Energy efficient opportunistic cooperative transmission with different ratio combinings: from a new perspective

Energy efficient opportunistic cooperative transmission with different ratio combinings: from a new perspective
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不同比率组合的节能机会协作传动:新视角

DOI:
10.1007/s11432-013-4886-6
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发表时间:
2013-05
期刊:
Science China Information Sciences
影响因子:
--
通讯作者:
Rui Yun
Rui Yun
中科院分区:
其他
文献类型:
--
作者:
Wang Qian;He MeiQi;Song Wei;Rui Yun

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在协作通信中,基于目的地可用的信道状态信息(CSI),可以在接收器处使用不同的比率组合方法。本文主要研究在放大转发(AF)协作通信中,采用不同的比合并方法时的最优能量分配问题。更具体地说,我们考虑了一系列重要的基本问题,包括加权总能量约束下的信噪比最大化、信噪比加权约束下的加权总能量最小化、中断概率约束下的加权总能量最小化和瞬时加权总能量约束下的中断概率最小化,并分析了这些问题之间的关系。我们首先考虑最大比合并(MRC),并推导出精确的解析解。为了揭示资源分配问题中的关键因素,我们进一步将多个参数的精确结果转化为只有两个量的更直观的结果。由此产生的解决方案为理解最优节能机会式协作传输提供了一个新的视角。然后,我们考虑了固定比合并(FRC),并证明了原问题不存在显式解析解。为此,在对目标函数进行凸性证明的基础上,利用数值凸优化方法得到问题的唯一解。通过数值计算和仿真研究,验证了理论分析的正确性。
In cooperative communication, different ratio combining approaches can be utilized at the receiver based on the channel state information (CSI) available to the destination. In this paper, we focus on optimal energy allocation under amplify-and-forward (AF) cooperative communications when various ratio combining methods are utilized. More specifically, we consider a suite of important and fundamental problems, including signal-to-noise ratio (SNR) maximization under a weighted total energy constraint, weighted total energy minimization under an SNR constraint, weighted total energy minimization under an outage probability constraint and outage probability minimization under instantaneous weighted total energy constraint, and analyze the relationship among these problems. We first consider maximal ratio combining (MRC) and derive exact analytical solutions. To reveal the key factors in the resource allocation problem, we further transform the exact results with multiple parameters into more intuitive results with only two quantities. The resulting solutions provide a new perspective to understand optimal energy-efficient opportunistic cooperative transmissions. We then consider fixed ratio combining (FRC) and show that an explicit analytical solution does not exist to the original problem. To this end, based on the convexity proofs for the objective functions, we utilize numerical convex optimizations to obtain the unique solution. Both numerical and simulation studies are conducted to validate our theoretical analysis.
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