基于群优化的多输入多输出 (ACO-MIMO) 均等化,用于低复杂度的模式分割多重复合 (MDM) 处理
Optics express
|November 22, 2024
概括
两个新的基于群优化 (ACO) 的模式划分多重复合 (MDM) 系统的方案显著降低了多输入多输出 (MIMO) 均化的计算复杂性. 这些ACO-MIMO方法有效地找到最佳参数,提高性能并减少算法调用.
科学领域:
- 光学通信是指光学通信.
- 信号处理 信号处理
- 计算智能是一种计算智能.
背景情况:
- 模式分割复杂化 (MDM) 系统在多输入多输出 (MIMO) 均等化中面临着计算复杂性的挑战.
- 对有限冲动响应 (FIR) 均衡器的步骤大小 (μ) 和点击次数 (K) 等参数进行优化对于性能至关重要.
- 现有的参数优化的方法是计算密集的.
研究的目的:
- 为MDM系统提出基于群优化 (ACO) 的新型MIMO等级方案.
- 为了减少与找到最佳等分参数 (μ-K组合) 相关的计算复杂性.
- 评估在循环循环传输系统中拟议方案的性能和稳定性.
主要方法:
- 开发两种基于ACO的MIMO均等化方案:固定的ACO-MIMO和随机的ACO-MIMO.
- 在三模循环循环传输系统中进行实验评估.
- 与传统的遗传算法 (GA) 和最的下降算法 (SDA) 方案进行比较.
主要成果:
- 与传统方法相比,ACO-MIMO方案显著降低了等分算法调用量,高达42.74% (固定) 和80.63% (随机).
- 在最佳的μ-K组合中达到高达99.34%的高成功率 (Phit).
- 在各种数据集和往返中表现出稳定的性能,证实了长途MDM传输的稳健运行.
结论:
- 拟议的 ACO-MIMO 方案有效地减轻了 MDM MIMO 均等化中的计算负担.
- 固定的和随机的 ACO-MIMO 方案都提供了显著的复杂性降低和参数优化的高精度.
- 在不同的比特错误率 (BER) 分布下,这两种 ACO-MIMO 方案的性能都是可比的.
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