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Trajectory-Unaware Path Loss Forecast in a Distributed Massive MIMO System based on a Multivariate BiLSTM Model
  • +2
  • Rodney Martinez Alonso ,
  • Robbert Beerten,
  • Achiel Colpaert,
  • Andrea P Guevara,
  • Sofie Pollin
Rodney Martinez Alonso
Department of Electrical Engineering (ESAT)

Corresponding Author:[email protected]

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Robbert Beerten
Department of Electrical Engineering (ESAT)
Achiel Colpaert
Department of Electrical Engineering (ESAT), IMEC
Andrea P Guevara
Department of Electrical Engineering (ESAT)
Sofie Pollin
Department of Electrical Engineering (ESAT)


Cell-free massive MIMO networks have recently emerged as an attractive solution capable of solving the performance degradation at the cell edge of cellular networks. For scalability reasons, usercentric clusters were recently proposed to serve users via a subset of APs. In the case of dynamic mobile scenarios, this form of network organization requires predictive algorithms for forecasting propagation parameters to maintain performance by proactively allocating new APs to a user. In this paper, we present a BiLSTM-based multivariate path loss forecasting algorithm. Thanks to the combination of dual prediction by the BiLSTM and diversity from multiple antennas, our model mitigates the error propagation typically faced by sequential neural networks for time-series forecasting. In the evaluated scenario, from 2 to 10 steps ahead, we reduce the propagation of the error by a factor of 18 compared to previous research on path loss forecasting by an LSTM time-series-based model. In contrast to parallel transformer solutions, the complexity cost of our algorithm is also significantly lower.
14 Feb 2024Submitted to TechRxiv
19 Feb 2024Published in TechRxiv