The 12 GHz frequency range is becoming a strong contender for indoor application in next generation 5G and 6G networks because it strikes a compromise between controllable signal attenuation and bandwidth availability, which answers the growing need for incredibly fast and robust wireless access. The behavior of propagation of signal in the 12 GHz band for a multi-room indoor corridor setting are examined in this work, with an emphasis on both LOS and NLOS scenarios. To evaluate the enhancement of signals in unreachable locations, measurements were made within adjacent rooms and down a corridor, with as well as without aluminium reflectors. The results show that whereas enclosed corridors behave like waveguides, open areas and NLOS zones show considerable attenuation. Numerous algorithms of machine learning and deep learning, which includes CNN, DNN, RNN, Random Forest, Decision Tree, and Gradient Boosting, were used to model and forecast signal strength across different locations. With a 0.0001 dB RMSE and 100% accuracy, the Decision Tree model performed the best. These results highlight how well the 12 GHz band works for high-speed indoor communication and how useful intelligent modeling is for improving wireless system architecture.

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Analysis of FR3 Band Signal Propagation in Indoor Environments for 5G and 6G Applications

  • Md. Anoarul Islam,
  • Biprajit Dutta,
  • Vivekananda Mukherjee,
  • Manabendra Maiti,
  • Quazi Mohmmad Alfred,
  • Rabindranath Bera,
  • Ardhendu Shekhar Biswas

摘要

The 12 GHz frequency range is becoming a strong contender for indoor application in next generation 5G and 6G networks because it strikes a compromise between controllable signal attenuation and bandwidth availability, which answers the growing need for incredibly fast and robust wireless access. The behavior of propagation of signal in the 12 GHz band for a multi-room indoor corridor setting are examined in this work, with an emphasis on both LOS and NLOS scenarios. To evaluate the enhancement of signals in unreachable locations, measurements were made within adjacent rooms and down a corridor, with as well as without aluminium reflectors. The results show that whereas enclosed corridors behave like waveguides, open areas and NLOS zones show considerable attenuation. Numerous algorithms of machine learning and deep learning, which includes CNN, DNN, RNN, Random Forest, Decision Tree, and Gradient Boosting, were used to model and forecast signal strength across different locations. With a 0.0001 dB RMSE and 100% accuracy, the Decision Tree model performed the best. These results highlight how well the 12 GHz band works for high-speed indoor communication and how useful intelligent modeling is for improving wireless system architecture.