A Noise Suppression Filter for Molecular Communication via Diffusion
Ruifeng Zheng, Lin Lin, Hao Yan
Abstract
Molecular communication via diffusion (MCvD) is one of the most feasible communication paradigms for nanonetworks, especially for bio-nanonetworks in water-rich biological environments. Noise is an unwanted signal that severely limits the receiver's ability to detect the wanted signal. The dominant noise in MCvD is counting noise, which is much different from traditional communication paradigms. However, noise suppression in MCvD has not been explicitly investigated. Therefore, it is necessary to propose a practical noise-suppression approach to diminish the impact of noise in MCvD. In this letter, we propose a simple but effective noise-suppression filter for MCvD, the I-filter. Moreover, the proposed noise-suppression filter has low computational complexity. Our work shows that the proposed filter performs much better than current noise-suppression approaches, the moving average algorithm, in the state-of-the-art detection schemes in terms of bit error rate (BER) performance in the severe noise scenarios.