<![CDATA[Advances in wireless communication technology face challenges in providing high channel capacity, energy efficiency, and transmitted signal quality in complex channels. Previous studies on Multiple Input Multiple Output (MIMO) with Intelligent Reflecting Surface (IRS) generally discuss theoretical models under ideal channel assumptions using the Semidefinite Relaxation (SDR) method, which exhibits high complexity and limited scalability. A research gap emerges due to the scarcity of studies on MIMO-IRS that address realistic optimization efficiency to maximize the Signal-to-Noise Ratio (SNR) in dynamic environments. This study aims to overcome these limitations through the integrated application of Alternating Optimization (AO) and Manifold Optimization (MO), which can handle non-convex problems more efficiently. The research is grounded in ontological, epistemological, and axiological aspects, employing experimental and simulation methods to optimize active beamforming at the Base Station and passive beamforming at the IRS while maintaining the unit modulus constraint. The results demonstrate that the AO-MO combination in MIMO-IRS increases channel capacity by up to 39.8%, SNR by up to 19.1%, and reduces computational time by more than fivefold compared to conventional methods without AO-MO. The contribution of this study lies in an optimization approach that efficiently enhances channel capacity and SNR without increasing computational complexity, enabling its application in wireless networks requiring high-speed and low-latency communication.]]>
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