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Journal articles on the topic 'Turbo-equalization'

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Published: 4 June 2021
Last updated: 25 May 2024

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1

Koetter, R., A. C. Singer, and M. Tuchler. "Turbo equalization." IEEE Signal Processing Magazine 21, no. 1 (January 2004): 67–80. http://dx.doi.org/10.1109/msp.2004.1267050.

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2

Raphaeli, D., and Y. Zarai. "Combined turbo equalization and turbo decoding." IEEE Communications Letters 2, no. 4 (April 1998): 107–9. http://dx.doi.org/10.1109/4234.664220.

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3

Jafri, Atif Raza, Amer Baghdadi, and Michel Jezequel. "Parallel MIMO Turbo Equalization." IEEE Communications Letters 15, no. 3 (March 2011): 290–92. http://dx.doi.org/10.1109/lcomm.2011.011311.102109.

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4

Tuchler, Michael, and Andrew C. Singer. "Turbo Equalization: An Overview." IEEE Transactions on Information Theory 57, no. 2 (February 2011): 920–52. http://dx.doi.org/10.1109/tit.2010.2096033.

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5

Jiang, S., L. Ping, H. Sun, and C. S. Leung. "Modified LMMSE Turbo Equalization." IEEE Communications Letters 8, no. 3 (March 2004): 174–76. http://dx.doi.org/10.1109/lcomm.2004.823434.

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6

Siegrist, M., T. Schorr, A. Dittrich, W. Sauer-Greff, and R. Urbansky. "Turbo Equalization Of Nonlinear ISI-channels Using High Rate FEC Codes." Advances in Radio Science 3 (May 12, 2005): 259–63. http://dx.doi.org/10.5194/ars-3-259-2005.

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Abstract. Turbo equalization is a widely known method to cope with low signal to noise ratio (SNR) channels corrupted by linear intersymbol interference (ISI) (Berrou and Galvieux, 1993; Hagenauer et al., 1997). Recently in this workshop it was reported that also for nonlinear channels a remarkable turbo decoding gain can be achieved (Siegrist et al., 2001). However, the classical turbo equalization relies on code rates at 1/3 up to 1/2 which makes it quite unattractive for high rate data transmission. Considering the potential of iterative equalization and decoding, we obtain a considerable turbo decoding gain also for high rate codes of less than 7% redundancy by using punctured convolutional codes and block codes.
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7

Sun, Lin, Mei Wang, Guoheng Zhang, Haisen Li, and Lan Huang. "Filtered Multitone Modulation Underwater Acoustic Communications Using Low-Complexity Channel-Estimation-Based MMSE Turbo Equalization." Sensors 19, no. 12 (June 17, 2019): 2714. http://dx.doi.org/10.3390/s19122714.

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Filtered multitone (FMT) modulation divides the communication band into several subbands to shorten the span of symbols affected by multipath in underwater acoustic (UWA) communications. However, there is still intersymbol interference (ISI) in each subband of FMT modulation degrading communication performance. Therefore, ISI suppression techniques must be applied to FMT modulation UWA communications. The suppression performance of traditional adaptive equalization methods often exploited in FMT modulation UWA communications is limited when the effect of ISI spans tens of symbols or large constellation sizes are used. Turbo equalization consisting of adaptive equalization and channel decoding can improve equalization performance through information exchanging and iterative processes. To overcome the shortcoming of traditional minimum mean square error (MMSE) equalization and effectively suppress the ISI with relatively low computation complexity, an FMT modulation UWA communication using low-complexity channel-estimation-based (CE-based) MMSE turbo equalization is proposed in this paper. In the proposed method, turbo equalization is first exploited to suppress the ISI in FMT modulation UWA communications, and the equalizer coefficients of turbo equalization are adjusted using the low-complexity CE-based MMSE algorithm. The proposed method is analyzed in theory and verified by simulation analysis and real data collected in the experiment carried out in a pool with multipath propagation. The results demonstrate that the proposed method can achieve better communication performance with a higher bit rate than the FMT modulation UWA communication using traditional MMSE adaptive equalization.
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8

Laot, C., A. Glavieux, and J. Labat. "Turbo equalization: adaptive equalization and channel decoding jointly optimized." IEEE Journal on Selected Areas in Communications 19, no. 9 (2001): 1744–52. http://dx.doi.org/10.1109/49.947038.

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9

Sun, Lin, and Haisen Li. "Multiple-Input-Multiple-Output Filtered Multitone Time Reversal Acoustic Communications Using Direct Adaptation-Based Turbo Equalization." Sensors 23, no. 13 (July 1, 2023): 6081. http://dx.doi.org/10.3390/s23136081.

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This paper proposes using direct adaptation (DA)-based turbo equalization in multiple-input-multiple-output (MIMO) filtered multitone (FMT) time reversal (TR) acoustic communications to jointly suppress noise, residual co-channel interference (CCI) and intersymbol interference (ISI) after the TR process. Soft information-based adaptive decision feedback equalization (ADFE) adjusted according to the recursive expected least squares (RELS) algorithm, including interference cancellation and decoding, is used to construct the DA-based turbo equalization. In the proposed method, soft information is exchanged between soft symbols with soft decisions of decoding iteratively, and interference suppression is proceeded successively and iteratively until the performance is stable. The principle of the proposed method is analyzed, and based on the acoustic channel responses measured in a real experiment, the performance is assessed in relation to that of anther two methods. Compared with the MIMO-FMT TR underwater acoustic communication using interference suppression without error control coding (ECC), the proposed method performs better, benefitting from the ECC included in turbo equalization. Additionally, compared with the MIMO-FMT TR underwater acoustic communication using interference suppression based on hard decision equalization and decoding, the proposed method exhibits superior performance by exploiting soft information.
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10

JIN, Yi-dan, Feng ZHANG, and Wei-ling WU. "Reduced-Complexity Turbo Equalization for Turbo Coded MIMO/OFDM Systems." Journal of China Universities of Posts and Telecommunications 13, no. 1 (March 2006): 93–98. http://dx.doi.org/10.1016/s1005-8885(07)60090-9.

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11

Kim, Yongguk, and Heung-Gyoon Ryu. "Blind Turbo Equalization System with Beamforming." Journal of Korea Information and Communications Society 38A, no. 10 (October 31, 2013): 850–57. http://dx.doi.org/10.7840/kics.2013.38a.10.850.

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12

Arlunno, Valeria, Antonio Caballero, Robert Borkowski, Darko Zibar, Knud J. Larsen, and Idelfonso Tafur Monroy. "Turbo Equalization for Digital Coherent Receivers." Journal of Lightwave Technology 32, no. 2 (January 2014): 275–84. http://dx.doi.org/10.1109/jlt.2013.2291956.

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13

Yellepeddi, Atulya, and James C. Preisig. "Adaptive Equalization in a Turbo Loop." IEEE Transactions on Wireless Communications 14, no. 9 (September 2015): 5111–22. http://dx.doi.org/10.1109/twc.2015.2432764.

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14

Rad, F. R., and Jaekyun Moon. "Turbo equalization utilizing soft decision feedback." IEEE Transactions on Magnetics 41, no. 10 (October 2005): 2998–3000. http://dx.doi.org/10.1109/tmag.2005.854447.

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15

Tuchler, M., R. Koetter, and A. C. Singer. "Turbo equalization: principles and new results." IEEE Transactions on Communications 50, no. 5 (May 2002): 754–67. http://dx.doi.org/10.1109/tcomm.2002.1006557.

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16

Mong-Suan Yee, Bee Leong Yeap, and L. Hanzo. "Radial basis function-assisted turbo equalization." IEEE Transactions on Communications 51, no. 4 (April 2003): 664–75. http://dx.doi.org/10.1109/tcomm.2003.810807.

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17

Zhang, Chuanzong, Zhongyong Wang, Carles Navarro Manchon, Peng Sun, Qinghua Guo, and Bernard Henri Fleury. "Turbo Equalization Using Partial Gaussian Approximation." IEEE Signal Processing Letters 23, no. 9 (September 2016): 1216–20. http://dx.doi.org/10.1109/lsp.2016.2581841.

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18

Kalantarova, Nargiz, Suleyman Serdar Kozat, and Alper T. Erdogan. "Robust Turbo Equalization Under Channel Uncertainties." IEEE Transactions on Signal Processing 60, no. 1 (January 2012): 261–73. http://dx.doi.org/10.1109/tsp.2011.2162505.

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19

Moon, Todd K., and Jacob H. Gunther. "Multiple-Access via Turbo Joint Equalization." IEEE Transactions on Communications 60, no. 10 (October 2012): 3001–10. http://dx.doi.org/10.1109/tcomm.2012.070912.110191.

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20

Wu, QiHui, ChunMing Zhao, and JinLong Wang. "Turbo iterative equalization for HSDPA systems." Science in China Series F: Information Sciences 50, no. 1 (February 2007): 105–12. http://dx.doi.org/10.1007/s11432-007-0011-z.

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21

Jiang, Sen, Hong Sun, and Ping Li. "Turbo equalization with Jointly Gaussian equalizer." Journal of Electronics (China) 22, no. 2 (March 2005): 118–24. http://dx.doi.org/10.1007/bf02688137.

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22

Barhumi, Imad. "Turbo equalization of doubly selective channels." Wireless Communications and Mobile Computing 14, no. 18 (October 15, 2012): 1691–703. http://dx.doi.org/10.1002/wcm.2306.

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23

Berdai, A., J. Y. Chouinard, and Huu Tue Huynh. "Adaptation of turbo coding and equalization in turbo equalization for time-varying and frequency-selective channels." Canadian Journal of Electrical and Computer Engineering 33, no. 2 (2008): 99–108. http://dx.doi.org/10.1109/cjece.2008.4621834.

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24

Bee Leong Yeap, Tong Hooi Liew, J. Hamorsky, and L. Hanzo. "Comparative study of turbo equalization schemes using convolutional, convolutional turbo, and block-turbo codes." IEEE Transactions on Wireless Communications 1, no. 2 (April 2002): 266–73. http://dx.doi.org/10.1109/7693.994820.

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25

Cuc, Adriana-Maria, Florin Lucian Morgoș, Adriana-Marcela Grava, and Cristian Grava. "Iterative Equalization and Decoding over an Additive White Gaussian Noise Channel with ISI Using Low-Density Parity-Check Codes." Applied Sciences 13, no. 22 (November 14, 2023): 12294. http://dx.doi.org/10.3390/app132212294.

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In this article we present an iterative system of equalization and decoding to manage the intersymbol interference over an additive white Gaussian noise (AWGN) channel. Following the classic turbo equalization scheme, the proposed system consists of low-density parity-check (LDPC) coding at the transmitter side; we applied a Log maximum a posteriori probability (Log-MAP) equalizer and min-sum LDPC decoding at the receiver side. The equalizer and decoder, linked through interleaving and deinterleaving, iteratively update each other’s information. We performed the performance analysis of the proposed system, bit error rate (BER) vs. signal-to-noise ratio (SNR), considering three different impulse responses of the channel (h). Our experimental results indicated that increasing the number of iterations performed by the LDPC decoder from 10 to 20 during the iterative process of equalization and decoding leads to better outcomes. The proposed system was compared with turbo equalization and separate equalization, performed before the decoding process with minimum mean-square error (MMSE) and LDPC decoding, in terms of BER vs. SNR, considering the three different h. Based on the analyzed results, it can be concluded that the equalization performance depends on both the impulse responses of the channel and the chosen decoding and equalization method; therefore, the equalization method does not always offer good results for any h.
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26

Baek, Chang-Uk, and Ji-Won Jung. "An Efficient Turbo Equalization for Faster than Nyquist Signal." International Journal of Signal Processing Systems 4, no. 3 (June 2016): 231–34. http://dx.doi.org/10.18178/ijsps.4.3.231-234.

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27

Adamu, Mohammed Jajere, Li Qiang, Rabiu Sale Zakariyya, Charles Okanda Nyatega, Halima Bello Kawuwa, and Ayesha Younis. "An Efficient Turbo Decoding and Frequency Domain Turbo Equalization for LTE Based Narrowband Internet of Things (NB-IoT) Systems." Sensors 21, no. 16 (August 8, 2021): 5351. http://dx.doi.org/10.3390/s21165351.

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This paper addresses the main crucial aspects of physical (PHY) layer channel coding in uplink NB-IoT systems. In uplink NB-IoT systems, various channel coding algorithms are deployed due to the nature of the adopted Long-Term Evolution (LTE) channel coding which presents a great challenge at the expense of high decoding complexity, power consumption, error floor phenomena, while experiencing performance degradation for short block lengths. For this reason, such a design considerably increases the overall system complexity, which is difficult to implement. Therefore, the existing LTE turbo codes are not recommended in NB-IoT systems and, hence, new channel coding algorithms need to be employed for LPWA specifications. First, LTE-based turbo decoding and frequency-domain turbo equalization algorithms are proposed, modifying the simplified maximum a posteriori probability (MAP) decoder and minimum mean square error (MMSE) Turbo equalization algorithms were appended to different Narrowband Physical Uplink Shared Channel (NPUSCH) subcarriers for interference cancellation. These proposed methods aim to minimize the complexity of realizing the traditional MAP turbo decoder and MMSE estimators in the newly NB-IoT PHY layer features. We compare the system performance in terms of block error rate (BLER) and computational complexity.
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28

Yang, Zhuangchun, Tianyi Liang, Zhourong Deng, and Youwen Zhang. "Improved proportionate FONLMS algorithm based direct adaptive Turbo equalization for MIMO underwater acoustic communications." MATEC Web of Conferences 283 (2019): 07003. http://dx.doi.org/10.1051/matecconf/201928307003.

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In this paper, a novel normalized least mean squares (NLMS) algorithm that jointly updates the efficient of the linear equalizer and soft interference canceller (SIC) in an adaptive turbo equalizer for multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications. To exploit the sparsity of MIMO UWA channels and enhance the convergence speed of adaptive equalization, improved proportionate fast self-optimized NLMS algorithm (IPFONLMS), is proposed to well adapt to sparse channel with the similar complexity as improve proportionate NLMS (IPNLMS) algorithm. Then we extend the proposed algorithm to the adaptive turbo equalization for MIMO UWA communications. The performance of the proposed adaptive algorithm is evaluated by numerical results. Simulation results show that the improved data efficiency and bit error ratio (BER) performance of the proposed receiver is achieved over adaptive turbo equalizer based on the IPNLMS algorithm.
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29

Pan, Zihao, Chen Xie, Heng Wang, Yimin Wei, and Daoxing Guo. "Blind Turbo Equalization of Short CPM Bursts for UAV-Aided Internet of Things." Sensors 22, no. 17 (August 29, 2022): 6508. http://dx.doi.org/10.3390/s22176508.

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With the surge of Internet of Things (IoT) applications using unmanned aerial vehicles (UAVs), there is a huge demand for an excellent complexity/power efficiency trade-off and channel fading resistance at the physical layer. In this paper, we consider the blind equalization of short-continuous-phase-modulated (CPM) burst for UAV-aided IoT. To solve the problems of the high complexity and poor convergence of short-burst CPM blind equalization, a novel turbo blind equalization algorithm is proposed based on establishing a new expectation–maximization Viterbi (EMV) algorithm and turbo scheme. Firstly, a low complexity blind equalization algorithm is obtained by applying the soft-output Lazy Viterbi algorithm within the EM algorithm iteration. Furthermore, a set of initializers that achieves a high global convergence probability is designed by the blind channel-acquisition (BCA) method. Meanwhile, a soft information iterative process is used to improve the system performance. Finally, the convergence, bit error rate, and real-time performance of iterative detection can be further improved effectively by using improved exchange methods of extrinsic information and the stopping criterion. The analysis and simulation results show that the proposed algorithm achieves a good blind equalization performance and low complexity.
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30

Assaf, Mohammad Razk, and Abdel-Nasser Assimi. "Iterative Interference Cancellation for Coded Filter Bank Multicarrier Systems." International Journal of Embedded and Real-Time Communication Systems 10, no. 4 (October 2019): 81–93. http://dx.doi.org/10.4018/ijertcs.2019100105.

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Filter bank multicarrier is one of the candidates for future communication systems. Simple equalization methods cannot be directly applied due to the high interference from adjacent channels. In this article, the authors derive a soft-input/soft-output (SISO) equalizer based on the minimum mean square error (MMSE) criterion for the bit-interleaved coded system using a filter bank multi-carrier scheme with offset quadrature amplitude modulation (FBMC/OQAM). The authors use this SISO-MMSE equalizer in a turbo-equalization scheme for each sub-carrier. The difficulty in this implementation comes from the required processing delay per turbo-iteration due to the non-causal nature of the interference in this system. Therefore, the number of turbo-iterations is limited in order to limit the processing delay. The authors evaluate the performance of the proposed turbo-equalizer over the International Telecommunication Union (ITU) Vehicular B channel by mean of numerical simulations. The obtained results show the effectiveness of the proposed equalizer in term of signal-to-noise ratio (SNR) gain.
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31

CHEN, Yen-Chih, and Yu Ted SU. "Turbo Equalization of Nonlinear TDMA Satellite Signals." IEICE Transactions on Communications E92-B, no. 3 (2009): 992–97. http://dx.doi.org/10.1587/transcom.e92.b.992.

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32

Kim, Kyeongyeon, Jun Won Choi, Suleyman S. Kozat, and Andrew C. Singer. "Low Complexity Turbo-Equalization: A Clustering Approach." IEEE Communications Letters 18, no. 6 (June 2014): 1063–66. http://dx.doi.org/10.1109/lcomm.2014.2316172.

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33

Nelson, J., A. Singer, and R. Koetter. "Linear turbo equalization for parallel isi channels." IEEE Transactions on Communications 51, no. 6 (June 2003): 860–64. http://dx.doi.org/10.1109/tcomm.2003.813178.

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34

Fertonani, Dario, Alan Barbieri, and Giulio Colavolpe. "Reduced-Complexity BCJR Algorithm for Turbo Equalization." IEEE Transactions on Communications 55, no. 11 (November 2007): 2224. http://dx.doi.org/10.1109/tcomm.2007.908492.

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35

Fertonani, Dario, Alan Barbieri, and Giulio Colavolpe. "Reduced-Complexity BCJR Algorithm for Turbo Equalization." IEEE Transactions on Communications 55, no. 12 (December 2007): 2279–87. http://dx.doi.org/10.1109/tcomm.2007.910638.

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36

Djordjevic, I. B., H. G. Batshon, M. Cvijetic, Lei Xu, and Ting Wang. "PMD Compensation by LDPC-Coded Turbo Equalization." IEEE Photonics Technology Letters 19, no. 15 (August 2007): 1163–65. http://dx.doi.org/10.1109/lpt.2007.901103.

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37

Ngo, Hoang Anh, Robert G. Maunder, and Lajos Hanzo. "Fully Parallel Turbo Equalization for Wireless Communications." IEEE Access 3 (2015): 2652–64. http://dx.doi.org/10.1109/access.2015.2503266.

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38

Sarestoniemi, M., T. Matsumoto, K. Kansanen, and J. Iinatti. "Turbo Diversity Based on SC/MMSE Equalization." IEEE Transactions on Vehicular Technology 54, no. 2 (March 2005): 749–52. http://dx.doi.org/10.1109/tvt.2004.841520.

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39

Reynolds, Daryl, and Xiaodong Wang. "Low-complexity Turbo-equalization for diversity channels." Signal Processing 81, no. 5 (May 2001): 989–95. http://dx.doi.org/10.1016/s0165-1684(00)00277-2.

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40

Song, S., A. C. Singer, and K. M. Sung. "Soft Input Channel Estimation for Turbo Equalization." IEEE Transactions on Signal Processing 52, no. 10 (October 2004): 2885–94. http://dx.doi.org/10.1109/tsp.2004.834270.

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41

Guo, Qinghua, and Li Ping. "LMMSE turbo equalization based on factor graphs." IEEE Journal on Selected Areas in Communications 26, no. 2 (2008): 311–19. http://dx.doi.org/10.1109/jsac.2008.080208.

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42

Drajic, Dejan, Kari Hooli, and Markku Juntti. "Turbo Coding with Equalization in WCDMA Downlink." Wireless Personal Communications 28, no. 4 (March 2004): 259–76. http://dx.doi.org/10.1023/b:wire.0000033599.08396.ef.

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43

Liu, L., and L. Ping. "An Extending Window MMSE Turbo Equalization Algorithm." IEEE Signal Processing Letters 11, no. 11 (November 2004): 891–94. http://dx.doi.org/10.1109/lsp.2004.835461.

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44

Takpaya, Kazi. "Performance of three stage turbo-equalization-decoding." Journal of Electronics (China) 20, no. 6 (November 2003): 439–45. http://dx.doi.org/10.1007/s11767-003-0058-y.

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45

Douillard, Catherine, Michel Jézéquel, Claude Berrou, Département Electronique, Annie Picart, Pierre Didier, and Alain Glavieux. "Iterative correction of intersymbol interference: Turbo-equalization." European Transactions on Telecommunications 6, no. 5 (September 1995): 507–11. http://dx.doi.org/10.1002/ett.4460060506.

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46

Talakoub, Shahram, Leila Sabeti, Behnam Shahrrava, and Majid Ahmadi. "An Improved Max-Log-MAP Algorithm for Turbo Decoding and Turbo Equalization." IEEE Transactions on Instrumentation and Measurement 56, no. 3 (June 2007): 1058–63. http://dx.doi.org/10.1109/tim.2007.894228.

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47

van Walree, P. A., and G. Leus. "Robust Underwater Telemetry With Adaptive Turbo Multiband Equalization." IEEE Journal of Oceanic Engineering 34, no. 4 (October 2009): 645–55. http://dx.doi.org/10.1109/joe.2009.2032997.

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48

Choi, Jun Won, Thomas J. Riedl, Kyeongyeon Kim, Andrew C. Singer, and James C. Preisig. "Adaptive Linear Turbo Equalization Over Doubly Selective Channels." IEEE Journal of Oceanic Engineering 36, no. 4 (October 2011): 473–89. http://dx.doi.org/10.1109/joe.2011.2158013.

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49

Ahn, Tae-Seok, Ji-Won Jung, Tae-Doo Park, and Dong-Won Lee. "Turbo Equalization for Covert communication in Underwater Channel." Journal of the Korea Institute of Information and Communication Engineering 20, no. 8 (August 31, 2016): 1422–30. http://dx.doi.org/10.6109/jkiice.2016.20.8.1422.

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50

Zheng, Yahong Rosa, Jingxian Wu, and Chengshan Xiao. "Turbo equalization for single-carrier underwater acoustic communications." IEEE Communications Magazine 53, no. 11 (November 2015): 79–87. http://dx.doi.org/10.1109/mcom.2015.7321975.

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