Emerging industrial and massive Internet of Things (IoT) applications increasingly rely on private 5G New Radio (NR) deployments operating in the 3.5 GHz Citizens Broadband Radio Service (CBRS) band. General Authorized Access (GAA) provides a low-cost spectrum opportunity for IoT-specific NR networks, but the absence of interference protection makes uplink reliability highly vulnerable to co-channel interference (CCI), especially in dense IoT deployments. Ensuring dependable IoT connectivity requires interference-aware channel pattern allocation coordinated by the Spectrum Access System (SAS) while accounting for IoT traffic characteristics and gateway constraints. This paper presents a game-theoretic optimization framework for channel pattern allocation for IoT NR small cells operating under the CBRS GAA tier. We first formulate CCI minimization as a binary quadratic program (BQP) and show its nonconvex and NP-hard nature. To address this complexity, we develop two complementary solutions: (i) a cooperative hedonic coalition formation algorithm that enables SAS-assisted clustering of IoT NR gateways, and (ii) a distributed potential game approach where IoT gateways autonomously select channel patterns using log-linear learning. Numerical evaluations under realistic IoT deployment and propagation models demonstrate significant gains in packet delivery ratio (PDR), reduced energy consumption of IoT devices through fewer retransmissions, and improved spectrum reliability compared to existing CBRS coexistence mechanisms. The proposed framework is shown to be robust under asymmetric interference conditions and achieves more equitable per-user reliability than existing coexistence mechanisms. The proposed approaches provide practical and scalable solutions for interference-aware private IoT NR networks in shared mid-band spectrum.

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.

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.

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.

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.

In this paper, we have presented the performance results of NR-compliant waveforms under nonlinear HPA channels which also can be candidates for B5G/6G satellite communication for integrated terrestrial and satellite networks.

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.

Reliable and QoS‐aware multimedia delivery is one of the most important requirements to satisfy end user needs. IEEE 802.11 networks have become one of the most popular Internet access methods owing to their low‐cost and fast connection speed. Multicasting is a transmission technique that can significantly increase network efficiency through the simultaneous delivery of the same multimedia content to multiple multicast group members. IEEE 802.11 describes several multicast protocols targeted to increase the robustness of audio and video delivery. Some researchers have investigated the performance of such multicast protocols in terms of their achievable throughput. However, for a better understanding of the suitability of the protocols for a QoS‐aware multimedia transmission, an in‐depth analysis of the achievable delay is needed. This study provides a detailed delay analysis and describes analytical models for estimating the average achievable delay of IEEE 802.11 multicast protocols co‐existing with legacy stations. The proposed analytical models are compared against simulation results under various conditions and parameter configurations. The simulation results validate the accuracy of the proposed models.

Small cell eNBs which are deployed in close to audio devices have high potential of causing acoustic interference. Thus, we investigate the impact of the physical channels and physical signals from the LTE small cell eNB. The spectral characteristics of physical channels/signals are examined through theoretical analysis and experiments. From the results, the influence of each physical channel/signal is evaluated in audible frequency band.

Efficient sharing of the network spectrum is required to meet the exponentially growing demand of wireless communications, and the role of the MAC protocols has been becoming more important. In this regard, a novel MAC protocol called the Renewal Access Protocol (RAP) was recently proposed. It was shown that the RAP achieves optimal throughput, high short-term fairness, and near-optimal delay performances when the number of nodes in the network is known to all nodes. However, it is not easy for a node to know the number of nodes in the network and the number of nodes is time varying. So we propose an adaptive version of the RAP called the Adaptive Renewal Access Protocol (A-RAP) in this paper. A node equipped with the A-RAP estimates the number of nodes utilizing two dimensional Backoff Selection State (BSS), the Estimated Number of Nodes (ENN) and phase, based on its transmission results. For an efficient design of the A-RAP, we analyze the performance of the A-RAP and carefully determine two key parameters, the adjustment probabilities and the numbers of phases. Numerical and simulation results are provided to verify that the proposed A-RAP achieves good throughput and short-term fairness as the RAP. The results also show that the A-RAP achieves better short-term fairness than other MAC protocols proposed in the open literature.

As a promising solution for handling super dense wireless networks, wireless local area networks (WLANs) have been intensively considered due to their wide availability. However, the contention-based MAC protocol in WLANs should be modified because of its inefficiency. To this end, we consider a recently proposed novel MAC protocol called the renewal access protocol (RAP). With the RAP, we analyze two strategies for resolving collisions efficiently and achieving optimal throughput performance in super dense WLANs: strategies without and with grouping. First, we analyze the asymptotic behavior of the RAP itself (i.e., without grouping) as the number of terminals goes to infinity. We show that the RAP can achieve optimal throughput even in super dense WLANs and the relevant optimal access probability can be derived in a closed form. Second, we propose a grouping strategy in the RAP called the grouped RAP (G-RAP). While a grouping strategy in the IEEE 802.11ah standard is based on time division, our G-RAP is based on transmission attempts. So the G-RAP does not waste channel resources. We show that the G-RAP achieves the optimal network throughput for any group structure if terminals use the optimal access probability that we derive. Our analytical results are validated by simulation.

This paper designs a resource allocation to minimize the total transmit power of a multi-band wireless network under the required rate constraint. The required rate splitting scheme is proposed to reduce the computational complexity of the multi-band resource allocation. On the basis of the primal decomposition method, the multi-band resource allocation is decomposed into multiple sub-band resource allocations and the required rate splitting. Numerical results show that the performance gap between the proposed resource allocation and the optimal resource allocation is negligible. It is also observed that the proposed resource allocation provides better performances than the conventional resource allocation.

The performance analysis of the multiple‐input multiple‐output interference alignment (IA) with user selection in small‐cell interference channels is investigated. In this system, the authors consider the user selection using spatially independent scheduling in which a transmitter sends the data stream of the selected user providing the maximum effective signal‐to‐noise ratio (SNR). As the first step, this study derives exact and approximate probability density functions of the effective SNR of the proposed IA with user selection. Next, exact closed‐form expressions of the bit‐error rate (BER) for the M ‐ary quadrature amplitude modulation, M ‐ary phase shift keying modulation and outage probability are presented. Using asymptotic analysis, the BER and outage probability of the proposed IA system with user selection are approximated, and the diversity order and SNR gain are quantified, respectively. Also, the authors derive tight closed‐form expression of the proposed system for ergodic capacity using the Taylor series expansion of logarithm function. The analytical results show that diversity order of the IA system with user selection is superior to that of the conventional iterative algorithms. Also, the ergodic capacity of the proposed system is superior to that of the conventional iterative algorithms at high SNR. This performance gain comes from multi‐user diversity. The analytical results are verified through simulation results.

This letter presents an alternative derivation of the Case IV of Pawula F-function and an approximation for the corresponding probability density function (pdf) in the presence of the Tikhonov distributed phase error. The pdf of Case IV is used to obtain new upper bounds for the conditional and average symbol-error probabilities of M-ary phase shift keying with phase error over additive white Gaussian noise channels. Numerical results demonstrate the validity of the upper bounds.

This letter presents a joint approach to the symbol-error probability (SEP) of coherent M-ary phase-shift keying in a situation where the phase error, quadrature error, and in-phase-quadrature (I-Q) gain mismatch problems take place all concurrently over an additive white Gaussian noise and arbitrary fading channel. A set of equations that characterizes the conditional SEP on an instantaneous fading signal-to-noise ratio is derived in the form of the Craig representation.

This paper investigates the average symbol error probability (SEP) performance of the coherent M-ary phase shift keying (M-PSK) system in a situation where the phase error, quadrature error, and I-Q gain mismatch problems take place all concurrently over an additive white Gaussian noise (AWGN) and Nakagami-m fading channel. An analytical expression for the average SEP is derived. Computer simulation results are included based on our analysis in order to examine the overall SEP performance of the M-PSK system for various combinations of the phase error, quadrature error, and I-Q gain mismatch values.

This letter provides a new expression for the probability of the phase angle between two vectors perturbed by Gaussian noise with in-phase/quadrature-phase (I/Q) unbalance, in order to simplify the analysis of the error probabilities of M-ary phase-shift keying (MPSK). The error probabilities, symbol-error rates, or bit-error rates for MPSK with I/Q balance or unbalance, can be presented straightforwardly from the derived expression. Because the newly derived result is provided in terms of the conventional first-order Gaussian Q-function and the joint Gaussian Q-function with a correlation coefficient dependent on M, it readily allows rapid evaluation for various cases of practical interest.