{"title":"利用机器学习技术在 X 波段使用多端口接收器实现微波功率放大器线性化","authors":"Sasan Tavoseh, Abbas Mohammadi","doi":"10.1007/s10470-024-02296-7","DOIUrl":null,"url":null,"abstract":"<div><p>Modern telecommunication systems require high-efficiency modulations with high PAPR. To have high efficiency while being linear, a linearization technique must be implemented. One of the efficient methods for linearization is the digital pre-distortion method. In this paper, a digital pre-distortion method using different types of neural networks is used to linearize PA. A six-port receiver in the digital pre-distortion loop is used to demodulate the output of the PA to the baseband and linearize the PA in the baseband. Using this receiver has reduced the cost, noise, and complexity of the demodulator used in the pre-distortion circuit. Adjacent channel power ratio (ACPR) has been used as a performance metric. According to the results, the BiLSTM network used in this paper is associated with a severe reduction in complexity and a significant improvement in the ACPR parameter compared to the other types of BiLSTM network previously used for linearization. It is observed that for the three input signals 16QAM, 64QAM, and OFDM with 600 MHz bandwidth, the maximum improvement in ACPR parameter using BiLSTM network is 25.2dB, 23.1dB, and 22.5dB in X-Band, respectively.</p></div>","PeriodicalId":7827,"journal":{"name":"Analog Integrated Circuits and Signal Processing","volume":"122 1","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Linearization of microwave power amplifier using multi-port receiver with machine learning techniques in X-band\",\"authors\":\"Sasan Tavoseh, Abbas Mohammadi\",\"doi\":\"10.1007/s10470-024-02296-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Modern telecommunication systems require high-efficiency modulations with high PAPR. To have high efficiency while being linear, a linearization technique must be implemented. One of the efficient methods for linearization is the digital pre-distortion method. In this paper, a digital pre-distortion method using different types of neural networks is used to linearize PA. A six-port receiver in the digital pre-distortion loop is used to demodulate the output of the PA to the baseband and linearize the PA in the baseband. Using this receiver has reduced the cost, noise, and complexity of the demodulator used in the pre-distortion circuit. Adjacent channel power ratio (ACPR) has been used as a performance metric. According to the results, the BiLSTM network used in this paper is associated with a severe reduction in complexity and a significant improvement in the ACPR parameter compared to the other types of BiLSTM network previously used for linearization. It is observed that for the three input signals 16QAM, 64QAM, and OFDM with 600 MHz bandwidth, the maximum improvement in ACPR parameter using BiLSTM network is 25.2dB, 23.1dB, and 22.5dB in X-Band, respectively.</p></div>\",\"PeriodicalId\":7827,\"journal\":{\"name\":\"Analog Integrated Circuits and Signal Processing\",\"volume\":\"122 1\",\"pages\":\"\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2024-12-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Analog Integrated Circuits and Signal Processing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10470-024-02296-7\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Analog Integrated Circuits and Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s10470-024-02296-7","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
Linearization of microwave power amplifier using multi-port receiver with machine learning techniques in X-band
Modern telecommunication systems require high-efficiency modulations with high PAPR. To have high efficiency while being linear, a linearization technique must be implemented. One of the efficient methods for linearization is the digital pre-distortion method. In this paper, a digital pre-distortion method using different types of neural networks is used to linearize PA. A six-port receiver in the digital pre-distortion loop is used to demodulate the output of the PA to the baseband and linearize the PA in the baseband. Using this receiver has reduced the cost, noise, and complexity of the demodulator used in the pre-distortion circuit. Adjacent channel power ratio (ACPR) has been used as a performance metric. According to the results, the BiLSTM network used in this paper is associated with a severe reduction in complexity and a significant improvement in the ACPR parameter compared to the other types of BiLSTM network previously used for linearization. It is observed that for the three input signals 16QAM, 64QAM, and OFDM with 600 MHz bandwidth, the maximum improvement in ACPR parameter using BiLSTM network is 25.2dB, 23.1dB, and 22.5dB in X-Band, respectively.
期刊介绍:
Analog Integrated Circuits and Signal Processing is an archival peer reviewed journal dedicated to the design and application of analog, radio frequency (RF), and mixed signal integrated circuits (ICs) as well as signal processing circuits and systems. It features both new research results and tutorial views and reflects the large volume of cutting-edge research activity in the worldwide field today.
A partial list of topics includes analog and mixed signal interface circuits and systems; analog and RFIC design; data converters; active-RC, switched-capacitor, and continuous-time integrated filters; mixed analog/digital VLSI systems; wireless radio transceivers; clock and data recovery circuits; and high speed optoelectronic circuits and systems.