From nominal to true a posteriori probabilities: an exact Bayesian theorem based probabilistic data association approach for iterative MIMO detection and decoding
From nominal to true a posteriori probabilities: an exact Bayesian theorem based probabilistic data association approach for iterative MIMO detection and decoding
It was conventionally regarded that the existing probabilistic data association (PDA) algorithms output the estimated symbol-wise a posteriori probabilities (APPs) as soft information. In this paper, however, we demonstrate that these probabilities are not the true APPs in the rigorous mathematicasense, but a type of nominal APPs, which are unsuitable for the classic architecture of iterative detection and decoding (IDD) aided receivers. To circumvent this predicament, we propose an exact Bayesian theorem based logarithmic domain PDA (EB-Log-PDA) method, whose output has similar characteristics to the true APPs, and hence it is readily applicable to the classic IDD architecture of multiple-input multiple-output (MIMO) systems using the general M-ary modulation. Furthermore, we investigate the impact of the PDA algorithms' inner iteration on the design of PDA-aided IDD receivers. We demonstrate that introducing inner iterations into PDAs, which is common practice in PDA-aided uncoded MIMO systems, would actually degrade the IDD receiver's performance, despite significantly increasing the overall computational complexity of the IDD receiver. Finally, we investigate the relationship between the extrinsic log-likelihood ratio (LLRs) of the proposed EB-Log-PDA and of the approximate Bayesian theorem based logarithmic domain PDA (AB-Log-PDA) reported in our previous work. We also show that the IDD scheme employing the EB-Log-PDA without incorporating any inner PDA iterations has an achievable performance close to that of the optimal maximum a posteriori (MAP) detector based IDD receiver, while imposing a significantly lower computational complexity in the scenarios considered.
Bayesian theorem, iterative detection and decoding, multiple-input–multiple-output, mimo, probabilistic data association, pda, m-ary modulation
2782-2793
Yang, Shaoshi
df1e6c38-ff3b-473e-b36b-4820db908e60
Lv, Tiejun
fb465673-1068-4cae-bb94-93ab1dd63f4d
Maunder, Robert G.
76099323-7d58-4732-a98f-22a662ccba6c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
July 2013
Yang, Shaoshi
df1e6c38-ff3b-473e-b36b-4820db908e60
Lv, Tiejun
fb465673-1068-4cae-bb94-93ab1dd63f4d
Maunder, Robert G.
76099323-7d58-4732-a98f-22a662ccba6c
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Yang, Shaoshi, Lv, Tiejun, Maunder, Robert G. and Hanzo, Lajos
(2013)
From nominal to true a posteriori probabilities: an exact Bayesian theorem based probabilistic data association approach for iterative MIMO detection and decoding.
IEEE Transactions on Communications, 61 (7), .
(doi:10.1109/TCOMM.2013.053013.120427).
Abstract
It was conventionally regarded that the existing probabilistic data association (PDA) algorithms output the estimated symbol-wise a posteriori probabilities (APPs) as soft information. In this paper, however, we demonstrate that these probabilities are not the true APPs in the rigorous mathematicasense, but a type of nominal APPs, which are unsuitable for the classic architecture of iterative detection and decoding (IDD) aided receivers. To circumvent this predicament, we propose an exact Bayesian theorem based logarithmic domain PDA (EB-Log-PDA) method, whose output has similar characteristics to the true APPs, and hence it is readily applicable to the classic IDD architecture of multiple-input multiple-output (MIMO) systems using the general M-ary modulation. Furthermore, we investigate the impact of the PDA algorithms' inner iteration on the design of PDA-aided IDD receivers. We demonstrate that introducing inner iterations into PDAs, which is common practice in PDA-aided uncoded MIMO systems, would actually degrade the IDD receiver's performance, despite significantly increasing the overall computational complexity of the IDD receiver. Finally, we investigate the relationship between the extrinsic log-likelihood ratio (LLRs) of the proposed EB-Log-PDA and of the approximate Bayesian theorem based logarithmic domain PDA (AB-Log-PDA) reported in our previous work. We also show that the IDD scheme employing the EB-Log-PDA without incorporating any inner PDA iterations has an achievable performance close to that of the optimal maximum a posteriori (MAP) detector based IDD receiver, while imposing a significantly lower computational complexity in the scenarios considered.
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TCOM-12-0427.pdf
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Published date: July 2013
Keywords:
Bayesian theorem, iterative detection and decoding, multiple-input–multiple-output, mimo, probabilistic data association, pda, m-ary modulation
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 344521
URI: http://eprints.soton.ac.uk/id/eprint/344521
PURE UUID: 0ca38529-81aa-4885-8bf2-34034fe3d477
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Date deposited: 24 Oct 2012 11:48
Last modified: 27 Oct 2023 01:49
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Contributors
Author:
Shaoshi Yang
Author:
Tiejun Lv
Author:
Robert G. Maunder
Author:
Lajos Hanzo
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