Janghyuk Youn

Recently, reconfigurable intelligent surfaces (RISs) have received much interest from both academia and industry due to their flexibility and cost-effectiveness in adjusting the phase and amplitude of wireless signals with low-cost passive reflecting elements. In particular, many RIS-aided techniques have been proposed to improve both data rate and energy efficiency for 6G wireless communication systems. In this paper, we propose a novel RIS-based channel randomization (RCR) technique for improving physical-layer security (PLS) for a time-division duplex (TDD) downlink cellular wire-tap network which consists of a single base station (BS) with multiple antennas, multiple legitimate pieces of user equipment (UE), multiple eavesdroppers (EVEs), and multiple RISs. We assume that only a line-of-sight (LOS) channel exists among the BS, the RISs, and the UE due to propagation characteristics of tera-hertz (THz) spectrum bands that may be used in 6G wireless communication systems. In the proposed technique, each RIS first pseudo-randomly generates multiple reflection matrices and utilizes them for both pilot signal duration (PSD) in uplink and data transmission duration (DTD) in downlink. Then, the BS estimates wireless channels of UE with reflection matrices of all RISs and selects the UE that has the best secrecy rate for each reflection matrix generated. It is shown herein that the proposed technique outperforms the conventional techniques in terms of achievable secrecy rates.

For the next generation of manufacturing, the industrial internet of things (IoT) has been considered as a key technology that enables smart factories, in which sensors transfer measured data, actuators are controlled, and systems are connected wirelessly. In particular, the wireless sensor network (WSN) needs to operate with low cost, low power (energy), and narrow spectrum, which are the most technical challenges for industrial IoT networks. In general, a relay-assisted communication network has been known to overcome scarce energy problems, and a spectrum-sharing technique has been considered as a promising technique for the radio spectrum shortage problem. In this paper, we propose a phase steering based hybrid cooperative relaying (PSHCR) technique for the generic relay-assisted spectrum-shared WSN, which consists of a secondary transmitter, multiple secondary relays (SRs), a secondary access point, and multiple primary access points. Basically, SRs in the proposed PSHCR technique operate with decode-and-forward (DF) relaying protocol, but it does not abandon the SRs that failed in decoding at the first hop. Instead, the SRs operate with amplify-and-forward (AF) protocol when they failed in decoding at the first hop. Furthermore, the SRs (regardless of operating with AF or DF protocol) that satisfy interference constraints to the primary network are allowed to transmit a signal to the secondary access point at the second hop. Note that phase distortion is compensated through phase steering operation at each relay node before second-hop transmission, and thus all relay nodes can operate in a fully distributed manner. Finally, we validate that the proposed PSHCR technique significantly outperforms the existing best single relay selection (BSR) technique and cooperative phase steering (CPS) technique in terms of outage performance via extensive computer simulations.

In this paper, we propose a novel intelligent reflecting surface (IRS) based wireless channel confusion technique for energy efficient jamming. Since IRS consists of passive reflecting elements, its amplitude and phase can be changed rapidly. Due to its rapid alternation of passive elements, IRS makes legitimate receiver to acquire wrong channel state information (CSI) by changing effective channel of legitimate network so that the channel at pilot signal slot and at data signal slots are different. As a result, channel confusion technique can effectively degrade the communication performance of legitimate network even legitimate transmitter uses much higher transmit power. The jamming performance of proposed IRS based channel confusion technique is evaluated by extensive computer simulation. Moreover, it is shown that proposed IRS based channel confusion technique is better jamming technique than Gaussian jamming technique.

In this paper, we propose a novel opportunistic interference alignment technique for spectrum-shared radar and uplink cellular communication systems where both systems are equipped with multiple antennas. In the proposed OIA technique, the radar system sends its signal so that the radar signal is received into interference space at base stations (BSs) of the cellular system, while each uplink user (UE) generates its transmit beamforming vector so that communication signals are received within interference space at the radar receiver. Moreover, to achieve better sum-rate performance of the cellular communication system, the BS selects the UEs which results in sufficiently small interference to other cells for the uplink communication. With the proposed OIA technique, detection performance of the radar system is protected, while the communication system achieves satisfactory sum-rate performance. Through extensive computer simulations, we show that the performances of both radar and communication systems with the proposed technique significantly outperform a conventional null-space projection based spectrum sharing scheme.

With the growth of the number of Internet of Things (IoT) devices, a wide range of wireless sensor networks (WSNs) will be deployed for various applications. In general, WSNs are constrained by limitations in spectrum and energy resources. In order to circumvent these technical challenges, we propose a novel cooperative phase-steering (CPS) technique with a simple on-off power control for generic spectrum sharing-based WSNs, which consists of a single secondary source (SS) node, multiple secondary relay (SR) nodes, a single secondary destination (SD) node, and multiple primary destination (PD) nodes. In the proposed technique, each SR node that succeeds in packet decoding from the SS and for which its interference power to the PD nodes is lower than a certain threshold is allowed to transmit the signal to the SD node. All SR nodes that are allowed to transmit signals to the SD node adjust the phase of their transmit signal such that the phase of received signals at the SD node from the SR nodes is aligned to a certain angle. Moreover, we mathematically analyze the outage probability of the proposed scheme. Our analytical and simulation results show that the proposed technique outperforms the conventional cooperative relaying schemes in terms of outage probability. Through extensive computer simulations, it is shown that the analytical results match well with the simulated outage probability as a lower bound.

In this paper, we propose a novel opportunistic downlink interference alignment with a beam selection (ODIA-BS) technique for fog-radio access networks (F- RANs). With joint transmit and receive beamforming strategies, inter-cell interference is effectively suppressed and intra-cell interference is perfectly eliminated. The performance in terms of sum-rate is improved by selecting a proper transmit beam through F- RAN with low signaling overheads. We also develop a spectrally-efficient ODIA-BS (SE-ODIA-BS) technique to further improve the sum-rate performance, where the scheduler at remote radio head selects users with the most orthogonal effective channel vector to each other. Through extensive computer simulations, it is shown that the sum-rate of the proposed ODIA-BS and SE-OIDA- BS techniques outperform other conventional schemes in the F-RAN environment.

In this paper, we propose a novel machine learning (ML) based link-to-system (L2S) mapping technique for inter-connecting a link-level simulator (LLS) and a system-level simulator (SLS). For validating the proposed technique, we utilized 5G K-Simulator, which was developed through a collaborative research project in Republic of Korea and includes LLS, SLS, and network-level simulator (NS). We first describe a general procedure of the L2S mapping methodology for 5G new radio (NR) systems, and then, we explain the proposed ML-based exponential effective signal-to-noise ratio (SNR) mapping (EESM) method with a deep neural network (DNN) regression algorithm. We compared the proposed ML-based EESM method with the conventional L2S mapping method. Through extensive simulation results, we show that the proposed ML-based L2S mapping technique yielded better prediction accuracy in regards to block error rate (BLER) while reducing the processing time.