Channel Estimation Techniques for Diffusion-Based Molecular Communications

TL;DR

This paper introduces a training-based framework for estimating the channel impulse response in molecular communication systems, deriving ML and LSSE estimators, analyzing their performance against the CR bound, and designing optimal training sequences.

Contribution

It presents the first comprehensive ML and LSSE estimators for CIR in molecular communications, along with CR bound analysis and training sequence design.

Findings

ML and LSSE estimators perform close to the CR bound

Optimal training sequences improve estimation accuracy

Simulation results validate theoretical analysis

Abstract

In molecular communication (MC) systems, the expected number of molecules observed at the receiver over time after the instantaneous release of molecules by the transmitter is referred to as the channel impulse response (CIR). Knowledge of the CIR is needed for the design of detection and equalization schemes. In this paper, we present a training-based CIR estimation framework for MC systems which aims at estimating the CIR based on the observed number of molecules at the receiver due to emission of a sequence of known numbers of molecules by the transmitter. In particular, we derive maximum likelihood (ML) and least sum of square errors (LSSE) estimators. We also study the Cramer Rao (CR) lower bound and training sequence design for the considered system. Simulation results confirm the analysis and compare the performance of the proposed estimation techniques with the CR lower bound.