How to optimize pilot allocation and power for fair, delay-constrained access?
Delay-Constrained Grant-Free Random Access in MIMO Systems: Distributed Pilot Allocation and Power Control
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This paper tackles the challenge of optimizing grant-free random access (GFRA) in a wireless network, where multiple devices need to share limited communication resources fairly and efficiently.
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The researchers frame the problem as a multi-agent learning task and propose a novel solution that combines deep learning with domain knowledge from wireless communication theory. This approach allows devices to learn distributed access policies that consider factors like data urgency and channel conditions to maximize both fairness and overall network performance. Importantly, the proposed method avoids the complex exploration phase of traditional reinforcement learning, leading to faster and more efficient learning.