Markov Chain Monte Carlo Detection for Frequency-Selective Channels Using List Channel Estimates

Wan, Hong; Chen, Rongrong; Choi, Jun Won; Singer, Andrew C.; Preisig, James C.; Farhang‐Boroujeny, Behrouz

doi:10.1109/jstsp.2011.2172913

Cited by 19 publications

(9 citation statements)

References 33 publications

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“…Furthermore, notable early work in [1,21,2] has tackled the problem of equalization and data detection in quasi-static multipath channels using MCMC methods. Also, over the years, several other work that deal with a single and multiple carrier modulation schemes in SISO and MIMO systems have applied MCMC methods [22].…”

Section: Contribution and Related Workmentioning

confidence: 99%

“…Furthermore, we consider circularly-symmetric Gaussian prior distributions for h 1 and h 2 which are given by…”

Section: Algorithm For Channel 11 Modelmentioning

confidence: 99%

“…Particularly, Markov Chain Monte Carlo (MCMC) methods [1,2] and particle filtering techniques have been extensively and successfully used in solving various communication problems [3]. Some of the problems that have been addressed by the Bayesian methods include channel estimation [4,5], equalization [6,7], synchronization [8], and data detection for single-antenna and for multiple-antenna systems [9] in both single-user and multi-user environments [10].…”

Section: Introductionmentioning

confidence: 99%

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Data detection in relay-based communication systems using Bayesian methods

Ghirmai

2015

Digital Signal Processing

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Section: Contribution and Related Workmentioning

confidence: 99%

“…Furthermore, we consider circularly-symmetric Gaussian prior distributions for h 1 and h 2 which are given by…”

Section: Algorithm For Channel 11 Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data detection in relay-based communication systems using Bayesian methods

Ghirmai

2015

Digital Signal Processing

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“…In recent years, in addition to the active investigation on single carrier transmissions such as the studies by Gomes et al (2008); Ling and Li (2012); Song et al (2010Song et al ( , 2008; Tao et al (2011Tao et al ( , 2010; Wan et al (2011);Xu et al (2013); and Zheng et al (2010), OFDM technique has been very actively pursued for underwater acoustic communications, due to its ability to handle long multipath channels Carrascosa & Stojanovic, 2010;Huang et al, 2011Huang et al, , 2013Kang et al, 2010;Kang & Iltis, 2008;Leus & van Walree, 2008;Li et al, 2009Li et al, , 2008Liu & Yang, 2013;Radosevic et al, 2013;Rojo & Stojanovic, 2010;Tao & Zheng, 2013;Tu et al, 2013;Wan et al, 2012;Wang et al, 2013;Xu et al, 2012;Zajic & Edelmann, 2013). In contrast with extensive research work, there is limited work on OFDM acoustic modem development.…”

Section: Underwater Acoustic Ofdmmentioning

confidence: 99%

Analysis of Underwater OFDM Performance During a 2-Month Deployment in Chesapeake Bay

Wan¹,

Zhou²,

Wilson³

et al. 2014

mar technol soc j

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A B S T R A C TUnderwater acoustic orthogonal-frequency-division-multiplexing (OFDM) modems have been deployed in an environmental monitoring application in the Chesapeake Bay, maintained by the National Oceanic and Atmospheric Administration (NOAA) Chesapeake Bay Office, bringing sensory data from underwater instruments to surface on an hourly basis since March 2012. This paper analyzes the recorded data sets collected over a 2-month period to understand the OFDM performance in this particular application. During online operations, there were 221 data files failed in decoding out of 1,310 data files recorded, with a packet success rate of 83.1%. Various offline processing techniques are applied to evaluate the possible performance improvement when the receiver complexity and processing delays are not of concern, including (i) iterative processing while treating intercarrier interference (ICI) as additive noise, (ii) explicit ICI mitigation in a progressive fashion, and (iii) datadriven sparsity factor optimization and effective noise weighting for improved channel estimation and multichannel data fusion. With these offline processing techniques, the packet success rate can be improved to nearly 97.0%. Both online and offline data analyses confirm that there were large temporal dynamics, and large wind speed and wave height led to low pilot signal-to-noise ratios that directly deteriorate the decoding performance. Through the study, we suggest several possible approaches to improve the communication reliability for long-term deployments in challenging shallow water environments.

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“…The receiver for user will apply the resampling operation on the th hydrophone and then perform OFDM demodulation to generate frequency-domain measurements (15) where the superscript stresses that the output is associated with user . We can represent the measurement on the th subcarrier as (16) where is the interference from user to user on the th subcarrier, and the channel coefficient can be expressed as (17) (18) Fig. 3.…”

Section: Sud-based Iterative Receivermentioning

confidence: 99%

Performance Results of Two Iterative Receivers for Distributed MIMO OFDM With Large Doppler Deviations

Huang

Zhou

Wang

2013

IEEE J. Oceanic Eng.

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This paper studies a distributed orthogonal frequency-division multiplexing (OFDM) system with multiple quasi-synchronous users, where different users may transmit different numbers of parallel data streams. The distinction from most existing work is that the multipath channels for different users have significantly different Doppler scales. Such a setting with two single-transmitter users was first studied in a recent publication by Tu et al. (2010). This paper presents two iterative receivers, termed as multiuser detection (MUD)-based receivers and single-user detection (SUD)-based receivers. The MUD-based receiver adopts a frequency-domain-oversampling front end on each receive element, then performs joint channel estimation and multiuser data detection iteratively. The SUD-based receiver adopts conventional single-user processing modules, but adds a critical step of multiuser interference (MUI) cancellation, where the MUI reconstruction explicitly considers different resampling factors used by different users. Experimental data sets from the 2010 Mobile Acoustic Communications Experiment (MACE10, Martha's Vineyard, MA) and the 2008 Surface Processes and Acoustic Communications Experiment (SPACE08) are used to emulate a distributed OFDM system with different numbers of users and different numbers of data streams per user. Performance results in different settings validate the effectiveness of the proposed iterative receivers. Index Terms-Distributed MIMO OFDM, Doppler scale, iterative receiver, multiuser interference cancellation, underwater acoustic (UWA) communications.

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Markov Chain Monte Carlo Detection for Frequency-Selective Channels Using List Channel Estimates

Cited by 19 publications

References 33 publications

Data detection in relay-based communication systems using Bayesian methods

Data detection in relay-based communication systems using Bayesian methods

Analysis of Underwater OFDM Performance During a 2-Month Deployment in Chesapeake Bay

Performance Results of Two Iterative Receivers for Distributed MIMO OFDM With Large Doppler Deviations

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