Sean Kwon

Jaehyup Seong, Juhwan Lee, Jungwoo Lee et al.

Dual-polarized transmission offers a promising approach to improve spectral efficiency in multiantenna networks by reusing frequency and time resources across orthogonal polarization domains. Building upon this advantage, this paper investigates interference management in mixed dual-polarized integrated satellite-terrestrial networks (MDP-ISTN), comprising a circularly polarized (CP) satellite sub-network and a linearly polarized (LP) terrestrial sub-network. To this end, we employ rate-splitting multiple access (RSMA), which enables flexible non-orthogonal transmission through partial interference decoding and partial interference treating-as-noise. Specifically, to jointly mitigate both inter-network interference between the CP low Earth orbit (LEO) satellite and LP terrestrial sub-networks as well as intra-network interference within each sub-network, we propose an MDP-RSMA framework that incorporates inter-network rate-splitting (RS) with a super-common message together with intra-network RS. Moreover, we account for practical challenges in MDP-ISTN, including polarization mismatch, channel depolarization, and imperfect channel state information at the transmitter. To maximize the minimum user rate among all satellite and terrestrial users, we formulate a robust precoder optimization problem and develop a weighted minimum mean square error (WMMSE)-based algorithm tailored to the proposed MDP-RSMA. Numerical results demonstrate that the proposed scheme significantly improves the minimum user rate over several baseline schemes across diverse MDP-ISTN scenarios.

Haemin Lee, Sean Kwon, Soyi Jung et al.

A successful deployment of drones provides an ideal solution for surveillance systems. Using drones for surveillance can provide access to areas that may be difficult or impossible to reach by humans or in-land vehicles gathering images or video recordings of a specific target in their coverage. Therefore, we introduces a data delivery drone to transfer collected surveillance data in harsh communication conditions. This paper proposes a Myerson auction-based asynchronous data delivery in an aerial distributed data platform in surveillance systems taking battery limitation and long flight constraints into account. In this paper, multiple delivery drones compete to offer data transfer to a single fixed-location surveillance drone. Our proposed Myerson auction-based algorithm, which uses the truthful second-price auction (SPA) as a baseline, is to maximize the seller's revenue while meeting several desirable properties, i.e., individual rationality and incentive compatibility while pursuing truthful operations. On top of these SPA-based operations, a deep learning-based framework is additionally designed for delivery performance improvements.

Won Joon Yun, MyungJae Shin, Soyi Jung et al.

Self-driving cars and autonomous driving research has been receiving considerable attention as major promising prospects in modern artificial intelligence applications. According to the evolution of advanced driver assistance system (ADAS), the design of self-driving vehicle and autonomous driving systems becomes complicated and safety-critical. In general, the intelligent system simultaneously and efficiently activates ADAS functions. Therefore, it is essential to consider reliable ADAS function coordination to control the driving system, safely. In order to deal with this issue, this paper proposes a randomized adversarial imitation learning (RAIL) algorithm. The RAIL is a novel derivative-free imitation learning method for autonomous driving with various ADAS functions coordination; and thus it imitates the operation of decision maker that controls autonomous driving with various ADAS functions. The proposed method is able to train the decision maker that deals with the LIDAR data and controls the autonomous driving in multi-lane complex highway environments. The simulation-based evaluation verifies that the proposed method achieves desired performance.