In this work, we study the impact of THz signaling on controlling protein conformational changes. Specifically, due to the imposed variability of the intra-body medium on signal propagation, we study the effect of the channel randomness on THz-induced protein interactions. To do so, we demonstrate how the impinging nanoantenna force affects the energy absorbed by the protein structure. We specifically focus on analyzing the impact of the slowly varying force on the capability to control the dynamics of the desired protein population. We later introduce the probability of selectivity outage as a metric that is calculated based on a pre-defined selectivity threshold. Our results indicate that a trade-off must exist in the system since the higher the selectivity threshold, the higher the probability of selectivity outage, which increases the constraints imposed on the intra-body system design. The presented work provides a better understanding and characterization of the electromagnetically triggered protein molecules, their micro-environment, and their interaction with surrounding particles.

In this paper, we propose a theoretical intra-body propagation model for signal transmission in the THz frequency band. The channel of interest is a blood vessel composed of red blood cells (RBCs), where propagation occurs between a nanoantenna transmitter and a protein receiver. The presented model accounts for signal losses due to molecular absorption and scattering through both line-of-sight (LOS) and non-line-of-sight (NLOS) links. The RBCs between the antenna and the protein act as obstacles that attenuate the signal power giving rise to shadowing. By conducting Monte Carlo simulations, we develop the random characteristics of the transmission medium. Inspired by radar systems, an expression for the received power is derived using the bistatic radar model and the total path loss is computed through the different links. The results are validated by means of electromagnetic wave propagation simulations. The presented work indicates that a reliable communication link exists between the nanoantenna and the protein through both the LOS and NLOS transmission. Our work facilitates the accurate design of in-vivo wireless nanosensor networks and paves the path towards selective intra-body interactions.

It has been established that interfacing Terahertz (THz) band signals with protein molecules excites their resonant modes. In this work, we develop a model that bridges the mechanical system of proteins, modeled as harmonic oscillators, and the probability of protein conformal changes. We deploy the Langevin stochastic equation under the influence of an external force to capture the protein dynamics. The average energy driving the protein to alter its conformation is derived and used to determine the probability of protein folding. Our numerical analysis results show that the energy transferred from the nanoantenna and stored in the protein is capable of inducing a conformal change in the protein. This illustrates the power of THz waves in enabling protein interactions with high selectivity. It also sheds light on various opportunities that impact applications concerning targeted therapy, biosensing as well as disease control and prevention.

Nanosized devices operating inside the human body open up new prospects in the healthcare domain. On the one hand, molecular communication enables biological nanomachines to communicate by exchanging molecules and performing application-dependent tasks. On the other hand, electromagnetic (EM) nano-communication points to the Terahertz Band (0.1-10 THz) as the frequency range for communication among nano-biosensors. In this paper, we propose a stimuli-responsive paradigm which integrates EM and molecular communication by stimulating proteins in the human body. Our model capitalizes on the fact that proteins act as an interface between both mediums, in which triggering proteins by THz waves changes their conformational structure. This allows biochemical and biomechanical activities to be carried out in a controlled manner. The stochasticity involved in the folding and unfolding of proteins is modeled using a Markov chain. A closed form expression for the mutual information rate by which proteins receive information is derived and maximized to find the capacity. By illustrating the information rates theoretically achievable, we hope to spark research into the EM-based control of protein networks.

In order to exploit the inherent sparsity of an OFDM multipath wireless channel, this paper presents a novel estimation technique based on the ℓ1-LS criterion that relies on the selection of the optimal regularization parameter. This term is proven in the paper to be intimately related to the Signal to Noise Ratio (SNR) and Sparsity Degree (ρ) of the channel. Therefore, in order to take full advantage of the ℓ1-LS framework, an ad-hoc technique for estimating blindly the SNR and ρ of the problem is also introduced. The performance of the novel algorithm is compared to a number of reference techniques. Two standard measures, namely the Mean Square Error (MSE) of the estimated channel and the Symbol Error Rate (SER) of the OFDM system validate the robustness of the proposed algorithm. Indeed, this novel technique achieves better estimation along with better spectrum efficiency.

Embedding a sparse penalty in conventional Least Mean Square (LMS) adaptive algorithms is an established strategy to enhance the performance and robustness against noise in the estimation of sparse plants, such as wireless mul-tipath channels. In this paper we review the most prominent NLMS-based algorithms with ℓp-norm constraint, discussing the underlying mechanisms that lead to improvement gains in sparse scenarios. Simulation results validate the analysis and comparative discussion. Given that adaptive algorithms operating in time domain deteriorate with correlated signals, we propose hereby a novel frequency-domain (FD) ℓp-NLMS that performs in such situations. Simulation results indicate that the proposed method outperforms its time-domain counterparts not only in convergence rate but more importantly in residual misalignment. This important result has not been echoed so far.