论文标题
学习以优势智能为中心的电力分配
Learning Centric Power Allocation for Edge Intelligence
论文作者
论文摘要
尽管机器型通信（MTC）设备会生成大量数据，但由于能量和计算功率有限，它们通常无法处理此数据。为此，已经提出了Edge Intelligence，该智能收集分布式数据并在边缘执行机器学习。但是，这种范式需要最大程度地提高学习表现，而不是通信吞吐量，为此，著名的水和最高公平算法效率低下，因为它们仅根据无线渠道的质量分配资源。本文提出了一种以学习为中心的功率分配（LCPA）方法，该方法基于经验分类错误模型分配无线电资源。为了了解对LCPA的见解，得出了渐近最佳解决方案。该解决方案表明，发射功率与通道增益成反比，并与学习参数成倍扩展。实验结果表明，所提出的LCPA算法显着优于其他功率分配算法。
While machine-type communication (MTC) devices generate massive data, they often cannot process this data due to limited energy and computation power. To this end, edge intelligence has been proposed, which collects distributed data and performs machine learning at the edge. However, this paradigm needs to maximize the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient since they allocate resources merely according to the quality of wireless channels. This paper proposes a learning centric power allocation (LCPA) method, which allocates radio resources based on an empirical classification error model. To get insights into LCPA, an asymptotic optimal solution is derived. The solution shows that the transmit powers are inversely proportional to the channel gain, and scale exponentially with the learning parameters. Experimental results show that the proposed LCPA algorithm significantly outperforms other power allocation algorithms.
