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Journal articles on the topic 'Fixed –Complexity Sphere Decoder (FSD)'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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1

Wang, Mengdi, Kai Yang, and Yixiao Luo. "Long prediction horizon model predictive control for pmsm based on fixed-complexity sphere decoder." Journal of Physics: Conference Series 3033, no. 1 (2025): 012009. https://doi.org/10.1088/1742-6596/3033/1/012009.

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Abstract Long prediction horizon model predictive control (MPC) enhances control system performance. However, its computational complexity grows exponentially with the prediction horizon, which affects real-time performance. To solve this problem, an optimization method based on the fixed-complexity sphere decoder (FSD) is proposed in this paper. FSD adopts a fixed exploration path and hierarchical optimization strategy to reduce the computation load while ensuring system performance near the optimal level. This paper presents a control strategy for permanent magnet synchronous motors (PMSM) b
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2

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 d
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3

Mao, Xin Yu, Shu Bo Ren, and Hai Ge Xiang. "Reduced Complexity FSD Algorithm for MIMO Detection." Applied Mechanics and Materials 195-196 (August 2012): 259–64. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.259.

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Detection is a major challenge for the utilization of multiple-input multiple-output (MIMO) system. Even the fixed sphere decoding (FSD), which is known for its simplicity in calculation, requests too much computation in high order modulation and large number antenna system especially for mobile battery-operated devices. In this paper, a reduced FSD algorithm is proposed to simplify the calculation complexity of the FSD while maintaining the performance at the same time. Simulation results in a 4×4, 16-QAM system show that up to 89% calculation can be saved while the performance drop is less t
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4

Lai, Kuei-Chiang, Cheng-Chieh Huang, and Jiun-Jie Jia. "Variation of the Fixed-Complexity Sphere Decoder." IEEE Communications Letters 15, no. 9 (2011): 1001–3. http://dx.doi.org/10.1109/lcomm.2011.072011.110916.

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5

Jalden, J., L. G. Barbero, B. Ottersten, and J. S. Thompson. "The Error Probability of the Fixed-Complexity Sphere Decoder." IEEE Transactions on Signal Processing 57, no. 7 (2009): 2711–20. http://dx.doi.org/10.1109/tsp.2009.2017574.

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6

Wu, Bin, and Guido Masera. "Analysis on parallel implementations of fixed-complexity sphere decoder." Science China Information Sciences 56, no. 4 (2012): 1–11. http://dx.doi.org/10.1007/s11432-011-4441-2.

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7

LEI, Sheng, Xin ZHANG, Cong XIONG, and Dacheng YANG. "An Efficient Statistical Pruning Algorithm for Fixed-Complexity Sphere Decoder." IEICE Transactions on Communications E94-B, no. 3 (2011): 834–37. http://dx.doi.org/10.1587/transcom.e94.b.834.

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8

Xiong, Cong, Xin Zhang, Kai Wu, and Dacheng Yang. "A simplified fixed-complexity sphere decoder for V-BLAST systems." IEEE Communications Letters 13, no. 8 (2009): 582–84. http://dx.doi.org/10.1109/lcomm.2009.091039.

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9

Roger, Sandra, Alberto Gonzalez, Vicenc Almenar, and Gerald Matz. "An Efficient Fixed-Complexity Sphere Decoder with Quantized Soft Outputs." IEEE Communications Letters 16, no. 11 (2012): 1828–31. http://dx.doi.org/10.1109/lcomm.2012.091712.121702.

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10

Shen, Hong, and Chun-ming Zhao. "Efficient Iterative Detection Based on the Soft Fixed-complexity Sphere Decoder." Journal of Electronics & Information Technology 34, no. 7 (2013): 1659–64. http://dx.doi.org/10.3724/sp.j.1146.2011.00931.

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11

Xi Chen, Guanghui He, and Jun Ma. "VLSI Implementation of a High-Throughput Iterative Fixed-Complexity Sphere Decoder." IEEE Transactions on Circuits and Systems II: Express Briefs 60, no. 5 (2013): 272–76. http://dx.doi.org/10.1109/tcsii.2013.2251954.

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12

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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13

Wu, B., and G. Masera. "Efficient VLSI implementation of soft-input soft-output fixed-complexity sphere decoder." IET Communications 6, no. 9 (2012): 1111. http://dx.doi.org/10.1049/iet-com.2011.0310.

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14

Zheng, Chengwei, Xuezheng Chu, John McAllister, and Roger Woods. "Real-Valued Fixed-Complexity Sphere Decoder for High Dimensional QAM-MIMO Systems." IEEE Transactions on Signal Processing 59, no. 9 (2011): 4493–99. http://dx.doi.org/10.1109/tsp.2011.2159213.

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15

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 (2014): 180–87. http://dx.doi.org/10.7840/kics.2014.39a.4.180.

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16

Barbero, L. G., and J. S. Thompson. "Extending a Fixed-Complexity Sphere Decoder to Obtain Likelihood Information for Turbo-MIMO Systems." IEEE Transactions on Vehicular Technology 57, no. 5 (2008): 2804–14. http://dx.doi.org/10.1109/tvt.2007.914064.

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17

Jiang, Hua, Lizhen Shen, Hao Cheng, and Guoqing Liu. "Algebraic Number Precoded OFDM Transmission for Asynchronous Cooperative Multirelay Networks." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/862020.

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This paper proposes a space-time block coding (STBC) transmission scheme for asynchronous cooperative systems. By combination of rotated complex constellations and Hadamard transform, these constructed codes are capable of achieving full cooperative diversity with the analysis of the pairwise error probability (PEP). Due to the asynchronous characteristic of cooperative systems, orthogonal frequency division multiplexing (OFDM) technique with cyclic prefix (CP) is adopted for combating timing delays from relay nodes. The total transmit power across the entire network is fixed and appropriate p
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18

Nguyễn Minh Thường. "An Analysis of Valid Nodes Distribution for Sphere Decoding in the MIMO Wireless Communication System." Journal of Research and Development on Information and Communication Technology 2021, no. 2 (2021): 47–54. http://dx.doi.org/10.32913/mic-ict-research.v2021.n2.985.

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Sphere Decoding (SD) algorithms can achieve a quasi-maximum likelihood (ML) decoder performance over Gaussian multiple input-multiple output (MIMO) channels with much lower complexity compared to the exhaustive search method. The SD algorithm is based on a closest lattice point search over a limited search space (hypersphere). On top of that, QR-decomposition simplifies the SD linear system's matrix to be an upper triangle matrix. The solution solver then is done by searching in the exponentially expanding search tree, started from the top with only a single node then increases by M times ever
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19

Mathur, Garima, Mohammad Salim, and R. P. Yadav. "A Novel Approach for Sphere Decoder MIMO System." October 5, 2013. https://doi.org/10.5281/zenodo.9239.

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In sphere decoding techniques, it was seen that the generalized sphere decoder algorithms have been applied to decode the MIMO systems. The transmitted vector is determined by decoding a sequence of determined sub problems.In this paper, the proposed sphere decoder has promised considerable performance as compared to the generalized sphere decoder. The proposed sphere decoding algorithm follows an adaptive radius selection approach for reducing the computational complexity as compared to the generalized sphere decoding algorithm and with the ideal ML decoder which applies on the QPSK signaling
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20

Mohaisen, Manar, and KyungHi Chan. "Efficiency Improvement of the Fixed-complexity Sphere Decoder." KSII Transactions on Internet and Information Systems, 2011, 330–43. http://dx.doi.org/10.3837/tiis.2011.02.005.

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21

Manar, Mohaisen, and KyungHi Chang. "Efficiency Improvement of the Fixed-complexity Sphere Decoder." KSII Transactions on Internet and Information Systems, 2011, 494–507. http://dx.doi.org/10.3837/tiis.2011.03.002.

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22

Chen, Yen-Ming, Ya-Xin Dai, Shih-Jie Jhang, and Yeong-Luh Ueng. "An Efficient Soft-output Fixed-complexity Sphere Decoder for Large QAM Constellations." IEEE Transactions on Vehicular Technology, 2025, 1–15. https://doi.org/10.1109/tvt.2025.3588111.

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23

Kim, Minjoon. "A Design of Fixed-complexity Sphere Decoder Combined with Interference Mitigation Algorithm for Downlink MU-MIMO Systems." IEEE Access, 2022, 1. http://dx.doi.org/10.1109/access.2022.3212765.

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