摘要
针对单一的反射链路模型无法准确衡量超大规模智能反射面(extremely large-scale reconfigurable intelligent suface,XL-RIS)系统环境的问题,构造了一种XL-RIS近场和基站(base station,BS)远场区域相叠加的通信模型;在此基础上,为了减少训练开销,提出了一种高效波束训练方案。首先,综合考虑近场球面波束和远场平面波束路径增益,引入影响系数F,推导了适配XL-RIS近场和BS远场重叠区域的信道模型;此外,为了提高接收信号功率、使直射波束和反射波束同相相加,引入相位修正参数,构造了匹配重叠区域的训练码本;最后,针对该模型设计了一种可变步长的空间分层方案,具体而言,空间的每一层采样间隔由原点沿半径向外依次递增。仿真结果表明,在信噪比为0时,双链路混合场信道模型能达到完美信道条件下93.7%的速率性能,相对于近场反射模型和远场反射模型分别提升了57.6%和205.4%;新的空间分层方案与传统的分层方案相比平均可达速率误差在1%以内,但训练开销减少了63.6%。
Aiming at the problem that a single reflected link model cannot accurately measure the environment of extremely large-scale reconfigurable intelligent suface(XL-RIS)system,a communication model of superimposed XLRIS near-field region and BS far-field region was constructed.On this basis,an efficient beam training scheme was proposed to reduce the training cost.Firstly,considering the path gain of near-field spherical beam and far-field planar beam,the influence coefficient F was introduced to derive the channel model suitable for XL-RIS near-field and BS far-field superposition region.In addition,in order to improve the received signal power and make the direct beam and the reflected beam in phase superposition,the phase correction parameter was introduced,and the training codebook matching the hybrid near-far field channel was constructed.Finally,a spatial layering scheme with variable step size was designed for the model.Specifically,the sampling interval of each layer increases from the origin along the radius.The simulation results show that the dual link hybrid near-far field model can achieve 93.7%rate performance under perfect channel condition when the SNR is 0,and the rate performance is improved by 57.6%and 205.4%compared with the near-field reflection model and the far-field reflection model respectively.The average reachable rate error of the new spatial layering scheme is less than 1%compared with the traditional layering scheme,but the training cost is reduced by 63.6%.
作者
杨黎明
邱多
李俊峰
YANG Liming;QIU Duo;LI Junfeng(School of Communications and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
出处
《电信科学》
北大核心
2025年第8期76-85,共10页
Telecommunications Science
基金
重庆市自然科学基金创新发展联合基金(中国星网)(No.CSTB2023NSCQ-LZX0114)。
