K-Means Clustering-Aided Non-Coherent Detection for Molecular Communications

Xuewen Qian (1, 2) , Marco Di Renzo (1, 2, 3) , Andrew Eckford (4)

1 Université Paris-Saclay
2 L2S - Laboratoire des signaux et systèmes
3 CNRS - Centre National de la Recherche Scientifique
4 York University [Toronto]

In this paper, we consider non-coherent detection for molecular communication systems in the presence of inter-symbol-interference. In particular, we study non-coherent detectors based on memorybits-based thresholds in order to achieve low bit-error-ratio (BER) transmission. The main challenge of realizing detectors based on memory-bits-based thresholds is to obtain the channel state information based only on the received signals. We tackle this issue by reformulating the thresholds through intermediate variables, which can be obtained by clustering multi-dimensional data from the received signals, and by using the K-means clustering algorithm. In addition to estimating the thresholds, we show that the transmitted bits can be retrieved from the clustered data. To reduce clustering errors, we propose iterative clustering methods from one-dimensional to multi-dimensional data, which are shown to reduce the BER. Simulation results are presented to verify the effectiveness of the proposed methods.

Mots clés: non-coherent detection; K-means clustering; Molecular communications; inter-symbol interference; channel state information

Domaines: Electronique

Xuewen Qian, Marco Di Renzo, Andrew Eckford. K-Means Clustering-Aided Non-Coherent Detection for Molecular Communications. IEEE Transactions on Communications, 2021, 69 (8), pp.5456 - 5470. ⟨10.1109/TCOMM.2021.3075523⟩. ⟨hal-03358065⟩