Joongheon Kim

On-demand video streaming is becoming a killer application for wireless networks. Recent information-theoretic results have shown that a combination of caching on the users' devices and device-to-device (D2D) communications yields throughput scalability for very dense networks, which represent critical bottlenecks for conventional cellular and wireless local area network (WLAN) technologies. In this paper, we consider the implementation of such caching D2D systems where each device pre-caches a

This paper proposes fast millimeter-wave (mm-wave) beam training protocols with receive beamforming. Both IEEE standards and the academic literature have generally considered beam training protocols involving exhaustive search over all possible beam directions for both the beamforming initiator and responded However, this operation requires a long time (and thus overhead) when the beamwidth is quite narrow such as for mm-wave beams (1° in the worst case). To alleviate this problem, we propose tw

The urban aerial mobility (UAM) system, such as drone taxi or air taxi, is one of future on-demand transportation networks. Among them, electric vertical takeoff and landing (eVTOL) is one of UAM systems that is for identifying the locations of passengers, flying to the positions where the passengers are located, loading the passengers, and delivering the passengers to their destinations. In this paper, we propose a distributed deep reinforcement learning where the agents are formulated as eVTOL

Millimeter-wave (mm-wave) transmission is a promising method for increasing capacity in next-generation cellular systems. However, since mm-wave signals are too weak to (i) do long-distance communication and (ii) survive in non-line-of-sight situations, multi-hop relaying is required. We thus study in this paper a multi-hop routing protocol at mm-wave (38 GHz and 28GHz) frequencies. Due to the great and steadily increasing importance of video streaming in cellular applications, we pay special at

This paper addresses an adaptive and dynamic localized scheme unique to hierarchical clustering protocols in wireless sensor networks, while reducing the consumption of residual energy of cluster heads and as a result delivering a prolonged sensor network lifetime. Our proposed scheme, low-energy localized clustering (LLC) aims to minimize energy consumption of cluster heads while the entire sensor network is still being covered. For achieving this goal, LLC dynamically regulates the radius of e

This paper proposes two novel algorithms for adaptive crowdsourcing in 60-GHz medical imaging big-data platforms, namely, a max-weight scheduling algorithm for medical cloud platforms and a stochastic decision-making algorithm for distributed power-and-latency-aware dynamic buffer management in medical devices. In the first algorithm, medical cloud platforms perform a joint queue-backlog and rate-aware scheduling decisions for matching deployed access points (APs) and medical users where APs are

With the evolution of various advanced driver assistance system (ADAS) platforms, the design of autonomous driving system is becoming more complex and safety-critical. The autonomous driving system simultaneously activates multiple ADAS functions; and thus it is essential to coordinate various ADAS functions. This paper proposes a randomized adversarial imitation learning (RAIL) method that imitates the coordination of autonomous vehicle equipped with advanced sensors. The RAIL policies are trai

Wireless systems operating in the 60 GHz band are promising for achieving broadband connectivity to/from high-speed trains. However, the high mobility creates new challenges on the physical and medium access control (MAC) layer and requires a dedicated infrastructure of closely-spaced base stations near the train tracks. We develop an optimization framework for selecting the pairs between infrastructure stations and the antennas on the train to achieve sum rate maximization. We investigate the m

In beyond 5G and 6G network scenarios, the use of satellites has been actively discussed for extending target monitoring areas, even for extreme circumstances, where the monitoring functionalities can be realized due to the usage of millimeter-wave wireless links. This paper designs an efficient scheduling algorithm which minimizes overlapping monitoring areas among observation satellite constellation. In order to achieve this objective, a quantum optimization based algorithm is used because the

Wireless streaming of high-definition video is a promising application for 60GHz links, since multi-Gigabit/s data rates are possible. In particular we consider a sports stadium broadcasting system where video signals from multiple cameras are transmitted to a central location. Due to the high pathloss of 60GHz radiation over the large distances encountered in this setting, the use of relays is required. This paper designs a quality-aware coding and relaying algorithm for maximization of the ove

This paper proposes a joint optimization framework for minimizing high-definition (HD) video coding rates and selecting optimal relay nodes in 60 GHz millimeter-wave (mmWave) IEEE 802.11ad very high throughput (VHT) wireless systems. While IEEE 802.11ad VHT aims to support uncompressed HD video wireless transmission, its major limitation is the extremely high attenuation even in line-of-sight situations, which leads to a short admissible distance between transmitter and receivers and/or the nece

As an important part of IoTization trends, wireless sensing technologies have been involved in many fields of human life. In cellular network evolution, the long term evolution advanced (LTE-A) networks including machine-type communication (MTC) features (named LTE-M) provide a promising infrastructure for a proliferation of Internet of things (IoT) sensing platform. However, LTE-M may not be optimally exploited for directly supporting such low-data-rate devices in terms of energy efficiency sin

This demo abstract describes an initial design of a new adaptive video streaming protocol for device-to-device WiFi-based mobile platforms and its software implementation. For the demonstration, two mobile servers and two mobile users will be deployed verifying that our device-to-device adaptive video streaming implementation works with desirable user experience.

This paper proposes a novel multi-agent deep reinforcement learning (MADRL)-based positioning algorithm for multiple unmanned aerial vehicles (UAVs) collaboration in mobile access applications where the UAVs work as mobile base stations. The primary objective of the proposed algorithm is to establish reliable mobile access networks for vehicle-to-everything (V2X) communications. This paper jointly considers energy-efficient UAV operation and reliable wireless communication services for realizing