Survey and taxonomy of clustering algorithms in 5G Abstract   The largescale deployment of fifth generation (5G) is expected to produce a massive amount of data with high variability due to ultradensification and the rapid increase in a heterogeneous range of applications and services (e.g., virtual reality, augmented reality, and driverless vehicles), and network devices (e.g., smart gadgets and sensors). Clustering organizes network topology by segregating nodes with similar interests or behaviors in a network into logical groups in order to achieve networklevel and clusterlevel enhancements, particularly cluster stability, load balancing, social awareness, fairness, and quality of service. Clustering has been investigated to support mobile user equipment (UE) in access networks, whereby UEs form clusters themselves and may connect to BSs. In this paper, we present a comprehensive survey of the research work of clustering schemes proposed for various scenarios in 5G networks and highlight various aspects of clustering schemes, including objectives, challenges, metrics, characteristics, performance measures. Furthermore, we present open issues of clustering in 5G.

A Distributed EnergyEfficient Resource Allocation Mechanism for Multimedia Broadband Services in 5G Ultradense Networks Trafficintensive Multimedia Broadband Services (MBS) lead to the explosive mobile traffic growth in 5G network, and Ultradense Networks (UDNs) is a potential solution for it. However, the densification of large numbers of Smallcell Base Stations (SBSs) in UDN faces huge energy consumption challenge. Therefore in this paper, we propose a distributed energyefficient resource allocation mechanism for 5G UDN meeting MBS usersxe2x80x99 QoS. First, we use Kmeans algorithm to cluster the BSs, reducing the complexity of resource allocation. Then, we propose a BSsleeping algorithm in each cluster to reduce the overall network energy consumption. Finally, we adopt an iterative transmit power optimization method in each cluster to further improve the Energy Efficiency (EE) of UDN. Simulation results show that the proposed approach can effectively reduce the energy consumption and can improve the EE of UDN by nearly 23% while guaranteeing the QoS of MBS User Equipments (UEs).