Abhishek K. Gupta

We study the impact of deploying reconfigurable intelligent surfaces (RISs) in mitigating coverage gaps and enhancing transmission reliability in an industrial internet of things (IIoT) network. First, we consider a single blockage scenario and characterize the correlation between blocking events of the base station (BS)-user and the RIS-user links and study its impact on the probability of establishing a viable reflected link. Then, by considering multiple blockages, we derive the distribution of the signal to noise ratio (SNR) as a function of data size, blockage density, the number of RISs, and the deployment area. We analyze the impact of normalized blockage radius and identify the threshold beyond which the assumption of independent blockages deviates from the ground truth of correlated blocking. Finally, we compare the outage performance of this RIS-assisted system with that operated with network-controlled relays, and demonstrate that while the relays provide a higher reliability beyond a certain blockage threshold, increasing the number of RISs may help mitigate this effect. These insights offer valuable design guidelines for deploying RIS-aided IIoT networks in dense blockage environments.

In this work, we develop the channel model of a molecular communication (MC) system with molecules propagating via anomalous diffusion in a confined environment, in particular, inside a spherical region. The MC system consists of an absorbing receiver located at the center of the region and a point transmitter along whereas the outer boundary is fully reflecting. We first obtain the concentration profile of molecules inside the region at a given time. Further, we derive the hitting rate and hitting probability to characterize the channel.

This work focuses on the development of an analytical framework to study a diffusion-assisted molecular communication-based network of nano-machines (NMs) with a clustered initial deployment to detect a target in a three-dimensional (3D) medium. Leveraging the Poisson cluster process to model the initial locations of clustered NMs, we derive the analytical expression for the target detection probability with respect to time along with relevant bounds. We also investigate a single-cluster scenario. All the derived expressions are validated through extensive particle-based simulations. Furthermore, we analyze the impact of key parameters, such as the mean number of NMs per cluster, the density of the cluster, and the spatial spread, on the detection performance. Our results show that detection probability is greatly influenced by clustering, and different spatial arrangements produce varying performances. The results offer a better understanding of how molecular communication systems should be designed for optimal target detection in nanoscale and biological environments.

This work focuses on the development of an analytical framework to study a diffusion-assisted molecular communication-based network of nano-machines (NMs) with a clustered initial deployment to detect a target in a three-dimensional (3D) medium. Leveraging the Poisson cluster process to model the initial locations of clustered NMs, we derive the analytical expression for the target detection probability with respect to time along with relevant bounds. We also investigate a single-cluster scenario. All the derived expressions are validated through extensive particle-based simulations. Furthermore, we analyze the impact of key parameters, such as the mean number of NMs per cluster, the density of the cluster, and the spatial spread, on the detection performance. Our results show that detection probability is greatly influenced by clustering, and different spatial arrangements produce varying performances. The results offer a better understanding of how molecular communication systems should be designed for optimal target detection in nanoscale and biological environments.

We study the impact of deploying reconfigurable intelligent surfaces (RISs) in mitigating coverage gaps and enhancing transmission reliability in an industrial internet of things (IIoT) network. First, we consider a single blockage scenario and characterize the correlation between blocking events of the base station (BS)-user and the RIS-user links and study its impact on the probability of establishing a viable reflected link. Then, by considering multiple blockages, we derive the distribution of the signal to noise ratio (SNR) as a function of data size, blockage density, the number of RISs, and the deployment area. We analyze the impact of normalized blockage radius and identify the threshold beyond which the assumption of independent blockages deviates from the ground truth of correlated blocking. Finally, we compare the outage performance of this RIS-assisted system with that operated with network- controlled relays, and demonstrate that while the relays provide a higher reliability beyond a certain blockage threshold, increasing the number of RISs may help mitigate this effect. These insights offer valuable design guidelines for deploying RIS-aided IIoT networks in dense blockage environments.

Molecular communication (MC) offers a groundbreaking approach to communication inspired by biological signaling. It is particularly suited for environments where traditional electromagnetic methods fail, such as fluid mediums or within the human body. This study focuses on addressing a major challenge in MC systems: inter symbol interference (ISI), which arises due to the random, diffusive propagation of molecules. We propose a novel technique that leverages transmission shaping to mitigate ISI effectively by designing optimal transmission pulse (or sequence) for symbols. Our approach centers on solving a multi-objective optimization problem that aims to maximize the separability of individual symbol's responses within the symbol duration while matching the interference caused by molecular spillover for all symbols. By making ISI of each symbol similar, the approach reduces the effect of previous symbols and thus not require any adaptive computations. We introduce a geometric analogy involving two families of ellipses to derive the optimal solution. Analytical insights are supported by numerical simulations to design optimized transmission profiles to enhance the resilience toward ISI. The proposed transmission shaping method is evaluated through symbol error rate (SER). These results mark a significant step forward in developing robust and efficient MC systems, opening doors to advanced applications in bio-inspired and nano-scale communication technologies.