Chaotic-mapping and Gaussian perturbation-based multi-channel constant modulus blind equalization
In multi-channel communication simulation systems, inconsistencies in amplitude and phase between channels can degrade system performance, making channel equalization technology essential. Unlike traditional equalizer designs, blind equalization algorithms do not require training sequences, improvin...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2025-05-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/thesisDetails#10.11959/j.issn.1000-0801.2025101 |
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| Summary: | In multi-channel communication simulation systems, inconsistencies in amplitude and phase between channels can degrade system performance, making channel equalization technology essential. Unlike traditional equalizer designs, blind equalization algorithms do not require training sequences, improving system efficiency and not interfering with the simulation process. The improved constant modulus blind equalization algorithm based on particle swarm optimization is a new blind equalization method that introducing particle swarm optimization to find the optimal solution for the equalizer, thereby improving the convergence speed of the algorithm. However, this algorithm is sensitive to initial parameters and is prone to get stuck in local optimum. Constant weights and learning factors can increase the steady-state mean square error, resulting in uneven local and global search capabilities. To address these issues, an improved particle swarm constant modulus blind equalization algorithm based on chaotic-mapping and Gaussian perturbation was proposed. After simulation verification, the performance of the proposed algorithm has been improved. The sensitivity to parameters set in the early stages of the algorithm is reduced. The fitness decreases by 0.011 after stabilization. When the symbol error rate reaches <inline-formula><alternatives><math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><msup><mrow><mn mathvariant="normal">10</mn></mrow><mrow><mo>-</mo><mn mathvariant="normal">3</mn></mrow></msup></math><graphic specific-use="big" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/A31A9D60-3E53-4dc7-8437-7C17ACCEA2E1-M002.jpg"><?fx-imagestate width="5.58799982" height="2.53999996"?></graphic><graphic specific-use="small" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="alternativeImage/A31A9D60-3E53-4dc7-8437-7C17ACCEA2E1-M002c.jpg"><?fx-imagestate width="5.58799982" height="2.53999996"?></graphic></alternatives></inline-formula> level, the signal-to-noise ratio decreases more compared to traditional algorithms. The mean square error is reduced by 1.77 dB, and intersymbol interference is reduced by 0.64 dB. In addition, by comparing different inertia weight schemes, it is further verified that the proposed algorithm achieves faster convergence speed and lower inter-symbol interference. |
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| ISSN: | 1000-0801 |