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Journal articles on the topic 'MIMO decoder'

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Author: Grafiati

Published: 14 March 2023

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1

Alghoniemy, Masoud, and Ahmed H. Tewfik. "MIMO Cube Decoder." Journal of Communications Software and Systems 7, no. 3 (September 22, 2011): 104. http://dx.doi.org/10.24138/jcomss.v7i3.176.

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An insight on the lattice decoder for flat-fading multiple antenna wireless communications systems is presented in this paper. In particular, we show that by formulating the decoding problem as a bounded-error subset selection, theresultant decoder finds the nearest lattice point to the received signal vector such that the search is bounded inside a hypercube centered at the received vector. The dimensions and orientation of the hypercube can be adjusted based on the diversity of the channel in order to improve its performance. The search for the nearest codeword to the received signal vector is solved by modeling the problem as an Integer Program (IP). Simulation shows that the proposed decoder is inferior to the Sphere Decoder (SD) by about 1-dB while its complexity is superior to the Sphere Decoder at very low signal to noise ratio.

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2

Mao, Yun, Ying Guo, Jun Peng, Xueqin Jiang, and Moon Ho Lee. "Double-Layer Low-Density Parity-Check Codes over Multiple-Input Multiple-Output Channels." International Journal of Antennas and Propagation 2012 (2012): 1–6. http://dx.doi.org/10.1155/2012/716313.

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We introduce a double-layer code based on the combination of a low-density parity-check (LDPC) code with the multiple-input multiple-output (MIMO) system, where the decoding can be done in both inner-iteration and outer-iteration manners. The present code, called low-density MIMO code (LDMC), has a double-layer structure, that is, one layer defines subcodes that are embedded in each transmission vector and another glues these subcodes together. It supports inner iterations inside the LDPC decoder and outeriterations between detectors and decoders, simultaneously. It can also achieve the desired design rates due to the full rank of the deployed parity-check matrix. Simulations show that the LDMC performs favorably over the MIMO systems.

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3

Ali, Sajid, Sara Shakil Qureshi, and Syed Ali Hassan. "Quaternion Codes in MIMO System of Dual-Polarized Antennas." Applied Sciences 11, no. 7 (April 1, 2021): 3131. http://dx.doi.org/10.3390/app11073131.

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The use of quaternion orthogonal designs (QODs) to describe point-to-point communication among dual-polarized antennas has the potential to provide higher rate orthogonal and quasi-orthogonal complex designs exploiting polarization diversity among space and time diversities. Furthermore, it is essential to have a space time block code (STBC) which offers a linear and decoupled decoder which quasi-orthogonal designs fail to attain. In this paper, we show how the realm of quaternions unexpectedly offers us a possible solution and codes obtained from quaternion designs mostly achieve both linear and decoupled decoders. This motivated us to perform an indispensable search for QODs such that the code rate is bounded below by 1/2 and does not sharply decrease as the number of transmit antennas increases. It is shown that three famous recursive techniques do not satisfy this criteria and their code rates decrease rather rapidly. Therefore, we propose another method of constructing quaternion designs suitable for any number of transmit antennas and verify that these attain linear and decoupled decoders with the system model based on quaternionic channel. It is shown that such designs outperform others in terms of transmit diversity, code rates and the optimality of the proposed decoder is validated through simulation results.

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4

Harbi, Yahya, ALI AL-JANABI, Hayder Almusa, Marwa Chafii, and Alister Burr. "Iterative Interference Cancellation for Multi-Carrier Modulation in MIMO-DWT Downlink Transmission." Journal of Telecommunications and the Digital Economy 9, no. 4 (December 5, 2021): 75–87. http://dx.doi.org/10.18080/jtde.v9n4.426.

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The Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) scheme represents the dominant radio interface for broadband multicarrier communication systems. However, with insufficient Cyclic Prefixes (CP), Inter-Symbol Interference (ISI) and Inter-Carrier Interference (ICI) occur due to the time-varying multipath channel. This means that the performance of the system will be degraded. In this paper, we investigate the interference problem for a MIMO Discrete Wavelet Transform (MIMO-DWT) system under the effect of the downlink LTE channel. A Low-Density Parity-Check (LDPC) decoder is used to estimate the decoded signal. The proposed iterative algorithm uses the estimated decoded signal to compute the components required for ICI/ISI interference reduction. In this paper, Iterative Interference Cancellation (IIC) is employed to mitigate the effects of interference that contaminates the received signal due to multiple antenna transmission and a multipath channel. An equalizer with minimum mean square error is considered. We compare the performance of our proposed algorithm with the traditional MIMO-OFDM scheme in terms of bit error probability under insufficient CP. Simulation results verify that significant improvements are achieved by using IIC and MIMO-IIC for both systems.

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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

Milford, David, and Magnus Sandell. "Simplified Quantisation in a Reduced-Lattice MIMO Decoder." IEEE Communications Letters 15, no. 7 (July 2011): 725–27. http://dx.doi.org/10.1109/lcomm.2011.051011.110485.

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7

Mathur, Garima, Mohammad Salim, and R. Yadav. "A Novel Approach for Sphere Decoder MIMO System." British Journal of Mathematics & Computer Science 4, no. 1 (January 10, 2014): 22–32. http://dx.doi.org/10.9734/bjmcs/2014/5614.

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8

Jiménez-Pacheco, Alberto, Ángel Fernández-Herrero, and Javier Casajús-Quirós. "Design and Implementation of a Hardware Module for MIMO Decoding in a 4G Wireless Receiver." VLSI Design 2008 (January 31, 2008): 1–8. http://dx.doi.org/10.1155/2008/312614.

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Future 4th Generation (4G) wireless multiuser communication systems will have to provide advanced multimedia services to an increasing number of users, making good use of the scarce spectrum resources. Thus, 4G system design should pursue both higher-transmission bit rates and higher spectral efficiencies. To achieve this goal, multiple antenna systems are called to play a crucial role. In this contribution we address the implementation in FPGAs of a multiple-input multiple-output (MIMO) decoder embedded in a prototype of a 4G mobile receiver. This MIMO decoder is part of a multicarrier code-division multiple-access (MC-CDMA) radio system, equipped with multiple antennas at both ends of the link, that is able to handle up to 32 users and provides raw transmission bit-rates up to 125 Mbps. The task of the MIMO decoder is to appropriately combine the signals simultaneously received on all antennas to construct an improved signal, free of interference, from which to estimate the transmitted symbols. A comprehensive explanation of the complete design process is provided, including architectural decisions, floating-point to fixed-point translation, and description of the validation procedure. We also report implementation results using FPGA devices of the Xilinx Virtex-4 family.

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9

Cerato, Barbara, Guido Masera, and Emanuele Viterbo. "Enabling VLSI Processing Blocks for MIMO-OFDM Communications." VLSI Design 2008 (March 24, 2008): 1–10. http://dx.doi.org/10.1155/2008/351962.

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Multi-input multi-output (MIMO) systems combined with orthogonal frequency-division multiplexing (OFDM) gained a wide popularity in wireless applications due to the potential of providing increased channel capacity and robustness against multipath fading channels. However these advantages come at the cost of a very high processing complexity and the efficient implementation of MIMO-OFDM receivers is today a major research topic. In this paper, efficient architectures are proposed for the hardware implementation of the main building blocks of a MIMO-OFDM receiver. A sphere decoder architecture flexible to different modulation without any loss in BER performance is presented while the proposed matrix factorization implementation allows to achieve the highest throughput specified in the IEEE 802.11n standard. Finally a novel sphere decoder approach is presented, which allows for the realization of new golden space time trellis coded modulation (GST-TCM) scheme. Implementation cost and offered throughput are provided for the proposed architectures synthesized on a 0.13 CMOS standard cell technology or on advanced FPGA devices.

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10

JIN, Xianglan, Dong-Sup JIN, Jong-Seon NO, and Dong-Joon SHIN. "Diversity Analysis of MIMO Decode-and-Forward Relay Network by Using Near-ML Decoder." IEICE Transactions on Communications E94-B, no. 10 (2011): 2828–36. http://dx.doi.org/10.1587/transcom.e94.b.2828.

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11

Marosits, Ádám, Zsolt Tabi, Zsófia Kallus, Péter Vaderna, István Gódor, and Zoltán Zimborás. "Exploring Embeddings for MIMO Channel Decoding on Quantum Annealers." Infocommunications journal 13, no. 1 (2021): 11–17. http://dx.doi.org/10.36244/icj.2021.1.2.

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Quantum Annealing provides a heuristic method leveraging quantum mechanics for solving Quadratic Unconstrained Binary Optimization problems. Existing Quantum Annealing processing units are readily available via cloud platform access for a wide range of use cases. In particular, a novel device, the D-Wave Advantage has been recently released. In this paper, we study the applicability of Maximum Likelihood (ML) Channel Decoder problems for MIMO scenarios in centralized RAN. The main challenge for exact optimization of ML decoders with ever-increasing demand for higher data rates is the exponential increase of the solution space with problem sizes. Since current 5G solutions can only use approximate methodologies, Kim et al. [1] leveraged Quantum Annealing for large MIMO problems with phase shift keying and quadrature amplitude modulation scenarios. Here, we extend upon their work and present embedding limits for both more complex modulation and higher receiver / transmitter numbers using the Pegasus P16 topology of the D-Wave Advantage system.

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12

Koo, Jihun, Jaehoon Kim, Yongsuk Kim, and Jaeseok Kim. "A Parallel Sphere Decoder Algorithm for High-order MIMO System." Journal of the Institute of Electronics and Information Engineers 51, no. 5 (May 25, 2014): 11–19. http://dx.doi.org/10.5573/ieie.2014.51.5.011.

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13

Liu, Chao. "A Fast Generalized Complex Sphere Decoder for MIMO Systems." Journal of Electronics & Information Technology 30, no. 5 (March 14, 2011): 1189–92. http://dx.doi.org/10.3724/sp.j.1146.2006.01407.

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14

HATAKAWA, Yasuyuki, Noriaki MIYAZAKI, and Toshinori SUZUKI. "Performance Evaluation of MIMO-OFDM with Twin Turbo Decoder." IEICE Transactions on Communications E92-B, no. 1 (2009): 228–36. http://dx.doi.org/10.1587/transcom.e92.b.228.

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15

Romano, Gianmarco, Domenico Ciuonzo, Pierluigi Salvo Rossi, and Francesco Palmieri. "Low-complexity dominance-based sphere decoder for MIMO systems." Signal Processing 93, no. 9 (September 2013): 2500–2509. http://dx.doi.org/10.1016/j.sigpro.2013.02.011.

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16

Shao, Z. Y., S. W. Cheung, and T. I. Yuk. "Semi-definite relaxation decoder for 256-QAM MIMO system." Electronics Letters 46, no. 11 (2010): 796. http://dx.doi.org/10.1049/el.2010.1149.

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17

Mohammed, Karim, and Babak Daneshrad. "A MIMO Decoder Accelerator for Next Generation Wireless Communications." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 18, no. 11 (November 2010): 1544–55. http://dx.doi.org/10.1109/tvlsi.2009.2025590.

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18

Greenberg, Maxim, Moshe Nazarathy, and Meir Orenstein. "Efficient hierarchical list decoder for massive optical MIMO Transmission." Optics Express 16, no. 2 (2008): 718. http://dx.doi.org/10.1364/oe.16.000718.

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19

Huang, Chung-Jung, Chang-Shen Lee, Wei-Ho Chung, and Ta-Sung Lee. "A geometry based efficient decoder for underdetermined MIMO systems." Digital Signal Processing 41 (June 2015): 60–69. http://dx.doi.org/10.1016/j.dsp.2015.03.005.

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20

Costa, Bruno Felipe, and Taufik Abrão. "MIMO Precoding for Correlated Fading Channels." Journal of Circuits, Systems and Computers 25, no. 05 (February 25, 2016): 1650041. http://dx.doi.org/10.1142/s0218126616500419.

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This contribution proposes a precoder-decoder design aiming to improve the performance of multiple-input–multiple-output (MIMO) detectors under correlated fading channels. The MIMO detection principle namely minimum mean squared error (MMSE) detector is analyzed under such channel condition. The proposed approach deploys the channel state information (CSI) aiming to estimate the level of spatial correlation channel, namely normalized correlation index [Formula: see text] and uses this information to improve the MIMO system performance. Furthermore, the impact of the [Formula: see text] estimation errors on the performance, as well the performance degradation for different levels of correlation have been analyzed and compared with the classical MMSE-MIMO detector operating under uncorrelated channels and perfect channel estimation.

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21

Hou, Siyun, Wengen Li, Tianying Liu, Shuigeng Zhou, Jihong Guan, Rufu Qin, and Zhenfeng Wang. "MIMO: A Unified Spatio-Temporal Model for Multi-Scale Sea Surface Temperature Prediction." Remote Sensing 14, no. 10 (May 14, 2022): 2371. http://dx.doi.org/10.3390/rs14102371.

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Sea surface temperature (SST) is a crucial factor that affects global climate and marine activities. Predicting SST at different temporal scales benefits various applications, from short-term SST prediction for weather forecasting to long-term SST prediction for analyzing El Niño–Southern Oscillation (ENSO). However, existing approaches for SST prediction train separate models for different temporal scales, which is inefficient and cannot take advantage of the correlations among the temperatures of different scales to improve the prediction performance. In this work, we propose a unified spatio-temporal model termed the Multi-In and Multi-Out (MIMO) model to predict SST at different scales. MIMO is an encoder–decoder model, where the encoder learns spatio-temporal features from the SST data of multiple scales, and fuses the learned features with a Cross Scale Fusion (CSF) operation. The decoder utilizes the learned features from the encoder to adaptively predict the SST of different scales. To our best knowledge, this is the first work to predict SST at different temporal scales simultaneously with a single model. According to the experimental evaluation on the Optimum Interpolation SST (OISST) dataset, MIMO achieves the state-of-the-art prediction performance.

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22

Abdalla, Kasim, and Sameer Alrufaiaat. "A New Robust Decoding Technique of Four Transmitters MIMO STBC System based on FastICA Algorithm." International Journal of Intelligent Engineering and Systems 14, no. 1 (February 28, 2021): 181–91. http://dx.doi.org/10.22266/ijies2021.0228.18.

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A new robust decoding technique which designed of Multiple-Input Multiple-Output Space–Time Block Code (MIMO-STBC) using Fast Independent Component Analysis (Fast-ICA) based on proposed mixing model has been performed in this paper. This decoding technique is characterized by i) complexity is very low, ii) the speed is high and iii) BER performance is excellent. It can be achieved with any MIMO STBC system with a fewer pilot symbols number. Also, it is reduced decoding time into 1/8 by innovating a simple strategy referred by one source extraction method. Also, this paper includes suitable initializing for the de-mixing vector to solve the ambiguities problem of sign and source of blind source separation (BSS). To test the proposed technique, four transmitters (4Tx) STBC MIMO system was implemented using MATLAB2018. It also found that excellent BER performance associated with a high number of symbols per frame (about 8012 symbols). The simulation results show that the new decoder works for any number of receiver antenna (Nr = 2, 4 and 5). As compare with classical decoding algorithm, it is found that the new decoder provides coding gain (at BER =10-6 ) equal to 1 dB,1.45 dB and 1.76 dB when Nr = 2,4 and 8 respectively, using only 2~3 iterations only.

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23

Koo, Jihun, Soo-Yong Kim, and Jaeseok Kim. "A parallel collaborative sphere decoder for a MIMO communication system." Journal of Communications and Networks 16, no. 6 (December 2014): 620–26. http://dx.doi.org/10.1109/jcn.2014.000108.

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24

Yokota, Yuji, and Hiroshi Ochi. "Complexity Reduction of Higher Order MIMO Decoder Using Group Detection." Journal of Signal Processing 20, no. 1 (2016): 21–29. http://dx.doi.org/10.2299/jsp.20.21.

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25

Tao Cui and C. Tellambura. "An efficient generalized sphere decoder for rank-deficient MIMO systems." IEEE Communications Letters 9, no. 5 (May 2005): 423–25. http://dx.doi.org/10.1109/lcomm.2005.1431159.

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26

Barbero, Luis, and John Thompson. "Fixing the Complexity of the Sphere Decoder for MIMO Detection." IEEE Transactions on Wireless Communications 7, no. 6 (June 2008): 2131–42. http://dx.doi.org/10.1109/twc.2008.060378.

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27

León, Juan Francisco Castillo, Ulises Pineda Rico, and Enrique Stevens-Navarro. "Complexity-Improved Sphere Decoder for MIMO Systems Using PSK Modulations." Procedia Technology 3 (2012): 52–60. http://dx.doi.org/10.1016/j.protcy.2012.03.006.

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28

Li, Qingwei, and Zhongfeng Wang. "Reduced Complexity K-Best Sphere Decoder Design for MIMO Systems." Circuits, Systems & Signal Processing 27, no. 4 (June 3, 2008): 491–505. http://dx.doi.org/10.1007/s00034-008-9039-6.

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29

Li, Yongzhi, Cheng Tao, Yapeng Li, Liu Liu, and Tao Zhou. "Investigation of Sphere Decoder and Channel Tracking Algorithms for Media-Based Modulation over Time-Selective Channels." Wireless Communications and Mobile Computing 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/2509824.

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The performance of media-based modulation (MBM) systems, where additional information can be conveyed by the indices of the channel states created by RF mirrors, over time-selective channels is investigated. By transforming the MBM system model into a traditional MIMO system model, we first propose a reduced complexity sphere decoder algorithm. Then two channel tracking algorithms, which are based on least mean square adaptive filter and recursive least-squares adaptive filter, are employed in order to combat the performance loss caused by the time-varying channels. Numerical results show that the proposed sphere decoder and these two channel tracking algorithms perform well in MBM systems.

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30

Maulida, Zuhrotul, Wahyul Amien Syafei, and Imam Santoso. "IMPLEMENTASI MIMO DECODER BERBASIS METODE NON-LINEAR UNTUK MENINGKATKAN KINERJA WLAN 802.11ac." TRANSIENT 6, no. 3 (November 9, 2017): 496. http://dx.doi.org/10.14710/transient.6.3.496-502.

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Kebutuhan sistem komunikasi nirkabel yang mendukung laju data dan kinerja yang semakin tinggi mendorong perkembangan teknologi WLAN. Teknologi ini mengerucut pada penggunaan teknologi terkini, yaitu MIMO-OFDM. Implementasi OFDM pada perkembangan WLAN dimulai sejak IEEE 802.11a, hingga saat ini mencapai 900 Mbps pada IEEE 802.11ac. Dengan penggunaan kanal yang sama untuk beberapa data pada setiap antenna, dibutuhkan teknik khusus untuk mendapatkan kembali informasi yang dikirim. Dua teknik yang umum digunakan adalah berbasis metode linear, yaitu ZF dan MMSE. Keduanya memiliki kompleksitas yang rendah, tetapi kinerjanya juga rendah. Teknik yang dikenal optimal adalah berbasis non-linear, yaitu MLD. Teknik ini memiliki kinerja paling baik tetapi tingkat kompleksitasnya tinggi. Untuk menengahi kedua metode tersebut, dikembangkanlah metode non-linear yang sub-optimal, seperti K-Best, Trellis, dan Sphere Detection. Ketiga metode ini memiliki kompleksitas yang rendah dan menghasilkan kinerja yang baik. Pada penelitian ini akan dilakukan simulasi dan analisa terhadap kinerja MIMO Decoder yang berbasis metode non-linear, yaitu K-Best, Trellis, dan Sphere Detection. Simulasi dilakukan pada MCS 5, 6, dan 7 dengan konfigurasi 6x6 dan 40 MHz bandwidth. Konfigurasi ini akan menghasilkan laju data sebesar 720 Mbps, 810 Mbps, dan 900 Mbps.

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31

Gimmler-Dumont, Christina, Frank Kienle, Bin Wu, and Guido Masera. "A System View on Iterative MIMO Detection: Dynamic Sphere Detection versus Fixed Effort List Detection." VLSI Design 2012 (April 22, 2012): 1–14. http://dx.doi.org/10.1155/2012/826350.

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Multiple-antenna systems are a promising approach to increase the data rate of wireless communication systems. One efficient possibility is spatial multiplexing of the transmitted symbols over several antennas. Many different MIMO detector algorithms exist for this spatial multiplexing. The major difference between different MIMO detectors is the resulting communications performance and implementation complexity, respectively. Particularly closed-loop MIMO systems have attained a lot of attention in the last years. In a closed-loop system, reliability information is fed back from the channel decoder to the MIMO detector. In this paper, we derive a basic framework to compare different soft-input soft-output MIMO detectors in open- and closed-loop systems. Within this framework, we analyze a depth-first sphere detector and a breadth-first fixed effort detector for different application scenarios and their effects on area and energy efficiency on the whole system. We present all system components under open- and closed-loop system aspects and determine the overall implementation cost for changing an open-loop system in a closed-loop system.

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32

Li, Jialing, I.-Tai Lu, and Enoch Lu. "Robust MMSE Transceiver Designs for Downlink MIMO Systems with Multicell Cooperation." International Journal of Digital Multimedia Broadcasting 2010 (2010): 1–19. http://dx.doi.org/10.1155/2010/815704.

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Therobust-generalized iterative approach(Robust-GIA),robust-fast iterative approach(Robust-FIA), androbust-decoder covariance optimization approach(Robust-DCOA) are proposed for designing MMSE transceivers of downlink multicell multiuser MIMO systems with per-cell and per-antenna power constraints and possibly imperfect channel state information. TheRobust-DCOAis the most restrictive but is always optimum, theRobust-GIAis the most general, and theRobust-FIAis the most efficient. When theRobust-DCOAis applicable and the decoder covariance matrices are full rank, the three proposed approaches are equivalent and all provide the optimum solution. Numerical results show that the proposed robust approaches outperform their non-robust counterparts in various single-cell and multicell examples with different system configurations, channel correlations, power constraints, and cooperation scenarios. Moreover, performances of the robust approaches are insensitive to estimation errors of channel statistics (correlations and path loss). With cell-cooperation, cell edge interference problems can be remedied without reducing the number of data streams by using the proposed robust approaches.

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33

Aggarwal, Monika. "Performance of MIMO-OFDM system using Linear Maximum Likelihood Alamouti Decoder." International Journal of Distributed and Parallel systems 3, no. 3 (May 31, 2012): 121–31. http://dx.doi.org/10.5121/ijdps.2012.3312.

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34

Jothikumar, Ramya, and Nakkeeran Rangaswamy. "Complexity reduction by sign prediction in tree traversal of MIMO decoder." IEICE Electronics Express 11, no. 17 (2014): 20140628. http://dx.doi.org/10.1587/elex.11.20140628.

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35

MIYAOKA, Yuya, Yuhei NAGAO, Masayuki KUROSAKI, and Hiroshi OCHI. "RTL Design of High-Speed Sorted QR Decomposition for MIMO Decoder." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E95.A, no. 11 (2012): 1991–97. http://dx.doi.org/10.1587/transfun.e95.a.1991.

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36

王, 海泉. "Performance Analysis of the MRC Decoder for a Massive MIMO System." Hans Journal of Wireless Communications 04, no. 06 (2014): 126–35. http://dx.doi.org/10.12677/hjwc.2014.46020.

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37

Barbero, L. G., and J. S. Thompson. "Performance of the complex sphere decoder in spatially correlated MIMO channels." IET Communications 1, no. 1 (2007): 122. http://dx.doi.org/10.1049/iet-com:20050414.

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38

Budihal, Suneeta V., and R. M. Banakar. "Search Radius Setting and Updation for Sphere Decoder for MIMO systems." Procedia Computer Science 171 (2020): 2087–96. http://dx.doi.org/10.1016/j.procs.2020.04.225.

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39

Myllylä, Markus, Markku Juntti, and Joseph R. Cavallaro. "Implementation aspects of list sphere decoder algorithms for MIMO-OFDM systems." Signal Processing 90, no. 10 (October 2010): 2863–76. http://dx.doi.org/10.1016/j.sigpro.2010.04.014.

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40

Jothikumar, Ramya, and Nakkeeran Rangaswamy. "A Modified Euclidean Norm Computation for Complexity Reduction in MIMO Decoder." Wireless Personal Communications 85, no. 3 (June 23, 2015): 1251–59. http://dx.doi.org/10.1007/s11277-015-2838-8.

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41

Mohamed, Mohamed I. A., Karim Mohammed, and Babak Daneshrad. "Energy Efficient Programmable MIMO Decoder Accelerator Chip in 65-nm CMOS." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 22, no. 7 (July 2014): 1481–90. http://dx.doi.org/10.1109/tvlsi.2013.2272058.

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42

Koo, Jihun, Yongsuk Kim, and Jaeseok Kim. "An Extendable Fixed-Complexity Sphere Decoder for Downlink Multi-User MIMO Communication System." Journal of Korea Information and Communications Society 39A, no. 4 (April 30, 2014): 180–87. http://dx.doi.org/10.7840/kics.2014.39a.4.180.

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43

SHEN, CHUNG-AN, AHMED M. ELTAWIL, and KHALED N. SALAMA. "EVALUATION FRAMEWORK FOR K-BEST SPHERE DECODERS." Journal of Circuits, Systems and Computers 19, no. 05 (August 2010): 975–95. http://dx.doi.org/10.1142/s0218126610006554.

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While Maximum-Likelihood (ML) is the optimum decoding scheme for most communication scenarios, practical implementation difficulties limit its use, especially for Multiple Input Multiple Output (MIMO) systems with a large number of transmit or receive antennas. Tree-searching type decoder structures such as Sphere decoder and K-best decoder present an interesting trade-off between complexity and performance. Many algorithmic developments and VLSI implementations have been reported in literature with widely varying performance to area and power metrics. In this semi-tutorial paper we present a holistic view of different Sphere decoding techniques and K-best decoding techniques, identifying the key algorithmic and implementation trade-offs. We establish a consistent benchmark framework to investigate and compare the delay cost, power cost, and power-delay-product cost incurred by each method. Finally, using the framework, we propose and analyze a novel architecture and compare that to other published approaches. Our goal is to explicitly elucidate the overall advantages and disadvantages of each proposed algorithms in one coherent framework.

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Liu, Qingli, Jiaxu Sun, Shaoming Qiu, Yana Lv, and Xiuli Du. "A Convolutional Self-Attention Network for CSI Reconstruction in MIMO System." Wireless Communications and Mobile Computing 2023 (January 27, 2023): 1–10. http://dx.doi.org/10.1155/2023/2922232.

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A convolutional self-attention network-based channel state information reconstruction method is presented to address the issue of low reconstruction accuracy of channel state information in Multiple-Input Multiple-Output (MIMO) at a high compression rate. First, an encoder-decoder structure-based channel state information reconstruction model is built. The feature is extracted by the encoder’s convolutional network, and the information is compressed by adding an attention block. At the same time, the compressed information is nonuniform quantized to prevent the transmission process from using up too much bandwidth. A dequantization module and an attention block are added to the decoder to reduce the impact of noise on the matrix, converting the continuous value into a discrete value to increase reconstruction accuracy and using the long-time cosine annealing training approach. According to the simulation results, when compared to CsiNet, Lightweight CNN, CRNet, and CLNet, convergence speed is improved by 17.64%, indoor reconstruction precision is improved by an average of 37.4%, and outside reconstruction accuracy is improved by an average of 32.5% under all compressions.

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Agustin, Desy, Nachwan Mufti Adriansyah, and Muhsin. "Enhancement of Twice Quasi Orthogonal Space Time Block Coded (QOSTBC) Performance System with Zero Forcing EVCM Decoder." MATEC Web of Conferences 218 (2018): 03009. http://dx.doi.org/10.1051/matecconf/201821803009.

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In today’s modern telecommunications systems, makes the number of studies and development of multiple antennas and multiple-input multiple-output (MIMO) systems to achieve high reliability and low complexity. One attractive approach to improve that performance is using technique transmit diversity which is spacetime block coding and receiver diversity i.e. zero forcing EVCM (ZF EVCM). Although some earlier MIMO standards were develop some space-time codes like (O-STBC)and (Q-OSTBC) to provide high reliability but they are limited able to achieve orthogonality. In this research will be proposed a MIMO system scheme which is an improvement of QOSTBC that used a transmission diversity technique. This improvement from QOSTBC is Twice QOSTBC uses a provision in two codeword matrices to be sent are arranged diagonally so as to have higher levels of orthogonality. In this case Twice QOSTBC highly structured (4x1) can be replaced as an equivalent EVCM channel H. The proposed Twice-QOSTBC’s results outperform other QOSTBC techniques with a difference around 3 dB for single-input multi-input (MISO) input configuration at 10-6 BER and receiver ZF EVCM has a very similar structure as the code matrix S of the underlying Twice QSTBC which can eliminates the system complexity.

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Huang, Ping, Yue Heng Li, and Mei Yan Ju. "Efficient LLR Optimization Based on GMI for LDPC Coded BICM Systems." Applied Mechanics and Materials 519-520 (February 2014): 1016–20. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.1016.

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In this paper, A new on-line LLR optimization method based on the generalized mutual information (GMI) maximization is proposed for LDPC coded BICM systems. As the BP decoder is sensitive to imperfect soft demodulator output information, it is necessary to correct the LLRs to provide the reliable information for BP decoder. The proposed method searched the scalar factors to maximize the GMI for each bit channel, and applied them to the demodulator output LLRs. Different from the previous reference, the proposed found the factors by on-line computation with minor complexity. This characteristic makes it is more effective in the real systems where the channel changes rapidly. Numerical and simulation results verify that the proposed method achieves noticeable performance improvement for MIMO-BICM systems with LDPC codes.

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Kim, Minjoon, Jangyong Park, Hyunsub Kim, and Jaeseok Kim. "Efficient Near-Optimal Detectionwith Generalized Sphere Decoder for Blind MU-MIMO Systems." ETRI Journal 36, no. 4 (August 1, 2014): 682–85. http://dx.doi.org/10.4218/etrij.14.0213.0506.

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Alrashdi, Ayed M., Abdullah E. Alrashdi, Amer Alghadhban, and Mohamed A. H. Eleiwa. "Optimum GSSK Transmission in Massive MIMO Systems Using the Box-LASSO Decoder." IEEE Access 10 (2022): 15845–59. http://dx.doi.org/10.1109/access.2022.3148329.

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PARK, Jangyong, Yunho JUNG, and Jaeseok KIM. "A Low Complexity Fixed Sphere Decoder with Statistical Threshold for MIMO Systems." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E98.A, no. 2 (2015): 735–39. http://dx.doi.org/10.1587/transfun.e98.a.735.

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Ali, Noor S., Kasim K. Abdalla, and Sameer A. Kadhim. "BER Performance Improvement of Alamouti MIMO-STBC Decoder Using Mutual Information Method." Journal of Physics: Conference Series 1530 (May 2020): 012016. http://dx.doi.org/10.1088/1742-6596/1530/1/012016.

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