Channel Estimation of IRS-OTFS Communication System with Meta-learning Algorithm
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摘要: 针对高多普勒场景下智能反射表面(IRS)辅助多用户通信系统存在的信道估计传输开销大的问题，该文结合正交时频空间(OTFS)调制特点构造一种IRS-OTFS通信系统，充分发挥IRS和OTFS的性能优势，并在此基础上提出一种学习率自适应的模型无关元学习(MAML)算法。对IRS-OTFS多用户信道估计任务做离线训练，根据各任务的收敛速度自适应地调整学习率，防止训练失衡，并利用信道之间的相关性和MAML算法的少样本、泛化特性得到全局模型和适应性模型，快速学习新用户信道的传输特性，降低传输开销，提高信道估计准确性。理论分析和仿真结果表明，该算法在信道传输条件相同的情况下，将传输开销降低了大约50%，并相对于基准算法有4.8 dB左右的性能提升。Abstract: Focusing on the problem of large channel estimation transmission overhead in Intelligent Reflective Surface IRS) assisted multi-user communication system in high Doppler scenario, an IRS-OTFS communication system is constructed based on the characteristics of Orthogonal Time-Frequency Space (OTFS) modulation, which gives full play to the performance advantages of IRS and OTFS, and on this basis, a Model-Agnostic Meta-Learning (MAML) algorithm with adaptive learning rate is proposed. The IRS-OTFS multi-user channel estimation task is trained offline, the learning rate is adaptively adjusted according to the convergence speed of each task to prevent training imbalance, and the correlation between channels and the few samples and generalization characteristics of MAML algorithm are used to obtain global models and adaptive models, so as to quickly learn the transmission characteristics of new user channels, reduce transmission overhead, and improve the accuracy of channel estimation. Theoretical analysis and simulation results show that the algorithm reduces the transmission overhead by about 50% under the same channel transmission conditions, and has a performance improvement of about 4.8 dB compared with the benchmark algorithm.
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1 学习率自适应的MAML算法信道估计步骤
输入：多用户训练任务集(包括支持集$ {\boldsymbol{D}}_k^{{\mathrm{sup}}} $和查询集$ {\boldsymbol{D}}_k^{{\mathrm{que}}} $)，
$ {{\boldsymbol{D}}^{{\mathrm{sup}}}} = {\left\{ {{\boldsymbol{D}}_k^{{\mathrm{sup}}}} \right\}_{k = 1,2, \cdots ,K}} $, $ {{\boldsymbol{D}}^{{\mathrm{que}}}} = {\left\{ {{\boldsymbol{D}}_k^{{\mathrm{que}}}} \right\}_{k = 1,2, \cdots ,K}} $；目标
用户数据集$ {{\boldsymbol{D}}^{\mathrm{Tar}}} $，学习率参数$ \alpha (0) $,$ \beta $,$ \gamma $元训练阶段： 1: for 迭代轮次$ t $ do 2: for 每个信道估计任务$ k $ do 3: 根据式(19)更新当前训练任务的内循环学习率$ {\alpha _k}(t) $ 4: 根据式(21)更新当前用户信道估计任务的适应性参数
$ {{\boldsymbol{\varphi}} _k}(t) $5: end for 6: 根据式(22)更新得到全局模型参数$ {{\boldsymbol{\theta}} ^*} $ 7: end for 针对新用户信道任务的微调阶段： 8: 初始化目标任务的模型参数为$ {{\boldsymbol{\theta}} ^*} $ 9: for 微调次数 do 10: 根据式(23)更新新用户的模型参数$ {{\boldsymbol{\varphi}} _{\mathrm{Tar}}} $ 11:end for 输出：全局模型参数$ {{\boldsymbol{\theta}} ^*} $；目标用户的适应性模型参数$ {{\boldsymbol{\varphi}} _{\mathrm{Tar}}} $ 
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表 1 仿真参数表
参数 值 载波频率(GHz) 28 BS端天线数 32 IRS无源反射元件数 32 批大小 20 内优化学习率 $ {10^{ - 3}} $ 外优化学习率 $ {10^{ - 4}} $ 卷积层数目 16
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表 2 卷积层参数设置表
层数 $ {C^{(l)}} $ $ N_{{\mathrm{SF}}}^{(l)} $ $ W_x^{(l)} $ $ W_y^{(l)} $ conv1,onv9 2 32 3 3 conv2～conv7 32 32 3 3 conv10～conv15 32 32 3 3 conv8, onv16 32 2 3 3
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