Bender's Decomposition for Optimization Design Problems in Communication Networks

TL;DR

This paper reviews the application of Benders decomposition, a powerful optimization technique, across various communication network design problems, highlighting its variants, applications, challenges, and future prospects.

Contribution

It provides a comprehensive overview of Benders decomposition variants and their applications in diverse communication network design problems, aiding future research and implementation.

Findings

Different BD variants are effective in various network types.

BD helps in formulating efficient solutions for complex network problems.

Challenges in applying BD include computational complexity and problem-specific adaptations.

Abstract

Various types of communication networks are constantly emerging to improve the connectivity services and facilitate the interconnection of various types of devices. This involves the development of several technologies, such as device-to-device communications, wireless sensor networks and vehicular communications. The various services provided have heterogeneous requirements on the quality metrics such as throughput, end-to-end latency and jitter. Furthermore, different network technologies have inherently heterogeneous restrictions on resources, e.g., power, interference management requirements, computational capabilities, etc. As a result, different network operations such as spectrum management, routing, power control and offloading need to be performed differently. Mathematical optimization techniques have always been at the heart of such design problems to formulate and propose computationally efficient solution algorithms. One of the existing powerful techniques of mathematical optimization is Benders decomposition (BD), which is the focus of this article. Here, we briefly review different BD variants that have been applied in various existing network types and different design problems. These main variants are the classical, the combinatorial, the multi-stage, and the generalized BD. We discuss compelling BD applications for various network types including heterogeneous cellular networks, infrastructure wired wide area networks, smart grids, wireless sensor networks, and wireless local area networks. Mainly, our goal is to assist the readers in refining the motivation, problem formulation, and methodology of this powerful optimization technique in the context of future networks. We also discuss the BD challenges and the prospective ways these can be addressed when applied to communication networks' design problems.