The 5G networks are broadly characterized by three unique features: ubiquitous connectivity, extremely low latency, and extraordinary high-speed data transfer. The challenge of 5G is to assure the network performance and different quality of service (QoS) requirements of different services, such as machine type communication (MTC), enhanced mobile broad band (eMBB), and ultra-reliable low latency communications (URLLC) over 5G networks. Unlike the previous ''one size fits all'' system, the softwarization, slicing and network capability exposure of 5G provide dynamic programming capabilities for QoS assurance. With the increasing complexity and dynamics of the network behaviors, it is non-trivial for a programmer to develop traditional software codes to schedule the network resources based on expert knowledge, especially when there is no quantitative relationship among the network events and the QoS anomalies. Machine learning is a computer technology that gives computer systems the ability to learn with data and improve performance and accuracy of decision making on a specific task, without being explicitly programmed. The areas of machine learning and communication technology are converging. Supervised learning based QoS assurance architecture for 5G networks was proposed in this paper. The supervised machine learning mechanisms can intelligently learn the network environment and react to dynamic situations. They can learn from the fore passed QoS related information and anomalies, and further reconstruct the relationship between the fore passed data and the current QoS related anomalies automatically and accurately. They, then, can trigger automatic mitigation or provide suggestions. The supervised machine learning mechanisms can also predict future QoS related anomalies with high confidence. In this paper, a case study for QoS anomaly root cause tracking based on decision tree was given to validate the proposed framework architecture.

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Recently, edge caching has emerged as a new data sponsoring scheme, where the devices on the edge network, e.g., 4G/5G base stations, cache video contents and directly delivery them to mobile video users. Such caching schemes have significantly decreased the backhaul congestion, reduce the delivery latency, thus attract more mobile video users and monetize a huge number of videos. In this paper, we consider a hybrid caching schemes system, which combines edge cache sponsoring (ECS) and traditional cellular data sponsoring (CDS), where the content providers display advertisements to mitigate the cellular data downloading expense and provide attractive contents for mobile video users. In the system, we investigate the cooperative scenario and the competitive scenario between the two sponsoring schemes, and a mobile video user can select one scheme or neither of them for the requests of a video. In the first scenario, we focus on achieving the maximal total benefit of content providers by optimizing CDS and ECS schemes together. In the second scenario, we separately optimize ECS and CDS schemes and maximize the benefits of corresponding content providers. To illustrate the effectiveness of the two scenarios, we formulate the system as a two-stage game: 1) in the first stage, content providers (i.e., leaders) choose the sponsor effort in sponsor schemes (cooperatively or competitively); 2) in the second stage, mobile video users (i.e., followers) select their sponsor preferences. We conduct numerical results to explore the sub-game perfect equilibrium and find that the joint utilization of the two sponsor schemes can benefit the mobile video users, i.e., when ECS and CDS compete with each other, MUs can benefit 36% ∼ 140% more than the case where there exists only one sponsor scheme. While when CPs cooperate with each other, their total payoff is maximized, the mobile video users' payoff also increases. Moreover, we find that ECS can benefit the content providers more than CDS when sponsor revenue is high, and this indicates that ECS is a promising sponsor scheme for the high-value contents. INDEX TERMS Data sponsoring, edge caching, content delivery networks.

Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS SN2 Different User Nighbor Indirect Neighbor SN1 These ids are set of ids belonging to different users These IDs are set of IDs belonging to the same user FIGURE 4. We divide the IDs in social network 1 and social network 2 into three types: Different User, Neighbor, and Indirect Neighbor by the method of linking ID.

With the growing utilization of intelligent unmanned aerial vehicle (UAV) clusters in both military and civilian domains, the routing protocol of flying ad-hoc networks (FANETs) has promised a crucial role in facilitating cluster communication. However, the highly dynamic nature of the network topology, owing to the rapid movement and changing direction of aircraft nodes, as well as frequent accesses and exits from the network, has resulted in an increased interruption rate of FANETs links. While traditional protocols can satisfy basic network service quality (QoS) requirements in mobile ad-hoc networks (MANETs) with relatively fixed topology changes, they may fail to achieve optimal routes and consequently restrict information dissemination in FANETs with topology changes, which ultimately leads to elevated packet loss and delay. This paper undertakes an in-depth investigation of the challenges faced by current routing protocols in high dynamic topology scenarios, such as delay and packet loss. It proposes a Q-learning empowered highly dynamic, and latency-aware routing algorithm for flying ad-hoc networks (QEHLR). Traditional routing algorithms are unable to effectively route packets in highly dynamic FANETs; hence, this paper employs a Q-learning method to learn the link status in the network and effectively select routes through Q-values to avoid connection loss. Additionally, the remaining time of the link or path lifespan is incorporated into the routing protocol to construct the routing table. QEHLR can delete predicted failed links based on network status, thereby reducing packet loss caused by failed route selection. Simulations show that the enhanced algorithm significantly improves the packet transmission rate, which addresses the challenge of routing protocols’ inability to adapt to various mobility scenarios in FANETs with dynamic topology by introducing a calculation factor based on the QEHLR protocol. The experimental results indicate that the improved routing algorithm achieves superior network performance.