arXiv subjects

Seongah Jeong

Publications and source records attributed to Seongah Jeong.

At least 19 recordsLinked to original sources

arXiv2025

Exploring the Near and Far-Field Coexistence for RIS-Assisted ISAC Systems: An Adaptive Bandwidth Splitting Approach

Integrated sensing and communication (ISAC) enables the joint use of spectrum and hardware resources for radar sensing and data transmission, serving as a key enabler of next-generation wireless networks. However, most existing ISAC studies have been limited to operation within a single frequency band and have not been designed to adapt to diverse wireless propagation environments or user configurations. To address these limitations, this paper investigates a reconfigurable intelligent surface (RIS)-assisted ISAC system employing an adaptive bandwidth-splitting strategy under near-field (NF) and far-field (FF) coexistence. The system comprises a full-duplex access point (AP), an RIS and multiple users, where an ISAC user (IU) is both a sensing target and a communication user in the NF region, while communication-only users (CUs) rely on the RIS and experience either NF or FF propagation depending on their placement. The proposed system jointly exploits traditional sensing-only (SO) and ISAC bands and adopts uplink non-orthogonal multiple access (NOMA) for simultaneous transmission. We formulate a joint optimization problem for the receive beamforming vector, bandwidth-splitting ratio, and RIS phase shifts to minimize the Cramer-Rao bound (CRB) under rate and resource constraints. An efficient algorithm is developed based on an alternating optimization (AO) framework combined with semi-definite relaxation (SDR). Numerical results demonstrate that the proposed approach significantly outperforms conventional schemes that operate solely in either the ISAC or SO band, achieving superior performance across various RIS and user configurations under hybrid NF and FF coexistence scenarios.

eess.SP↗

arXiv2025

Joint Optimization of Computation Offloading and Resource Allocation in ISAC-assisted SAGIN-based IoT

In this letters, an energy-efficient integrated sensing and communication (ISAC) for space-air-ground integrated network (SAGIN)-based Internet of Things (IoT) systems is proposed to facilitate wide coverage and real-time 6G services. For processing a sizable data collected at a IoT device, a hybrid edge computing scheme is applied with the cloudlets mounted at autonomous aerial vehicle (AAV) and low earth orbit (LEO) satellite, where the AAV with multiple antennas performs uplink sensing of the nearby target. With the aim of minimizing the total AAV's energy consumption, we optimize the duration of training and data phase and the bit allocation coupled with the offloading ratio under the constraints for offloading and sensing. Via simulations, the superiority of the proposed algorithm is verified to be pronounced with the sufficient mission time and the high sensing performance constraint.

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arXiv2024

Fronthaul Compression and Beamforming Optimization for Secure Cell-free ISAC Systems

This letter aims to provide sensing capabilities for a potential eavesdropper, while simultaneously enabling the secure communications with the legitimate users in a cell-free multipleinput multiple-output system with limited fronthaul links. In order to maximize the sensing performance, the joint design of fronthaul compression and beamforming is proposed considering the constraints on the finite fronthaul-capacity links and the maximum power along with the worst-case secrecy rate requirements. To this end, we propose an algorithmic solution based on the minorization-maximization method and semidefinite programming relaxation techniques, whose performance superiority is verified via simulations compared to the reference schemes such as distributed sensing and random beamforming.

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arXiv2024

Joint Mechanical and Electrical Adjustment of IRS-aided LEO Satellite MIMO Communications

In this correspondence, we propose a joint mechanical and electrical adjustment of intelligent reflecting surface (IRS) for the performance improvements of low-earth orbit (LEO) satellite multiple-input multiple-output (MIMO) communications. In particular, we construct a three-dimensional (3D) MIMO channel model for the mechanically-tilted IRS in general deployment, and consider two types of scenarios with and without the direct path of LEO-ground user link due to the orbital flight. With the aim of maximizing the end-to-end performance, we jointly optimize tilting angle and phase shift of IRS along with the transceiver beamforming, whose performance superiority is verified via simulations with the Orbcomm LEO satellite using a real orbit data.

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arXiv2024

Secrecy Enhancement for UAV-enabled Integrated Sensing and Communication System

In this correspondence, we propose an unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) system, where a full-duplex UAV equipped with uniform planar array (UPA) is adopted as a base station for the multiuser downlink communications, while sensing and jamming a passive ground eavesdropper. The goal of this work is to maximize the sum secrecy rate of ground users subject to the constraints of sensing accuracy and UAV's operational capability by jointly optimizing the transceiver beamforming and UAV's trajectory. To this end, we develop the algorithmic solution based on block coordinate descent (BCD) and semidefinite programming (SDP) relaxation techniques, whose performance is verified via simulations indicating its efficacy in improving communication security with the sufficient mission period.

eess.SP↗

arXiv2024

Cell-Free MIMO Perceptive Mobile Networks: Cloud vs. Edge Processing

Perceptive mobile networks implement sensing and communication by reusing existing cellular infrastructure. Cell-free multiple-input multiple-output, thanks to the cooperation among distributed access points, supports the deployment of multistatic radar sensing, while providing high spectral efficiency for data communication services. To this end, the distributed access points communicate over fronthaul links with a central processing unit acting as a cloud processor. This work explores four different types of PMN uplink solutions based on Cell-free multiple-input multiple-output, in which the sensing and decoding functionalities are carried out at either cloud or edge. Accordingly, we investigate and compare joint cloud-based decoding and sensing (CDCS), hybrid cloud-based decoding and edge-based sensing (CDES), hybrid edge-based decoding and cloud-based sensing (EDCS) and edge-based decoding and sensing (EDES). In all cases, we target a unified design problem formulation whereby the fronthaul quantization of signals received in the training and data phases are jointly designed to maximize the achievable rate under sensing requirements and fronthaul capacity constraints. Via numerical results, the four implementation scenarios are compared as a function of the available fronthaul resources by highlighting the relative merits of edge- and cloud-based sensing and communications. This study provides guidelines on the optimal functional allocation in fronthaul-constrained networks implementing integrated sensing and communications.

cs.IT↗

arXiv2024

LR-FHSS Transceiver for Direct-to-Satellite IoT Communications: Design, Implementation, and Verification

This paper proposes a long range-frequency hopping spread spectrum (LR-FHSS) transceiver design for the Direct-to-Satellite Internet of Things (DtS-IoT) communication system. The DtS-IoT system has recently attracted attention as a promising nonterrestrial network (NTN) solution to provide high-traffic and low-latency data transfer services to IoT devices in global coverage. In particular, this study provides guidelines for the overall DtS-IoT system architecture and design details that conform to the Long Range Wide-Area Network (LoRaWAN). Furthermore, we also detail various DtS-IoT use cases. Considering the multiple low-Earth orbit (LEO) satellites, we developed the LR-FHSS transceiver to improve system efficiency, which is the first attempt in real satellite communication systems using LR-FHSS. Moreover, as an extension of our previous work with perfect synchronization, we applied a robust synchronization scheme against the Doppler effect and co-channel interference (CCI) caused by LEO satellite channel environments, including signal detection for the simultaneous reception of numerous frequency hopping signals and an enhanced soft-output-Viterbi-algorithm (SOVA) for the header and payload receptions. Lastly, we present proof-of-concept implementation and testbeds using an application-specific integrated circuit (ASIC) chipset and a field-programmable gate array (FPGA) that verify the performance of the proposed LR-FHSS transceiver design of DtS-IoT communication systems. The laboratory test results reveal that the proposed LR-FHSS-based framework with the robust synchronization technique can provide wide coverage, seamless connectivity, and high throughput communication links for the realization of future sixth-generation (6G) networks.

eess.SY↗

arXiv2024

AIRS-assisted Vehicular Networks with Rate-Splitting SWIPT Receivers: Joint Trajectory and Communication Design

In this correspondence, we propose to use an intelligent reflective surface (IRS) installed on unmanned aerial vehicle (UAV), referred to as aerial IRS (AIRS), for vehicular networks, where simultaneous wireless information and power transfer (SWIPT) receivers to concurrently allow information decoding (ID) and energy harvesting (EH) are equipped at the battery-limited vehicles. For efficiently supporting the multiple moving vehicles, we adopt rate-splitting multiple access (RSMA) technique. With the aim of maximizing the sum rate of vehicles, we jointly optimize trajectory and phase shift design of AIRS, transmit power and rate allocation for RSMA along with power splitting ratio for SWIPT implementation. Via simulations, the superior performances of the proposed algorithm are validated compared to the conventional partial optimizations.

cs.IT↗

arXiv2023

Rate-splitting Multiple Access for Hierarchical HAP-LAP Networks under Limited Fronthaul

In this correspondence, we propose hierarchical high-altitude platform (HAP)-low-altitude platform (LAP) networks with the aim of maximizing the sum-rate of ground user equipments (UEs). The multiple aerial radio units (RUs) mounted on HAPs and LAPs are managed by the central unit (CU) via constrained fronthaul links. The limitation of fronthaul capacity can be addressed through quantization, employing the cloud radio access network (C-RAN) architecture. For spectral efficiency, we adopt the rate-splitting multiple access (RSMA), leveraging the advantages of both space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA). To achieve this, we jointly optimize rate splitting, transmit power allocation, quantization noise variance, and UAV placement using an alternating optimization (AO) approach coupled with successive convex approximation (SCA) and the weighted minimum mean square error (WMMSE) method. Numerical results validate the superior performance of the proposed method compared to benchmark schemes, including partial optimizations or those without the assistance of LAPs.

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arXiv2023

Energy-Efficient Secure Offloading System Designed via UAV-Mounted Intelligent Reflecting Surface for Resilience Enhancement

With increasing interest in mmWave and THz communication systems, an unmanned aerial vehicle (UAV)-mounted intelligent reflecting surface (IRS) has been suggested as a key enabling technology to establish robust line-of-sight (LoS) connections with ground nodes owing to their free mobility and high altitude, especially for emergency and disaster response. This paper investigates a secure offloading system, where the UAV-mounted IRS assists the offloading procedures between ground users and an access point (AP) acting as an edge cloud. In this system, the users except the intended recipients in the offloading process are considered as potential eavesdroppers. The system aims to achieve the minimum total energy consumption of battery-limited ground user devices under constraints for secure offloading accomplishment and operability of UAV-mounted IRS, which is done by optimizing the transmit power of ground user devices, the trajectory and phase shift matrix of UAV-mounted IRS, and the offloading ratio between local execution and edge computing based on the successive convex approximation (SCA) algorithms. Numerical results show that the proposed algorithm can provide the considerable energy savings compared with local execution and partial optimizations.

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arXiv2023

Cache-assisted Mobile Edge Computing over Space-Air-Ground Integrated Networks for Extended Reality Applications

Extended reality-enabled Internet of Things (XRI) provides the new user experience and the sense of immersion by adding virtual elements to the real world through Internet of Things (IoT) devices and emerging 6G technologies. However, the computational-intensive XRI tasks are challenging for the energy-constrained small-size XRI devices to cope with, and moreover certain data requires centralized computing that needs to be shared among users. To this end, we propose a cache-assisted space-air-ground integrated network mobile edge computing (SAGIN-MEC) system for XRI applications, consisting of two types of edge servers mounted on an unmanned aerial vehicle (UAV) and low Earth orbit (LEO) equipped with cache and the multiple ground XRI devices. For system efficiency, the four different offloading procedures of the XRI data are considered according to the type of information, i.e., shared data and private data, as well as the offloading decision and the caching status. Specifically, the private data can be offloaded to either UAV or LEO, while the offloading decision of the shared data to the LEO can be determined by the caching status. With the aim of maximizing the energy efficiency of the overall system, we jointly optimize UAV trajectory, resource allocation and offloading decisions under latency constraints and UAV's operational limitations by using the alternating optimization (AO)-based method along with Dinkelbach algorithm and successive convex optimization (SCA). Via numerical results, the proposed algorithm is verified to have the superior performance compared to conventional partial optimizations or without cache.

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arXiv2023

Transceiver Design and Performance Analysis for LR-FHSS-based Direct-to-Satellite IoT

This paper presents a novel transceiver design aimed at enabling Direct-to-Satellite Internet of Things (DtS-IoT) systems based on long range-frequency hopping spread spectrum (LR-FHSS). Our focus lies in developing an accurate transmission method through the analysis of the frame structure and key parameters outlined in Long Range Wide-Area Network (LoRaWAN) [1]. To address the Doppler effect in DtS-IoT networks and simultaneously receive numerous frequency hopping signals, a robust signal detector for the receiver is proposed. We verify the performance of the proposed LR-FHSS transceiver design through simulations conducted in a realistic satellite channel environment, assessing metrics such as miss detection probability and packet error probability.

cs.AR↗

arXiv2023

Energy-Efficient Vehicular Edge Computing with One-by-one Access Scheme

With the advent of ever-growing vehicular applications, vehicular edge computing (VEC) has been a promising solution to augment the computing capacity of future smart vehicles. The ultimate challenge to fulfill the quality of service (QoS) is increasingly prominent with constrained computing and communication resources of vehicles. In this paper, we propose an energy-efficient task offloading strategy for VEC system with one-by-one scheduling mechanism, where only one vehicle wakes up at a time to offload with a road side unit (RSU). The goal of system is to minimize the total energy consumption of vehicles by jointly optimizing user scheduling, offloading ratio and bit allocation within a given mission time. To this end, the non-convex and mixed-integer optimization problem is formulated and solved by adopting Lagrange dual problem, whose superior performances are verified via numerical results, as compared to other benchmark schemes.

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arXiv2023

Marine IoT Systems with Space-Air-Sea Integrated Networks: Hybrid LEO and UAV Edge Computing

Marine Internet of Things (IoT) systems have grown substantially with the development of non-terrestrial networks (NTN) via aerial and space vehicles in the upcoming sixth-generation (6G), thereby assisting environment protection, military reconnaissance, and sea transportation. Due to unpredictable climate changes and the extreme channel conditions of maritime networks, however, it is challenging to efficiently and reliably collect and compute a huge amount of maritime data. In this paper, we propose a hybrid low-Earth orbit (LEO) and unmanned aerial vehicle (UAV) edge computing method in space-air-sea integrated networks for marine IoT systems. Specifically, two types of edge servers mounted on UAVs and LEO satellites are endowed with computational capabilities for the real-time utilization of a sizable data collected from ocean IoT sensors. Our system aims at minimizing the total energy consumption of the battery-constrained UAV by jointly optimizing the bit allocation of communication and computation along with the UAV path planning under latency, energy budget and operational constraints. For availability and practicality, the proposed methods were developed for three different cases according to the accessibility of the LEO satellite, ``Always On," ``Always Off" and ``Intermediate Disconnected", by leveraging successive convex approximation (SCA) strategies. Via numerical results, we verify that significant energy savings can be accrued for all cases of LEO accessibility by means of joint optimization of bit allocation and UAV path planning compared to partial optimization schemes that design for only the bit allocation or trajectory of the UAV.

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arXiv2022

Hybrid UAV-enabled Secure Offloading via Deep Reinforcement Learning

Unmanned aerial vehicles (UAVs) have been actively studied as moving cloudlets to provide application offloading opportunities and to enhance the security level of user equipments (UEs). In this correspondence, we propose a hybrid UAV-aided secure offloading system in which a UAV serves as a helper by switching the mode between jamming and relaying to maximize the secrecy sum-rate of UEs. This work aims to optimize (i) the trajectory of the helper UAV, (ii) the mode selection strategy and (iii) the UEs' offloading decisions under the constraints of offloading accomplishment and the UAV's operational limitations. The solution is provided via a deep deterministic policy gradient (DDPG)-based method, whose superior performance is verified via a numerical simulation and compared to those of traditional approaches.

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arXiv2021

Collaborative Cloud and Edge Mobile Computing in C-RAN Systems with Minimal End-to-End Latency

Mobile cloud and edge computing protocols make it possible to offer computationally heavy applications to mobile devices via computational offloading from devices to nearby edge servers or more powerful, but remote, cloud servers. Previous work assumed that computational tasks can be fractionally offloaded at both cloud processor (CP) and at a local edge node (EN) within a conventional Distributed Radio Access Network (D-RAN) that relies on non-cooperative ENs equipped with one-way uplink fronthaul connection to the cloud. In this paper, we propose to integrate collaborative fractional computing across CP and ENs within a Cloud RAN (C-RAN) architecture with finite-capacity two-way fronthaul links. Accordingly, tasks offloaded by a mobile device can be partially carried out at an EN and the CP, with multiple ENs communicating with a common CP to exchange data and computational outcomes while allowing for centralized precoding and decoding. Unlike prior work, we investigate joint optimization of computing and communication resources, including wireless and fronthaul segments, to minimize the end-to-end latency by accounting for a two-way uplink and downlink transmission. The problem is tackled by using fractional programming (FP) and matrix FP. Extensive numerical results validate the performance gain of the proposed architecture as compared to the previously studied D-RAN solution.

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arXiv2019

Energy-Efficient Task Offloading for Vehicular Edge Computing: Joint Optimization of Offloading and Bit Allocation

With the rapid development of vehicular networks, various applications that require high computation resources have emerged. To efficiently execute these applications, vehicular edge computing (VEC) can be employed. VEC offloads the computation tasks to the VEC node, i.e., the road side unit (RSU), which improves vehicular service and reduces energy consumption of the vehicle. However, communication environment is time-varying due to the movement of the vehicle, so that finding the optimal offloading parameters is still an open problem. Therefore, it is necessary to investigate an optimal offloading strategy for effective energy savings in energy-limited vehicles. In this paper, we consider the changes of communication environment due to various speeds of vehicles, which are not considered in previous studies. Then, we jointly optimize the offloading proportion and uplink/computation/downlink bit allocation of multiple vehicles, for the purpose of minimizing the total energy consumption of the vehicles under the delay constraint. Numerical results demonstrate that the proposed energy-efficient offloading strategy significantly reduces the total energy consumption.

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arXiv2017

Optimization of Massive Full-Dimensional MIMO for Positioning and Communication

Massive Full-Dimensional multiple-input multiple-output (FD-MIMO) base stations (BSs) have the potential to bring multiplexing and coverage gains by means of three-dimensional (3D) beamforming. Key technical challenges for their deployment include the presence of limited-resolution front ends and the acquisition of channel state information (CSI) at the BSs. This paper investigates the use of FD-MIMO BSs to provide simultaneously high-rate data communication and mobile 3D positioning in the downlink. The analysis concentrates on the problem of beamforming design by accounting for imperfect CSI acquisition via Time Division Duplex (TDD)-based training and for the finite resolution of analog-to-digital converter (ADC) and digital-to-analog converter (DAC) at the BSs. Both \textit{unstructured beamforming} and a low-complexity \textit{Kronecker beamforming} solution are considered, where for the latter the beamforming vectors are decomposed into separate azimuth and elevation components. The proposed algorithmic solutions are based on Bussgang theorem, rank-relaxation and successive convex approximation (SCA) methods. Comprehensive numerical results demonstrate that the proposed schemes can effectively cater to both data communication and positioning services, providing only minor performance degradations as compared to the more conventional cases in which either function is implemented. Moreover, the proposed low-complexity Kronecker beamforming solutions are seen to guarantee a limited performance loss in the presence of a large number of BS antennas.

cs.IT↗