DBMC-aNOMAly: Asynchronous NOMA with Pilot-Symbol Optimization Protocol for Diffusion-Based Molecular Communication Networks

TL;DR

The work tackles efficient multiple access in diffusion-based molecular networks by introducing asynchronous NOMA (DBMC-NOMA) and a pilot-symbol optimization protocol (DBMC-aNOMAly). It derives a comprehensive BEP model for a KTX, single RX diffusion channel, and benchmarks against MDMA and TDMA, showing that offset-aware NOMA can match or exceed traditional schemes while avoiding worst-case synchronization configurations. The DBMC-aNOMAly protocol employs simple pilot-based thresholds, a WCAM mechanism to avoid problematic offsets, and optional molecule-count adjustments, with Monte Carlo validation demonstrating robust BEP reductions across network sizes, noise levels, and sampling jitter. The approach emphasizes CRN-implementable operations and sets the stage for future experimental validation and realistic biochemical-network implementations.

Abstract

Multiple access (MA) schemes can enable cooperation between multiple nodes in future diffusion-based molecular communication (DBMC) networks. Non-orthogonal MA for DBMC networks (DBMC-NOMA) is a promising option for efficient simultaneous MA using a single molecule type. Expanding significantly upon previous work on the topic, this paper addresses the question of parameter optimization and bit error probability (BEP) reduction in an asynchronous network using DBMC-NOMA. First, we analytically derive the associated BEP and use the result for a thorough comparison with other MA schemes like time-division and molecule-division MA. We show that the asynchronous nature of the system can be exploited for performance gain, and the upper-bound performance can be achieved in all circumstances by avoiding a few worst-case offset configurations. Subsequently, we propose DBMC-aNOMAly, a pilot-symbol-based optimization protocol for asynchronous DBMC-NOMA, and extensively evaluate it using Monte-Carlo simulations. DBMC-aNOMAly is shown to provide robust BEP reduction for different network sizes, noise levels, subjected to sampling jitter, as well as for changing conditions during runtime, particularly, compared to protocols in previous work. DBMC-aNOMAly consists of a set of simple operations such as comparisons and additions, deliberately designed to be implementable with chemical reaction networks, setting up future work on the realistic modeling of the protocol.

Figures (17)

• Figure 1: Schematic representation of the DBMC-aNOMAly protocol, including NOMA transmission from many TX to a single RX, the pilot-symbol-based parameter optimization scheme, as well as the beacon sent out by the RX to avoid worst-case offset scenarios.
• Figure 2: DBMC scenario with K point TX at distances $d_1$, $d_2$ ... $d_K$ from a spherical RX.
• Figure 3: Comparison of resource allocation for different MA schemes. The triangle and circle shape signify different molecule types, the color is associated with different TX, and the horizontal placement in the time slots shows the allocation of transmissions over time.
• Figure 4: SIC implementation strategies: the diagram on the left shows the classical description of a SIC procedure, in which knowledge of the individual channel impulse response (CIR) is used to subtract the respective contribution from TX$_i$, $\lambda_i$, from the sampled signal $n_\mathrm{s}$. On the right, the equivalent threshold-adaptation-based version is shown, which uses a set of thresholds $\tau_i^{\hat{s}_{i-1}}$ for every TX resulting in a binary-tree-like structure, in which the same sample is compared many times.
• Figure 5: Analytical BEP, $P_{\mathrm{e},i}$ over the detection thresholds on the $x$-axis, for a DBMC-NOMA system with $K=2$TX. MCS results superimposed for validation. We show $P_\mathrm{e,1}(\tau_1)$, which can be expressed independent of TX$_2$. Then, for optimal choice $\tau_1^*$, we depict $P_\mathrm{e,2}(\tau_1^*, \tau_2^0)$. Numbers of emitted molecules are set to $N_\mathrm{TX,1} = 10^6, N_\mathrm{TX,2}\approx 0.5\cdot 10^6$. All other parameters according to Table \ref{['tab:analytical_parameters']}.