TY - JOUR
T1 - Error rate improvement in underwater MIMO communications using sparse partial response equalization
AU - Roy, Subhadeep
AU - Duman, Tolga M.
AU - McDonald, Vincent Keyko
N1 - Funding Information:
Manuscript received July 04, 2007; revised March 27, 2008 and November 06, 2008; accepted December 15, 2008. First published April 17, 2009; current version published May 13, 2009. The 2005 Makai Experiment was supported primarily by the Office of Naval Research (ONR) High Frequency Initiative. The work of T. M. Duman was supported in part by the Multidisciplinary University Research Initiative (MURI) Project N00014-07-0739. This work was supported by the In-House Laboratory Independent Research (ILIR) program at SPAWAR and by the ONR SignalEx project for the construction of the MIMO transmit system. Associate Editor: H.-C. Song.
PY - 2009
Y1 - 2009
N2 - Shallow-water horizontal underwater acoustic (UWA) channels are characterized by a time-varying multipath structure with a long delay spread. This causes significant signal distortion in phase coherent transmissions by introducing intersymbol interference (ISI), which can span several hundreds of symbols. The multipath structure is usually very sparse. This paper proposes an adaptive sparse partial response equalizer (SPRE) well matched with the channel characteristics to mitigate the ISI efficiently, and communicate reliably. Unlike the conventional equalizers that try to eliminate the ISI completely, the proposed structure performs equalization in two steps. In the first step, the channel is equalized to a judiciously chosen target impulse response (TIR), thereby leaving controlled residual ISI at the output of the SPRE. In the second step, the residual ISI is further mitigated by a near-optimal, graph-based, low-complexity symbol detection algorithm known as belief propagation (BP). We demonstrate that due to the sparse nature of the long ISI channel, and the well-matched adaptive TIR, this two-step equalization process is particularly useful. The idea here can be compared to what is done in the magnetic recording literature [i.e., partial response maximum-likelihood (ML) equalization] to avoid excessive noise enhancement, with the difference that the TIR needs to be designed adaptively using training symbols and that in the second step, instead of using a ML detector, a BP algorithm is used. The proposed SPRE algorithm is designed for a general multiple-input-multiple-output (MIMO) system and it provides soft outputs for the transmitted bits as a by-product of the BP-type equalization algorithm. Hence, it can be efficiently employed in coded systems (e.g., turbo or low-density-parity-check-coded, interleaved systems) with an iterative decoding/equalization scheme, providing significant improvements in achievable error rates. Finally, we note that the proposed SPRE scheme also incorporates a phase locked loop (PLL) structure so as to track the Doppler shift introduced by the channel. To evaluate the effectiveness of the proposed equalizer, we compare it with several decision feedback equalizers (DFEs) proposed by Stojanovic et al (J. Ocean. Eng. vol. 19, no. 1, pp. 100-111, Jan. 1994), Roy et al (Proc. MTS/IEEE TECHNO-OCEAN Conf., vol. 1, pp. 26-33, Nov. 2004), and Roy et al (J. Ocean. Eng., vol. 32, no. 3, pp. 663-688, Jul. 2004) both through simulations and experimental results. The results demonstrate that the SPRE receiver outperforms the decision-feedback-based receivers when the channel has long, yet sparse (possibly, nonminimum phase) structure, by reducing the frame error rates (FERs), improving the post-equalization signal-to-noise ratio (SNR), and decreasing the required number of turbo iterations (reduced latency).
AB - Shallow-water horizontal underwater acoustic (UWA) channels are characterized by a time-varying multipath structure with a long delay spread. This causes significant signal distortion in phase coherent transmissions by introducing intersymbol interference (ISI), which can span several hundreds of symbols. The multipath structure is usually very sparse. This paper proposes an adaptive sparse partial response equalizer (SPRE) well matched with the channel characteristics to mitigate the ISI efficiently, and communicate reliably. Unlike the conventional equalizers that try to eliminate the ISI completely, the proposed structure performs equalization in two steps. In the first step, the channel is equalized to a judiciously chosen target impulse response (TIR), thereby leaving controlled residual ISI at the output of the SPRE. In the second step, the residual ISI is further mitigated by a near-optimal, graph-based, low-complexity symbol detection algorithm known as belief propagation (BP). We demonstrate that due to the sparse nature of the long ISI channel, and the well-matched adaptive TIR, this two-step equalization process is particularly useful. The idea here can be compared to what is done in the magnetic recording literature [i.e., partial response maximum-likelihood (ML) equalization] to avoid excessive noise enhancement, with the difference that the TIR needs to be designed adaptively using training symbols and that in the second step, instead of using a ML detector, a BP algorithm is used. The proposed SPRE algorithm is designed for a general multiple-input-multiple-output (MIMO) system and it provides soft outputs for the transmitted bits as a by-product of the BP-type equalization algorithm. Hence, it can be efficiently employed in coded systems (e.g., turbo or low-density-parity-check-coded, interleaved systems) with an iterative decoding/equalization scheme, providing significant improvements in achievable error rates. Finally, we note that the proposed SPRE scheme also incorporates a phase locked loop (PLL) structure so as to track the Doppler shift introduced by the channel. To evaluate the effectiveness of the proposed equalizer, we compare it with several decision feedback equalizers (DFEs) proposed by Stojanovic et al (J. Ocean. Eng. vol. 19, no. 1, pp. 100-111, Jan. 1994), Roy et al (Proc. MTS/IEEE TECHNO-OCEAN Conf., vol. 1, pp. 26-33, Nov. 2004), and Roy et al (J. Ocean. Eng., vol. 32, no. 3, pp. 663-688, Jul. 2004) both through simulations and experimental results. The results demonstrate that the SPRE receiver outperforms the decision-feedback-based receivers when the channel has long, yet sparse (possibly, nonminimum phase) structure, by reducing the frame error rates (FERs), improving the post-equalization signal-to-noise ratio (SNR), and decreasing the required number of turbo iterations (reduced latency).
KW - Belief propagation (BP) detection
KW - Multiple-input-multiple-output (MIMO)
KW - Partial response equalization
KW - Sparse equalization
KW - Turbo equalization (TE)
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U2 - 10.1109/JOE.2009.2014658
DO - 10.1109/JOE.2009.2014658
M3 - Article
AN - SCOPUS:67349255761
SN - 0364-9059
VL - 34
SP - 181
EP - 201
JO - IEEE Journal of Oceanic Engineering
JF - IEEE Journal of Oceanic Engineering
IS - 2
ER -