Research on beamforming optimization in intelligent reflective surface-assisted wireless communication systems
Abstract
This paper investigates the optimization of beamforming in intelligent reflecting surface (IRS)-assisted heterogeneous wireless communication systems, aiming to meet the stringent requirements of ultra-reliable low- latency communication (URLLC). By designing the transmit beamforming vectors at the small cell (SC), the proposed algorithm transforms a non-convex optimization problem into a solvable convex form using successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques. Simulation results demonstrate that the proposed method significantly enhances system throughput and user data rates compared to benchmark al- gorithms, especially under varying IRS configurations and transmission power levels. The study confirms the effectiveness and scalability of IRS in improving network performance in URLLC scenarios.