An underwater acoustic (UWA) network uses multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) to achieve high data rate with high amplitude that results in terms of augmented peak to average power ratio (PAPR). This significant PAPR deemed as a stumbling block to degrade the performance of MIMO-OFDM in UWA communication by causing nonlinear distortion within the high-power amplifier (HPA). While PAPR mitigation techniques have been suggested for the single input single output UWA case, there has been limited research work on controlling the PAPR dilemma in the MIMO-OFDM UWA case. To address this flaw, this article proposes a modified behavior of the artificial bee colony algorithm at the transmission end, which is further cross-verified using machine learning techniques. The simulation results show that the proposed technique outperforms current state-of-the-art PAPR diminishing methods, which is further enhanced in accordance with varying neuron counts and population size. Following that, we also analyzed and compared energy efficiency and performance gain of our proposed technique along with various advanced techniques. Furthermore, a desirable ratio between new and trained data was obtained to improve network efficiency by keeping PAPR values low.

中文翻译：

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基于群智能和机器学习的多输入多输出正交频分复用水声通信系统降低峰均功率比

水下声学 (UWA) 网络使用多输入多输出正交频分复用 (MIMO-OFDM) 来实现高数据速率和高幅度，从而提高峰均功率比 (PAPR)。这种显着的 PAPR 被视为通过在高功率放大器 (HPA) 内引起非线性失真而降低 UWA 通信中 MIMO-OFDM 性能的绊脚石。虽然已经针对单输入单输出 UWA 情况提出了 PAPR 缓解技术，但在 MIMO-OFDM UWA 情况下控制 PAPR 困境的研究工作有限。为了解决这个缺陷，本文提出了人工蜂群算法在传输端的改进行为，并使用机器学习技术进一步交叉验证。模拟结果表明，所提出的技术优于当前最先进的 PAPR 减小方法，该方法根据不同的神经元数量和种群大小得到进一步增强。之后，我们还分析和比较了我们提出的技术以及各种先进技术的能效和性能增益。此外，通过保持低 PAPR 值，获得了新数据和训练数据之间的理想比率，以提高网络效率。