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Journal articles on the topic 'Iterative receiver'

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Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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1

Qiao, Gang, Zeeshan Babar, Feng Zhou, Lu Ma, and Xue Li. "Low-Complexity Progressive MIMO-OFDM Receiver for Underwater Acoustic Communication." Symmetry 11, no. 3 (March 11, 2019): 362. http://dx.doi.org/10.3390/sym11030362.

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Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) proves to be a better choice for high speed underwater acoustic (UWA) communication as it increases the data rate and solves the bandwidth limitation issue; however, at the same time, it increases the design challenges and complexity of the receivers. Inter-Symbol Interference (ISI) and Inter-Carrier Interference (ICI) are introduced in the received signal by the extended multipath and Doppler shifts along with different types of noises due to the noisy acoustic channel. Here we propose two iterative receivers: one is ICI unaware iterative MIMO-OFDM receiver, which uses a novel cost function threshold based soft information decision feedback method. The second one is ICI aware progressive iterative MIMO-OFDM receiver, which adapts and increases the progressions according to the level of ICI present in the received signal, while fully utilizing the soft information from the previous iterations, therefore reducing the complexity. Orthogonal Matching pursuit (OMP) channel estimation, low density parity check (LDPC) decoding and minimum mean square error (MMSE) equalization schemes are exploited by both the receivers. The proposed receivers are analyzed and compared with the standard Alamouti MIMO receiver as a reference and also compared with the non-iterative, basic turbo iterative and non-progressive iterative MIMO receivers. Simulations and experimental results prove the efficiency and effectiveness of the proposed receivers.

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2

Qiao, Gang, Zeeshan Babar, Lu Ma, and Xue Li. "Cost Function based Soft Feedback Iterative Channel Estimation in OFDM Underwater Acoustic Communication." Infocommunications journal, no. 1 (2019): 29–37. http://dx.doi.org/10.36244/icj.2019.1.4.

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Underwater Acoustic (UWA) communication is mainly characterized by bandwidth limited complex UWA channels. Orthogonal Frequency Division Multiplexing (OFDM) solves the bandwidth problem and an efficient channel estimation scheme estimates the channel parameters. Iterative channel estimation refines the channel estimation by reducing the number of pilots and coupling the channel estimator with channel decoder. This paper proposes an iterative receiver for OFDM UWA communication, based on a novel cost function threshold driven soft decision feedback iterative channel technique. The receiver exploits orthogonal matching pursuit (OMP) channel estimation and low density parity check (LDPC) coding techniques after comparing different channel estimation and coding schemes. The performance of the proposed receiver is verified by simulations as well as sea experiments. Furthermore, the proposed iterative receiver is compared with other non-iterative and soft decision feedback iterative receivers.

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3

Ligorría, Juan Pablo, and Charles J. Ammon. "Iterative deconvolution and receiver-function estimation." Bulletin of the Seismological Society of America 89, no. 5 (October 1, 1999): 1395–400. http://dx.doi.org/10.1785/bssa0890051395.

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Abstract We describe and apply an iterative, time-domain deconvolution approach to receiver-function estimation and illustrate the reliability and advantages of the technique using synthetic- and observation-based examples. The iterative technique is commonly used in earthquake time-function studies and offers several advantages in receiver-function analysis such as intuitively stripping the largest receiver-function arrivals from the observed seismograms first and then the details; long-period stability by a priori constructing the deconvolution as a sum of Gaussian pulses; and easy generalization to allow multiwaveform deconvolution for a single receiver-function estimate.

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4

Shan, Shuwei, Hanwen Luo, and Wentao Song. "Iterative receiver for OFDM-SDMA system." Journal of Electronics (China) 21, no. 5 (September 2004): 359–65. http://dx.doi.org/10.1007/bf02687936.

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5

El Chall, Rida, Fabienne Nouvel, Maryline Hélard, and Ming Liu. "Performance and Complexity Evaluation of Iterative Receiver for Coded MIMO-OFDM Systems." Mobile Information Systems 2016 (2016): 1–22. http://dx.doi.org/10.1155/2016/7642590.

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Multiple-input multiple-output (MIMO) technology in combination with channel coding technique is a promising solution for reliable high data rate transmission in future wireless communication systems. However, these technologies pose significant challenges for the design of an iterative receiver. In this paper, an efficient receiver combining soft-input soft-output (SISO) detection based on low-complexity K-Best (LC-K-Best) decoder with various forward error correction codes, namely, LTE turbo decoder and LDPC decoder, is investigated. We first investigate the convergence behaviors of the iterative MIMO receivers to determine the required inner and outer iterations. Consequently, the performance of LC-K-Best based receiver is evaluated in various LTE channel environments and compared with other MIMO detection schemes. Moreover, the computational complexity of the iterative receiver with different channel coding techniques is evaluated and compared with different modulation orders and coding rates. Simulation results show that LC-K-Best based receiver achieves satisfactory performance-complexity trade-offs.

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6

Jiang, Hong Rui, and Kyung Sup Kwak. "Space–Time Block Coding Iterative Multiuser Receiver." Journal of Circuits, Systems and Computers 12, no. 01 (February 2003): 19–30. http://dx.doi.org/10.1142/s0218126603000817.

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We present a multiuser receiver for CDMA systems with the combination of turbo channel coding and space–time block coding. A turbo scheme based on multiuser detection, soft interference cancellation and decoding is provided, and the algorithms for space–time decoding and separately interference suppressing are derived in this paper. The multiuser detection consists of multiuser interference suppression and single-user space–time decoding. Then we develop the iterative multiuser receiver based on the soft estimates of the interfering users' symbols. Moreover, simulation is given to verify the effectiveness of the multiuser receiver.

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7

Wang, Yinzhi, and Gary L. Pavlis. "Generalized iterative deconvolution for receiver function estimation." Geophysical Journal International 204, no. 2 (December 16, 2015): 1086–99. http://dx.doi.org/10.1093/gji/ggv503.

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8

Lyu, Yibo, Lin Wang, Guofa Cai, and Guanrong Chen. "Iterative Receiver for $M$-ary DCSK Systems." IEEE Transactions on Communications 63, no. 11 (November 2015): 3929–36. http://dx.doi.org/10.1109/tcomm.2015.2425877.

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9

Kim, Y. J. D., and J. Bajcsy. "Iterative receiver for faster-than-Nyquist broadcasting." Electronics Letters 48, no. 24 (November 22, 2012): 1561–62. http://dx.doi.org/10.1049/el.2012.3346.

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10

Lehnigk-Emden, T., U. Wasenmüller, C. Gimmler, and N. Wehn. "Analysis of iteration control for turbo decoders in turbo synchronization applications." Advances in Radio Science 7 (May 18, 2009): 139–44. http://dx.doi.org/10.5194/ars-7-139-2009.

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Abstract. Wireless data transmission results in frequency and phase offsets of the signal in the receiver. In addition, the received symbols are corrupted by noise. Therefore, synchronization and channel coding are vital parts of each receiver in digital communication systems. By combining the phase and frequency synchronization with an advanced iterative channel decoder (inner loop) e.g. turbo codes in an iterative way (outer loop), the communications performance can be further increased. This principle is referred to as turbo synchronization. The energy consumption and the peak throughput of the system depend on the number of iterations for both loops. An advanced iteration control can decrease the mean number of needed iterations by detecting correctly decoded blocks. This leads to a dramatic energy saving or to an increase of throughput. In this paper we present a new stopping criterion for decodable blocks for turbo decoding in interrelation with turbo synchronization. Furthermore the implementation complexity of the turbo decoder is shown on a Xilinx FPGA.

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11

Suyama, Satoshi, Junichi Onodera, Hiroshi Suzuki, and Kazuhiko Fukawa. "Decision-directed phase noise compensation for millimeter-wave single carrier systems with iterative frequency-domain equalization." International Journal of Microwave and Wireless Technologies 2, no. 3-4 (July 8, 2010): 399–408. http://dx.doi.org/10.1017/s1759078710000516.

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This paper proposes a receiver that repeats iterative frequency-domain equalization (FDE) and decision-directed phase noise compensation (DD-PNC) to alleviate degradation due to the phase noise for millimeter-wave single carrier (SC) systems. High bit-rate SC-FDE transceivers based on the single-chip Si RF-CMOS IC technology in the 60-GHz millimeter-wave band have been extensively studied for wireless personal area network (WPAN) systems, and the relatively large phase noise in a phase-locked loop (PLL) synthesizer severely degrades transmission performance. In an initial processing of the proposed receiver, a cyclic prefix (CP)-based phase noise compensator (CP-PNC) removes the phase noise from a time-domain received signal by using CP, which is known to the receiver, and the channel is equalized by the iterative FDE using the conventional minimum mean-square-error (MMSE) weight. In an iterative processing, DD-PNC estimates the phase noise each symbol by exploiting an output of a channel decoder, and then compensates the time-domain received signal for the phase noise by using the estimate. In order to equalize the compensated received signal, the iterative FDE performs both the MMSE filtering and residual inter-symbol interference cancelation using the decoder output. Computer simulations following the 60-GHz WPAN standard demonstrate that in the 64QAM with the coding rate of 3/4, the proposed receiver with three iterations can drastically remove the phase noise of −85 dBc/Hz at 1 MHz offset, and that it can achieve excellent transmission performance.

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12

Xiang, Luping, Yusha Liu, Chao Xu, Robert G. Maunder, Lie-Liang Yang, and Lajos Hanzo. "Iterative Receiver Design for Polar-Coded SCMA Systems." IEEE Transactions on Communications 69, no. 7 (July 2021): 4235–46. http://dx.doi.org/10.1109/tcomm.2021.3068733.

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13

Boujemaa, Hatem, Raphael Visoz, and Antoine O. Berthet. "Iterative low-complexity receiver for the UMTS downlink." Annales des Télécommunications 59, no. 5-6 (May 2004): 696–712. http://dx.doi.org/10.1007/bf03179693.

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14

Qi Wang, Zhaocheng Wang, and Linglong Dai. "Iterative Receiver for Hybrid Asymmetrically Clipped Optical OFDM." Journal of Lightwave Technology 32, no. 22 (November 15, 2014): 4471–77. http://dx.doi.org/10.1109/jlt.2014.2358611.

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15

Park, Sangjoon. "QR-BP Based Iterative Receiver for MIMO Systems." Journal of Korean Institute of Communications and Information Sciences 45, no. 12 (December 31, 2020): 2084–87. http://dx.doi.org/10.7840/kics.2020.45.12.2084.

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16

Stienstra, D., A. K. Khandani, and Wen Tong. "Iterative multiuser turbo-code receiver for ds-cdma." IEEE Transactions on Vehicular Technology 52, no. 2 (March 2003): 365–73. http://dx.doi.org/10.1109/tvt.2002.807142.

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17

Ulukus, S., and A. Yener. "Iterative Transmitter and Receiver Optimization for CDMA Networks." IEEE Transactions on Wireless Communications 3, no. 6 (November 2004): 1879–84. http://dx.doi.org/10.1109/twc.2004.837453.

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18

Marey, Mohamed, and Octavia A. Dobre. "Iterative Receiver Design for Uplink OFDMA Cooperative Systems." IEEE Transactions on Broadcasting 62, no. 4 (December 2016): 936–47. http://dx.doi.org/10.1109/tbc.2016.2606892.

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19

Kim, Kyeong Jin, Theodoros A. Tsiftsis, and Robert Schober. "Semiblind Iterative Receiver for Coded MIMO-OFDM Systems." IEEE Transactions on Vehicular Technology 60, no. 7 (September 2011): 3156–68. http://dx.doi.org/10.1109/tvt.2011.2158672.

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20

Nefedov, Nikolai, Markku Pukkila, Raphaël Visoz, and Antoine O. Berthet. "Iterative receiver concept for TDMA packet data systems." European Transactions on Telecommunications 14, no. 5 (2003): 457–69. http://dx.doi.org/10.1002/ett.4460140509.

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21

Gülünay, Necati. "The diminishing residual matrices method for surface-consistent statics — A review." GEOPHYSICS 82, no. 4 (July 1, 2017): V257—V274. http://dx.doi.org/10.1190/geo2016-0602.1.

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The diminishing residual matrices (DRM) method can be used to surface-consistently decompose individual trace statics into source and receiver components. The statics to be decomposed may either be first-arrival times after the application of linear moveout associated with a consistent refractor as used in refraction statics or residual statics obtained by crosscorrelating individual traces with corresponding model traces (known as pilot traces) at the same common-midpoint (CMP) location. The DRM method is an iterative process like the well-known Gauss-Seidel (GS) method, but it uses only source and receiver terms. The DRM method differs from the GS method in that half of the average common shot and receiver terms are subtracted simultaneously from the observations at each iteration. DRM makes the under-constrained statics problem a constrained one by implicitly adding a new constraint, the equality of the contribution of shots and receivers to the solution. The average of the shot statics and the average of the receiver statics are equal in the DRM solution. The solution has the smallest difference between shot and receiver statics profiles when the number of shots and the number of receivers in the data are equal. In this case, it is also the smallest norm solution. The DRM method can be derived from the well-known simultaneous iterative reconstruction technique. Simple numerical tests as well as results obtained with a synthetic data set containing only the field statics verify that the DRM solution is the same as the linear inverse theory solution. Both algorithms can solve for the long-wavelength component of the statics if the individual picks contain them. Yet DRM method is much faster. Application of the method to the normal moveout-corrected CMP gathers on a 3D land survey for residual statics calculation found that pick-decompose-apply-stack stages of the DRM method need to be iterated. These iterations are needed because of time and waveform distortions of the pilot traces due to the individual trace statics. The distortions lessen at every external DRM iteration.

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22

Zafaruddin, S. M., Shankar Prakriya, and Surendra Prasad. "Iterative Receiver Based on SAGE Algorithm for Crosstalk Cancellation in Upstream Vectored VDSL." ISRN Communications and Networking 2011 (June 30, 2011): 1–15. http://dx.doi.org/10.5402/2011/586574.

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We propose the use of an iterative receiver based on the Space Alternating Generalized Expectation maximization (SAGE) algorithm for crosstalk cancellation in upstream vectored VDSL. In the absence of alien crosstalk, we show that when initialized with the frequency-domain equalizer (FEQ) output, the far-end crosstalk (FEXT) can be cancelled with no more real-time complexity than the existing linear receivers. In addition, the suggested approach does not require offline computation of the channel inverse and thus reduces the receiver complexity. In the presence of alien crosstalk, there is a significant gap between the rate performance of the linear receivers as compared with the single-user bound (SUB). The proposed receiver is shown to successfully bridge this gap while requiring only a little extracomplexity. Computer simulations are presented to validate the analysis and confirm the performance of the proposed receiver.

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23

He, Y., and A. Bilgic. "Iterative least squares method for global positioning system." Advances in Radio Science 9 (August 1, 2011): 203–8. http://dx.doi.org/10.5194/ars-9-203-2011.

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Abstract. The efficient implementation of positioning algorithms is investigated for Global Positioning System (GPS). In order to do the positioning, the pseudoranges between the receiver and the satellites are required. The most commonly used algorithm for position computation from pseudoranges is non-linear Least Squares (LS) method. Linearization is done to convert the non-linear system of equations into an iterative procedure, which requires the solution of a linear system of equations in each iteration, i.e. linear LS method is applied iteratively. CORDIC-based approximate rotations are used while computing the QR decomposition for solving the LS problem in each iteration. By choosing accuracy of the approximation, e.g. with a chosen number of optimal CORDIC angles per rotation, the LS computation can be simplified. The accuracy of the positioning results is compared for various numbers of required iterations and various approximation accuracies using real GPS data. The results show that very coarse approximations are sufficient for reasonable positioning accuracy. Therefore, the presented method reduces the computational complexity significantly and is highly suited for hardware implementation.

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24

Zhang, Yongxing, Wenping Ge, Pengju Zhang, Mengyao Gao, and Gecheng Zhang. "A Joint Detection and Decoding Scheme for PC-SCMA System Based on Pruning Iteration." Symmetry 12, no. 10 (October 1, 2020): 1624. http://dx.doi.org/10.3390/sym12101624.

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Polar coding and sparse code multiple access (SCMA) are key technologies for 5G mobile communication, the joint design of them has a great significance to improve the overall performance of the transmitter-receiver symmetric wireless communication system. In this paper, we firstly propose a pruning iterative joint detection and decoding algorithm (PI-JDD) based on the confidence stability of resource nodes. Branches to be updated are dynamically pruned to avoid redundant iterative, which is able to reduce 24~50% complexity while achieving the approximate error performance of traditional serial joint iterative detection and decoding algorithm S-JIDD. Then, to further reduce the bit error rate (BER) of the receiver, a cyclic redundancy check (CRC) termination mechanism is added at the end of each joint iteration to avoid the convergence error caused by decoding deviation. Simulation results show that the addition of an early stopping criterion can achieve a remarkable performance gain compared with the S-JIDD algorithm. More importantly, the combined algorithm of the two proposed schemes can reduce the computational complexity while achieving better error performance.

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25

Liu, Shu Yang, Jian Ping Li, and Chao Shi Cai. "An Advanced Self-Adaptive Iterative Decoding Scheme Based on Stopping Criterion." Advanced Materials Research 179-180 (January 2011): 167–73. http://dx.doi.org/10.4028/www.scientific.net/amr.179-180.167.

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This paper proposes an advanced iterative decoding scheme based on stopping criterion for BICM-ID embedded turbo codes. A fixed iterative number scheme has been used in the receiver and lower complexity than the conventional schedule can be achieved. To acquire a smaller total iterative number and fewer calculations of CE, the maximum and minimum iterative numbers (Imax/Imin) are introduced in this proposed scheme. However, iterative numbers which correspond to different SNRs are varied. In order to receive better flexibility, both Imax and Imin, whose values are not unique, are determined by the statistics of the average iterative numbers and the CG criterion. Simulation results confirm that, compared with the fixed iterative number scheme, similar BER performances and much lower complexity can be achieved. Further improvement of the adaptability results from this self-adaptive option of Imax/Imin.

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26

Yang, Yuan, and Hai-lin Zhang. "A simplified MMSE-based iterative receiver for MIMO systems." Journal of Zhejiang University-SCIENCE A 10, no. 10 (October 2009): 1389–94. http://dx.doi.org/10.1631/jzus.a0920172.

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27

Dhivagar, B., Kiran Kuchi, and K. Giridhar. "An Iterative DFE Receiver for MIMO SC-FDMA Uplink." IEEE Communications Letters 18, no. 12 (December 2014): 2141–44. http://dx.doi.org/10.1109/lcomm.2014.2361787.

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28

Park, S. Y., S. K. Choi, and C. G. Kang. "Complexity-reduced iterative MAP receiver for spatial multiplexing systems." IEE Proceedings - Communications 152, no. 4 (2005): 432. http://dx.doi.org/10.1049/ip-com:20041140.

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29

Bai, Lin, and Jinho Choi. "Lattice Reduction-Based MIMO Iterative Receiver Using Randomized Sampling." IEEE Transactions on Wireless Communications 12, no. 5 (May 2013): 2160–70. http://dx.doi.org/10.1109/twc.2013.031813.120611.

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30

Schmitt, Lars, and Heinrich Meyr. "A Systematic Framework for Iterative Maximum Likelihood Receiver Design." IEEE Transactions on Communications 58, no. 7 (July 2010): 2035–45. http://dx.doi.org/10.1109/tcomm.2010.07.080357.

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31

Huang, Nuo, Jun-Bo Wang, Cunhua Pan, Jin-Yuan Wang, Yijin Pan, and Ming Chen. "Iterative Receiver for Flip-OFDM in Optical Wireless Communication." IEEE Photonics Technology Letters 27, no. 16 (August 15, 2015): 1729–32. http://dx.doi.org/10.1109/lpt.2015.2438338.

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32

Zhang, Xiaohui, Hongxiang Li, Wenqi Liu, and Junling Qiao. "Iterative IQ Imbalance Compensation Receiver for Single Carrier Transmission." IEEE Transactions on Vehicular Technology 66, no. 9 (September 2017): 8238–48. http://dx.doi.org/10.1109/tvt.2017.2690963.

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33

Weitkemper, P., K. Zielinski, K. D. Kammeyer, and R. Laur. "Optimized power allocation for iterative multiuser detection for a SC-CDMA uplink." Advances in Radio Science 5 (June 13, 2007): 273–78. http://dx.doi.org/10.5194/ars-5-273-2007.

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Abstract. This paper deals with optimization of the received power profile for iterative parallel and successive interference cancellation (PIC/SIC) in coded CDMA systems. For practical implementation additional constraints should be applied. This paper focuses on the maximum tolerable bit error rate. It will be shown that optimized power profile can considerably gain the overall system performance. Due to unequal required receive powers an allocation to certain users can be done with respect to their individual power constraints. This is important especially in near-far scenarios. Beside these constraints also the maximum number of iterations is implemented due to limiting the computational complexity in the receiver.

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34

Zheng, Zi Wei. "Iterative Channel Estimation Scheme for the WLAN Systems with the Multiple-Antenna Receivers." Advanced Engineering Forum 6-7 (September 2012): 871–75. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.871.

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Alleviate the multipath delay spread and suitable for broadband transmission efficiency, orthogonal frequency division multiplexing wireless local area network (WLAN) is widely used to assist inverse fast Fourier transform and fast Fourier transform operation domain. Orthogonal frequency division multiplexing is a blow to the broadcast channel multipath fading and high data throughput, transmission, wireless fading channel method, which is widely used to support high performance bandwidth-efficient wireless multimedia services. Several times in the transmitter and receiver antenna technology allows data transfer rate and spectrum efficiency and the use of multiple transmit antennas and multiple receive antennas through spatial processing. High-precision channel estimation scheme is very important wideband multi-carrier orthogonal frequency complex WLAN systems use multiple antenna receiver based division of labor and the overall multi-carrier orthogonal frequency multiplexing division of performance-based WLAN system is to crucial antenna to receive the symbol error rate. In this article, the iterative channel estimation scheme proposed multi-carrier orthogonal frequency division multiplexed using multiple antennas receiver-based WLAN system.

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35

Ilahi, A. K., M. F. R. Auly, D. A. Zaky, A. Abdullah, R. P. Nugroho, S. K. Suhardja, A. D. Nugraha, et al. "Early Results of Time Domain Receiver Function Data Processing in Mt Merapi and Mt Merbabu." IOP Conference Series: Earth and Environmental Science 873, no. 1 (October 1, 2021): 012055. http://dx.doi.org/10.1088/1755-1315/873/1/012055.

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Abstract The receiver function method is a method to image the earth subsurface by utilizing Ps conversion waves that are formed due to the velocity boundary. In general, the receiver function method estimates depth of structures such as the mantle-crust boundary by deconvoluting the vertical component from the horizontal component. Typical receiver function data processing is done in the frequency domain where the deconvolution process can be seen as a division between two components. In this study, we tried to reprocess the data using a deconvolution technique in time domain, popularly known as iterative time-domain deconvolution. The principle of iterative time domain deconvolution consists of iterative cross-correlation between the horizontal and vertical component. We use data from the DOMERAPI seismic station network located in the vicinity of Mt Merapi and Mt Merbabu. Mt Merapi is one of the most active volcanoes in the world with frequent eruptions and located at the ring of fire chain volcano in Indonesia. Note that the previous receiver function study in this region showed complex signals at some stations that may be related to sediment at shallow sediment and possible layers of low velocity zone that interfering main signal for a crust-mantle boundary. Our current results show iterative time domain RFs have clearer and smoother signal than the frequency domain that help interpreting the waveform signals. We estimate a range of crust thickness between 26-31 km near Mt Merapi. However, we noticed that iterative time domain calculation requires longer computation time and input signal.

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36

Wahibi, Issam, Meryem Ouzzif, Jérôme Le Masson, and Samir Saoudi. "Stationary Interference Cancellation in Upstream Coordinated DSL Using a Turbo-MMSE Receiver." International Journal of Digital Multimedia Broadcasting 2008 (2008): 1–8. http://dx.doi.org/10.1155/2008/428037.

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We investigate the scenario of an upstream coordinated DSL transmission in presence of spatial-correlated noise. Joint signal processing helps mitigate this noise and reduce internal interference effects between coordinated users. We propose to couple noise whitening with a mean-squared error iterative receiver in order to approach the matched filter bound of the DSL coordinated system. The convergence of the iterative scheme in this scenario is predicted using EXIT charts under realistic transmission conditions.

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37

Tsukamoto, Masaki, and Yasunori Iwanami. "Complexity Reduced MIMO Interleaved SC-FDMA Receiver with Iterative Detection." International Journal of Communications, Network and System Sciences 07, no. 12 (2014): 508–18. http://dx.doi.org/10.4236/ijcns.2014.712051.

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38

Li, Lin Tao, Peng Zheng, and Jin Hao. "Performance Analysis and Implementation of SISO Iterative Receiver for SCCC." Applied Mechanics and Materials 644-650 (September 2014): 3763–66. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.3763.

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Serial concatenated convolutional codes (SCCC) have gained a great interest in recent years for outperforming PCCC at high SNR and lower error floor. In this paper, the BER performance of SCCC based on different decoding algorithm such as Log-MAP algorithm, Max-Log-MAP algorithm and improved Max-Log-MAP algorithm are investigated. In order to achieve a flexible high throughput, low complexity and multi-rate decoding process, some improved methods are proposed.

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39

IMAMURA, Jun, Satoshi DENNO, Daisuke UMEHARA, and Masahiro MORIKURA. "A Virtual Layered Space-Frequency Receiver Architecture with Iterative Decoding." IEICE Transactions on Communications E94-B, no. 7 (2011): 1994–2002. http://dx.doi.org/10.1587/transcom.e94.b.1994.

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40

Jayati, Ari, Wirawan Wirawan, Titiek Suryani, and Endroyono Endroyono. "Iterative Receiver Compensation of HPA Nonlinearity in MIMO-GFDM System." International Journal of Intelligent Engineering and Systems 13, no. 4 (August 31, 2020): 260–70. http://dx.doi.org/10.22266/ijies2020.0831.23.

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41

Du, Zhengfeng, Dongfeng Yuan, Hailiang Xiong, Hongji Xu, and Deqiang Wang. "Iterative receiver design for general nonorthogonal unitary space-time constellations." IEICE Electronics Express 9, no. 6 (2012): 464–69. http://dx.doi.org/10.1587/elex.9.464.

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42

YOO, Namsik, Jong-Hyen BAEK, and Kyungchun LEE. "Iterative Robust MMSE Receiver for STBC under Channel Information Errors." IEICE Transactions on Communications E99.B, no. 10 (2016): 2228–35. http://dx.doi.org/10.1587/transcom.2015ebp3496.

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43

Wei, Fan, and Wen Chen. "Low Complexity Iterative Receiver Design for Sparse Code Multiple Access." IEEE Transactions on Communications 65, no. 2 (February 2017): 621–34. http://dx.doi.org/10.1109/tcomm.2016.2631468.

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Sahin, Serdar, Antonio Maria Cipriano, Charly Poulliat, and Marie-Laure Boucheret. "A Framework for Iterative Frequency Domain EP-Based Receiver Design." IEEE Transactions on Communications 66, no. 12 (December 2018): 6478–93. http://dx.doi.org/10.1109/tcomm.2018.2863724.

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Li, Xiang, Jing Wu, Wei Heng, and Yang Huang. "Iterative Receiver Design for Probabilistic Constellation Shaping in ISI Channel." IEEE Access 8 (2020): 210478–89. http://dx.doi.org/10.1109/access.2020.3037807.

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Escalante, Christian, Yu J. Gu, and Mauricio Sacchi. "Simultaneous iterative time-domain sparse deconvolution to teleseismic receiver functions." Geophysical Journal International 171, no. 1 (October 2007): 316–25. http://dx.doi.org/10.1111/j.1365-246x.2007.03511.x.

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Zhao, Liang, and Jianhua Ge. "Iterative Receiver in Time-Frequency Domain for Underwater Acoustic Communications." Procedia Engineering 30 (2012): 844–51. http://dx.doi.org/10.1016/j.proeng.2012.01.936.

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CHEN, R., X. FAN, Y. XU, and H. ZHANG. "Newly-Built Iterative Receiver and Hardware Implementation for V-BLAST." IEICE Transactions on Communications E90-B, no. 2 (February 1, 2007): 377–80. http://dx.doi.org/10.1093/ietcom/e90-b.2.377.

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Wehinger, J., and C. F. Mecklenbrauker. "Iterative CDMA Multiuser Receiver With Soft Decision-Directed Channel Estimation." IEEE Transactions on Signal Processing 54, no. 10 (October 2006): 3922–34. http://dx.doi.org/10.1109/tsp.2006.880200.

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Knievel, C., P. A. Hoeher, A. Tyrrell, and G. Auer. "Multi-Dimensional Graph-Based Soft Iterative Receiver for MIMO-OFDM." IEEE Transactions on Communications 60, no. 6 (June 2012): 1599–609. http://dx.doi.org/10.1109/tcomm.2012.042712.110108.

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