# Online Admission Control and Rebalancing in Payment Channel Networks

arXiv: https://arxiv.org/abs/2209.11936 · HTML: https://mlsanity.com/paper/2209.11936/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2022.

## Built on by (most cited first, 1 of 1)

- [Deep Reinforcement Learning-based Rebalancing Policies for Profit Maximization of Relay Nodes in Payment Channel Networks](https://mlsanity.com/paper/2210.07302/) (2022)

## Abstract (arXiv)

Payment channel networks (PCNs) are a promising technology to improve the scalability of cryptocurrencies. PCNs, however, face the challenge that the frequent usage of certain routes may deplete channels in one direction, and hence prevent further transactions. In order to reap the full potential of PCNs, recharging and rebalancing mechanisms are required to provision channels, as well as an admission control logic to decide which transactions to reject in case capacity is insufficient. This paper presents a formal model of this optimisation problem. In particular, we consider an online algorithms perspective, where transactions arrive over time in an unpredictable manner. Our main contributions are competitive online algorithms which come with provable guarantees over time. We empirically evaluate our algorithms on randomly generated transactions to compare the average performance of our algorithms to our theoretical bounds. We also show how this model and approach differs from related problems in classic communication networks.