No abstract

No abstract

Inter-symbol and co-channel interferences restrict the capacity of molecular communication (MC) systems. In this study, the effect of these interferences on the data rate of MC systems is investigated to design an efficient MC system. To this end, the authors propose an analytical model for a diffusion-based MC system comprised of two nanomachines when they exploit On/Off keying modulation. They model the Brownian motion of molecules in a one-dimensional environment as a wiener process and the life expectancy of diffused molecules as an exponential process. First, they consider the inter-symbol interference to derive the data rate of the MC system as a function of the receiver decision threshold and the symbol time duration. Hence, they propose an algorithm to obtain the optimal values of MC system parameters. Then, the effect of cochannel interference is considered by assuming parallel MC systems. They propose a minimum distance between adjacent MC systems that their co-channel interferences effect to be negligible. Moreover, they verify the accuracy of the analytical results by Monte-Carlo simulations. Results show a remarkable improvement in the data rate of MC systems. The derived results may find application in nanonetworks where nanomachines connect together to perform complex tasks.

Due to the ever increasing data rate demand of beyond 5G networks and considering the wide range of Orthogonal Frequency Division Multipllexing (OFDM) technique in cellular systems, it is critical to reduce pilot overhead of OFDM systems in order to increase data rate of such systems. Due to sparsity of multipath channels, sparse recovery methods can be exploited to reduce pilot overhead. OFDM pilots are utilized as random samples for channel impulse response estimation. We propose a three-step sparsity recovery algorithm which is based on sparsity domain smoothing. Time domain residue computation, sparsity domain smoothing, and adaptive thresholding sparsifying are the three-steps of the proposed scheme. To the best of our knowledge, the proposed sparsity domain smoothing based thresholding recovery method known as SDS-IMAT has not been used for OFDM sparse channel estimation in the literature. Pilot locations are also derived based on the minimization of the measurement matrix coherence. Numerical results verify that the performance of the proposed scheme outperforms other existing thresholding and greedy recovery methods and has a near-optimal performance. The effectiveness of the proposed scheme is shown in terms of mean square error and bit error rate.

No abstract