Collaborative spatial reuse in wireless networks via selfish multi-armed bandits
Next-generation wireless deployments are characterized by being dense and
uncoordinated, which often leads to inefficient use of resources and poor performance. To
solve this, we envision the utilization of completely decentralized mechanisms to enable
Spatial Reuse (SR). In particular, we focus on dynamic channel selection and Transmission
Power Control (TPC). We rely on Reinforcement Learning (RL), and more specifically on
Multi-Armed Bandits (MABs), to allow networks to learn their best configuration. In this work …
uncoordinated, which often leads to inefficient use of resources and poor performance. To
solve this, we envision the utilization of completely decentralized mechanisms to enable
Spatial Reuse (SR). In particular, we focus on dynamic channel selection and Transmission
Power Control (TPC). We rely on Reinforcement Learning (RL), and more specifically on
Multi-Armed Bandits (MABs), to allow networks to learn their best configuration. In this work …
[引用][C] Collaborative spatial reuse in wireless networks via selfish multi-armed bandits
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