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In this paper we consider pilot contamination problem in the uplink of a multi-cell massive MIMO. Each base station (BS) is equipped with M (unlimited) antennas serving K (large but finite) users. Further, we let τ (pilot length) be an arbitrary but small value. As such, pilot optimization is formulated as a multi-objective optimization problem (MOP). It is shown that, when power control is enabled,...
On the design of a hybrid renewable energy system multiple objectives are in general required to be optimized simultaneously. This study presents a general multi-objective combinatorial model for optimizing the hybrid PV-wind-diesel-battery system configuration. The model considers four objectives, i.e., minimizing the lifetime system cost, lifetime CO2 and SO2 emissions and maximizing the system...
The preference-inspired co-evolutionary algorithm using goal vectors (PICEA-g) has been demonstrated to perform well on multi-objective problems. The superiority of PICEA-g originates from the smart fitness assignment, that is, candidate solutions are co-evolved with goal vectors along the search. In this study, we identify a limitation of this fitness assignment method, and propose an enhanced fitness...
Preference-inspired co-evolutionary algorithms (PICEAs) are a novel class of population-based approaches for multi-objective optimization. PICEA-g is one realization of PICEAs in which goal vectors are taken as preferences and are co-evolved with the candidate solutions during the search. The performance of PICEA-g is affected by the distribution of the co-evolved goal vectors. In PICEA-g, new goal...
The quality of an approximation set usually includes two aspects-- approaching distance and spreading diversity. This paper introduces a new technique for assessing the diversity of an approximation to an exact Pareto-optimal front. This diversity is assessed by using an ldquoexposure degreerdquo of the exact Pareto-optimal front against the approximation set. This new technique has three advantages:...
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