Design of Intelligence Reflector Metasurface Using Deep Learning Neural Network for 6G Adaptive Beamforming

Work on identifying the various techniques for 6G wireless networks has already begun as the present specification for 5G networks nears conclusion. Reconfigurable Intelligent Surfaces (RISs) are one of these potentially useful technologies for 6G service providers. They provide unparalleled levels of freedom in terms of wireless channel engineering, allowing the system to change the channel's properties whenever and however it chooses. Nonetheless, such qualities need a thorough understanding of the reaction of the related meta-surface under all conceivable operational situations. Analytical models and complete wave simulations may both be used to gain a better knowledge of the radiation pattern features, although both have inaccuracies under specific situations and are exceedingly computationally intensive. As a result, in this study, we offer a unique neural network-based technique for description of the meta-surfaces response that is both rapid and accurate. We look at a variety of scenarios and show how the proposed methodology can be used in them. In particular, we show that our technique is capable of learning and predicting the parameters driving the reflected wave radiation pattern with the accuracy of a complete wave simulation (98.8%-99.8%) while using just a fraction of the time and computer complexity of an analytical simulation. The above finding and approach will be particularly useful in the design, defect tolerance, and servicing of the hundreds of RISs which will be installed in the 6G distributed system.