机器学习辅助的毫米波无线收发器电路设计
Machine Learning Assisted Design of mmWave Wireless Transceiver Circuits
AI总结：
针对毫米波收发器电路设计迭代耗时、受非线性与复杂权衡影响的问题，本研究以28-GHz收发器为对象，将传统与大规模机器学习方法融入设计流程以根据规格预测参数，提升设计效率，并探讨了未来研究方向。
摘要：
随着第五代（5G）及即将到来的第六代（6G）通信对提供高数据吞吐量与相对低延迟展现出巨大需求，毫米波（mmWave）技术成为实现预期性能与任务的关键支撑组件。在此背景下，过去几十年间，毫米波集成电路（IC）从单个模块设计到复杂系统设计均吸引了大量研究兴趣。然而，模拟或射频电路涉及的高度非线性特性与复杂权衡关系，使其设计过程十分复杂。制造技术的快速发展也因更严格的要求，导致设计过程耗时不断增加。本论文首先研究了28-GHz收发器电路，给出了详细原理图及相关性能指标。在此基础上，选取了两个包含异构独立模块的目标系统，并在发射端与接收端分别进行了验证。随后，将部分传统方法与大规模机器学习（ML）方法融入所选系统的设计流程，用于根据期望规格预测电路参数，从而规避传统方法中典型的耗时迭代过程。最后，从电路设计与机器学习算法两个角度探讨了若干潜在研究方向。
英文摘要：
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.
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