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Adaptive power allocation for chase combining HARQ based low-complexity MIMO systems

机译:用于基于HARQ的低复杂度MIMO系统追赶的自适应功率分配

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This paper deals with energy-efficient adaptive power allocation for an incremental multiple-input multiple-output (IMIMO) system employing hybrid automatic repeat request (HARQ) with Chase combining (CC), to minimize its rate-outage probability under a constraint on average energy consumption per data packet. We first provide the rate-outage probability expressions for the considered IMIMO system, and use Gauss-Legendre approximation to convert them into a tractable form and formulate a non-convex optimization problem that can be solved by an interior-point algorithm for finding a local optimum. Next, to further reduce the solution complexity, using an asymptotically equivalent approximation of the rate-outage probability expression, we approximate the non-convex optimization problem as a geometric programming problem (GPP), for which a solution can be obtained using convex optimization algorithms. Illustrative results indicate that the proposed power allocation (PPA) offers significant gains in energy savings as compared to the equal-power allocation (EPA), and the less complex GPP approach can provide a closer performance to the exact method at lower values of rate-outage probability.
机译:本文针对采用混合自动重发请求(HARQ)和Chase组合(CC)的增量多输入多输出(IMIMO)系统的节能自适应功率分配,以在平均约束下最大程度地降低其断电率每个数据包的能耗。我们首先为所考虑的IMIMO系统提供速率中断概率表达式,然后使用Gauss-Legendre逼近将其转换为易于处理的形式,并提出一个非凸优化问题,该问题可以通过内点算法来求解,以找到局部区域。最佳。接下来,为了进一步降低解的复杂性,我们使用速率中断概率表达式的渐近等效近似,将非凸优化问题近似为几何规划问题(GPP),可以使用凸优化算法来获得解。说明性结果表明,与等功率分配(EPA)相比,拟议的功率分配(PPA)在节能方面有显着提高,而复杂程度较低的GPP方法可以在较低的rate-value值下提供比精确方法更接近的性能中断概率。

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