To initiate mobile communication, user devices must undergo authentication, authorization, and session setup, all handled by the mobile core network (MCN). Thus, an MCN outage can critically disrupt these procedures and degrade the user quality of experience (QoE). To address this, we propose the soft guarantee MCN (SG-MCN), an architecture inspired by the soft guarantee behavior from three-tier web systems. This technique achieves quick responses by bypassing bottleneck-prone datatier processing and utilizing information from the application and presentation tiers. SG-MCN adopts this behavior to enhance resiliency while maintaining compliance with 3GPP security standards. Specifically, it verifies devices in a presentation-tierequivalent function using a device-side-encrypted identifier and a precomputed identifier from the data tier. Mutual authentication is then performed using a precomputed authentication vector. Authorization and session setup are speculatively performed using enriched per-device contexts retrieved in previous procedures. Emulation results demonstrate that the SG-MCN sustains QoE scores close to those of the MCN under normal conditions, even in normal conditions with additional processing overhead and in outage conditions where data-tier functions are unavailable.

Reliable Moon-Earth communication is essential for long-term human presence on the Moon. Ensuring reliable communication requires careful consideration of various factors, including the choice of communication protocols. For high-latency deep-space links, the Bundle Protocol (BP) is designed to handle disruptions. At the same time, the Artemis program envisions IP-based wireless technologies, such as Wi-Fi and mobile networks, on the lunar surface. However, since these two approaches rely on different protocol stacks, a gateway is needed to bridge IP and BP, allowing end devices to communicate seamlessly without BP awareness. This paper presents a gateway bridging IP and BP networks and evaluates three implementations based on their termination layer. Through emulated Moon-Earth communication experiments, we show that application performance varies significantly with termination layer selection, highlighting the need for mission-specific gateway design.

In mobile networks, network performance can be severely degraded by a signaling storm, a phenomenon characterized by control plane (C-plane) congestion due to excessive signaling messages. This paper identifies congestion of inter-site links and overload of a specific NF instance as factors leading to a signaling storm. To address these problems, we propose a combined placement and routing algorithm approach. Firstly, our joint placement and routing algorithms perform geographically distributed deployment of C-plane NF instances according to the fluctuating mobility patterns of each user equipment (UE). Secondly, our routing algorithm identifies an appropriate C-plane NF instance to manage each UE, considering both the utilization rate and the number of contexts possessed by each NF instance. Extensive simulation evaluations demonstrate that our proposed methods significantly reduce inter-site signaling messages compared to existing placement scenarios. Furthermore, the dispersion of C-plane NF instances’ utilization rate is reduced, enhancing network performance and efficiency.

Cellular Vehicle-to-Everything (C-V2X) is attracting attention as a key enabler for advanced driving systems. The increase in data packets triggered by C-V2X will raise the risk of congestion in the mobile network and lead to the degradation of C-V2X service quality. To tackle this issue, automotive edge computing consortium (AECC) proposed an opportunistic data transfer (ODT) framework to suppress the data rate of nonpriority C-V2X services. However, the AECC proposal cannot support data rate control of each C-V2X service with consideration of variations in service requirements when services share a single packet data unit (PDU) session of a mobile network. This paper proposes an ODT framework that overcomes the limitation of the AECC proposal. The evaluation shows that the proposed framework improved the throughput of a non-priority C-V2X service, which was assumed to be service impact due to the suppression, by up to 241% compared to the AECC proposal.

With the 5th Generation mobile communication system (5G), ultrahigh capacity and ultralow latency communication are realized. Toward the next generation, i.e., 6th Generation mobile communication system (6G), even higher capacity or lower latency is needed. This paper focuses on the significant processing delay in the control plane when a User Equipment (UE) state transitions from Radio Resource Control (RRC)-IDLE to RRC-CONNECTED. On the transition, many interactions exist between a UE and a Base Station (BS) or a mobile core system, such as a connection establishment or context data exchange. We propose eliminating the effect of processing delay by introducing an Always-Connected Enablement BS (ACE-BS). We conduct experiments in an actual local 5G environment with UEs, BSs, and mobile cores, demonstrating the low latency communication by using the ACE-BS.

Widely used stream processing systems, e.g., Structured Streaming in Apache Spark, traditionally adopt static strategies for resource allocation. Despite the fact that streaming applications often involve variable loads and long run time, static strategies allocate a fixed amount of resources to meet the traffic peak, which leads to a severe inefficiency. Dynamic resource provisioning, which manipulates the number of Executors at run time, provides a great opportunity to improve resource utilization and to limit the platform cost. However, adapting the mechanism to latency-sensitive applications is difficult, since each newly added Executor incurs more initialization overhead. The overhead is mainly caused by class loading in JVM, and is incurred every time a new Executor comes in, which may result in service level objective (SLO) violations. In this paper, we propose a new warm-up mechanism that provides a semi-isolated environment inside a cluster to conduct the warm-up on newly added Executors. The proposed component, the named Warm-up Manager, automatically creates and manages warm-up jobs by modifying jobs in production to allow the new Executor to load the necessary classes. The mechanism aims to reduce the initialization overhead and to enable latency-sensitive streaming applications to apply dynamic strategies. We implemented a prototype on Structured Streaming, and the evaluation found that the warm-up overhead imposed on the task was reduced to 4% in the best case scenario.

Group mobility in mobile networks is responsible for dynamic changes in user accesses to base stations, which eventually lead to degradation of network quality of service (QoS). In particular, the rapid movement of a dense group of users intensively accessing the network, such as passengers on a train passing through a densely populated area, significantly affects the perceived network QoS. For better design and operation of mobile network facilities and functions in response to this issue, monitoring group mobility and modeling the access patterns in group mobility scenarios are essential. In this paper, we focus on fast and dense group mobility and mobile network signaling data (control-plane data), which contains information related to mobility and connectivity. Firstly, we develop a lightweight method of group mobility detection to extract train passengers from all users' signaling data without relying on precise location information about users, e.g., based on GPS. Secondly, based on the same signaling data and the results obtained by the detection method, we build connected/idle duration models for train users and non-train users. Finally, we leverage these models in mobile network simulations to assess the effectiveness of a dynamic base station switching/orientation scheme to mitigate QoS degradation with low power consumption in a group mobility scenario. The obtained models reveal that train users consume 3.5 times more resources than non-train users, which proves that group mobility has a significant effect on mobile networks. The simulation results show that the dynamic scheme of base station improves users' perceived throughput, latency and jitter with small amount of additional power consumption in case of a moderate number of train users, but its ineffectiveness with larger number of train users is also shown. This would suggest that group mobility detection and the obtained connection/idle duration models based solely on control-plane data analytics are usable and useful for the development of mobility-aware functions in base stations.

In order to continue to provide high quality communication services, mobile network operators want to detect the degradation of the communication quality when it occurs. Since actively evaluating LTE network performance is costly and time consuming, mobile network operators passively monitor the network. A finite state machine is a powerful approach for monitoring the network condition. However, in actual LTE network, it is not sufficient to validate the conformance between the specification machine and the implementation machine. Therefore, this paper proposes a LTE network monitoring method using statistics of state transition. In the proposed method, we first define the states according to 3GPP standards, then, we measure the state transition by referring to LTE C-Plane messages. Also, we measure the state transition probability and time interval for the transition. Finally we check the statistics of the state transition to detect the degradation of the communication quality. In order to evaluate the proposed method, we introduce an example of detecting ping-pong handovers using actual traffic data. We show the potential usefulness of the proposed method for practical use.

Group mobility in mobile networks is responsible for dynamic changes of network utilization as well as concentrated accesses to base stations that eventually lead to a degradation of network quality. In particular, a fast-moving group of users intensively accessing the network, such as passengers on a train running in a densely populated area, affects the perceived network quality. Efficiently evaluating and monitoring the performance of the network for these specific users is thus crucial. In this paper, we focus on the analysis of network signaling data, which contains information related to mobility and connectivity. We propose a practical and automated group movement detection method to detect train passengers without relying on the location of users and build network utilization models for train users and non-train users. The models reveal that train users consume 3.5 times more resources than non-train users, which proves that group mobility has a significant influence on mobile networks.