Multi-view Semantic-aware Resource Allocation for Edge AI

Jiawei Luo; Hussein Saleh; Mohamad Assaad

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arXiv · 2610.34532

Multi-view Semantic-aware Resource Allocation for Edge AI

Jiawei Luo·Hussein Saleh·Mohamad Assaad

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Abstract

Semantic communication is emerging as a pivotal paradigm to overcome wireless resource scarcity in 6G networks. This paper proposes a novel end-to-end framework for semantics-aware resource allocation in multi-user Edge AI. We extend the Opportunistic Information Bottleneck (OIB) to a multi-view setting, termed the Multi-view Gaussian Information Bottleneck (MGIB), by fusing complementary source views without variational approximation or network retraining. A resource allocation framework is then formulated for joint compression-level selection, transmission mode selection (i.e., the number of active sources per group), and PRB assignment. The problem is formulated as a Mixed-Integer Linear Program (MILP), solved efficiently via a proposed Lagrangian Relaxation (LR) algorithm, and benchmarked against the NP-hard optimal solution. Simulations demonstrate that the proposed LR scheme achieves near-optimal utility, and doubles PRB efficiency versus average allocation.

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BibTeX RIS

Jiawei Luo, Hussein Saleh, Mohamad Assaad. 2026-10-25. Multi-view Semantic-aware Resource Allocation for Edge AI. https://arxiv.org/abs/2610.34532

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