Digital Twin--Driven Adaptive Wavelet Strategy for Efficient 6G Backbone Network Telemetry

TL;DR

This paper introduces a novel MERA-inspired adaptive wavelet framework that guarantees perfect reconstruction and energy conservation, outperforming classical wavelets in 6G network telemetry compression.

Contribution

It establishes a rigorous link between MERA tensor networks and paraunitary filter banks, enabling learned wavelets with exact orthogonality for efficient 6G telemetry compression.

Findings

Learned filters outperform classical wavelets by 0.5-3.8 dB PSNR.

Framework preserves the Hurst exponent within estimation uncertainty.

Demonstrated effectiveness on MAWI backbone traces from 2020-2025.

Abstract

Classical orthogonal wavelets guarantee perfect reconstruction but rely on fixed bases optimized for polynomial smoothness, achieving suboptimal compression on signals with fractal spectral signatures. Conversely, learned methods offer adaptivity but typically enforce orthogonality via soft penalties, sacrificing structural guarantees. This work establishes a rigorous equivalence between Multiscale Entanglement Renormalization Ansatz (MERA) tensor networks and paraunitary filter banks. The resulting framework learns adaptive wavelets while enforcing exact orthogonality through manifold-constrained optimization, guaranteeing perfect reconstruction and energy conservation throughout training. Validation on Long-Range Dependent (LRD) network traffic demonstrates that learned filters outperform classical wavelets by 0.5--3.8~dB PSNR on six MAWI backbone traces (2020--2025, 314~Mbps--1.75~Gbps) while preserving the Hurst exponent within estimation uncertainty ($|\Delta H| \le 0.03$). These results establish MERA-inspired wavelets as a principled approach for telemetry compression in 6G digital twin synchronization.