Machine Learning Techniques for Equalizing Nonlinear Distortion (2020)

By Rob Thompson, Comcast; Xiaohua Li, State University of New York at Binghamton

Since the early 2000s, the cable television (CATV) industry has been playing its part in the Artificial Intelligence (AI) community by deploying equalization technology to enable its digital signals to survive varying frequency response conditions within its cable plants. Simon Haykin describes how the perceptron and the adaptive filter using the least mean squares (LMS) algorithm are naturally related.

Equalization has evolved into a powerful tool, enabling the CATV industry to achieve communication efficiencies once thought impossible -- but that story is not quite complete. The limits of equalization may extend beyond the linear frequency response, and cancel the nonlinear responses commonly associated with nodes and other active devices which use power amplifiers (PAs). Achieving nonlinear equalization requires new equalization methods, like receiver post-distorter equalization, where techniques include AI models, such as deep neural networks (DNNs). Furthermore, researchers have been advancing nonlinear distortion cancellation via other methods, including peak-to-average-power ratio (PAPR) reduction, and digital pre-distortion (DPD). These technologies are beginning to show up in newer generation devices, where demands for radio frequency (RF) output power is high, while keeping power consumption low, like the full duplex DOCSIS (FDX) remote PHY device (RPD) nodes. DPD technologies cancel the contribution of the transmitting device only. More aggressive nonlinear distortion cancellation methods may be accomplished by advanced DNN approaches, such as incorporating input features derived from Volterra series models, which has become a popular model for no linear distortion that can be used to describe multiple nonlinearity orders and memory. Then efficiencies across the CATV network could be considered, either by higher node RF output power, or more efficient PA architecture/bias within the node, amplifier, and/or customer premise equipment (CPE). This paper will propose how current CATV equalization systems could be enhanced to cancel severe nonlinear distortion based on some of these novel approaches to nonlinear equalization.

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