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Spectrum Allocation for Network Slices with Inter-Numerology Interference using Deep Reinforcement Learning
2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications
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 aggregatedoi:10.1109/pimrc48278.2020.9217107 dblp:conf/pimrc/ZambiancoV20 fatcat:3bdq554hbjgtblbjj4jecm4xwq