One primary challenge hindering the development of Terahertz communication is the significant non-ideal device attributes including phase noise (PN) and in-phase/quadrature-phase (I1Q) imbalance that is hard to model offline. Deep learning (DL)-based models can learn and fit channels and non-ideal characteristics by the actual transmission data. This paper proposes two novel constellation shaping methods to limit the distribution of the constellation points in the DL-based modulation for Terahertz communication, in order to resist non-ideal effects in both the training and testing stages. Simulation results show that, without any extra compensation modules, the DL-based modulation and normalization can greatly mitigate non-ideal effects such as PN and I1Q imbalance. In addition to restricted constellation distribution, the proposed normalization and corresponding training methods can learn to have diagrams with smaller peak-to-average power ratio (PAPR), and thus can potentially support a higher average transmission power.


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    Title :

    Learning of Constellation Shaping with Maximum Norms for Terahertz Communication


    Contributors:
    Che, Bo (author) / He, Qi (author) / Tan, Zun (author) / Chen, Zhi (author)


    Publication date :

    2024-06-24


    Size :

    3604925 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





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