Relevant bibliographies by topics / Decision feedback equalization

Academic literature on the topic 'Decision feedback equalization'

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Published: 4 June 2021
Last updated: 28 January 2023

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Journal articles on the topic "Decision feedback equalization"

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Williamson, D., R. A. Kennedy, and G. W. Pulford. "Block decision feedback equalization." IEEE Transactions on Communications 40, no. 2 (1992): 255–64. http://dx.doi.org/10.1109/26.129188.

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Wood, R. "Enhanced decision feedback equalization." IEEE Transactions on Magnetics 26, no. 5 (1990): 2178–80. http://dx.doi.org/10.1109/20.104660.

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Ilic, Jovana, and Thomas Strohmer. "Sparsity Enhanced Decision Feedback Equalization." IEEE Transactions on Signal Processing 60, no. 5 (May 2012): 2422–32. http://dx.doi.org/10.1109/tsp.2012.2189387.

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Ariyavisitakul, S., N. R. Sollenberger, and L. J. Greenstein. "Tap-selectable decision-feedback equalization." IEEE Transactions on Communications 45, no. 12 (1997): 1497–500. http://dx.doi.org/10.1109/26.650219.

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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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Nelson, J. K., A. C. Singer, U. Madhow, and C. S. McGahey. "BAD: Bidirectional Arbitrated Decision-Feedback Equalization." IEEE Transactions on Communications 53, no. 2 (February 2005): 214–18. http://dx.doi.org/10.1109/tcomm.2004.841980.

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Altekar, S. A., A. E. Vityaev, and J. K. Wolf. "Decision-feedback equalization via separating hyperplanes." IEEE Transactions on Communications 49, no. 3 (March 2001): 480–86. http://dx.doi.org/10.1109/26.911455.

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Sahin, Serdar, Antonio Maria Cipriano, Charly Poulliat, and Marie-Laure Boucheret. "Iterative Decision Feedback Equalization Using Online Prediction." IEEE Access 8 (2020): 23638–49. http://dx.doi.org/10.1109/access.2020.2970340.

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Leclert, A., and P. Vandamme. "Decision Feedback Equalization of Dispersive Radio Channels." IEEE Transactions on Communications 33, no. 7 (July 1985): 676–84. http://dx.doi.org/10.1109/tcom.1985.1096356.

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Iqbal, Naveed, Azzedine Zerguine, and Naofal Al-Dhahir. "Decision Feedback Equalization using Particle Swarm Optimization." Signal Processing 108 (March 2015): 1–12. http://dx.doi.org/10.1016/j.sigpro.2014.07.030.

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Dissertations / Theses on the topic "Decision feedback equalization"

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Rice, Michael, Gayatri Narumanchi, and Mohammad Saquib. "Decision Feedback Equalization for SOQPSK." International Foundation for Telemetering, 2012. http://hdl.handle.net/10150/581839.

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This paper investigates a fractionally-spaced decision-feedback equalization technique for Shaped Offset Quadrature Phase Shift Keying (SOQPSK). The kernel of the block-based feedback algorithm is to estimate the intersymbol interference and cancel it from the samples used to make the bit decisions. This process refines the bit estimates sequentially, thereby increasing the probability of obtaining accurate estimates. The simulated bit error rate performance of the decision-feedback technique shows a 1 dB improvement over MMSE-equalized SOQPSK-TG over channels derived from multipath channel measurements at Cairns Army Airfield, Ft. Rucker, Alabama and Edwards AFB, California.
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Senol, Sinan. "Performance Comparison Of Adaptive Decision Feedback Equalizer And Blind Decision Feedback Equalizer." Master's thesis, METU, 2004. http://etd.lib.metu.edu/upload/1023746/index.pdf.

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The Decision Feedback Equalizer (DFE) is a known method of channel equalization which has performance superiority over linear equalizer. The best performance of DFE is obtained, commonly, with training period which is used for initial acquisiton of channel or recovering changes in the channel. The training period requires a training sequence which reduces the bit transmission rate or is not possible to send in most of the situations. So, it is desirable to skip the training period. The Unsupervised (Blind) DFE (UDFE) is such a DFE scheme which has no training period. The UDFE has two modes of operation. In one mode, the UDFE uses Constant Modulus Algorithm (CMA) to perform channel acquisition, blindly. The other mode is the same as classical decision-directed DFE. This thesis compares the performances of the classical trained DFE method and the UDFE. The performance comparison is done in some channel environments with the problem of timing error present in the received data bearing signal. The computer aided simulations are done for two stationary channels, a time-varying channel and a frequency selective Rayleigh fading channel to test the performance of the relevant equalizers. The test results are evaluted according to mean square error (MSE), bit-error rate (BER), residual intersymbol interference (RISI) performances and equalizer output diagrams. The test results show that the UDFE has an equal or, sometimes, better performance compared to the trained DFE methods. The two modes of UDFE enable it to solve the absence of training sequence.
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Kennedy, Rodney Andrew, and rodney kennedy@anu edu au. "Operational Aspects of Decision Feedback Equalizers." The Australian National University. Research School of Physical Sciences and Engineering, 1989. http://thesis.anu.edu.au./public/adt-ANU20050418.151329.

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The central theme is the study of error propagation effects in decision feedback equalizers (DFEs). The thesis contains: a stochastic analysis of error propagation in a tuned DFE; an analysis of the effects of error propagation in a blindly adapted DFE; a deterministic analysis of error propagation through input-output stability ideas; and testing procedures for establishing correct tap convergence in blind adaptation. To a lesser extent, the decision directed equalizer (DDE) is also treated.¶ Characterizing error propagation using finite state Markov process (FSMP) techniques is first considered. We classify how the channel and DFE parameters affect the FSMP model and establish tight bounds on the error probability and mean error recovery time of a tuned DFE. These bounds are shown to be too conservative for practical use and highlight the need for imposing stronger hypotheses on the class of channels for which a DFE may be effectively used.¶ In blind DFE adaptation we show the effect of decision errors is to distort the adaptation relative to the use of a training sequence. The mean square error surface in a LMS type setting is shown to be a concatenation of quadratic functions exposing the possibility of false tap convergence to undesirable DFE parameter settings. Averaging analysis and simulation are used to verify this behaviour on some examples.¶ Error propagation in a tuned DFE is also examined in a deterministic setting. A finite error recovery time problem is set up as an input-output stability problem. Passivity theory is invoked to prove that a DFE can be effectively used on a channel satisfying a simple frequency domain condition. These results give performance bounds which relate well with practice.¶ Testing for false tap convergence in blind adaptation concludes our study. Simple statistic output tests are shown to be capable of discerning correct operation of a DDE. Similar tests are conjectured for the DFE, supported by proofs for the low dimensional cases.
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Abdulrahman, Majeed Carleton University Dissertation Engineering Electrical. "Decision feedback equalization with cyclostationary interference for DSL." Ottawa, 1989.

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Wang, Rujiang. "Mitigating error propagation of decision feedback equalization in boradband communications." Doctoral thesis, Université Laval, 2008. http://www.theses.ulaval.ca/.

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Michaelides, John Frixou. "Nonlinear adaptive filtering for echo cancellation and decision feedback equalization." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66261.

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Wang, Rujiang. "Mitigating Error Propagation of Decision Feedback Equalization in Broadband Communications." Thesis, Université Laval, 2008. http://www.theses.ulaval.ca/2008/25328/25328.pdf.

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Xiaoqi, Han. "Nonlinear Equalization Based on Decision Feedback Equalizer for Optical Communication System." Miami University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=miami1386170540.

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Adnan, Rubyet. "Blind Equalization for Tomlinson-Harashima Precoded Systems." Thesis, University of Canterbury. Electrical and Computer Engineering, 2007. http://hdl.handle.net/10092/1130.

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At a communications receiver the observed signal is a corrupted version of the transmitted signal. This distortion in the received signal is due to the physical characteristics of the channel, including multipath propagation, the non-idealities of copper wires and impulse noise. Equalization is a process to combat these distortions in order to recover the original transmitted signal. Roughly stated, the equalizer tries to implement the inverse transfer function of the channel while taking into account the channel noise. The equalizer parameters can be tuned to this inverse transfer function using an adaptive algorithm. In many cases, the algorithm uses a training sequence to drive the equalizer parameters to the optimum solution. But, for time-varying channels or multiuser channels the use of a training sequence is inefficient in terms of bandwidth, as bandwidth is wasted due to the periodic re-transmission of the training sequence. A blind equalization algorithm is a practical method to eliminate this training sequence. An equalizer adapted using a blind algorithm is a key component of a bandwidth efficient receiver for broadcast and point-to-multipoint communications. The initial convergence performance of a blind adaptive equalizer depends on the higher-order statistics of the transmitted signal. In modern digital systems, Tomlinson-Harashima precoding (THP) is often used for signal shaping and to mitigate the error propagation problem of a decision feedback equalizer (DFE). The concept of THP comes from pre-equalization. In fact, it is a nonlinear form of pre-equalization, which bounds the higher-order statistics of the transmitted signal. But, THP and blind equalization are often viewed as incompatible equalization techniques. In this research, we give multiple scenarios where blind equalization of a THP-encoded signal might arise. With this motivation we set out to answer the question, can a blind equalizer successfully acquire a THP-encoded signal? We investigate the combination of a Tomlinson-Harashima precoder on the transmitter side and a blind equalizer on the receiver side. By bounding the kurtosis of the THP-encoded signal, we show that THP actually aids the initial convergence of blind equalization. We find that, as the symbol constellation size increases, the THP-encoded signal kurtosis approaches that of a uniform distribution, not a Gaussian. We investigate the compatibility of blind equalization with THP-encoded signals for both SISO and MIMO systems. In a SISO system, conventional blind algorithms can be used to counter the distortions introduced in the received signal. However, in a MIMO system with multiple users, the other users act as interferers on the desired user's signal. Hence, modified blind algorithms need to be applied to mitigate these interferers. For both SISO and MIMO systems, we show that the THP encoder ensures that the signal distribution approaches a non-Gaussian distribution. Using Monte Carlo simulations, we study the effects of Tomlinson-Harashima precoding on the performance of Bussgang-type blind algorithms and verify our theoretical analysis. The major contributions of this thesis are: • A demonstration that a blind equalizer can successfully acquire a THP-encoded signal for both SISO and MIMO systems. We show that THP actually aids blind equalization, as it ensures that the transmitted signal is non-Gaussian. • An analytical quantification of the effects of THP on the transmitted signal statistics. We derive a novel bound on the kurtosis of the THP-encoded signal. • An extension of the results from a single-user SISO scenario to multiple users and a MIMO scenario. We demonstrate that our bound and simulated results hold for these more general cases. Through our work, we have opened the way for a novel application of training sequence-less equalization: to acquire and equalize THP-encoded signals. Using our proposed system, periodic training sequences for a broadcast or point-to-multipoint system can be avoided, improving the bandwidth efficiency of the transceiver. Future modem designs with THP encoding can make use of our advances for bandwidth efficient communication systems.
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McGinty, Nigel, and nigel mcginty@defence gov au. "Reduced Complexity Equalization for Data Communication." The Australian National University. Research School of Information Sciences and Engineering, 1998. http://thesis.anu.edu.au./public/adt-ANU20050602.122741.

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Optimal decision directed equalization techniques for time dispersive communication channels are often too complex to implement. This thesis considers reduced complexity decision directed equalization that lowers complexity demands yet retains close to optimal performance. The first part of this dissertation consists of three reduced complexity algorithms based on the Viterbi Algorithm (VA) which are: the Parallel Trellis VA (PTVA); Time Reverse Reduced State Sequence Estimation (TR-RSSE); and Forward-Backward State Sequence Detection (FBSSD). The second part of the thesis considers structural modifications of the Decision Feedback Equalizer (DFE), which is a special derivative of the VA, specifically, optimal vector quantization for fractionally spaced DFEs, and extended stability regions for baud spaced DFEs using passivity analysis are investigated.¶ For a special class of sparse channels the VA can be decomposed over a number of independent parallel trellises. This decomposition will be called the Parallel Trellis Viterbi Algorithm and can have lower complexity than the VA yet it retains optimal performance. By relaxing strict sparseness constraints on the channel a sub-optimal approach is proposed which keeps complexity low and obtains good performance.¶ Reduced State Sequence Estimation (RSSE) is a popular technique to reduce complexity. However, its deficiency can be the inability to adequately equalize non-minimum phase channels. For channels that have energy peaks in the tail of the impulse response (post-cursor dominant) RSSE's complexity must be close to the VA or performance will be poor. Using a property of the VA which makes it invariant to channel reversal, TR-RSSE is proposed to extend application of RSSE to post-cursor dominant channels.¶ To further extend the class of channels suitable for RSSE type processing, FBSSD is suggested. This uses a two pass processing method, and is suited to channels that have low energy pre and post-cursor. The first pass generates preliminary estimates used in the second pass to aid the decision process. FBSSD can range from RSSE to TR-RSSE depending on parameter settings.¶ The DFE is obtained when the complexity of RSSE is minimized. Two characterizing properties of the DFE, which are addressed in this thesis, are feedback and quantization. A novel fractionally spaced (FS) DFE structure is presented which allows the quantizer to be generalized relative to the quantizer used in conventional FS-DFEs. The quantizer can be designed according to a maximum a posteriori criterion which takes into account a priori statistical knowledge of error occurrences. A radically different quantizer can be obtained using this technique which can result in significant performance improvements.¶ Due to the feedback nature of the DFE a form of stability can be considered. After a decision error occurs, a stable DFE will, after some finite time and in the absence of noise, operate error free. Passivity analysis provides sufficient conditions to determine a class of channels which insures a DFE will be stable. Under conditions of short channels and small modulation alphabets, it is proposed that conventional passivity analysis can be extended to account for varying operator gains, leading to weaker sufficient conditions for stability (larger class of channels).
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Books on the topic "Decision feedback equalization"

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Wiedmann, Ralf. An adaptive all-pass filter for decision feedback equalization. 1997.

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Wiedmann, Ralf. An adaptive all-pass filter for decision feedback equalization. 1997.

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Engelbrecht, Linda M. A DAC and comparator for a 100MHz decision feedback equalization loop. 1996.

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Gao, Hairong. Design of high-speed summing circuitry and comparator for adaptive parallel multi-level decision feedback equalization. 1997.

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Gao, Hairong. Design of high-speed summing circuitry and comparator for adaptive parallel multi-level decision feedback equalization. 1997.

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Book chapters on the topic "Decision feedback equalization"

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Bergmans, Jan W. M. "Partial-Response and Decision-Feedback Equalization." In Digital Baseband Transmission and Recording, 265–300. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4757-2471-4_6.

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Sur, Samarendra Nath, Rabindranath Bera, and Bansibadan Maji. "Decision Feedback Equalization for MIMO Systems." In Intelligent Computing and Applications, 205–12. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2268-2_22.

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Cioffi, J. M., and G. D. Forney. "Generalized Decision-Feedback Equalization for Packet Transmission with ISI and Gaussian Noise." In Communications, Computation, Control, and Signal Processing, 79–127. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6281-8_4.

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Guo, Yecai, and Juanjuan Ji. "Variable Momentum Factor Decision Feedback Blind Equalization Algorithm Based on Constant Parameter Error Function." In Communications in Computer and Information Science, 282–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19853-3_41.

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Pulikanti, Laxmaiah, Pradeep Goutam, Bipsa Purushothaman, K. G. Dileep, and S. V. Hari Prasad. "Bit Synchronization and Delayed Decision Feedback Equalization for EDGE BTS - Hardware Implementation on TMS320C6424 TI DSP." In Wireless and Satellite Systems, 73–82. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53850-1_9.

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"Decision Feedback Equalization." In Multi-Gigabit Transmission over Multimode Optical Fibre, 509–83. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470059044.ch10.

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"Zero-Forcing Decision Feedback Equalization." In Channel Equalization for Wireless Communications, 57–68. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118105283.ch3.

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"MMSE and ML Decision Feedback Equalization." In Channel Equalization for Wireless Communications, 99–113. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118105283.ch5.

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Sur, S., R. Bera, and B. Maji. "Decision feedback equalization for large scale MIMO system." In Foundations and Frontiers in Computer, Communication and Electrical Engineering, 295–98. CRC Press, 2016. http://dx.doi.org/10.1201/b20012-59.

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Chang, Yao-Jen, and Chia-Lu Ho. "Adaptive Fuzzy Neural Filtering for Decision Feedback Equalization and Multi-Antenna Systems." In Adaptive Filtering. InTech, 2011. http://dx.doi.org/10.5772/16336.

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Conference papers on the topic "Decision feedback equalization"

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Wood, R. "Enhanced decision feedback equalization." In International Conference on Magnetics. IEEE, 1990. http://dx.doi.org/10.1109/intmag.1990.734620.

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Huang, Gillian, Andrew Nix, and Simon Armour. "Decision feedback equalization in SC-FDMA." In 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, 2008. http://dx.doi.org/10.1109/pimrc.2008.4699507.

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Sainte-Agathe, Frederique, and Hikmet Sari. "Iterative Frequency-Domain Decision-Feedback Equalization." In 2006 3rd International Symposium on Wireless Communication Systems. IEEE, 2006. http://dx.doi.org/10.1109/iswcs.2006.4362247.

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Rad, F. R., and J. Moon. "Turbo equalization utilizing soft decision feedback." In INTERMAG Asia 2005: Digest of the IEEE International Magnetics Conference. IEEE, 2005. http://dx.doi.org/10.1109/intmag.2005.1464232.

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Hwasun Yoo, Sunghwan Ong, Changeon Kang, and Daesik Hong. "Prediction based direct blind decision feedback equalization." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1005188.

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Dietl, Guido, Sabik Salam, Wolfgang Utschick, and Josef A. Nossek. "Conjugate gradient based MMSE Decision Feedback Equalization." In 2008 IEEE International Symposium on Wireless Communication Systems. IEEE, 2008. http://dx.doi.org/10.1109/iswcs.2008.4726111.

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Ilic, Jovana, Thomas Strohmer, and Raymond Guan. "Decision feedback equalization with sparsity driven thresholding." In 2010 44th Asilomar Conference on Signals, Systems and Computers. IEEE, 2010. http://dx.doi.org/10.1109/acssc.2010.5757660.

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Lim, W. G., R. A. Kennedy, and T. D. Abhayapala. "Adaptive Decision Feedback Equalization under Parallel Adaptation." In 2008 IEEE International Conference on Communications. IEEE, 2008. http://dx.doi.org/10.1109/icc.2008.98.

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Zhang, Chao, and Changyong Pan. "Low Complexity Iterative Frequency Domain Decision Feedback Equalization." In 2010 IEEE Vehicular Technology Conference (VTC 2010-Fall). IEEE, 2010. http://dx.doi.org/10.1109/vetecf.2010.5594372.

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Oletu, Grace, and Predrag Rapajic. "Modified Iterative Decision Feedback Equalization for Communication Systems." In 2013 8th EUROSIM Congress on Modelling and Simulation (EUROSIM). IEEE, 2013. http://dx.doi.org/10.1109/eurosim.2013.104.

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