Symbol-by-Symbol Maximum Likelihood Detection for Cooperative Molecular Communication
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In this paper, symbol-by-symbol maximum likelihood (ML) detection is proposed for a cooperative diffusion-based molecular communication (MC) system. In this system, a fusion center (FC) chooses the transmitter (TX) symbol that is more likely, given the likelihood of its observations from multiple receivers (RXs), where the TX sends a common information symbol to all RXs. The transmission of a sequence of binary symbols and the resultant intersymbol interference are considered in the cooperative MC system. We propose five ML detection variants according to different constraints on the knowledge at the FC. These five variants demonstrate trade-offs between their performance and the information available. The system error probabilities for three ML detector variants are derived, two of which are in closed form. Numerical and simulation results show that the ML detection variants provide lower bounds on the error performance of the simpler cooperative variants and demonstrate that these simpler cooperative variants have error performance comparable to ML detectors when the reporting from RXs to the FC is noisy.
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