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基于水声信道稀疏结构的最大相关熵快速盲均衡算法

Fast Blind Equalization Algorithms for Sparse Underwater Acoustic Channel Structures Under Maximum Correntropy Criterion

  • 摘要: 针对水下突发式数据链路中稀疏多途与脉冲噪声并存导致的盲均衡收敛慢、鲁棒性不足问题,提出一种基于水声信道稀疏结构的最大相关熵准则快速盲均衡方法。该方法在恒模代价中引入最大相关熵准则,以抑制脉冲噪声引起的大误差样本对梯度更新的干扰;同时,针对传统随机梯度方法受小步长约束而收敛缓慢的问题,采用优序最小化思想构造局部快速更新方向。进一步结合水声信道稀疏结构,通过比例归一化机制增强主径抽头更新,从而实现均衡器抽头的快速稳定收敛。仿真结果表明,在稀疏信道场景下,提出的方法在海洋噪声与脉冲噪声干扰下相较于CMA,MCMA,MSEI等现有典型盲均衡算法有着更低的误符号率和更快的收敛速度。

     

    Abstract: To address the slow convergence and insufficient robustness of blind equalization in underwater acoustic burst communication links, where sparse multipath and impulsive noise coexist, a fast blind equalization method based on the sparse structure of underwater acoustic channels and the maximum correntropy criterion is proposed. The proposed method introduces themaximum correntropy criterion into the constant-modulus cost to suppress the disturbance of large-error samples caused by impulsive noise on gradient updating. A majorization-minimization strategy is then employed to construct a local fast update direction, thereby alleviating the slow convergence of conventional stochastic-gradient methods constrained by small step sizes. Furthermore, by exploiting the sparse structure of underwater acoustic channels, a proportionate normalization mechanism is introduced to enhance the update of dominant taps while suppressing unnecessary perturbations on non-dominant taps, thus achieving fast and stable convergence of the equalizer coefficients. Simulation results show that, under ocean ambient noise and impulsive noise, the proposed method achieves lower symbol error rate and faster convergence than typical blind equalization algorithms such as CMA, MCMA, and MSEI under sparse channel conditions.

     

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