Horizontal Handover Based on Fuzzy Logic Design For mobile Communication Networks

Handover represents an essential mechanism within wireless communication systems to maintain con-tinuous connectivity. Specific criteria and goals direct the handover decision-making framework. This research aims to present an extensive investigation into deploying fuzzy logic alongside an adaptive neuro-fuzzy inference system (ANFIS) framework for improving horizontal handover decisions and identifying optimal cells within mobile network environments. The investigation encompasses three phases: initially establishing handover determination, subsequently evaluating potential target cell ac-cessibility, and ultimately identifying the most suitable cell for user allocation. ANFIS, employing a Sugeno fuzzy model, operates within an adaptive system architecture that facilitates learning and ad-justments. A hybrid learning algorithm effectively trains the fuzzy inference system while managing epoch quantities throughout training, consequently minimizing errors. MATLAB simulations imple-ment the proposed framework. Simulation outcomes demonstrate that this approach strengthens deci-sion-making procedures and optimizes optimal cell identification for users. Moreover, the selection process by ANFIS outperforms traditional fuzzy logic by 16.3 to 35.86% within an availability range of 0.73 to 0.77.