QCell: Self-optimization of Softwarized 5G Networks through Deep Q-learning

Abstract

With the unprecedented rise in traffic demand and mobile subscribers, real-time fine-grained optimization frame-works are crucial for the future of cellular networks. Indeed, rigid and inflexible infrastructures are incapable of adapting to the massive amounts of data forecast for 5G networks. Network softwarization, i.e., the approach of controlling “everything” via software, endows the network with unprecedented flexibility, al-lowing it to run optimization and machine learning-based frame-works for flexible adaptation to current network conditions and traffic demand. This work presents QCell, a Deep Q-Network-based optimization framework for softwarized cellular networks. QCell dynamically allocates slicing and scheduling resources to the network base stations adapting to varying interference con-ditions and traffic patterns. QCell is prototyped on Colosseum, the world's largest network emulator, and tested in a variety of network conditions and scenarios. Our experimental results show that using QCell significantly improves user's throughput (up to 37.6%) and the size of transmission queues (up to 11.9%), decreasing service latency.