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Tradeoff Analysis and Joint Optimization of Link-Layer Energy Efficiency and Effective Capacity Toward Green Communications

Yu, Wenjuan and Musavian, Leila and Ni, Qiang (2016) 'Tradeoff Analysis and Joint Optimization of Link-Layer Energy Efficiency and Effective Capacity Toward Green Communications.' IEEE Transactions on Wireless Communications, 15 (5). pp. 3339-3353. ISSN 1536-1276

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A joint optimization problem of link-layer energy efficiency (EE) and effective capacity (EC) in a Nakagami-m fading channel under a delay-outage probability constraint and an average transmit power constraint is considered and investigated in this paper. First, a normalized multi-objective optimization problem (MOP) is formulated and transformed into a single-objective optimization problem (SOP), by applying the weighted sum method. The formulated SOP is then proved to be continuously differentiable and strictly quasiconvex in the optimum average input power, which turns out to be a cup shape curve. Furthermore, the weighted quasiconvex tradeoff problem is solved by first using Charnes-Cooper transformation and then applying Karush-Kuhn-Tucker (KKT) conditions. The proposed optimal power allocation, which includes the optimal strategy for the link-layer EE-maximization problem and the EC-maximization problem as extreme cases, is proved to be sufficient for the Pareto optimal set of the original EE-EC MOP. Moreover, we prove that the optimum average power level monotonically decreases with the importance weight, but strictly increases with the normalization factor, the circuit power and the power amplifier efficiency. Simulation results confirm the analytical derivations and further show the effects of fading severeness and transmission power limit on the tradeoff performance.

Item Type: Article
Uncontrolled Keywords: Quality-of-service; delay-outage probability constraint; effective capacity; energy efficiency; multi-objective optimization problem; weighted sum method
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 30 Jan 2017 19:27
Last Modified: 23 Sep 2022 18:43

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