Deep learning (DL)-based beam training schemes have enhanced the transmission capacity for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Nevertheless, the effects of noise and model complexity are still bottlenecks for most of those approaches. In this paper, we propose a hybrid convolutional neural network (CNN) encoder-based Transformer (HCNT) DL model containing robust channel expression and a scoring-based optimal beam decision. First, the hybrid CNN encoder parallelly tackles the real and imaginary components of the sampled channel collected by active sensors of the adopted semi-passive IRS with the grouped and point-wise convolution as the alternative to the complicated serial process. Second, we convert the continuous channel expression into binary sequences by leaky integrated-and-fire (LIF) mechanism seeking a robust representation against the noise effects. Finally, feature attention mechanism determines the prediction of the optimal beam by the scores of the relationship between potential direction and quantized binary tokens. Experimental results show that the proposed HCNT outperforms the existing schemes achieving a higher spectral efficiency under different noise levels with lower complexity.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    IRS-Assisted mmWave Massive MIMO Systems Beam Training with Hybrid CNN Encoder-based Transformer Deep Learning Model


    Contributors:
    Urakami, Taisei (author) / Jia, Haohui (author) / Chen, Na (author) / Okada, Minoru (author)


    Publication date :

    2023-10-10


    Size :

    1704047 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Beam Squint Effect in Multi-Beam mmWave Massive MIMO Systems

    Afeef, Liza / Arslan, Huseyin | IEEE | 2022



    Beam Prediction for mmWave Massive MIMO using Adjustable Feature Fusion Learning

    Yang, Sicheng / Ma, Jianpeng / Zhang, Shun et al. | IEEE | 2022


    mmWave massive MIMO vehicular communications

    Cheng, Xiang / Gao, Shijian / Yang, Liuqing | TIBKAT | 2023