COGN-NDAN: A Semantic-Aware Cross-Layer Protocol with Neural Feedback Integration for Neuro-Symbiotic 6G Networks

Abstract

The transition towards 6G networks necessitates a paradigm shift from bit-centric transmission to semantic-aware, goal-oriented communication. Traditional protocols such as TCP/IP operate agnostically to content semantics and fail to leverage redundancy in AI-driven applications, particularly in bandwidth-constrained, latency-critical scenarios such as Neuro-Digital Brain-Computer Interfaces (BCI) and Urban Air Mobility (UAM) systems. This paper introduces COGN-NDAN (Cognitive Orchestration & Green Networking — NeuroDigital Adaptive Network), a cross-layer protocol for neuro-symbiotic 6G networks. COGN-NDAN couples two complementary mechanisms. First, a physically grounded semantic-communication layer (standard RF/electromagnetic transmission) that encodes task-specific features instead of raw bit streams, achieving 87–92% bandwidth reduction while preserving semantic fidelity ≥ 0.85. Second, a neuro-symbiotic control loop in which EEG-derived cognitive-load biomarkers (P300 amplitude, theta-beta ratio, spectral entropy) modulate network prioritization, encoder compression, and RIS beam steering. Here “neuro” denotes strictly the EEG biomarker pipeline and its QoS feedback; all RF and beamforming behavior remains conventional electromagnetics. We present a cross-layer architecture unifying Physical Layer technologies (Near-Field Beamforming, Reconfigurable Intelligent Surfaces) with a Semantic Network Layer employing neural traffic classification. Comprehensive simulation results demonstrate that COGN-NDAN outperforms baseline stacks (gRPC, MQTT, CoAP) and the DeepSC semantic-communication baseline by achieving 45% reduction in end-to-end latency, 40% improvement in energy efficiency, and superior robustness at low SNR regimes, with all gains statistically significant. We further integrate SentinelX, a cryptographic security module, to defend against adversarial semantic perturbations and injection attacks. Validation across heterogeneous traffic and hardware configurations demonstrates feasibility for deployment within the Nexus infrastructure.

Received: 16 May 2026 | Revised: 23 July 2026 | Accepted: 28 September 2026

Conflicts of Interest
The author declares that there are no conflicts of interest to this work.

Data Availability Statement
The simulation code (NS-3 modules), experimental datasets, and manuscript source are being prepared for open-source release via the repository at https://github.com/FernandoMay/cogn-ndan.

Author Contribution Statement
Fernando May Fuentes: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing–original draft, Writing–review and editing, and Visualization, Validation, Resources, Data curation, Supervision, Project administration.