Small cell placement and interference management in heterogeneous networks using multi-objective genetic algorithm

Wu, Chih-Feng; Lee, C. N.

doi:10.1109/icasi.2017.7988281

Cited by 3 publications

(1 citation statement)

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“…Compared with classical search methods, GA reduces the chances of convergence towards local optimum due to its possibility to simultaneously evaluate a large number of candidate solutions. In the reviewed literature, we found various HetNets multi‐objective optimization problems successfully solved by using GAs 17,18,30,37,38 . The availability of NSGA‐II in MATLAB's multi‐objective optimization toolbox facilitated its rapid incorporation with a previously developed simulation tool for path loss estimation 31 .…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithm for biobjective optimization of indoor LTE femtocell deployment

Figueras-Benítez

Badra

2020

Int J Communication

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The optimization of indoor femtocell base stations (FBS) placement is a highly complex problem in which multiple performance figures can be considered. In this paper, we propose a biobjective optimization approach based on nondominated sorting genetic algorithm II (NSGA-II) to solve the indoor FBS deployment problem in residential scenarios where a single cell is deployed within the coverage of a single dominant interfering external macro base station (MBS). The optimization problem is formulated with the objective of jointly minimizing outage probability and maximizing normalized downlink average throughput. Several conditions are simultaneously considered under the framework of Monte Carlo simulations, such as the effect of co-channel interference in uplink and downlink, buildings with arbitrary layouts, realistic transmission schemes and antenna radiation patterns, random user distributions, and a well-established empirical path loss model. The proposed algorithm, which determines the location and orientation of the FBS's antenna that provides a suitable compromise between the two objective functions, is evaluated in this paper. First, two different experiments are proposed to investigate the algorithm convergence; then, statistical analyses are formulated to characterize the relation between the values achieved for both objective functions; finally, the computational load is evaluated. Results indicate the convergence of the proposed method towards the real Pareto front and a significant reduction of the computational time with respect to a reference method based on exhaustive search, while pointing at new criteria for the selection of the objective functions.

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Section: Introductionmentioning

confidence: 99%