From Immune Cells to Self-Organizing Ultra-Dense Small Cell Networks

TLDR

Operators are densifying macrocellular networks with low‑power base stations to meet future traffic demands, creating heterogeneous ultra‑dense small‑cell networks that face backhaul, capacity, and dynamic traffic challenges. The study aims to enhance energy efficiency and improve cell‑edge throughput by applying an artificial immune system–based self‑organizing network approach. The authors develop a mathematical AIS model that autonomously activates or deactivates small cells based on local traffic, and evaluate it via system‑level simulations across different activation speeds and deactivation delays. Simulations with spatio‑temporally varying traffic and geo‑location uncertainty show the AIS‑based SON approach is robust.

Abstract

In order to cope with the wireless traffic demand explosion within the next decade, operators are underlying their macrocellular networks with low power base stations in a more dense manner. Such networks are typically referred to as heterogeneous or ultra-dense small cell networks, and their deployment entails a number of challenges in terms of backhauling, capacity provision, and dynamics in spatio-temporally fluctuating traffic load. Self-organizing network (SON) solutions have been defined to overcome these challenges. Since self-organization occurs in a plethora of biological systems, we identify the design principles of immune system self-regulation and draw analogies with respect to ultra-dense small cell networks. In particular, we develop a mathematical model of an artificial immune system (AIS) that autonomously activates or deactivates small cells in response to the local traffic demand. The main goal of the proposed AIS-based SON approach is the enhancement of energy efficiency and improvement of cell-edge throughput. As a proof of principle, system level simulations are carried out in which the bio-inspired algorithm is evaluated for various parameter settings, such as the speed of small cell activation and the delay of deactivation. Analysis using spatio-temporally varying traffic exhibiting uncertainty through geo-location demonstrates the robustness of the AIS-based SON approach proposed.