A unified alpha-fairness scheme for clustered cell-free networking is achieved via closed-form deterministic equivalents and tight continuous relaxations that are exactly equivalent to the integer program in four cases of alpha.
Rate-constrained network decomposition for clustered cell-free networking,
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A DRL-based framework for clustered cell-free networking reduces channel estimation overhead to a single measurement per AP and adapts to user mobility, outperforming prior clustering methods in simulations across multiple objectives.
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Achieving $\alpha$-Fairness in Clustered Cell-Free Networking: A Tight Relaxation Approach
A unified alpha-fairness scheme for clustered cell-free networking is achieved via closed-form deterministic equivalents and tight continuous relaxations that are exactly equivalent to the integer program in four cases of alpha.
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Leveraging Deep Reinforcement Learning for Clustered Cell-Free Networking Over User Mobility
A DRL-based framework for clustered cell-free networking reduces channel estimation overhead to a single measurement per AP and adapts to user mobility, outperforming prior clustering methods in simulations across multiple objectives.