Intelligent Paging Strategy for Multi-Carrier CDMA System

TL;DR

This paper proposes an intelligent paging strategy for multi-Carrier CDMA systems that dynamically assigns paging algorithms based on traffic demand predicted by a neural network, improving resource utilization and reducing service time.

Contribution

It introduces a neural network-based dynamic algorithm assignment mechanism for paging in CDMA systems, enhancing efficiency over static methods.

Findings

High prediction accuracy with a correlation coefficient of 0.99.

Improved paging performance through dynamic algorithm switching.

Enhanced radio resource utilization and reduced service time.

Abstract

Subscriber satisfaction and maximum radio resource utilization are the pivotal criteria in communication system design. In multi-Carrier CDMA system, different paging algorithms are used for locating user within the shortest possible time and best possible utilization of radio resources. Different paging algorithms underscored different techniques based on the different purposes. However, low servicing time of sequential search and better utilization of radio resources of concurrent search can be utilized simultaneously by swapping of the algorithms. In this paper, intelligent mechanism has been developed for dynamic algorithm assignment basing on time-varying traffic demand, which is predicted by radial basis neural network; and its performance has been analyzed are based on prediction efficiency of different types of data. High prediction efficiency is observed with a good correlation coefficient (0.99) and subsequently better performance is achieved by dynamic paging algorithm assignment. This claim is substantiated by the result of proposed intelligent paging strategy.