Flexure-FET-Based Receiver with Competitive Binding for Interference Mitigation in Molecular Communication

TL;DR

The paper tackles interference in molecular communication by integrating a competitive binding framework into a Flexure-FET receiver. It develops a biorecognition model with competing ligands for a shared receptor, governed by dissociation constants $K_j$, leading to an equilibrium condition and an iterative solver. It couples this with a transduction model for the Flexure-FET, deriving binding noise and flicker noise contributions and two SNR definitions, $SNR_1$ (intrinsic noise only) and $SNR_2$ (interferer noise included), and analyzes SEP under 1-bit and 2-bit WSK. The results show that accounting for competitive binding improves detection reliability in high-interference environments and offers tuning knobs in ligand concentrations and receptor affinities, with potential extensions to odor-based MC for IoE and healthcare applications.

Abstract

Molecular communication (MC), a biologically inspired technology, enables applications in nanonetworks and the Internet of Everything (IoE), with great potential for intra-body systems such as drug delivery, health monitoring, and disease detection. This paper extends our prior work on the Flexure-FET MC receiver by integrating a competitive binding model to enhance performance in high-interference environments, where multiple molecular species coexist in the reception space. Previous studies have largely focused on ligand concentration estimation and detection, without fully addressing the effects of inter-species competition for receptor binding. Our proposed framework captures this competition, offering a more biologically accurate model for multitarget environments. By incorporating competition dynamics, the model improves understanding of MC behavior under interference. This approach enables fine-tuning of receptor responses by adjusting ligand concentrations and receptor affinities, thereby optimizing the performance of the Flexure-FET MC receiver. Comprehensive analysis shows that accounting for competitive binding is crucial for improving reliability and accuracy in complex MC systems. Factors such as signal-to-noise ratio (SNR), symbol error probability (SEP), interferer concentration, and receptor dynamics are shown to significantly affect performance. The proposed framework highlights the need to manage these factors effectively. Results demonstrate that modeling interference through competitive binding offers a realistic system perspective and allows tuning of receiver response, enabling robust detection in environments with multiple coexisting species.

Figures (10)

• Figure 1: The Flexure-FET-based MC receiver, with $W$ denoting the width, $L$ the length, $H$ the microbeam's thickness, $y_d$ the dielectric thickness, and $y_0$ the air gap.
• Figure 2: Spring-mass system representation of the Flexure-FET: (a) before capture, and (b) after capture jain2012flexure.
• Figure 3: Generalized signal flow diagram of a MC-Rx kuscu2016physical.
• Figure 4: The equivalent circuit of Flexure-FET MC-Rx.
• Figure 5: Normalized Sensitivity (S) as a function of interferer molecule concentration $[L_2]_0$ for different $k_2^+$ values.