“Optimizing RF Receiver Performance with Circuit-centric Genetic Algorithm”
This paper presents a highly efficient method for optimizing parameters in analog/high-frequency circuits, specifically targeting the performance parameters of a radio-frequency (RF) receiver. The goal is to maximize the receiver’s performance by reducing power consumption and noise figure while increasing conversion gain. The authors propose a novel approach called the Circuit-centric Genetic Algorithm (CGA) to address the limitations observed in the traditional Genetic Algorithm (GA).
One of the key advantages of the CGA is its simplicity and computational efficiency compared to existing deep learning models. Deep learning models often require significant computational resources and extensive training data, which may not always be readily available in the context of analog/high-frequency circuit optimization. The CGA, on the other hand, offers a simpler inference process that can more effectively leverage available circuit parameters to optimize the performance of the RF receiver.
Furthermore, the CGA offers significant advantages over manual design and the conventional GA in terms of finding optimal points. Manual design can be a time-consuming and iterative process, requiring the designer to experiment with various circuit parameters to identify the best combination. The conventional GA, while automated, can still be computationally expensive and may not always guarantee finding the superior optimum points. The CGA, with its circuit-centric approach, aims to mitigate the designer’s workload by automating the search for the best parameter values while also enhancing the likelihood of finding superior optimum points.
Looking ahead, it would be interesting to see the CGA being applied to more complex analog/high-frequency circuits beyond RF receivers. The authors demonstrate the feasibility of the method in optimizing a receiver, but its potential application in other circuit types could greatly benefit the field. Additionally, future research could explore the combination of CGA with other optimization techniques, further enhancing its efficiency and effectiveness in tuning circuit parameters.