This study proposes a regression diffusion model for predicting spectrum efficiency (SE) in 5G networks, addressing limitations of traditional analytical models in dense urban environments. Using 5G user equipment (UE) measurement data collected in Seoul, we developed a model that leverages SINR (Signal-to-Interference-plus-Noise Ratio) and TBS (Transport Block Size) as input variables with classifier-free guidance and hybrid loss functions. Experimental results demonstrate improved performance with 26.37% MAPE compared to alternative methods. The model accurately captures SE distribution characteristics and temporal variations, contributing to enhanced simulation accuracy for 5G network management in dense urban areas.

While the telecommunications landscape is undergoing a significant transformation with the advent of 5G technology, the continued importance of Long Term Evolution (LTE) is also emphasized due to its widespread adoption and reliability. In this circumstance, mobile network operators must continue to uphold their obligation to ensure Quality of Service (QoS) for LTE users. The actual LTE Base Station (BS) signal measurement data can be effectively exploited in the evaluation of user-perceived QoS. In this work, we utilize Downlink Control Information (DCI) data obtained from LTE BSs through a recently developed platform. The DCI data contains downlink information experienced by all users within the cell, but to assess user-perceived performance, it is necessary to distinguish each active session. We call the grouping of DCI messages that correspond to a continuous service in one active session ‘bundling’. While previous bundling methods have mostly focused on the time gap between DCI messages, we extract features based on the LTE standard that DCI can exhibit at the start of an active session. By combining these features with a probabilistic approach , we establish criteria for implementing bundling. In addition, through the proposed bundling methodology, we analyze the active session duration, number of active sessions, cell edge user performance, etc. The results validate the effectiveness of our bundling approach.

Accurate prediction of 5G SINR (Signal-to-Interference-and-Noise Ratio) in urban environments is essential for the effective operation of 5G networks. To date, SINR prediction has predominantly been performed through channel modeling using statistical approaches. However, these methods have limitations in adequately accounting for geospatial information, such as buildings, in urban areas. In this study, we propose a deep learning model that predicts the SINR of user equipment (UE) at specific points by fully incorporating geospatial information from digital elevation model (DEM) data and 5G base station (BS) locations. The prediction results demonstrate that our model provides more accurate predictions compared to existing methods and closely aligns with actual measurements.

This poster presents a method to cluster LTE base stations based on their locations to diagnose the usage of fragmented spectrum resources. The growing demand for low-frequency spectrum in next-generation networks highlights the need to evaluate the adequacy of LTE spectrum utilization. We analyze resource block usage data from a congested area in Korea, provided monthly by mobile network operators. Density-based clustering and Voronoi diagrams are applied to divide regions and assess total spectrum utilization. Probabilistic modeling using the areas of the regions provides an indicator of spectrum utilization.

We collected channel measurements using smart phones in actual cellular environments. The measurement data contains physical cell identifier (PCI) information, but not the base station (BS) locations. Separately, the BS locations can be obtained from the spectrum information system [1] operated by the government. To match the measured PCIs with the BS locations, we generated a radio environment map (REM) based on the geographic information system (GIS). Utilizing the generated REM as a reference model, we matched the measured PCIs with the corresponding BSs. As the result, the distance between the user equipment (UE) and the BS was obtained, and the distance-dependent channel characterization became available. Finally, this paper presents the distance distribution of path loss and coverage based on the measurements.

The benefits of dedicated spectrum for Private 5G(P5G) networks are driving demand for it. Implementing dedicated spectrum requires accurate estimates of system capacity for the envisioned deployment scenario. Industrial automation applications have stringent service requirements for latency and availability. In a time division duplex system, the satisfaction of these requirements should be considered based on the link configuration. We present a novel analytical framework based on a non-stationary queuing model that carefully accounts for these features in the calculation of appropriate system capacity for P5G. We provide example calculations for some device deployment scenarios.

With rapid growth of the number of mobile smart devices, the resource usage for video streaming, as one of the mostly popular mobile applications, also increases drastically. Thus, understanding the practical resource usage in current LTE-A systems becomes more important, which may serve for the future deployment of mobile communication systems. In this paper, we first introduce a methodology to measure the practical resource usage pattern of the mobile video streaming by taking YouTube service as a representative example. Then, we analyze the data measured from the LTE-A systems in South Korea to investigate the required data rate to support high definition (HD) video streaming and its relation to spectral efficiency, signal to interference and noise ratio (SINR), etc. In addition, we also analyze the resource assignment pattern in the time domain when streaming.

In 3G-WLAN overlay network environments, a UE is apt to lose the WLAN signal without any notice by moving while it connects to WLAN because it can be apart from the WLAN coverage suddenly. In this case the on-going services are disrupted by unexpected link corruption. Hence, it needs to execute quickly the handover to 3G network as soon as it detects the lost of WLAN signal. However, as the 3G access has many network signaling operations additionally including IP mobility operations for service continuity, it requests more enough processing time for the operations, which increases the handover latency causing the service interruption. Therefore it is important to predict the time when the UE will get out of the WLAN coverage. By the handover prediction, the UE can prepare the connection to 3G network before breaking off the WLAN connection. This paper shows several handover prediction algorithms to reduce the handover latency by fast handover triggering. The correctness of the algorithms is compared by simulation.

Generally, in UMTS-WLAN overlay networks, when a UE moves from UMTS to WLAN, it can make a choice of handover to WLAN on its own initiative and break off the UMTS connection after finishing the initial WLAN access operations such as an association, a subscriber authentication, etc. Therefore, the UE can seamlessly switch to the new access system and the handover delay is not significant problem. However, when the handover is happened from WLAN to UMTS, the UE is apt to lose the WLAN signal without warning because it can suddenly stray from the WLAN coverage. As the UE cannot communicate with any systems until it accesses to a UMTS system or another WLAN system, the current session in use may be abruptly terminated if the handover is too delayed and hence the handover delay has a great effect on the seamless mobility. This paper represents a UMTS-WLAN overlay network model and describes our developed UMTS-WLAN handover testbed. We show the test results and the analysis of handover delay happened during handover from WLAN to UMTS.

During handover between heterogeneous networks such as the 3G mobile telecommunication systems and the Wireless LAN systems, a subscriber authentication, the Mobile IP operations for IP mobility and an accessing to a data gateway are heavy burdens due to its time-taken operations and overheads. So they are main factors of handover delays. This paper proposes the handover strategies for reducing handover delays in the UMTS (Universal Mobile Telecommunications System) and the Wireless LAN overlay networks. For decreasing the delays, mandatory use of a fast re-authentication is applied and newly added features of one for the UMTS are introduced. And simplified MIPv6 operations and an optimized gateway approach to minimize the network access time are proposed. These proposed approaches are analyzed and evaluated with the results of an authentication test bed and a network simulation.

By mutual complemented reason of wide coverage and high data transmission, the integration and handover of SG network and WLAN is regarded as an important function for 3G beyond (B3G) mobile communication systems. The seamless mobility is one of the basic requirements for handover between these heterogeneous networks. In this paper, we present a seamless handover mechanism for UMTS and WLAN within the framework of handover architecture, IP mobility and authentication mechanism. Firstly, we review the 3GPP-specific integration architecture of UMTS and WLAN, WLAN UE-initiated tunnel mechanism, IP mobility and authentication mechanisms of both networks. Then we propose the configuration of mobile IPv6 based UMTS-WLAN overlay network and the security mechanism for reducing the handover delay. Lastly we show the operation of seamless handover in our proposed network model.