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.

Multi-radio access technology (multi-RAT) enabled mobile edge computing (MEC) has emerged as a promising paradigm for supporting heterogeneous applications. However, efficiently managing resources for both ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services in large-scale networks remains challenging. In this paper, we investigate a joint optimization problem involving user association and bandwidth allocation in multi-RAT-enabled MEC systems. We propose a novel cooperative evolutionary framework operated based on the interplay between inner and outer agents to efficiently optimize large-scale networks. Extensive simulation results demonstrate that the proposed approach significantly outperforms the conventional single-RAT MEC system and several representative evolutionary computation algorithms.

Multi-radio access technology (multi-RAT) enabled mobile edge computing (MEC) has emerged as a promising paradigm for supporting diverse applications with heterogeneous service requirements. However, efficiently managing resources to accommodate both ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services remains challenging, especially in large-scale networks. In this paper, we investigate a joint optimization problem involving user association, task offloading, power and bandwidth allocation, and scheduling policies within a multi-RAT-enabled MEC system to efficiently address the heterogeneous demands of URLLC and eMBB services. We first formulate a generalized optimization problem and mathematically derive optimal power and task offloading strategies to reduce the search space. We then propose improved scheduling algorithms that sequentially update scheduling decisions based on arrival times at the edge server. Furthermore, we develop a matrix-based cooperative evolutionary computation framework with inner and outer agents to efficiently handle the large-scale optimization problem. Extensive simulation results demonstrate that our proposed approach significantly outperforms conventional scheduling methods and representative evolutionary algorithms.

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.

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.

Fast Mobile IPv6 has been proposed to reduce latency and packet loss inherent to the handover process. However the previous research did not consider the packet loss according to the tunnel management for Fast Mobile IPv6. In the procedure of binding updates and fast binding updates, a mobile node has to establish new tunnels after receiving binding acknowledgement messages. While completing the tunnel creation and routing update, the mobile node can not handle packets incoming and outgoing through the new tunnels. This drawback causes packet loss of impeding the seamless handover. To reduce this packet loss, we have developed an effective tunnel management scheme called Dual Mode Tunneling (DMT). It employs decapsulation-only tunnels before using typical tunnels which can do an encapsulation and decapsulation. Through implementation and experimental study for vertical handover between 3G long term evolution and wireless local area network, we have evaluated the performance of the proposed scheme.

Mobile IPv6 (MIPv6) used to support IP mobility in heterogeneous network such as the universal mobile telecommunications system (or long term evolution system) and 802.11 wireless local area networks (WLANs). But MIPv6 has well-known packet loss during movement detection and binding update, but it has another packet loss while creating new tunnel and updating routing path. That is, when the home agent sends a binding acknowledgement (BA) message to mobile node after receiving a binding update (BU) message, the new tunneled packets are also sent to MN with the BA message. After receiving the BA message, the MN creates new tunnel and modifies routing information. But while completing these procedures, the MN can not handle new tunneled packets incoming together the BA message. To solve this problem, this paper proposes the dual tunneling mechanism(DTM) to reduce the additional loss using the reception- only tunnel. In this paper, we analyze the packet delay time in overlay and non-overlay network in case of general mobile IPv6 and DTM, and observe the handover experimental result for the 3G LTE-WLAN network.

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.