This work presents a new multi-chemical experimental platform for molecularcommunication where the transmitter can release different chemicals. Thisplatform is designed to be inexpensive and accessible, and it can be expandedto simulate different environments including the cardiovascular system andcomplex network of pipes in industrial complexes and city infrastructures. Todemonstrate the capabilities of the platform, we implement a time-slottedbinary communication system where a bit-0 is represented by an acid pulse, abit-1 by a base pulse, and information is carried via pH signals. The channelmodel for this system, which is nonlinear and has long memories, is unknown.Therefore, we devise novel detection algorithms that use techniques frommachine learning and deep learning to train a maximum-likelihood detector.Using these algorithms the bit error rate improves by an order of magnituderelative to the approach used in previous works. Moreover, our system achievesa data rate that is an order of magnitude higher than any of the previousmolecular communication platforms.
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