CORTEXA
← Browse
arxiveess.SP2026-06-27

Macro--Micro Decision-Making in 6G Networks: An Agent-Based Framework for the Resource-Fungibility Landscape Resource-Fungibility Landscape

Sayanti Ghosh, Indrakshi Dey, Nicola Marchetti

A defining feature of 6G networks is that performance depends not only on the quantity of available resources (e.g., spectrum, antennas, cache memory, compute, and fronthaul bandwidth) but also on their \emph{fungibility}, i.e., the ability of one resource to substitute for another under changing conditions. We argue that the fungibility landscape of a distributed 6G system is governed by two coupled decision scales: \emph{micro} decisions made locally by agents and \emph{macro} outcomes that emerge at the network level. Existing distributed-optimization approaches largely conflate these scales. To address this gap, we develop an agent-based-modeling (ABM) framework that separates macro and micro decisions through three operator-controllable macro choices, three micro hyperparameters, and three structural metrics. We establish six key results: (i) a two-timescale decomposition theorem, (ii) a structural-metric basis theorem, (iii) a macro--micro design rule with closed-form factorization of the emergent breakdown threshold, (iv) a fungibility--resilience monotonicity proposition, (v) a connectivity--substitutability duality theorem, and (vi) a multi-application generalization proposition. Numerical results visualize the macro fungibility landscape and the micro decision-sensitivity region for a representative 6G deployment.

View free PDFSource page

Related papers

arxiveess.SP2026-06-30

Towards a Joint Task-Oriented and Generative Semantic Communication Framework for 6G Networks

Soheyb Ribouh, Phil Polo Ditsia Di Ngoma

Semantic Communication (SC) has emerged as a key enabler for 6G wireless systems by transmitting task-relevant meaning rather than raw data, thereby significantly reducing bandwidth consumption while preserving communication intent. In this work, we propose an end-to-end OFDM-bas…

View free PDFSource page
arxiveess.SP2026-07-19

Inverse-Reinforcement Learning Enabled Digital Twin for Intent-based Drone Networks

Jiahao Wang, Ruimin Yang, Hanzhi Yu, Huaiyu Dai, Ye Hu

In this paper, the problem of the trajectory design for an intent-based drone operating in resource-constrained, dynamic wireless network environments is studied. In the considered model, the drone acts as a supplementary base station that navigates among ground user clusters to…

View free PDFSource page
arxiveess.SPphysics.optics2026-06-25

Low Complexity Kolmogorov-Arnold Network-based DPD for Analog RoF Fronthaul

Carlos Daniel Fontes da Silva, Tianyu Jiang, Lu Zhang, Vjaceslavs Bobrovs, Xianbin Yu, Xiaodan Pang, et al.

This paper proposes and demonstrates experimentally for the first time a Kolmogorov-Arnold Network (KAN)-based digital predistortion (DPD) model, named envelope time-delay KAN (ETDKAN), for mitigating nonlinear distortions in analog radio-over-fiber (A-RoF) systems. The ETDKAN mo…

View free PDFSource page
arxiveess.SP2026-07-22

WARA: A Closed-Loop Multi-Agent Framework for Wireless Optimization Autoresearch

Yuan Guo, Yilong Chen, Chao Hu, Xianghao Yu, Liang Hong, Jie Xu

Large language model (LLM) agents have shown growing capabilities in tool use, code execution, artifact inspection, and iterative revision, creating new opportunities for automating scientific research. To the best of our knowledge, this paper presents the first end-to-end autore…

View free PDFSource page
arxiveess.SP2026-07-14

LiTCom: A Lightweight Transmitter and Inference-Capable Receiver Framework for 6G Uplink

Chunmei Xu, Siqi Zhang, Zhi Ding, Yi Ma, Rahim Tafazolli

This paper introduces LiTCom, a lightweight transmitter and inference-capable receiver framework, designed to enable robust 6G uplink communication under low signal-to-noise (SNR) conditions. It embraces the resource asymmetry between edge devices and the network infrastructure.…

View free PDFSource page
arxiveess.SP2026-07-01

B2X Networks: Joint Design of Communication and Control for Embodied Intelligence

Yuanwei Liu, Xu Gan, Zhaolin Wang, Chongjun Ouyang, Hao Jiang, Zongyao Zhao, et al.

This article proposes the concept of \emph{brain-body-to-everything (B2X)} networks to facilitate the integration of wireless networks and embodied intelligence. In this framework, the \emph{brain} refers to the intelligence functions for reasoning, planning, and decision-making,…

View free PDFSource page