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Factor graph based message passing algorithms for joint phase-noise estimation and decoding in OFDM-IM

Factor graph based message passing algorithms for joint phase-noise estimation and decoding in OFDM-IM
Factor graph based message passing algorithms for joint phase-noise estimation and decoding in OFDM-IM
In order to glean benefits from orthogonal frequency division multiplexing combined with index modulation (OFDM-IM) in the presence of strong Phase-Noise (PHN), in this paper, low-complexity joint PHN estimation and decoding methods are developed in the framework of message passing on a factor
graph. Both the Wiener process and the truncated discrete cosine transform (DCT) expansion model are considered for approximating the PHN variation. Then based on these a factor graph is constructed for explicitly representing the joint estimation and detection problem. Taking full account of the sparse and structured a priori information arriving from the soft-in soft-out (SISO) decoder of a turbo receiver, a modified generalized approximate message passing (GAMP) algorithm is invoked for decoupling the frequency-domain symbols. In the decoupling step, mean field (MF) approximation is employed for solving the unknown nonlinear transform matrix problem imposed by PHN. Furthermore, merged belief propagation and MF (BP-MF) methods amalgamated both with sequential and parallel message passing schedules are introduced and compared to the proposed GAMP based algorithms in terms of their bit error ratio (BER) vs. complexity. Our simulation results demonstrate the efficiency of the proposed algorithms in the presence of both perfect and
imperfect channel state information.
0090-6778
Shi, Qiaolin
52eee4bb-2c53-41e1-8f67-5ebcd173143a
Wu, Nan
f26dc0e1-7da9-4c52-a5a8-d6387a8853f2
Wang, Hua
d2464222-af0f-49da-a6bb-8770dc1f0b11
Ma, Xiaoli
628d90e2-8682-4c26-9685-49b970525000
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Shi, Qiaolin
52eee4bb-2c53-41e1-8f67-5ebcd173143a
Wu, Nan
f26dc0e1-7da9-4c52-a5a8-d6387a8853f2
Wang, Hua
d2464222-af0f-49da-a6bb-8770dc1f0b11
Ma, Xiaoli
628d90e2-8682-4c26-9685-49b970525000
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Shi, Qiaolin, Wu, Nan, Wang, Hua, Ma, Xiaoli and Hanzo, Lajos (2020) Factor graph based message passing algorithms for joint phase-noise estimation and decoding in OFDM-IM. IEEE Transactions on Communications. (doi:10.1109/TCOMM.2020.2973080).

Record type: Article

Abstract

In order to glean benefits from orthogonal frequency division multiplexing combined with index modulation (OFDM-IM) in the presence of strong Phase-Noise (PHN), in this paper, low-complexity joint PHN estimation and decoding methods are developed in the framework of message passing on a factor
graph. Both the Wiener process and the truncated discrete cosine transform (DCT) expansion model are considered for approximating the PHN variation. Then based on these a factor graph is constructed for explicitly representing the joint estimation and detection problem. Taking full account of the sparse and structured a priori information arriving from the soft-in soft-out (SISO) decoder of a turbo receiver, a modified generalized approximate message passing (GAMP) algorithm is invoked for decoupling the frequency-domain symbols. In the decoupling step, mean field (MF) approximation is employed for solving the unknown nonlinear transform matrix problem imposed by PHN. Furthermore, merged belief propagation and MF (BP-MF) methods amalgamated both with sequential and parallel message passing schedules are introduced and compared to the proposed GAMP based algorithms in terms of their bit error ratio (BER) vs. complexity. Our simulation results demonstrate the efficiency of the proposed algorithms in the presence of both perfect and
imperfect channel state information.

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Accepted/In Press date: 2 February 2020
e-pub ahead of print date: 11 February 2020

Identifiers

Local EPrints ID: 437877
URI: http://eprints.soton.ac.uk/id/eprint/437877
ISSN: 0090-6778
PURE UUID: 5b9544a5-4320-4c62-aa8e-cc861546d668
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 21 Feb 2020 17:31
Last modified: 07 Oct 2020 01:33

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