4.7 Article

Reliability analysis and ABC based optimization for CoMP-enabled systems over Nakagami-m fading

Journal

APPLIED SOFT COMPUTING
Volume 117, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.asoc.2021.108399

Keywords

ABC; CoMP; Nakagami-m fading; Reliability analysis; Resource allocation

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This article investigates the reliable analysis and joint optimization over Nakagami-m fading for coordinated multi-point (CoMP) assisted multi-cell systems. By proposing the ABC algorithm framework, the joint sub-carrier assignment and power allocation problem for reliability optimization are successfully solved. Simulation results show that the proposed frameworks can significantly enhance the reliability of user equipment.
Providing reliable communications has become one of the major goals for machine-type-oriented applications. In this article, reliable analysis and joint optimization over Nakagami-m fading are investigated for coordinated multi-point (CoMP) assisted multi-cell systems. First, the reliability analysis model for user equipment (UE) served by multiple base stations (BSs) via CoMP technique over Nakagami-m fading is presented. The exact closed-form expression for reliability estimation based on the received signal-to-interference-plus-noise ratio (SINR) value over Nakagami-m fading is derived and verified. Then, the joint sub-carrier assignment and power allocation problem for reliability optimization is formulated. The formulated problem is proved to be NP-hard. Bio-inspired artificial bee colony algorithm (ABC) is thus invoked to tackle this problem, and three ABC based joint optimization frameworks, namely Two-Step ABC Optimization algorithm (TSABC), ABC Combinatorial Optimization algorithm (ABCCO), and Heuristic Two-Step Optimization algorithm (HTSO), are proposed. Simulation results show that the UE reliability can be significantly enhanced by these proposed frameworks. It is also showcased that the proposed ABCCO obtains optimized reliability of 16 nines within 700 generations for most scenarios, which outperforms TSABC, HTSO, and conventional genetic algorithm (GA). (C) 2021 Published by Elsevier B.V.

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