5G ağlarda numerolojı kaynaklı kırışım minimizasyonu: Derin takviyeli öğrenme dayalı yaklaşım

The use of 5G technologies has become prevalent over the years with the increased use of mobile devices, video services, and more. At the same time, 5G technology brings lower latency, higher reliability, and higher throughput than 4G technology. creation of network slices via software-defined networks and network virtualization functions enables these improvements. A network slice provides flexibility to the network. Each slice can be dynamically configured within itself via SDN/NVF. The network meets diverse requirements of diverse services by creating a network slice. At the higher level, NVF is responsible for creating and managing network slices. The reflection of a network slice on the physical layer is RAN slicing. With the nu- merology concept merging with 5G, the radio spectrum and corresponding resources become configurative in a manner of bandwidth, sub-carrier spacing, etc. However, the numerology solution has one drawback. Changing sub-carrier spacing in the resource grid destroys the orthogonality principle in the traditional OFDM signals. This leads the network to face new interference, inter-numerology interference. The solutions that enable these improvements have also brought optimization problems that we have not faced In 4G networks. The main problem is about opti- mization since 5G technology requires heterogeneous signals for heterogeneous services. Therefore, the optimal allocation of limited resources in order to prevent this problem is the essential aim of this paper, with the minimization of inter-numerology interference and maximizing channel capacity.