Year: 2025
Type: article
Abstract: Antenna selection is an appealing energy-efficient solution for multiple-input multiple-output (MIMO) systems. This paper handled the antenna selection problem in MIMO systems as a multi-class classification task, and propounded an antenna selection technique for energy efficiency (EE) maximization based on Fast-Adaptive Base Class-Boost (Fast-ABC-Boost) which boosts the classification performance of many "weak" classifiers, i.e. regression trees, to produce a powerful "committee" to make classification decision. Simulation results prove the superiority of Fast-ABC-Boost over the up-to-date learning method based on Deep Reinforcement Learning (DRL) and the conventional optimization driven Cyclic Binary Particle Swarm Optimization (CBPSO) approach in terms of EE performance with feasible complexity.
Source: Scientific Reports
Authors: Xiaofeng Yang, Jie Qiu, Danlei Mo
Institution: Yulin Normal University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Award: G7364717539
OpenAlex ID: W4415923555