Joongheon Kim

Transmission of high-definition (HD) video is a promising application for 60 GHz wireless links, since very high transmission rates (up to several Gbit/s) are possible. In particular we consider a sports stadium broadcasting system where signals from multiple cameras are transmitted to a central location. Due to the high pathloss of 60 GHz radiation over the large distances encountered in this scenario, the use of relays might be required. The current paper analyzes the joint selection of the ro

This paper proposes an adaptive and dynamic localized scheme unique to hierarchical clustering in RFID networks, while reducing the overlapping areas of clusters and consequently reducing collisions among RFID readers. Drew on our LLC scheme that adjusts cluster coverage to minimize energy consumption, low-energy localized clustering for RFID networks (LLCR) addresses RFID reader anti-collision problem in this paper. LLCR is a RFID reader anti-collision algorithm that minimizes collisions by min

This paper proposes an application algorithm based on a quantum approximate optimization algorithm (QAOA) for wireless scheduling problems. QAOA is one of the promising hybrid quantum-classical algorithms to solve combinatorial optimization problems and it provides great approximate solutions to non-deterministic polynomial-time (NP) hard problems. QAOA maps the given problem into Hilbert space, and then it generates the Hamiltonian for the given objective and constraint. Then, QAOA finds proper

Green tide, which is a serious water pollution problem, is caused by the complex relationships of various factors, such as flow rate, several water quality indicators, and weather. Because the existing methods are not suitable for identifying these relationships and making accurate predictions, a new system and algorithm is required to predict the green tide phenomenon and also minimize the related damage before the green tide occurs. For this purpose, we consider a new network model using smart

This paper proposes feasible and satisfactory system design parameters to mitigate the impact of interference on real-time high-definition video streaming in infrastructure-to-vehicle (I2V) telematic platforms utilizing 60-GHz radiation and the corresponding IEEE 802.11ad baseband. The analysis captures the impact of interference on the 60-GHz I2V vehicle (IV), which is caused by multiple interference sources through the 60-GHz wireless transmissions from nearby I2V base stations (IBSs) to their

The emergence of UHF RFID as one of the dominant technology trends has posed numerous unique challenges to researchers. This letter presents a novel, theoretically-grounded collision arbitration protocol, called TPC-CA, which optimally controls transmission power of RFID interrogators and thereby reducing redundant interrogator collisions

This paper proposes an energy-efficient cluster head selection method in the wireless ad hoc network by using a hybrid quantum-classical approach. The wireless ad hoc network is divided into several clusters via cluster head selection, and the performance of the network topology depends on the distribution of these clusters. For an energy-efficient network topology, none of the selected cluster heads should be neighbors. In addition, all the selected cluster heads should have high energy-consump

This paper presents numerical simulation results to study the impact of the co-existence between a fixed service (FS) system and 5G small cell networks at 28-, 38-, and 60-GHz millimeter-wave (mmWave) frequency bands. For this paper, two scenarios are considered: aggregation of interference from small cells into an FS receiver from base stations (BSs) to their associated user equipment (UE) (downlink) and the aggregation of cellular interference at the FS receiver from UEs to their associated BS

This paper conducts the feasibility study for discussing about the interference impacts in advanced fog computing (FC) networks with 60 GHz millimeter-wave wireless technology. With the concept of FC, cloud computing services can be utilized with local computing devices (FC devices) those are located nearby user terminals [edge devices (EDs)] for low-latency communications. Since EDs can be densely deployed in urban areas, wireless hyperconnection should be supportable in FC networks. For the sy

This paper proposes a novel dynamic offloading decision method which is inspired by deep reinforcement learning (DRL). In order to realize real-time communications in mobile edge computing systems, an efficient task offloading algorithm is required. When the decision of actions (offloading enabled, i.e., computing in clouds or offloading disabled, i.e., computing in local edges) is made by the proposed DRL-based dynamic algorithm in each unit time, it is required to consider real-time/seamless d

This paper discusses the stochastic and strategic control of 60 GHz millimeter-wave (mmWave) wireless transmission for distributed and mobile virtual reality (VR) applications. In VR scenarios, establishing wireless connection between VR data-center (called VR server (VRS) ) and head-mounted VR device (called VRD ) allows various mobile services. Consequently, utilizing wireless technologies is obviously beneficial in VR applications. In order to transmit massive VR data, the 60 GHz mmWave wirel

Federated learning-enabled edge devices train global models by sharing them while avoiding local data sharing. In federated learning, the sharing of models through communication between several clients and central servers results in various problems such as a high latency and network congestion. Moreover, battery consumption problems caused by local training procedures may impact power-hungry clients. To tackle these issues, federated edge learning (FEEL) applies the network edge technologies of

Orthogonal Frequency Division Multiplexing (OFDM) is a popular multicarrier technique used to attain high spectral efficiencies. It also has other advantages such as multipath tolerance and ease of implementation. However, OFDM based systems suffer from high Peak-to-Average Power Ratio (PAPR) problem. Because of the nonlinearity of the power amplifiers, the high PAPR causes significant distortion in the transmitted signal for millimeter-wave (mmWave) systems. To alleviate the high PAPR problem,

This study proposes a novel multi-module deep neural network framework which aims at improving intelligent long-term traffic forecasting. Following our previous system, the internal architecture of the new system adds deep learning modules that enable data separation during computation. Thus, prediction becomes more accurate in many sections of the road network and gives dependable results even under possible changes in weather conditions during driving. The performance of the framework is then