A Novel ML-Based Symbol Detection Pipeline for Molecular Communication

Molecular Communication (MC) is the process of sending information by the use of particles instead of electromagnetic (EM) waves. This change in paradigm allows the use of MC in areas where EM transmission is undesirable. These include underground, underwater and even intra-body communications. While this novel paradigm promises new areas for communication, one of the major setbacks is its relatively low throughput caused by the propagation speed. This can be improved by decreasing the symbol duration; however, this can be a detriment to the correct decoding of symbols. This paper proposes a novel symbol detection pipeline to increase the possible throughput without increasing the error rate of the communication. This is based on a machine-learning algorithm for classification tasks using an L-point discrete time moving average filter and a wide range of features. Extensive simulations with long sequences at different signal-to-noise ratio (SNR) values were performed to determine how well the proposed method detects symbols. The results show that our method can detect symbols received when On-Off Keying (OOK) modulations are used with a 10 dB gain, even when transmissions with untrained SNR values occur.