Page 50 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 3 – Internet of Bio-Nano Things for health applications

ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 3

Table 1 – List of Acronyms
Acronym Description Acronym Description
Ach Acetylcholine MAP Maximum A posteriori Probability
ANN Arti icial Neural Network MC Molecular Communication
ASK Amplitude Shift Keying MF Matched Filter
AWGN Additive White Gaussian Noise MIMO Multiple‑Input Multiple‑Output
BA Biological Agent MISO Multiple‑Input Single‑Output
BCDA Block Coordinate Descent Algorithm MMC Mobile Molecular Communication
BCZ Bovine serum albumin protein conjugated ZnO MMSE Minimum Mean Square Error
nano‑sphere
BER Bit Error Rate MoSK Molecule Shift Keying
BFGS Broyden‑Fletcher‑Goldfarb‑Shanno mRNA Messenger Ribonucleic acid
BSA Bovine Serum Albumin MSSK Molecular Space Shift Keying
CDF Cumulative Distribution Function MTSK Molecule Transition Shift Keying
CIR Channel Impulse Response NP Neyman‑Pearson
CNT Carbon Nano‑Tube OOK On‑Off keying
CSI Channel State Information OSK Order Shift Keying
CSK Concentration Shift Keying PAM Pulse Amplitude Modulation
CTAT Count‑To‑A‑Threshold PDF Probability Density Function
CTMP Continuous Time Markov Process PDMS Polydimethylsiloxane
DFE Decision Feedback Equalizer PMF Probability Mass Function
DFF Decision Feedback Filter PNN Probabilistic Neural Network
DNA Deoxyribonucleic acid PPM Pulse Position Modulation
ECF Extracellular Fluid PSO Particle Swarm Optimization
ECS Extracellular Space QMSSK Quadrature Molecular Space Shift Keying
EGFETs Electrolyte‑Gated Field‑Effect Transistors RF LC Radio Frequency inductance capacitance
FC Fusion Center RKHS Reproducing Kernel Hilbert Space
FCM Fuzzy C‑mean RNN Recurrent Neural Network
FET Field‑Effect Transistor RS Reed‑Solomon
FRET Forster Resonance Energy Transfer RTSK Release Time Shift Keying
G‑LOD Generalized‑Local Optimum Detector SBRNN Sliding Bidirectional Recurrent Neural Network
G‑LRT Generalized‑Likelihood Ratio Test SEP Symbol Error Probability
HCl Hydrochloric acid SIMO Single‑Input Multiple‑Output
IG Inverse Gaussian SINR Signal‑to‑Interference plus Noise Ratio
ILI Inter‑Link Interference Si‑NW Silicon‑Nano Wire
IoBNT Internet‑of‑Bio‑Nano Things SISO Single‑Input Single‑Output
ISI Inter‑Symbol Interference SNR Signal‑to‑Noise Ratio
LED Light Emitting Diode SPIONs Super‑Paramagnetic Iron Oxide Nanoparticles
LLR Log‑Likelihood Ratio SVM Support Vector Machine
LM Levenberg‑Marquardt SWCNT Single‑Walled Carbon Nano‑Tube
LMS Least Mean Square TDD Targeted Drug Delivery
LOC Lab‑on‑a‑Chip TS Takagi‑Sugeno
LSTM Long Short Term Memory ZF Zero Forcing

2. TRANSMISSION AND DETECTION WITH suboptimal detection schemes were derived by maximiz‑
1
STATIC NANO‑MACHINES ing the mutual information between transmitted and re‑
ceived symbols in the presence of ISI. In this work, per‑
2.1 Static nano‑machines in pure diffusive fect synchronization between the transmitter and the re‑
channel ceiver is assumed to simplify the analysis. In contrast to
OOK, the work in [33] considered a Molecule Shift Key‑
ing (MoSK modulation scheme in which different types of
2.1.1 Single transmitter and single receiver‑ molecules are sent to transmit different symbols. In this
based MC systems work, a linear and time‑invariant model for signal prop‑
agation is assumed and the noise is modeled as the ad‑
In [32], authors employed an On‑Off Keying (OOK) mod‑ ditive, uncorrelated, and non‑stationary random variable
ulation scheme at the transmitter where a ixed number with zero mean and variance dependent on the magnitude
of molecules were transmitted for bit‑1 and no molecules of the signal.
were transmitted for bit‑0. For this setup, optimal and 1 In contrast to suboptimal detection scheme, optimal detection scheme
requires a priori probability.

38 © International Telecommunication Union, 2021