Hierarchical attention-driven multi-agent reinforcement learning for resource allocation in cell-free massive MIMO with integrated sensing and communication

Author Identifier (ORCID)

Wei Ni: https://orcid.org/0000-0002-4933-594X

Abstract

Cell-Free massive MIMO (CF mMIMO), as a cornerstone for future wireless communication networks, can significantly enhance spectral efficiency, reduce energy consumption, and achieve seamless coverage. This paper investigates dynamic resource allocation in CF mMIMO systems provisioning integrated sensing and communication (ISAC) and involving multiple users and heterogeneous services. To address the rapidly varying channels, high complexity, task-dependent relevance and insufficient information sharing among access points (APs) arising from ISAC requirements, we propose a novel Hierarchical Attention-Driven Multi-Agent Reinforcement Learning (HAD-MARL) framework. A higher-layer “Commander Controller” outputs the AP-user pairing matrix, which is passed to a lower-layer “Worker Controller” that leverages the central attention mechanism to perform fine-grained resource allocation. Simulations demonstrate that HAD-MARL achieves about a 10% improvement in system utility score compared to benchmarks, with the performance gains becoming more pronounced as the number of APs and users increases. We empirically demonstrate and visualize the effect of the central attention mechanism on the formation of efficient collaboration among APs.

Keywords

Cell-free massive MIMO, central attention mechanism, hierarchical reinforcement learning, integrated sensing and communication (ISAC), resource allocation

Document Type

Journal Article

Date of Publication

1-1-2026

Volume

12

Publication Title

IEEE Transactions on Cognitive Communications and Networking

Publisher

IEEE

School

School of Engineering

Funders

National Natural Science Foundation of China (42171404, 62271352) / Shanghai Engineering Research Center for Blockchain Applications And Services (19DZ2255100) / Fundamental Research Funds for the Central Universities (22120250094)

Comments

Jiang, M., Liu, E., Ni, W., Wang, R., Hu, S., Xia, L., Geng, Y., Niyato, D., & Jamalipour, A. (2026). Hierarchical attention-driven multi-agent reinforcement learning for resource allocation in cell-free massive MIMO with integrated sensing and communication. IEEE Transactions on Cognitive Communications and Networking, 12, 6955–6969. https://doi.org/10.1109/TCCN.2026.3676012

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First Page

6955

Last Page

6969

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Link to publisher version (DOI)

10.1109/TCCN.2026.3676012