# GAO-FCNN-Enabled Beamforming of the RIS-Assisted Intelligent Communication System
## 1 Introduction  
With the continuous evolution of communication technology, especially the vigorous development of fifth generation (5G) technology, a host of applications have become increasingly comprehensive, such as intelligent manufacturing, smart cities, telemedicine, and so on. In this context, the reconfigurable intelligent surface (RIS), as an emerging wireless communication technology, has demonstrated a significant potential for the future of 6G communication systems [1; 2]. In references [3; 4], the investigation of the optimization of attainable rates in unmanned aerial vehicle (UAV) scenarios in multiple-input single-output (MISO) networks was conducted. A new perspective on the application of an RIS in dynamic environments was provided by this study. In reference [5], a capacity optimization algorithm for RIS-assisted multiple-input multiple-output (MIMO) communication systems was proposed. Specifically, an RIS is composed of many adjustable passive reflection components. By adaptively controlling the phase and amplitude of each reflective unit, the beamforming and directional discipline of the incident signal can be realized. As a consequence, the signal reception quality was enhanced, and the multi-path interference was eliminated and improved effectively [6]. Additionally, efficient reuse of space resources can be achieved by an RIS through the precise beamforming with reduced user interference. The authors in [7] showed that the coverage performance of the system can be greatly improved by optimizing beamforming in the RIS-assisted network. It provided an important theoretical basis for subsequent research. On this basis, the authors in [8] further explored the MIMO channel covariance matrix as well as optimized the autonomous phaseshifts and/or magnitudes of the RIS. Moreover, the beam can be dynamically adjusted to accommodate user position changes to ensure a sustained effect of interference suppression. Unlike the conventional intelligent large-scale MIMO communication system [9], an RIS incorporating a passive reflective surface was introduced in the modern mobile communication systems without any transmitting or receiving function modules. The key benefit of integrating the RIS lies in its unparalleled capacity to manipulate the propagation trajectory [10] of incoming signals dynamically. Furthermore, the space resource reuse was achieved through the meticulous tuning. And phase shifts and amplitudes can be controlled by the myriads of reflective elements embedded within the RIS surface. This process offered a high level of precision and adaptability that exceeded traditional signal control methods [11; 12; 13]. Based on this, significant improvements of signal coverage and communication efficiency can be achieved without additional energy consumption. As a consequence, the potential performance of the RIS can be realized through close collaboration among the base station (BS), user terminals, and the RIS. Additionally, the joint optimization of beam assignment parameters was studied to enhance the performance of the interruption probability [14; 15; 16; 17] and symbol error rate (SER) [18; 19; 20]. In reference [21], it was found that the security of the physical layer link was greatly improved. This improvement was obtained when beamforming vectors at the input point and the RIS were combined. However, the joint beamforming problem was complex and it was difficult to obtain a direct closed-form solution due to its highly nonconvex property [22; 23; 24; 25]. Traditional methods are often hindered by the stagnation in local optima, thereby the satisfactory performance is hard to achieve. Consequently, suboptimal methods are investigated by academic and industrial scientists. Among these schemes, the optimization methods based on the secondary planning [26] were explored due to its simplicity. The computational complexity was reduced by transforming the complex nonconvex problem into a tractable secondary programming problem through the convexation and linearization disciplines.