Peak to average power ratio alleviation by utilizing swarm intelligence along with machine learning for multiple input multiple output‐orthogonal frequency division multiplexing based underwater acoustic communication system

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. This research work has introduced a new swarm intelligence based modified artificial bee colony algorithm along with partial transmit sequence scheme. It effectively alleviates the nonlinearity of peak to average power ratio which is considered as a promising issue in the underwater acoustic network. We assessed the effectiveness of this proposed scheme in Matlab R2019a.