Demonstrates reduced energy consumption and maintained performance in compute-intensive applications using a learning-based AP clustering strategy in dynamic environments.
User-centric cell-free massive MIMO is a key 6G technology to meet strict latency and energy-efficiency demandsof compute-intensive applications. In practical O-RAN deployments, the central unit and distributed unit compute capacityfluctuates with network-slicing dynamics. Relying on fixed compute resources and access-point (AP) clustering based solely onradio conditions leads to radio–compute mismatches, which in turn create bottlenecks that degrade both latency and energyefficiency. To address this drawback, we propose a communication and computation-aware AP-clustering strategy with jointresource allocation, leveraging a multi-agent reinforcement learning framework. The learned policies reduce average device energy consumption by 45% versus a fixed-compute RL benchmark and by 70% versus an offloading-first FPC heuristic across 10–50 Mbit/s, while maintaining 98–100% deadline satisfaction.
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Dibaji et al. (2026) studied this question.
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