Error Probability Optimization for Non-Orthogonal Multiple Access in DBMC Networks

Non-orthogonal multiple access (NOMA) represents a promising option for differentiating multiple transmitters using only a single molecule type in a future diffusion-based molecular communication (DBMC) network. This paper addresses the bit error probability optimization of a DBMC-NOMA network with bio-nano-machines incapable of complex computations for classical optimization methods. We propose a pilot-symbol-based algorithm to approximate the optimal detection threshold and emitted number of transmitter molecules. Our solution is based on two algorithms for the separate optimization of thresholds and the number of molecules, which are applied alternatingly. Our Monte-Carlo simulation results show that the algorithm reliably approaches the global optimum parameter values regardless of initial values and signaling-molecule-to-noise ratio. Since it is composed of only a few basic operations, such as comparisons and additions, there is potential for an implementation using stochastic chemical reaction networks in future work.