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 internumerology 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.

The interference resulting from densification of access points and the coexistence of different numerologies within the same spectrum severely hinders inter-slice isolation. We propose a slice allocation policy that enforces inter-slice isolation by minimizing the inter-slice interference suffered by each virtual operator. In detail, we design a binary quadratic non-convex optimization problem that minimizes i) the interslice interference generated by interfering base stations and ii) the inter-slice interference generated by the multiplexing of spectrum slices having different numerologies. We also provide a heuristic algorithm to render the solution scalable in practical scenarios. We assess the performance of both approaches by evaluating the signal-to-interference-plus-noise ratio (SINR) associated to each slice through simulations. Results reveal that the heuristic algorithm provides a solution comparable with the optimal one on different minimization scenarios. Moreover, a considerable SINR improvement is observed with respect to a base-line scheme that does not account for inter-slice interference.

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Network slicing and mixed-numerology access schemes cover a central role to enable the flexible multi-service connectivity that characterizes 5G radio access networks (RAN). However, the interference generated by the simultaneous multiplexing of radio slices having heterogeneous subcarrier spacing can hinder the isolation of the different slices sharing the RAN and their effectiveness in meeting the application requirements. To overcome these issues, we design a radio resource allocation scheme that accounts for the inter-numerology interference and maximizes the aggregate network throughput. To overcome the computationally complexity of the optimal formulation, we leverage deep reinforcement learning (DRL) to design an agent capable of approximating the optimal solution exploiting a model-free environment formulation. We propose a multi-branch agent architecture, based on Branching Dueling Q-networks (BDQ), which ensures the agent scalability as the number of spectrum resources and network slices increases. In addition, we augment the agent learning performance by including an action mapping procedure designed to enforce the selection of feasible actions. We compare the agent performance to several benchmarks schemes. Results show that the proposed solution provides a good approximation of the optimal allocation in most scenarios.

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