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Hyperparameter-Free Transmit-Nonlinearity Mitigation Using a Kernel-Width Sampling Technique
IEEE Transactions on Communications ( IF 8.3 ) Pub Date : 2020-12-29 , DOI: 10.1109/tcomm.2020.3048045
Rangeet Mitra 1 , Georges Kaddoum 1 , Vimal Bhatia 2
Affiliation  

Nonlinear device characteristics present a severe performance-bottleneck for several upcoming next-generation wireless communication systems and prevent them from delivering high data-rates to the end-users. In this context, reproducing kernel Hilbert space (RKHS) based signal processing methods have gained widespread deployment and have been found to outperform classical polynomial-filtering-based solutions significantly. Furthermore, recent RKHS based techniques that rely on explicit feature-maps called random Fourier features (RFF) have emerged. These techniques alleviate the dependence on learning a dictionary and avoid the computations and errors incurred in dictionary-based learning. However, the performance of existing RKHS based solutions depends on choosing a suitable kernel-width. For the widely-used Gaussian kernel, we propose a methodology of assigning kernel-bandwidths that capitalizes on a stochastic sampling of kernel-widths using an ensemble drawn from a pre-designed probability density function. The technique is found to deliver a comparable convergence/error-rate performance to the scenario when the kernel-width is chosen by brute-force trial and error for tuning it for best performance. The desirable properties of the proposed kernel-sampling technique are supported by analytical proofs and are further highlighted by computer-simulations presented in the form of case studies in the context of next-generation communication systems.

中文翻译:

使用内核宽度采样技术的无超参数传输非线性缓解

非线性设备的特性为即将到来的下一代无线通信系统带来了严重的性能瓶颈,并阻止了它们向最终用户传递高数据速率。在这种情况下,基于重现内核希尔伯特空间(RKHS)的信号处理方法已得到广泛部署,并且已发现其性能明显优于基于经典多项式滤波的解决方案。此外,最近出现了基于RKHS的技术,这些技术依赖于称为随机傅立叶特征(RFF)的显式特征图。这些技术减轻了对学习词典的依赖,并避免了基于词典的学习中的计算和错误。但是,现有基于RKHS的解决方案的性能取决于选择合适的内核宽度。对于广泛使用的高斯核,我们提出了一种分配内核带宽的方法,该方法利用从预先设计的概率密度函数得出的集合,利用对内核宽度的随机采样来利用。当通过蛮力试验和错误选择内核宽度以对其进行调整以获得最佳性能时,发现该技术可提供与方案可比的收敛/错误率性能。所提出的内核采样技术的理想特性得到了分析证明的支持,并且在下一代通信系统的情况下,以案例研究的形式呈现的计算机模拟进一步突显了该特性。当通过蛮力试验和错误选择内核宽度以对其进行调整以获得最佳性能时,发现该技术可提供与方案可比的收敛/错误率性能。所提出的内核采样技术的理想特性得到了分析证明的支持,并且在下一代通信系统的情况下,以案例研究的形式呈现的计算机模拟进一步突显了该特性。当通过蛮力试验和错误选择内核宽度以对其进行调整以获得最佳性能时,发现该技术可提供与方案可比的收敛/错误率性能。所提出的内核采样技术的理想特性得到了分析证明的支持,并且在下一代通信系统的情况下,以案例研究的形式呈现的计算机模拟进一步突显了该特性。
更新日期:2020-12-29
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