Iterative Least Square‐Based Phase Optimized Intelligent Transmission With Large Intelligent Meta‐Surfaces

ABSTRACT Data transmission through large intelligent meta‐surfaces (LIMs) is one of the trending and key enabling technologies for 6G and beyond wireless communication networks. LIMs are more efficient than intelligent reflecting surfaces (IRS) by integrating large‐scale meta‐surfaces with embedded signal processing and sensing capabilities. As wireless communication evolves, channel characteristics remain highly unpredictable and uncontrollable. However, LIMs improve the wireless communication link's performance by modifying the phase shift of individual user signals from the base station. In this way, the proposed spread code‐assisted LIM uses an iterative least square‐based phase optimization (ILS‐PO) method that enhances the wireless link's overall reliability. Additionally, we utilize spreading to enhance the receiver performance for the end user. The proposed ILS‐PO method optimizes the phase shifts of LIM elements iteratively using the least square (LS) criterion to minimize the signal distortion and maximize the received signal power. This approach reduces the computational overhead compared to exhaustive optimization methods while maintaining robustness against imperfect channel state information (CSI). The bit error rate (BER), capacity, and throughput of the proposed method are compared with those of the decode‐and‐forward (DF) relaying system, the conventional IRS system with random phase optimization method under practical nonlinear and spatially correlated fading conditions. The simulation is performed under the existing orthogonal multiple access (OMA) scheme with imperfect CSI, and it evidently shows the superiority of the proposed ILS‐PO method for all practical scenarios.