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1. 重庆邮电大学超视距可信信息传输研究所,重庆 400065
2. 重庆邮电大学光电工程学院,重庆 400065
3. 重庆邮电大学通信与信息工程学院,重庆 400065
4. 重庆邮电大学光电信息感测与传输技术重庆市重点实验室博士后科研工作站,重庆 400065
[ "李国军(1978− ),男,四川资阳人,博士,重庆邮电大学教授,主要研究方向为复杂恶劣环境超视距无线通信与网络" ]
[ "龙锟(1999− ),男,湖南怀化人,重庆邮电大学硕士生,主要研究方向为信道均衡" ]
[ "叶昌荣(1989− ),男,重庆人,博士,重庆邮电大学副教授,主要研究方向为短波通信、信号处理、异构链路融合组网等" ]
[ "梁佳文(1999− ),男,陕西汉中人,重庆邮电大学硕士生,主要研究方向为信道均衡" ]
网络出版日期:2022-10,
纸质出版日期:2022-10-25
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李国军, 龙锟, 叶昌荣, 等. 高速移动环境下低复杂度OTSM迭代rake均衡方法[J]. 通信学报, 2022,43(10):86-93.
Guojun LI, Kun LONG, Changrong YE, et al. Iterative rake equalization method for low-complexity OTSM in high-speed mobile environment[J]. Journal on communications, 2022, 43(10): 86-93.
李国军, 龙锟, 叶昌荣, 等. 高速移动环境下低复杂度OTSM迭代rake均衡方法[J]. 通信学报, 2022,43(10):86-93. DOI: 10.11959/j.issn.1000-436x.2022203.
Guojun LI, Kun LONG, Changrong YE, et al. Iterative rake equalization method for low-complexity OTSM in high-speed mobile environment[J]. Journal on communications, 2022, 43(10): 86-93. DOI: 10.11959/j.issn.1000-436x.2022203.
摘 要:针对现有正交时序复用(OTSM)均衡方法抗噪性能不佳、计算复杂度高的问题,提出了一种基于最大比合并(MRC)的低复杂度迭代rake均衡器。其主要思想是利用MRC在时延-时域中迭代提取并相干合并发射符号的接收多径分量,以提高合并信号的信噪比。为加快MRC迭代算法收敛,设计了一种单抽头时频均衡器为其提供初始估计,随后结合外部纠错码进一步提高误码性能。仿真结果表明,基于MRC的迭代rake均衡器性能显著优于LMMSE线性均衡器,与目前广泛使用的高斯-赛德(GS)迭代均衡器相比,性能和计算复杂度都有较大提升。
Aiming at the problems of poor anti-noise performance and high computational complexity of the existing orthogonal time sequency multiplexing (OTSM) equalization methods
a low-complexity iterative rake equalizer based on maximum ratio combining (MRC) was proposed.The main idea was to use MRC to iteratively extract and coherently combine the received multipath components of the transmitted symbols in the delay-time domain to improve the signal-to-noise ratio of the combined signal.In order to speed up the convergence of the MRC iterative algorithm
a single-tap time-frequency equalizer was designed to provide an initial estimate
and then combined with an external error correction code to further improve the bit error performance.The simulation results show that the performance of the iterative rake equalizer based on MRC is significantly better than that of the LMMSE linear equalizer
and the performance and computational complexity are greatly improved compared with the currently widely used Gauss-Seidel (GS) iterative equalizer.
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