Applications of Inequalities to Optimization in Communication Networks: Novel Decoupling Techniques and Bounds for Multiplicative Terms Through Successive Convex Approximation

TL;DR

This paper introduces new decoupling bounds for multiplicative terms in communication network optimization problems, utilizing classical inequalities and a successive convex approximation algorithm to efficiently find stationary solutions.

Contribution

It develops novel bounds based on harmonic, geometric, arithmetic, and quadratic means for non-convex multiplicative terms, and proposes a new SCA algorithm using the AM upper bound for improved optimization.

Findings

The AM upper bound is convex and convergent under certain conditions.

The proposed SCA algorithm effectively finds stationary points in complex multiplicative optimization problems.

Numerical results demonstrate the method's effectiveness in network energy and quantum source optimization.

Abstract

In communication networks, optimization is essential in enhancing performance metrics, e.g., network utility. These optimization problems often involve sum-of-products (or ratios) terms, which are typically non-convex and NP-hard, posing challenges in their solution. Recent studies have introduced transformative techniques, mainly through quadratic and parametric convex transformations, to solve these problems efficiently. Based on them, this paper introduces novel decoupling techniques and bounds for handling multiplicative and fractional terms involving any number of coupled functions by utilizing the harmonic mean (HM), geometric mean (GM), arithmetic mean (AM), and quadratic mean (QM) inequalities. We derive closed-form expressions for these bounds. Focusing on the AM upper bound, we thoroughly examine its convexity and convergence properties. Under certain conditions, we propose a novel successive convex approximation (SCA) algorithm with the AM upper bound to achieve stationary point solutions in optimizations involving general multiplicative terms. Comprehensive proofs are provided to substantiate these claims. Furthermore, we illustrate the versatility of the AM upper bound by applying it to both optimization functions and constraints, as demonstrated in case studies involving the optimization of transmission energy and quantum source positioning. Numerical results are presented to show the effectiveness of our proposed SCA method.