KPI-oriented Probing Beam Selection and Beam Prediction for mmWave MISO communications

Millimeter wave (mmWave) communications present significant challenges in beam alignment and tracking (BA/T) due to the substantial overhead and delays associated with beam training in dynamic environments. Deep learning (DL) has emerged as a promising solution by exploiting spatial channel correlations. This work addresses the problem of data-driven probing beam selection and beam prediction, where a trained prediction model utilizes selected probing beams to optimize BA/T. Given the NP-hard combinatorial optimization problem, we propose an iterative strategy to reduce computational complexity, with each iteration focusing on selecting a probing beam. The training of the prediction models and the beam selection process are guided by a targeted key performance indicators (KPI). Extensive simulations and tests using both measured and simulated data from line of sight (LoS) and non-line of sight (NLoS) scenarios validate the effectiveness of the proposed strategies. The results show marked improvements in targeted KPIs, in NLoS environments such as DeepMIMO and 3GPP scenarios.

• Publication year

  2025

• Venue

  2025 IEEE/CIC International Conference on Communications in China (ICCC)

• Publication date

  2025-08-10

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/ICCC65529.2025.11148955
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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