Machine Learning Assisted Design of mmWave Wireless Transceiver Circuits

TL;DR

This paper explores the use of machine learning to accelerate the design of 28-GHz mmWave transceiver circuits for 5G/6G, demonstrating how ML can predict circuit parameters and reduce design time.

Contribution

It introduces ML integration into mmWave transceiver design, enabling faster parameter prediction and addressing complex trade-offs in high-frequency circuit development.

Findings

ML approaches successfully predict circuit parameters

Design process time is significantly reduced

Potential research directions are discussed

Abstract

As fifth-generation (5G) and upcoming sixth-generation (6G) communications exhibit tremendous demands in providing high data throughput with a relatively low latency, millimeter-wave (mmWave) technologies manifest themselves as the key enabling components to achieve the envisioned performance and tasks. In this context, mmWave integrated circuits (IC) have attracted significant research interests over the past few decades, ranging from individual block design to complex system design. However, the highly nonlinear properties and intricate trade-offs involved render the design of analog or RF circuits a complicated process. The rapid evolution of fabrication technology also results in an increasingly long time allocated in the design process due to more stringent requirements. In this thesis, 28-GHz transceiver circuits are first investigated with detailed schematics and associated performance metrics. In this case, two target systems comprising heterogeneous individual blocks are selected and demonstrated on both the transmitter and receiver sides. Subsequently, some conventional and large-scale machine learning (ML) approaches are integrated into the design pipeline of the chosen systems to predict circuit parameters based on desired specifications, thereby circumventing the typical time-consuming iterations found in traditional methods. Finally, some potential research directions are discussed from the perspectives of circuit design and ML algorithms.