Spectrum Allocation for Network Slices with Inter-Numerology Interference using Deep Reinforcement Learning

Network slicing and mixed-numerology schemes are essential technologies to efficiently accommodate different services in 5G radio access networks (RAN). To fully take advantage of these techniques, the design of spectrum slicing policies needs to account for the limited availability of the radio resources as well as the inter-numerology interference generated by slices employing different numerologies. In this context, we formulate a binary non-convex problem that maximizes the aggregate capacity of multiple network slices. The resulting spectrum allocation minimizes the inter-numerology interference under the frequent channel fluctuations characterizing the various users. To address the computational complexity of the designed objective function, we leverage deep reinforcement learning (DRL) to design a model-free solution computation. In detail, the trained centralized DRL agent exploits the channel fading statistic in order to provide a spectrum allocation that minimizes the inter-numerology interference. Results reveal that the proposed DRL scheme achieves performance that is comparable to the optimal one. It also outperforms a baseline scheme that statically allocate the radio resources.