Machine Learningbased SON function conflict resolution With the advent of 5G, it is expected that the heterogeneity of networks will increase significantly. This is due to the coexistence of different radio access technologies, servicespecific requirements for different slices, combined with a multitude of conflicting optimization goals including, coverage, capacity, various QoS requirements, energy savings, etc. This complexity results in an explosion of the optimization space, which turns the network management into an intractable problem. In order to solve this, we introduce cognitive capabilities into the network to discover the dynamics between different SON functions (SF) and then automatically derive rules to coordinate them without explicit human modeling. This is different from previous designs of SelfOrganizing Network (SON) functions based on single objective optimization approaches, where conflicts on the best configuration arise when more than one function is acting on the same cell. A wellstudied conflict happens when Mobility Load Balancing (MLB) and Mobility Robustness Optimization (MRO) are running simultaneously with their competing goals of capacity optimization and user quality. In this paper we propose a Machine Learning (ML) driven framework to automatically derive the systemxe2x80x99s model, considering the dynamics of the selected SFs, in order to ease the optimization process handling the underlying conflicts in an automated way.

Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges. The fifth generation (5G) wireless communication networks are currently being deployed, and beyond 5G (B5G) networks are expected to be developed over the next decade. Artificial intelligence (AI) technologies and, in particular, machine learning (ML) have the potential to efficiently solve the unstructured and seemingly intractable problems by involving large amounts of data that need to be dealt with in B5G. This article studies how AI and ML can be leveraged for the design and operation of B5G networks. We first provide a comprehensive survey of recent advances and future challenges that result from bringing AIML technologies into B5G wireless networks. Our survey touches different aspects of wireless network design and optimization, including channel measurements, modeling, and estimation, physicallayer research, and network management and optimization. Then, ML algorithms and applications to B5G networks are reviewed, followed by an overview of standard developments of applying AIML algorithms to B5G networks. We conclude this study by the future challenges on applying AIML to B5G networks.