Optimal location management in LTE networks using evolutionary techniques

Abstract

Summary Wireless network is considered a vital enabler in the world of information technology, specifically, LTE and LTE advanced networks, which are the latest technologies owing to their fast speed, robustness, and large bandwidth. However, in spite of the aforementioned advancements, signaling overhead poses critical challenges in terms of network availability, especially those caused by location management messages which are related to users’ mobility behavior. This paper seeks to address the problem of signaling overhead caused by the location management messages specifically, tracking area update (TAU) and paging by deploying three evolutionary algorithms, namely particle swarm optimization (PSO), artificial bee colony (ABC), and gravitational search algorithm (GSA). The deployed algorithms guarantee yielding the minimum values of the signaling overhead for TAU, paging, and the battery power consumption of the user. It is shown that ABC‐based algorithm has faster convergence and better signaling overhead when compared with other implemented algorithms. Moreover, the measured relative standard deviation (RSD) value of all algorithms shows low uncertainty of around 1% for the objective function and 3% for the paging, TAU, and power. Hence, the three applied optimization algorithms have proven to be efficient and reliable for solving the problem in a large‐scale environment.