Symbol-by-Symbol Maximum Likelihood Detection for Cooperative Molecular Communication

TL;DR

This paper introduces symbol-by-symbol maximum likelihood detection methods for cooperative molecular communication systems, analyzing error probabilities, optimizing molecule distribution, and comparing performance with simpler detection schemes.

Contribution

It proposes new ML detection variants for cooperative MC, derives error probabilities, and optimizes molecule allocation to improve detection accuracy.

Findings

ML detection variants outperform non-ML schemes in error probability

Equal molecule distribution among symmetric RXs minimizes error

Numerical results confirm the effectiveness of proposed ML detectors

Abstract

In this paper, symbol-by-symbol maximum likelihood (ML) detection is proposed for a cooperative diffusion-based molecular communication (MC) system. In this system, the transmitter (TX) sends a common information symbol to multiple receivers (RXs) and a fusion center (FC) chooses the TX symbol that is more likely, given the likelihood of its observations from all RXs. The transmission of a sequence of binary symbols and the resultant intersymbol interference are considered in the cooperative MC system. Three ML detection variants are proposed according to different RX behaviors and different knowledge at the FC. The system error probabilities for two ML detector variants are derived, one of which is in closed form. The optimal molecule allocation among RXs to minimize the system error probability of one variant is determined by solving a joint optimization problem. Also for this variant, the equal distribution of molecules among two symmetric RXs is analytically shown to achieve the local minimal error probability. Numerical and simulation results show that the ML detection variants provide lower bounds on the error performance of simpler, non-ML cooperative variants and demonstrate that these simpler cooperative variants have error performance comparable to ML detectors.