arXiv subjects

Wonjae Shin

Publications and source records attributed to Wonjae Shin.

At least 19 recordsLinked to original sources

arXiv2026

Rainbow Beamforming for Wideband LEO Satellite Communications: Principles, Applications, and Technical Challenges

Low Earth Orbit (LEO) satellite communications (SATCOM) has emerged as a key enabler of global connectivity for 6G networks. To overcome the significant path loss of space-to-ground links, high-gain directional beamforming (BF) is indispensable. As LEO systems evolve toward wider bandwidths to support data-intensive applications, however, they encounter a fundamental physical limitation known as the beam-squint effect, which induces frequency-dependent beam misalignment. Conventionally, the beam-squint effect has been treated as a critical performance impairment that must be mitigated. This article introduces a paradigm shift in wideband LEO satellite systems by redefining beam-squint as a valuable source of frequency-spatial diversity and presents the principles of rainbow BF. Rather than mitigating beam squint, rainbow BF deliberately exploits it to generate frequency-dependent beams, enabling different frequency components to illuminate distinct spatial directions using only a single or a small number of radio frequency chains. By supporting dynamic frequency-spatial beam allocation, rainbow BF offers enhanced flexibility and scalability for wideband LEO SATCOM. We further illustrate the benefits of rainbow BF through three representative LEO SATCOM applications: i) massive multiple access to overcome the latency and throughput bottlenecks of conventional beam hopping; ii) integrated sensing and communications for simultaneous target detection and data transmission; and iii) rapid satellite acquisition to reduce search overhead and improve link reliability. Finally, we discuss key implementation challenges and outline promising future research directions for rainbow BF in wideband LEO SATCOM.

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arXiv2026

Robust Rate-Splitting Design for Mixed Dual-Polarized Integrated Satellite-Terrestrial Networks Under Polarization Mismatch

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.

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arXiv2026

ISAC-Enabled Non-Terrestrial Networks for 6G: Design Principles, Standardization, Performance Tradeoffs, and Use Cases

Non-Terrestrial Networks (NTN) have emerged as a key enabler to fully realize the vision of integrated, intelligent, and ubiquitous connectivity in 6G systems. However, several operational challenges, including severe Doppler effects, interference, and latency, hinder the seamless integration of NTN and Terrestrial Networks (TN). In this context, Integrated Sensing and Communication (ISAC), which unifies sensing and communication functionalities within a common framework, offers great potential to address these challenges while enabling new network capabilities. Due to its complementary functionalities, ISAC can play a pivotal role in enhancing NTN performance, although its practical adoption requires a fundamental rethinking of existing architectural and standardization frameworks. Motivated by this need, this article examines key aspects of ISAC-enabled NTN, including architectural design principles, application scenarios, standardization challenges, and key performance tradeoffs. Finally, a representative case study is presented to illustrate major technical challenges and highlight promising future research directions for ISAC-enabled NTN.

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arXiv2026

Cooperative Multi-Satellite ISAC Networks: Centralized vs. Distributed Sensing

This paper investigates a downlink multi-satellite integrated sensing and communication (ISAC) network, in which multiple satellites simultaneously transmit ISAC signals to provide communication services to ground user equipments and enable cooperative sensing of airborne targets through multiple gateways. To support this dual functionality, we introduce communication and sensing beamforming designs based on uniform planar arrays with optimized power allocation. Building on these designs, we propose two cooperative sensing frameworks, namely centralized and distributed. In the centralized framework, each gateway forwards its sensing observations to a central unit (CU), where the positions of multiple targets are jointly estimated from the aggregated data. To mitigate the signaling overhead inherent in centralized processing, a distributed framework is further proposed, in which each gateway independently estimates target positions and transmits only the local estimates to the CU. To associate estimates from different gateways, a data association problem based on the squared Euclidean distance is formulated and efficiently solved using the Hungarian algorithm. The final target positions are then obtained by minimizing the distance error. Simulation results demonstrate that the proposed centralized and distributed frameworks significantly outperform existing sensing schemes while satisfying communication performance requirements. Finally, we evaluate the sensing-communication trade-off from the viewpoints of sensing accuracy and communication power consumption under the proposed frameworks.

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arXiv2025

Beyond Beam Sweeping: One-Shot Satellite Acquisition with Doppler-Aware Rainbow Beamforming

High-gain beamforming (BF) is essential for low Earth orbit (LEO) satellite communications to overcome severe path loss, but this requires acquiring precise satellite positions. Conventional satellite acquisition typically relies on time-domain beam sweeping, which incurs substantial overhead and latency. In this correspondence, we propose an efficient one-shot satellite acquisition framework that capitalizes on two phenomena traditionally regarded as impairments: i) Doppler effects and ii) beam-squint effects. Specifically, we derive a closed-form \emph{rainbow beamformer} that leverages beam-squint effects to align frequency-dependent beam directions with satellite positions inferred from their Doppler shifts. This approach enables reception from multiple satellites at once without requiring beam sweeping. To extract satellite position information, we develop three Doppler-aware angle estimation algorithms based on received signals. Simulation results demonstrate that the proposed method significantly outperforms conventional beam sweeping approaches in both acquisition accuracy and required time slots. These gains stem from the ability of the proposed rainbow BF to exploit the \emph{angle-dependent nature of Doppler shifts}, enabling full angular-domain coverage with a single pilot transmission and reception.

cs.IT↗

arXiv2025

Embracing Beam-Squint Effects for Wideband LEO Satellite Communications: A 3D Rainbow Beamforming Approach

Low Earth Orbit (LEO) satellite communications (SATCOM) offers high-throughput, low-latency global connectivity to a very large number of users. To accommodate this demand with limited hardware resources, beam hopping (BH) has emerged as a prominent approach in LEO SATCOM. However, its time-domain switching mechanism confines coverage to a small fraction of the service area during each time slot, exacerbating uplink throughput bottlenecks and latency issues as the user density increases. Meanwhile, wideband systems experience the beam-squint effect, where analog beamforming (BF) directions vary with subcarrier frequencies, potentially causing misalignment at certain frequencies, thereby hindering the performance of wideband SATCOM. In this paper, we aim to shift the paradigm in wideband LEO SATCOM from beam-squint as an impairment to beam-squint as an asset. Specifically, we put forth 3D rainbow BF employing a joint phase-time array (JPTA) antenna with true time delay (TTD) to intentionally widen the beam-squint angle, steering frequency-dependent beams toward distributed directions. This novel approach enables the satellite to serve its entire coverage area in a single time slot. By doing so, the satellite simultaneously receives uplink signals from a massive number of users, significantly boosting throughput and reducing latency. To realize 3D rainbow BF, we formulate a JPTA beamformer optimization problem and address the non-convex nature of the optimization problem through a novel joint alternating and decomposition-based optimization framework. Through numerical evaluations incorporating realistic 3D LEO SATCOM geometry, our numerical results demonstrate that the proposed rainbow BF-empowered LEO SATCOM achieves up to 2.8-fold increase in uplink throughput compared to conventional BH systems. These results mark a significant breakthrough for 6G wideband LEO SATCOM.

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arXiv2025

Enabling Full-Duplex LEO Satellite Systems with Non-Reciprocal BD-RIS-Assisted Beamforming

Low Earth orbit (LEO) satellites are a promising technology for providing low-latency, high-data-rate, and wide-coverage communication services. However, with growing demand for data transmission, future non-terrestrial networks (NTNs) require high spectral efficiency especially with low-gain antennas at the ground devices. This motivates the adoption of in-band full-duplex (FD) systems. In addition, the potential imbalance between downlink (DL) and uplink (UL) transmissions necessitates flexibility in resource allocation. To overcome these challenges, we propose an FD LEO satellite system, where the non-reciprocal beyond-diagonal reconfigurable intelligent surfaces (NR-BD-RIS) and multiple transmit and receive antennas are attached to the LEO satellite. NR-BD-RIS reflects the DL and UL signals by passive beamforming. By incorporating non-reciprocal components into the impedance network of RIS, the NR-BD-RIS breaks channel reciprocity, facilitating simultaneous support for multiple beam directions. To cover a wide coverage, we propose a time-sharing scheduling framework in which the NR-BD-RIS simultaneously serves multiple DL and multiple UL ground devices within each time slot. An optimization problem is defined to maximize the weighted sum-rate over the entire scheduling period. Numerical results demonstrate that the proposed NR-BD-RIS significantly performs better than both conventional BD-RIS and diagonal RIS (D-RIS) with respect to DL and UL sum-rate performance under both single-user (SU) and multiple-user (MU) cases. Additionally, NR-BD-RIS requires less frequent reconfiguration compared to the other two types of RIS, making it more practical for implementation.

eess.SP↗

arXiv2025

Space-Time Rate-Splitting Multiple Access for Multibeam LEO Satellite Networks

This paper proposes a novel space-time rate-splitting multiple access (ST-RSMA) framework for multibeam low Earth orbit (LEO) satellite communications (SATCOM) systems, where space-time coding is integrated into the common stream transmission. This design enables full diversity gain in the common stream transmission for all users, regardless of the uncertainty of the channel state information (CSI) and network load conditions, thereby overcoming the performance limitations of conventional RSMA that employs a single beamforming vector for all users. To further enhance performance, we develop a weighted minimum mean square error (WMMSE)-based algorithm tailored to ST-RSMA that jointly optimizes the power allocation for the common stream and the power/beamforming vectors for private streams, aiming to maximize the minimum user rate. Numerical results show that ST-RSMA significantly outperforms conventional RSMA and other multiple access techniques, offering a robust and scalable solution for LEO SATCOM.

cs.IT↗

arXiv2025

Transmitter-Side Beyond-Diagonal RIS-Enabled Integrated Sensing and Communications

Beyond diagonal reconfigurable intelligent surfaces (BD-RIS) have emerged as a promising technology for 6G wireless networks, offering more advanced control over electromagnetic wave propagation than conventional diagonal RIS. This paper proposes a novel integrated sensing and communication (ISAC) framework that incorporates BD-RIS at the transmitter. This not only opens the door to enhanced sensing and communication performance, but also alleviates the need for large-scale fully digital radio frequency (RF) chains at the transmitter. Based on the proposed system model, we formulate a normalized weighted optimization problem to jointly design the active beamforming and the BD-RIS scattering matrix with the aim of jointly minimizing the trace of the Cram\'er-Rao bound (CRB) for sensing targets and maximizing the sum rate (SR) for communication users. To address this highly coupled optimization problem, we propose a novel and low-complexity iterative algorithm that efficiently solves the active beamforming and scattering matrix subproblems by transforming each into a series of tractable projection problems with closed-form solutions. Numerical results show the appealing capability of the transmitter-side BD-RIS-aided ISAC over conventional diagonal RIS-aided ISAC in enhancing both sensing and communication performance. Moreover, compared to the classic iterative algorithm, the proposed algorithm offers enhanced dual-functional performance while significantly reducing the computational complexity.

eess.SP↗

arXiv2025

Interference Management for Integrated Sensing and Communications: A Multiple Access Perspective

The integrated sensing and communication (ISAC) technique has been considered a key enabler for 6G radio access networks. ISAC fulfills a brand new paradigm shift in wireless networks via the seamless interplay between communication and sensing within a unified network. However, the tight integration of these functionalities inevitably gives rise to various types of interference, posing significant challenges to existing ISAC waveform designs and rendering interference management a critical concern. Inspired by the development trajectory of wireless communications, different multiple access (MA) techniques, such as orthogonal multiple access (OMA), space-division multiple access (SDMA), and more recently, non-orthogonal multiple access (NOMA) and rate-splitting multiple access (RSMA), have been demonstrated to play a pivotal role in efficiently utilizing limited spectrum resources, designing ISAC waveforms, as well as managing inter-user interference and inter-functionality interference in ISAC. Notably, the interplay between MA and ISAC presents mutually beneficial integration. On the one hand, ISAC helps MA techniques better exploit their interference management capability beyond the communication-only networks. On the other hand, different MA techniques serve as promising solutions for inter-functionality and inter-user interference management in ISAC. In this paper, we deliver the first comprehensive tutorial of MA techniques in ISAC networks. Specifically, we illustrate the fundamental principles of ISAC, classify the diverse types of interference in different ISAC systems, and compare MA-assisted ISAC designs, highlighting their respective advantages and limitations. Moreover, we provide an outlook on the emerging applications and future research directions of different MA-assisted ISAC.

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arXiv2025

Optimal Task Offloading with Firm Deadlines for Mobile Edge Computing Systems

Under a dramatic increase in mobile data traffic, a promising solution for edge computing systems to maintain their local service is the task migration that may be implemented by means of Autonomous mobile agents (AMA). In designing an optimal scheme for task offloading to AMA, we define a system cost as a minimization objective function that comprises two parts. First, an offloading cost which can be interpreted as the cost of using computational resources from the AMA. Second, a penalty cost due to potential task expiration. To minimize the expected (timeaverage) cost over a given time horizon, we formulate a Dynamic programming (DP). However, the DP Equation suffers from the well-known curse of dimensionality, which makes computations intractable, especially for infinite system state space. To reduce the computational burden, we identify three important properties of the optimal policy and show that it suffices to evaluate the DP Equation on a finite subset of the state space only. We then prove that the optimal task offloading decision at a state can be inferred from that at its adjacent states, further reducing the computational load. We present simulations to verify the theoretical results and to provide insights into the considered system.

eess.SY↗

arXiv2025

PC-DeepNet: A GNSS Positioning Error Minimization Framework Using Permutation-Invariant Deep Neural Network

Global navigation satellite systems (GNSS) face significant challenges in urban and sub-urban areas due to non-line-of-sight (NLOS) propagation, multipath effects, and low received power levels, resulting in highly non-linear and non-Gaussian measurement error distributions. In light of this, conventional model-based positioning approaches, which rely on Gaussian error approximations, struggle to achieve precise localization under these conditions. To overcome these challenges, we put forth a novel learning-based framework, PC-DeepNet, that employs a permutation-invariant (PI) deep neural network (DNN) to estimate position corrections (PC). This approach is designed to ensure robustness against changes in the number and/or order of visible satellite measurements, a common issue in GNSS systems, while leveraging NLOS and multipath indicators as features to enhance positioning accuracy in challenging urban and sub-urban environments. To validate the performance of the proposed framework, we compare the positioning error with state-of-the-art model-based and learning-based positioning methods using two publicly available datasets. The results confirm that proposed PC-DeepNet achieves superior accuracy than existing model-based and learning-based methods while exhibiting lower computational complexity compared to previous learning-based approaches.

cs.LG↗

arXiv2025

Rate-Matching Framework for RSMA-Enabled Multibeam LEO Satellite Communications

With the goal of ubiquitous global connectivity, multibeam low Earth orbit (LEO) satellite communication (SATCOM) has attracted significant attention in recent years. The traffic demands of users are heterogeneous within the broad coverage of SATCOM due to different geological conditions and user distributions. Motivated by this, this paper proposes a novel rate-matching (RM) framework based on rate-splitting multiple access (RSMA) that minimizes the difference between the traffic demands and offered rates while simultaneously minimizing transmit power for power-hungry satellite payloads. Moreover, channel phase perturbations arising from channel estimation and feedback errors are considered to capture realistic multibeam LEO SATCOM scenarios. To tackle the non-convexity of the RSMA-based RM problem under phase perturbations, we convert it into a tractable convex form via the successive convex approximation method and present an efficient algorithm to solve the RM problem. Through the extensive numerical analysis across various traffic demand distribution and channel state information accuracy at LEO satellites, we demonstrate that RSMA flexibly allocates the power between common and private streams according to different traffic patterns across beams, thereby efficiently satisfying users non-uniform traffic demands. In particular, the use of common messages plays a vital role in overcoming the limited spatial dimension available at LEO satellites, enabling it to manage inter- and intra-beam interference effectively in the presence of phase perturbation.

cs.IT↗

arXiv2024

A Tutorial on Non-Terrestrial Networks: Towards Global and Ubiquitous 6G Connectivity

The International Mobile Telecommunications (IMT)-2030 framework recently adopted by the International Telecommunication Union Radiocommunication Sector (ITU-R) envisions 6G networks to deliver intelligent, seamless connectivity that supports reliable, sustainable, and resilient communications. Recent developments in the 3rd Generation Partnership Project (3GPP) Releases 17-19, particularly within the Radio Access Network (RAN)4 working group addressing satellite and cellular spectrum sharing and RAN2 enhancing New Radio (NR)/IoT for NTN, highlight the critical role NTN is set to play in the evolution of 6G standards. The integration of advanced signal processing, edge and cloud computing, and Deep Reinforcement Learning (DRL) for Low Earth Orbit (LEO) satellites and aerial platforms, such as Uncrewed Aerial Vehicles (UAV) and high-, medium-, and low-altitude platform stations, has revolutionized the convergence of space, aerial, and Terrestrial Networks (TN). Artificial Intelligence (AI)-powered deployments for NTN and NTN-IoT, combined with Next Generation Multiple Access (NGMA) technologies, have dramatically reshaped global connectivity. This tutorial paper provides a comprehensive exploration of emerging NTN-based 6G wireless networks, covering vision, alignment with 5G-Advanced and 6G standards, key principles, trends, challenges, real-world applications, and novel problem solving frameworks. It examines essential enabling technologies like AI for NTN (LEO satellites and aerial platforms), DRL, edge computing for NTN, AI for NTN trajectory optimization, Reconfigurable Intelligent Surfaces (RIS)-enhanced NTN, and robust Multiple-Input-Multiple-Output (MIMO) beamforming. Furthermore, it addresses interference management through NGMA, including Rate-Splitting Multiple Access (RSMA) for NTN, and the use of aerial platforms for access, relay, and fronthaul/backhaul connectivity.

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arXiv2024

Cooperative Learning-Based Framework for VNF Caching and Placement Optimization over Low Earth Orbit Satellite Networks

Low Earth Orbit Satellite Networks (LSNs) are integral to supporting a broad range of modern applications, which are typically modeled as Service Function Chains (SFCs). Each SFC is composed of Virtual Network Functions (VNFs), where each VNF performs a specific task. In this work, we tackle two key challenges in deploying SFCs across an LSN. Firstly, we aim to optimize the long-term system performance by minimizing the average end-to-end SFC execution delay, given that each satellite comes with a pre-installed/cached subset of VNFs. To achieve optimal SFC placement, we formulate an offline Dynamic Programming (DP) equation. To overcome the challenges associated with DP, such as its complexity, the need for probability knowledge, and centralized decision-making, we put forth an online Multi-Agent Q-Learning (MAQL) solution. Our MAQL approach addresses convergence issues in the non-stationary LSN environment by enabling satellites to share learning parameters and update their Q-tables based on distinct rules for their selected actions. Secondly, to determine the optimal VNF subsets for satellite caching, we develop a Bayesian Optimization (BO)-based learning mechanism that operates both offline and continuously in the background during runtime. Extensive experiments demonstrate that our MAQL approach achieves near-optimal performance comparable to the DP model and significantly outperforms existing baselines. Moreover, the BO-based approach effectively enhances the request serving rate over time.

cs.IT↗

arXiv2024

A Double-Difference Doppler Shift-Based Positioning Framework with Ephemeris Error Correction of LEO Satellites

In signals of opportunity (SOPs)-based positioning utilizing low Earth orbit (LEO) satellites, ephemeris data derived from two-line element files can introduce increasing error over time. To handle the erroneous measurement, an additional base receiver with a known position is often used to compensate for the effect of ephemeris error when positioning the user terminal (UT). However, this approach is insufficient for the long baseline (the distance between the base receiver and UT) as it fails to adequately correct Doppler shift measurement errors caused by ephemeris inaccuracies, resulting in degraded positioning performance. Moreover, the lack of clock synchronization between the base receiver and UT exacerbates erroneous Doppler shift measurements. To address these challenges, we put forth a robust double-difference Doppler shift-based positioning framework, coined 3DPose, to handle the clock synchronization issue between the base receiver and UT, and positioning degradation due to the long baseline. The proposed 3DPose framework leverages double-difference Doppler shift measurements to eliminate the clock synchronization issue and incorporates a novel ephemeris error correction algorithm to enhance UT positioning accuracy in case of the long baseline. The algorithm specifically characterizes and corrects the Doppler shift measurement errors arising from erroneous ephemeris data, focusing on satellite position errors in the tangential direction. To validate the effectiveness of the proposed framework, we conduct comparative analyses across three different scenarios, contrasting its performance with the existing differential Doppler positioning method. The results demonstrate that the proposed 3DPose framework achieves an average reduction of 90% in 3-dimensional positioning errors compared to the existing differential Doppler approach.

cs.IT↗

arXiv2024

Rate-Splitting for Joint Unicast and Multicast Transmission in LEO Satellite Networks with Non-Uniform Traffic Demand

Low Earth orbit (LEO) satellite communications (SATCOM) with ubiquitous global connectivity is deemed a pivotal catalyst in advancing wireless communication systems for 5G and beyond. LEO SATCOM excels in delivering versatile information services across expansive areas, facilitating both unicast and multicast transmissions via high-speed broadband capability. Nonetheless, given the broadband coverage of LEO SATCOM, traffic demand distribution within the service area is non-uniform, and the time/frequency/power resources available at LEO satellites remain significantly limited. Motivated by these challenges, we propose a rate-matching framework for non-orthogonal unicast and multicast (NOUM) transmission. Our approach aims to minimize the difference between offered rates and traffic demands for both unicast and multicast messages. By multiplexing unicast and multicast transmissions over the same radio resource, rate-splitting multiple access (RSMA) is employed to manage interference between unicast and multicast streams, as well as inter-user interference under imperfect channel state information at the LEO satellite. To address the formulated problems non-smoothness and non-convexity, the common rate is approximated using the LogSumExp technique. Thereafter, we represent the common rate portion as the ratio of the approximated function, converting the problem into an unconstrained form. A generalized power iteration (GPI)-based algorithm, coined GPI-RS-NOUM, is proposed upon this reformulation. Through comprehensive numerical analysis across diverse simulation setups, we demonstrate that the proposed framework outperforms various benchmarks for LEO SATCOM with uneven traffic demands.

cs.IT↗

arXiv2024

Rate-Splitting Multiple Access for GEO-LEO Coexisting Satellite Systems: A Traffic-Aware Throughput Maximization Precoder Design

The frequency coexistence between geostationary orbit (GEO) and low earth orbit (LEO) satellite systems is expected to be a promising approach for relieving spectrum scarcity. However, it is essential to manage mutual interference between GEO and LEO satellite systems for frequency coexistence. Specifically, \emph{in-line interference}, caused by LEO satellites moving near the line-of-sight path between GEO satellite and GEO users (GUs), can significantly degrade GEO system throughput. This paper put forth a novel rate-splitting multiple access (RSMA) with a super-common message for GEO-LEO coexisting satellite systems (CSS). By employing a super-common message that GUs can decode, GUs can mitigate the in-line interference by successive interference cancellation (SIC). Moreover, we formulate a traffic-aware throughput maximization (TTM) problem to satisfy the heterogeneous traffic demands of users by minimizing total unmet throughput demands (or user dissatisfaction). By doing so, the TTM precoder can be flexibly adjusted according to the interference leakage from LEO satellites to GUs and target traffic demands. Numerical results confirm that our proposed method ensures seamless connectivity even in the GEO-LEO in-line interference regime under imperfect channel state information (CSI) at both the transmitter and receiver.

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