Clustering and Optimization Algorithms to Enable Reliable 6G Mobile Molecular Communications

Molecular communications are envisioned to transform medicine and environmental sciences, but currently only small, isolated networks have been deployed. It is essential, then, to develop new mechanisms to enable information flow from remote users to nanometric biological machines through next-generation networks, such as 6G mobile technologies. 6G mobile networks are characterized by an extreme Quality-of-Service, but in the context of molecular communications, two requirements turn critical: ultra-massive and extremely reliable communications. Molecular communications are simplex, so there is typically no channel to transmit acknowledgment messages. Furthermore, several nanometric receptors concentrated in just some square micrometers are an ultra-massive device density for 6G base stations. In this paper, we propose a computational algorithm to make reliable and massive 6G mobile molecular communications feasible. The proposed algorithm employs clustering to create a real-time map with the positions of the biological nanometric machines, and particle swarm optimization to track the identity of the different machines while slowly moving. To handle ultra-massive density, clustering operates by defining which magnetic particles used as communication interface belong to the same biological machine. While the optimization mechanism considers the current and previous clustering results and a probabilistic model to determine the identity of each cell. Reliability is achieved by an acknowledgment message generated by a 6G transceiver when the biological machines reach the expected destination. Simulation tools are employed to validate the proposed solution. Results show that the identification error is less than 15%, and the reliability achieves a probability of up to 90%.