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Low-Complexity I/Q Imbalance Calibration Algorithm Based on Zero-IF Receiver
Journal of Circuits, Systems and Computers ( IF 0.9 ) Pub Date : 2022-08-24 , DOI: 10.1142/s0218126623500305
Zhi Li 1 , YaFeng Yao 1 , QunQun Zhou 1 , DongBin Fu 2
Affiliation  

Although the scheme of designing transceiver with zero-IF architecture reduces the design cost, complexity and power loss of the system, this architecture is more sensitive to the I/Q mismatch problem existing in the analog front-end and it is difficult to eliminate the image interference caused by the mismatch. Therefore, a fast and low-cost I/Q imbalance calibration algorithm is needed. As for the problem of I/Q imbalance in zero-IF receiver, a blind estimation algorithm for extracting the calibration parameters and signal compensation is proposed because of the complexity of calculation and hardware circuit, as well as the power consumption of resources under the traditional algorithm. Based on the statistical property of the signal, this algorithm has the specific characteristics of low computational complexity and being easy to implement in hardware circuit. Due to different ideas of parameters extraction, a special calibration model is designed. The result of simulation shows that the proposed algorithm has not only better performance in compensation parameters estimation, but also better calibration performance than the traditional algorithm. By comparing the calibration performance in the case of different gain imbalances or phase imbalances, we can see that the algorithm can maintain a good calibration performance under different imbalance conditions.



中文翻译:

基于零中频接收机的低复杂度 I/Q 不平衡校准算法

采用零中频架构设计收发器的方案虽然降低了系统的设计成本、复杂度和功率损耗,但这种架构对模拟前端存在的I/Q失配问题较为敏感,难以消除不匹配造成的图像干扰。因此,需要一种快速且低成本的 I/Q 不平衡校准算法。针对零中频接收机的I/Q不平衡问题,由于计算和硬件电路的复杂性,以及传统方法下资源的功耗,提出了一种用于提取校准参数和信号补偿的盲估计算法。算法。根据信号的统计特性,该算法具有计算复杂度低、易于硬件电路实现的特点。由于参数提取的思路不同,设计了一种特殊的标定模型。仿真结果表明,与传统算法相比,所提算法不仅具有更好的补偿参数估计性能,而且校准性能也更好。通过比较不同增益不平衡或相位不平衡情况下的校准性能,可以看出该算法在不同的不平衡条件下均能保持良好的校准性能。仿真结果表明,与传统算法相比,所提算法不仅具有更好的补偿参数估计性能,而且校准性能也更好。通过比较不同增益不平衡或相位不平衡情况下的校准性能,可以看出该算法在不同的不平衡条件下均能保持良好的校准性能。仿真结果表明,与传统算法相比,所提算法不仅具有更好的补偿参数估计性能,而且校准性能也更好。通过比较不同增益不平衡或相位不平衡情况下的校准性能,可以看出该算法在不同的不平衡条件下都能保持良好的校准性能。

更新日期:2022-08-24
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