| Summary: | Millimeter wave (mmWave) and terahertz (THz) massive MIMO architectures are pivotal in the advancement of mobile communications. These systems conventionally utilize codebooks to facilitate initial connection and to manage information transmission tasks. Traditional codebooks, however, are typically composed of numerous single-lobe beams, thus incurring substantial beam training overhead. While neural network-based approaches have been proposed to mitigate the beam training load, they sometimes fail to adequately consider the minority users dispersed across various regions. The fairness of the codebook coverage relies on addressing this problem. Therefore, we propose an imbalanced learning (IL) methodology for beam codebook construction, explicitly designed for scenarios characterized by an imbalanced user distribution. Our method begins with a pre-clustering phase, where user channels are divided into subsets based on their power response to combining vectors across distinct subareas. Then, each subset is refined by a dedicated sub-model, which contributes to the global model within each IL iteration. To facilitate the information exchange among sub-models during global updates, we introduce the focal loss mechanism. Our simulation results substantiate the efficacy of our IL framework in enhancing the performance of mmWave and THz massive MIMO systems under the conditions of imperfect channel state information and imbalanced user distribution.
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