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作 者:董榕恩 谢中毅 马海波 赵飞龙 束锋 DONG Rongen;XIE Zhongyi;MA Haibo;ZHAO Feilong;SHU Feng(School of Information and Communication Engineering,Hainan University,Haikou 570228,China;Purple Mountain Laboratories,Nanjing 210094,China;China Mobile Group Hainan Co.,Ltd.,Haikou 571250,China)
机构地区:[1]海南大学信息与通信工程学院,海口570228 [2]紫金山实验室,南京210094 [3]中国移动通信集团海南有限公司,海口571250
出 处:《电子与信息学报》2025年第1期138-146,共9页Journal of Electronics & Information Technology
基 金:国家自然科学基金(U22A2002,62071234);海南省科技专项基金(ZDKJ2021022);海南大学科研启动项目(KYQD(ZR)-21008);海南大学信息技术协同创新中心项目(XTCX2022XXC07)。
摘 要:作为一种通过算法智能地控制信号反射来重构无线通信环境的新技术,智能反射面(IRS)近年来受到了广泛关注。与传统的中继系统相比,IRS辅助的中继系统可有效节约成本和能耗,并显著提高系统性能。然而,配备离散移相器的IRS会导致相位量化误差,从而降低接收机的接收性能。为了分析IRS相位量化误差导致的性能损失,该文基于弱大数定律和瑞利分布,在瑞利信道下,推导了关于移相器量化比特数的双IRS辅助放大转发中继网络的信噪比性能损失与可达速率的闭合表达式。此外,基于Taylor级数展开表达式,推导了其近似性能损失闭合表达式。仿真结果表明,系统的信噪比和可达速率性能损失随着量化比特数的增加而逐渐减小,而随着IRS相移元件数的增加而逐渐增大。当量化比特数为4时,系统的信噪比和可达速率性能损失分别小于0.06 dB和0.03 bit/(s·Hz)。Objective Most existing research assumes that the Intelligent Reflecting Surface(IRS)is equipped with continuous phase shifters,which neglects the phase quantization error.However,in practice,IRS devices are typically equipped with discrete phase shifters due to hardware and cost constraints.Similar to the performance degradation caused by finite quantization bit shifters in directional modulation networks,discrete phase shifters in IRS systems introduce phase quantization errors,potentially affecting system performance.This paper analyzes the performance loss and approximate performance loss in a double IRS-aided amplify-and-forward relay network,focusing on Signal-to-Noise Ratio(SNR)and achievable rate under Rayleigh fading channels.The findings provide valuable guidance on selecting the appropriate number of quantization bit for IRS in practical applications.Methods Based on the weak law of large numbers,Euler’s formula,and Rayleigh distribution,closed-form expressions for the SNR performance loss and achievable rate of the discrete phase shifter IRS-aided amplify-and-forward relay network are derived.Additionally,corresponding approximate expressions for the performance loss are derived using the first-order Taylor series expansion.Results and Discussions The SNR performance loss at the destination is evaluated as a function of the number of IRS-1 elements(N),assuming that the number of IRS-2 elements(M)equals N(Fig.2).It is evident that,regardless of whether the scenario involves actual or approximate performance loss,the SNR performance loss decreases as the number of quantization bit(k)increases but increases as N grows.When k=1,the gap between the actual performance loss and the approximate performance loss widens with increasing N.This gap becomes negligible when k is greater than or equal to 2.Notably,when k=4,the SNR performance loss is less than 0.06 dB.Furthermore,both the SNR performance loss and approximate performance loss gradually decelerate as N increases towards a larger scale.The achievable
分 类 号:TN92[电子电信—通信与信息系统]
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