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Maximizing Energy Efficiency in Multiuser Multicarrier Broadband Wireless Systems: Convex Relaxation and Global Optimization Techniques

机译:在多用户多载波宽带无线系统中使能效最大化:凸松弛和全局优化技术

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摘要

A key challenge toward green communications is how to maximize energy efficiency by optimally allocating wireless resources in large-scale multiuser multicarrier orthogonal frequency-division multiple-access (OFDMA) systems. The quality-of-service (QoS)-constrained energy efficiency maximization problem is generally hard to solve due to the inverse transposition of the optimization operands in the optimization objective. We apply convex relaxation to make the problem quasiconcave with respect to power and concave with respect to the subcarrier indexing coefficients. The Karush–Kuhn–Tucker (KKT) optimality conditions lead to transcendental functions, where existing solutions are only numerically tractable. Different from the existing approaches, we apply the Maclaurin series expansion technique to transform the complex transcendental functions into simple polynomial expressions that allow us to obtain the global optimum in fast polynomial time, with the tractable upper bound of truncation error. With the new solution method, we propose a joint optimal allocation policy for both adaptive power and dynamic subcarrier allocations. We gain insight on the optimality, feasibility, and computational complexity of the joint optimal solution to show that the proposed scheme is theoretically and practically sound with fast convergence toward near-optimal solutions with an explicitly tractable truncation error. The simulation results confirm that the proposed scheme achieves a much higher energy efficiency performance with the guaranteed QoS and much lower complexity than existing approaches in the literature.
机译:绿色通信面临的主要挑战是如何通过在大型多用户多载波正交频分多址(OFDMA)系统中优化分配无线资源来最大程度地提高能源效率。服务质量(QoS)约束的能效最大化问题通常很难解决,因为优化目标中优化操作数的反位。我们应用凸松弛来使问题在功率上近似凹,而在副载波索引系数上则凹。 Karush–Kuhn–Tucker(KKT)最优性条件导致先验函数,其中现有解决方案仅在数值上易于处理。与现有方法不同,我们使用Maclaurin级数展开技术将复杂的先验函数转换为简单的多项式表达式,从而使我们能够在快速多项式时间内获得具有最优的截断误差上限。利用新的求解方法,我们针对自适应功率分配和动态子载波分配提出了一种联合最优分配策略。我们对联合最优解的最优性,可行性和计算复杂性有了深入的了解,以表明所提出的方案在理论上和实践上都是合理的,并且可以快速收敛到具有明显可忽略的截断误差的近最优解。仿真结果证实,与文献中的现有方法相比,所提出的方案在保证QoS的同时实现了更高的能效性能,并且复杂度大大降低。

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