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重庆邮电大学通信与信息工程学院,重庆 400065
Revised:2026-01-07,
Accepted:12 January 2026,
Online First:30 March 2026,
Published:20 April 2026
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郭晓金,雍鸣洁,王泽宇.基于模糊逻辑控制的自适应低相关序列设计算法[J].电信科学,
Guo Xiaojin,Yong Mingjie,Wang Zeyu.Adaptive low-coherence sequence design algorithm | based on fuzzy logic control[J].Telecommunications Science,
郭晓金,雍鸣洁,王泽宇.基于模糊逻辑控制的自适应低相关序列设计算法[J].电信科学, DOI:10.11959/j.issn.1000−0801.2026122.
Guo Xiaojin,Yong Mingjie,Wang Zeyu.Adaptive low-coherence sequence design algorithm | based on fuzzy logic control[J].Telecommunications Science, DOI:10.11959/j.issn.1000−0801.2026122.
具有低峰均功率比(peak-to-average power ratio,PAPR)的低相关序列在正交频分多址(orthogonal frequency division multiple access,OFDMA)等多载波通信系统中得到广泛应用。针对现有的基于几何碰撞模型的低相关序列设计算法(low-coherence sequence design algorithm,LOCEDA)缺乏对优化状态的自适应感知能力,以及在复杂的约束条件下收敛速度慢、性能不理想等问题,提出了一种基于模糊逻辑控制的自适应低相关序列设计算法(Fuzzy-Logic-Controlled Adaptive Low-Coherence Sequence Design Algorithm,FLC-LOCEDA)。该算法在现有的低相关序列设计算法的基础上,引入模糊逻辑控制器(fuzzy logic control,FLC)构建参数自适应调节机制。FLC根据算法迭代过程中的相关性改善率和PAPR违例程度实时计算序列更新步长与碰撞解决轮数,在满足严格PAPR约束的前提下自动调节算法的全局搜索和局部优化之间的平衡。通过仿真验证,与LOCEDA相比,FLC-LOCEDA在大幅降低时间复杂度的同时,显著提升了收敛速度。特别是在低PAPR约束条件下,该算法将收敛所需的迭代次数减少了约63.1%,并将最优互相关性进一步降低了约12.6%,表明其在解决多目标约束序列设计问题上具有有效性与鲁棒性。
Low-coherence sequences with low peak-to-average power ratio (PAPR) are widely applied in multicarrier communication systems such as orthogonal frequency division multiple access (OFDMA). To address the issues that the existing low-coherence sequence design algorithm (LOCEDA) based on a geometric collision model lacked adaptive sensing capability for optimization status and suffered from slow convergence speed and unsatisfactory performance under complex constraints
an adaptive low-coherence sequence design algorithm based on fuzzy logic control (FLC-LOCEDA) was proposed. Based on the existing LOCEDA
a fuzzy logic controller (FLC) was introduced to construct a parameter adaptive adjustment mechanism. The sequence updating step size and collision resolution rounds were calculated in real-time by the FLC according to the correlation improvement rate and PAPR violation degree during the iterative process
and the balance between global search and local optimization was automatically adjusted under the premise of strictly satisfying PAPR constraints. It was verified through simulations that
compared with the existing LOCEDA
the convergence speed was significantly improved and the time complexity was greatly reduced by FLC-LOCEDA. In particular
under low PAPR constraints
the number of iterations required for convergence was reduced by approximately 63.1%
and the optimal cross-correlation was further decreased by about 12.6%. The effectiveness and robustness of the FLC-LOCEDA algorithm in solving multi-objective constraint sequence design problems were demonstrated.
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