Closed-form congestion control via deep symbolic regression

arXiv:2405.01435v1 Announce Type: cross
Abstract: As mobile networks embrace the 5G era, the interest in adopting Reinforcement Learning (RL) algorithms to handle challenges in ultra-low-latency and high throughput scenarios increases. Simultaneously, the advent of packetized fronthaul networks imposes demanding requirements that traditional congestion control mechanisms cannot accomplish, highlighting the potential of RL-based congestion control algorithms. Although learning RL policies optimized for satisfying the stringent fronthaul requirements is feasible, the adoption of neural network models in real deployments still poses some …

abstract algorithms arxiv challenges congestion control cs.lg cs.ni form highlighting latency low mobile networks regression reinforcement reinforcement learning requirements type via