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Research PaperResearchia:202606.24036

Degeneracy-Aware Resilient Resource Allocation in Cell-Free Cache-Aided MU-MIMO Networks

Sayanti Ghosh

Abstract

Cell-free cache-aided multi-user multiple-input-multiple-output (MIMO) (CF-CA-MU-MIMO) networks improve spectral efficiency through coded multicast delivery and distributed spatial multiplexing, but their distributed architecture introduces vulnerabilities to jamming, cache-aware eavesdropping, Byzantine corruption, and pilot-contamination attacks. This paper develops a degeneracy-aware resilient framework based on four vulnerability-mode partitions (subfile, edge node, multicast stream, and use...

Submitted: June 24, 2026Subjects: Engineering; Chemical Engineering

Description / Details

Cell-free cache-aided multi-user multiple-input-multiple-output (MIMO) (CF-CA-MU-MIMO) networks improve spectral efficiency through coded multicast delivery and distributed spatial multiplexing, but their distributed architecture introduces vulnerabilities to jamming, cache-aware eavesdropping, Byzantine corruption, and pilot-contamination attacks. This paper develops a degeneracy-aware resilient framework based on four vulnerability-mode partitions (subfile, edge node, multicast stream, and user) and three attack-aware structural metrics: Degeneracy-Weighted Path Robustness (DWPRatt^{\mathrm{att}}), trust-aware Functional Substitution Score (FSStrust^{\mathrm{trust}}), and a robust degeneracy index (DkrobD_k^{\mathrm{rob}}). These metrics are incorporated into a fully decentralized consensus-based agent framework (DC-ABM) using trust-weighted trimmed-mean aggregation and adaptive trust evolution. Five theoretical results are established: (i) a tight top-mass concentration lemma, (ii) matching memory--rate--resilience achievability and converse bounds, (iii) a robust-degeneracy bound with outage characterization, (iv) a secrecy--cache coupling theorem, and (v) a Byzantine-robust mean-square convergence result with an explicit breakdown threshold fmax⁑f_{\max}. Simulations validate the analytical bounds and demonstrate 1.8Γ—1.8\times to 3Γ—3\times faster convergence than distributed alternating direction method of multipliers (ADMM), multi-agent reinforcement learning (MARL)/graph neural network (GNN)-based control, and Su--Vaidya consensus while maintaining throughput up to the predicted threshold fmaxβ‘β‰ˆ0.19f_{\max}\approx0.19.


Source: arXiv:2606.24611v1 - http://arxiv.org/abs/2606.24611v1 PDF: https://arxiv.org/pdf/2606.24611v1 Original Link: http://arxiv.org/abs/2606.24611v1

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Submission Info
Date:
Jun 24, 2026
Topic:
Chemical Engineering
Area:
Engineering
Comments:
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