Our paper “Convex Optimization of Distributed Cooperative Detection in Multi-Receiver Molecular Communication” (link to arXiv version) has been accepted to appear in IEEE Transactions on Molecular, Biological, and Multi-Scale Communications. Congratulations to our lead author Yuting Fang for her first journal paper, and to the other co-authors Nan Yang, Andrew Eckford, and Rodney Kennedy!
This paper looks at how voting rules can be used by multiple receivers who make their own decisions of data transmitted via a signal diffusing from a single source. Convex optimization is used to calculate the best decision thresholds at the receivers and at a fusion centre where the votes are collected. The full abstract is below.
Abstract: In this paper, the error performance achieved by cooperative detection among K distributed receivers in a diffusion-based molecular communication (MC) system is analyzed and optimized. In this system, the receivers first make local hard decisions on the transmitted symbol and then report these decisions to a fusion center (FC). The FC combines the local hard decisions to make a global decision using an N-out-of-K fusion rule. Two reporting scenarios, namely, perfect reporting and noisy reporting, are considered. Closed-form expressions are derived for the expected global error probability of the system for both reporting scenarios. New approximated expressions are also derived for the expected error probability. Convex constraints are then found to make the approximated expressions jointly convex with respect to the decision thresholds at the receivers and the FC. Based on such constraints, suboptimal convex optimization problems are formulated and solved to determine the optimal decision thresholds which minimize the expected error probability of the system. Numerical and simulation results reveal that the system error performance is greatly improved by combining the detection information of distributed receivers. They also reveal that the solutions to the formulated suboptimal convex optimization problems achieve near-optimal global error performance.