COLO: Combined osprey and lyrebird optimization for optimal antenna selection for massive MIMO system

Abstract

Massive MIMO (M-MIMO) design is essential for enhancing spatial multiplexing gains in modern communication systems, but it often compromises energy efficiency (EE). Selecting the optimal antenna subset is crucial for boosting EE without negatively impacting spectrum efficiency (SE). However, due to the exponential increase in processing time as antenna count rises, exhaustive search methods become impractical for large MIMO systems. To address this, a novel optimization approach for optimal antenna selection (OAS) is proposed, combining the Osprey Optimization Algorithm (OOA) and Lyrebird Optimization Algorithm (LOA) into a hybrid COLO algorithm. COLO introduces key innovations, including a Feature Dependency-based (FDB) selection technique, a Global Positioning Strategy (GPS) for better search guidance, and OOA integration for enhanced exploration and exploitation. This approach aims to maximize SE while improving system efficiency. The suggested COLO for the maximal scenario has a lower fitness of 4.55×10–09, whereas the traditional LOA, RPO, OOA, EHO, COA, CB-PSO, and GA+CSO+PSO models achieve a higher fitness than COLO. The performance of COLO-based OAS is evaluated against existing methods in terms of efficiency, antenna count, statistical analysis, and convergence, demonstrating its superiority in maximizing SE.