Researcher: Baydaş, O. Tansel
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Baydaş, O. Tansel
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Publication Metadata only Received signal and channel parameter estimation in molecular communications(IEEE-Inst Electrical Electronics Engineers Inc, 2024) ; Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; Baydaş, O. Tansel; Akan, Özgür Barış; ; College of Engineering;Molecular communication (MC) is a paradigm that employs molecules as information carriers, hence, requiring unconventional transceivers and detection techniques for the Internet of Bio-Nano Things (IoBNT). In this study, we provide a novel MC model that incorporates a spherical transmitter and receiver with partial absorption. This model offers a more realistic representation than receiver architectures in literature, e.g., passive or entirely absorbing configurations. An optimization-based technique utilizing particle swarm optimization (PSO) is employed to accurately estimate the cumulative number of molecules received. This technique yields nearly constant correction parameters and demonstrates a significant improvement of 5 times in terms of root mean square error (RMSE) compared to the literature. The estimated channel model provides an approximate analytical impulse response;hence, it is used for estimating channel parameters such as distance, diffusion coefficient, or a combination of both. The iterative maximum likelihood estimation (MLE) is applied for the parameter estimation, which gives consistent errors compared to the estimated Cramer-Rao Lower Bound (CLRB).Publication Metadata only Estimation and detection for molecular MIMO Communications in the internet of bio-nano things(IEEE, 2023) Department of Electrical and Electronics Engineering; Department of Electrical and Electronics Engineering; N/A; Department of Electrical and Electronics Engineering; Akan, Özgür Barış; Çetinkaya, Oktay; Baydaş, O. Tansel; Faculty Member; Researcher; Other; College of Engineering; College of Engineering; N/A; 6647; N/A; N/AFor the Internet of Bio-Nano Things (IoBNT) applications demanding high transmission rates, a well-modeled Molecular Communication (MC) channel is essential. The existing studies proposing multiple-input and multiple-output (MIMO) models for MC, however, often make the unrealistic assumption of using ideal receivers with perfect absorption. Hence, this paper proposes a molecular MIMO channel model with spherical transmitters and partially-absorbing ligand receptor-based receivers underpinned by four unique parameters. For the non-analytical nature of the MIMO channel, we use a supervised learning algorithm to estimate the number of molecules in the reception space. We evaluate the root mean square error (RMSE) of our solution, which returns consistent results. The estimation is used for ligand-receptor binding statistics, in which the intersymbol inference (ISI) and molecular interference are considered. We also propose two techniques based on convolutional and recurrent neural networks (CNN & RNN) as alternatives to the generic threshold-based detection. Our detectors outperform the threshold-based technique; specifically, the CNN-based method improves the mean bit error rate (BER) performance three times.