications and Networking Conference (IEEE WCNC 2011), Cancun, Mexico. 28 - 31 Mar 2011. pp. 1629-1634 . (doi:10.1109/WCNC.2011.5779379).

Record type: Conference or Workshop Item (Paper)

Abstract

A unified Bit-based Probabilistic Data Association (B-PDA) detection approach is proposed for Multiple-Input Multiple-Output (MIMO) systems employing high-order Quadrature Amplitude Modulation (QAM). The new approach transforms the symbol detection process of QAM to a bit-based process by introducing a Unified Matrix Representation (UMR) of QAM. Both linear natural and nonlinear Gray bit-to-symbol mapping schemes are considered. Our analytical and simulation results demonstrate that the linear natural mapping based B-PDA approach attains an improved detection performance, despite dramatically reducing the computational complexity in contrast to the conventional symbol-based PDA aided MIMO detector. Furthermore, it is shown that the linear natural mapping based B-PDA method is capable of approaching the lower bound performance provided by the nonlinear Gray mapping based B-PDA MIMO detector. Since the linear natural mapping based scheme is simpler and more applicable in practice than its nonlinear Gray mapping based counterpart, we conclude that in the context of the uncoded B-PDA MIMO detector it is preferable to use the linear natural bit-to-symbol mapping, rather than the nonlinear Gray mapping.

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Published date: 30 March 2011
Venue - Dates: 12th IEEE Wireless Communications and Networking Conference (IEEE WCNC 2011), Cancun, Mexico, 2011-03-28 - 2011-03-31
Keywords: probabilistic data association, pda, unified matrix representation, high-order qam, low complexity detection
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 271901
URI: http://eprints.soton.ac.uk/id/eprint/271901
PURE UUID: 58144a49-c0e4-4f9a-9374-f6cf6de7e217
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

Catalogue record

Date deposited: 12 Jan 2011 20:38
Last modified: 18 Mar 2024 02:34

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Contributors

Author: Shaoshi Yang
Author: Tiejun Lv
Author: Lajos Hanzo ORCID iD

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