Underwater optical wireless communication system: Deep learning CNN with NOMA-based performance analysis

Abstract

This research is looking forward improving the performance for underwater optical wireless communication (UOWC) by applying a Non-orthogonal multiple access (NOMA) technique. We also get the benefit of the advantage the transmission based on convolutional neural network hybrid with a long short-term memory cell. The relays selection and power optimization are two main parameters to enhance the UOWC system performance. In this work, we suppose a pairing method for NOMA nodes. By replacing the inner dense connections with convolution layers, this model is proposed to overcome high complexity and over fitting to improve the model performance. The obtained performance for sum rates show that NOMA outperforms the orthogonal multiple access system by ~ 6%. Applying a step-by-step sub-optimization algorithm (SSOPA) yields better results than using fixed power allocation (FPA), while using a global optimal power allocation algorithm (GOPA) increases the sum rates over both FPA and SSOPA. It is found that the improvement when using GOPA combined with CNN approach enhances the performance of sum rates by ~ 2.5% than using the independent-relay-aided NOMA (ICNOMA) for UOWC. The GOPA improvement is 1.2%, 2.5%, 8.7% over FPA and is 0.12%, 0.34%, 2.09% over SSOPA, for clear, pure, and coastal water, respectively. The ICNOMA outperforms both ordinary NOMA (ONOMA) and cooperative NOMA (CNOMA) without independent relay nodes. The ICNOMA achieves an improvement over ONOMA and CNOMA by 20.4% and 3.2%, respectively.