Channel Estimation of IRS-OTFS Communication System with Meta-learning Algorithm
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.
Intelligent Reflecting Surface (IRS)Meta-learningOrthogonal Time-Frequency Space (OTFS)Channel estimation
万方数据
维普
