Slime Mould Algorithm based Fuzzy Linear CFO Estimation in Wireless Sensor Networks

Abstract

Orthogonal Frequency-Division Multiplexing (OFDM) is a form of digital systems and a way of encoding digital data across multiple frequency components that is used in telecommunication services. Carrier Frequency Offset (CFO) inaccuracy is a serious disadvantage of OFDM. In this research, Fuzzy based Slime Mould optimization for CFO (FSM-CFO) has been proposed. The proposed FSM-CFO not only estimates the CFO with increased precision, but also allocates resources effectively, achieving maximum utilization of dynamic resources. Initially, Slime Mould Algorithm is utilized to extract the precise CFO. Additionally, the base station (BS) manages the Resource Units (RU), which could be used to distribute resources in such a manner that the user-requests are met. The resources are assigned using fuzzy rules, with the type of resource that is most appropriate for the nodes that have requested the resources being chosen. To assign resources to a certain job, fuzzy rules are devised. Once the residence time for the duration has passed, the highest-priority tasks receive the specific resources. Finally, experiments are carried out to demonstrate the effectiveness of the proposed method. The performance of the proposed method is evaluated in terms of Resource utilization, failure rate, life span, and energy consumption. According to the simulated results, the suggested configuration can perform better compared to the existing techniques.