Reconfigurable intelligent surfaces (RIS) are emerging as a promising technology for next-generation wireless communications, capable of mitigating severe propagation attenuation, enhancing spectral efficiency, and expanding signal coverage. This paper focuses on online millimeter-wave (mmWave) beam tracking for multi-RIS-assisted hybrid beamforming systems. We develop two novel beam tracking algorithms based on multi-agent deep reinforcement learning (DRL): a multi-agent deep deterministic policy gradient (MADDPG)-based algorithm for continuous-domain beam angle tracking and a multi-agent deep Q-network (MADQN)-based algorithm for codebook-based discrete-domain beam angle tracking. Both algorithms are designed to maximize the sum rate by jointly optimizing analog beamforming for the base station (BS) and reflection coefficients for multiple RISs in dynamic environments, leveraging historical information and without requiring current user position or channel information. After determining analog beamforming and RIS reflection coefficients, digital beamforming for the BS is constructed by estimating the end-to-end effective channel, which significantly reduces the overhead of channel estimation. Experimental results demonstrate that the proposed algorithms effectively adapt the analog beamformer and RIS reflection coefficients to account for user mobility, significantly outperforming existing benchmark schemes.

This study explores interference coordination for space-air-ground integrated networks under probabilistic line-of-sight (LoS) channel models, where the LoS probability of a wireless channel is statistically modeled based on the elevation angle between an unmanned aerial vehicle (UAV) and a ground node (GN). Unlike conventional approaches that simplify the model by neglecting either LoS or non-LoS (NLoS) channel components, we jointly optimize user scheduling, transmit power, and three-dimensional trajectory while fully accounting for both components. The objective is to maximize the minimum average spectral efficiency (SE) among GNs while ensuring the required SE for earth stations (ESs) served by satellites. Given the nonconvexity of the optimization problem, we decompose it into four subproblems and apply a successive convex approximation to make each subproblem convex with respect to the relevant optimization variable. Subsequently, we propose a low-complexity algorithm based on the block coordinate descent method to iteratively determine the optimal solution for each convex subproblem. Extensive simulations under various scenarios confirm that the UAV optimizes its horizontal and vertical trajectories to increase the LoS probability of the signal channel and the NLoS probability of the interference channel. This allows the UAV to serve GNs with high SE while reducing interference to the ESs. The results also demonstrate that the proposed scheme outperforms baseline schemes in terms of average SE by jointly optimizing the three-dimensional trajectory and communication resources.

In this paper, we explore the rigorous mathematical modeling of an unmanned aerial vehicle (UAV)-enabled parcel delivery to optimize a three-dimensional (3D) trajectory and pickup/drop-off strategy. Taking into account practical considerations including the avoidance of no-fly zones (NFZs) and the weight restrictions of the UAV, our goal is to jointly optimize the pickup and drop-off indicators, lengths of time slots, and horizontal and vertical trajectories, with the objective of minimizing the weighted-sum of completion time and energy consumption. To address the nonconvexity of the formulated problem, which involves mixed-integer nonlinear programming, we first apply a successive convex approximation to transform the nonconvex problem into a convex one for optimization variables. Moreover, we utilize a penalty convex-concave procedure to maintain the binary nature of integer variables. Finally, for the relaxed convex problem, we propose a low-complexity algorithm that derives the suboptimal UAV strategy iteratively. The simulation results demonstrate the effectiveness of the proposed strategy in establishing 3D trajectories for specific objectives and completely avoiding NFZs while maintaining the binary nature of the pickup and drop-off indicators. Furthermore, the comparative study provides insight into the trade-offs between time-minimization and energy-minimization strategies, offering the flexibility to choose the most suitable approach based on the specific service requirements and objectives.

Integrated sensing and communications (ISAC) is a promising technology for sixth-generation (6G) networks, especially when combined with extremely large-scale antenna arrays (ELAAs) for near-field sensing. In this paper, we investigate a holographic-ELAA-enabled near-field ISAC system, where a dual-functional base station simultaneously transmits communication and sensing signals. By adopting the conditional mutual information as the sensing metric, we formulate a sensing-centric beamforming optimization problem subject to a transmit power constraint and the amplitude constraints of the holographic surface. To solve the resulting problem, we decompose it into digital beamforming and holographic beamforming subproblems and propose an alternative algorithm. Numerical results show that the proposed design achieves improved sensing performance compared with conventional schemes.

This paper studies a cooperative relaying communication scheme that employs a single passive reconfigurable intelligent surface (RIS), utilizing integer forcing (IF) as a multiple-input multiple-output (MIMO) technique. In the case of IF-based transceivers, the transmitter sends independently encoded data streams using the same lattice code, and the receiver decodes integer-linear combinations of codewords instead of decoding each codeword separately. Although the flexible decoding provided by IF improves achievable rates compared to conventional separate decoding, the integer-linear combinations observed at the IF-based receiver must remain unchanged throughout the codeword's duration, even in the presence of channel variations, to enable the decoding of summed codewords. Motivated by this fact, we introduce a novel strategy tailored for IF that involves adjusting the reflection matrix of the RIS to reduce fluctuations in the resulting end-to-end channel between the transmitter and receiver throughout codeword transmission, which we refer to as channel stabilization. Furthermore, we develop a novel IF-based transceiver scheme called successive cancellation IF (SC-IF), which effectively integrates successive IF (S-IF) sum decoding with minimum mean square error-successive interference cancellation (MMSE-SIC) individual decoding within a unified MIMO framework to achieve improved performance. Simulation results demonstrate that when a large number of reflective elements are employed in the RIS, the proposed channel stabilization scheme significantly outperforms benchmark schemes that aim to individually optimize the reflection matrix for each sub-block, and the proposed SC-IF can achieve a rate comparable to the theoretical upper bound represented by the joint maximal likelihood (ML) receiver.

For change detection in synthetic aperture radar (SAR) imagery, amplitude change detection (ACD) and coherent change detection (CCD) are widely employed. However, time-series SAR data often contain noise and variability introduced by system and environmental factors, requiring mitigation. Additionally, the stability of SAR signals is preserved when calibration accounts for temporal and environmental variations. Although ACD and CCD techniques can detect changes, spatial variability outside the primary target area introduces complexity into the analysis. This study presents a robust change detection methodology designed to identify urban changes using KOMPSAT-5 time-series data. A comprehensive preprocessing framework—including coregistration, radiometric terrain correction, normalization, and speckle filtering—was implemented to ensure data consistency and accuracy. Statistical homogeneous pixels (SHPs) were extracted to identify stable targets, and coherence-based analysis was employed to quantify temporal decorrelation and detect changes. Adaptive thresholding and morphological operations refined the detected changes, while small-segment removal mitigated noise effects. Experimental results demonstrated high reliability, with an overall accuracy of 92%, validated using confusion matrix analysis. The methodology effectively identified urban changes, highlighting the potential of KOMPSAT-5 data for post-disaster monitoring and urban change detection. Future improvements are suggested, focusing on the stability of InSAR orbits to further enhance detection precision. The findings underscore the potential for broader applications of the developed SAR time-series change detection technology, promoting increased utilization of KOMPSAT SAR data for both domestic and international research and monitoring initiatives.

The synthetic aperture radar (SAR) offset tracking method is extensively employed to accurately measure significant surface displacements resulting from phenomena such as glacier melting, volcanic eruptions, and earthquakes, particularly when the interferometric phase lacks coherence. However, a trade-off exists between the resolution and accuracy of SAR offset tracking, determined by the selected kernel sizes. Hence, choosing optimal kernel sizes is crucial in the application of this method. In this study, we applied SAR offset tracking with a coarse-to-fine strategy, applying the kernel sizes coarsely and then fine. This approach allows for improved observational precision while maintaining resolution compared to general single-kernel offset tracking results. Applying this technique to SAR imagery from KOMPSAT-5, a South Korean X-band SAR satellite, enabled the precise observation of surface displacements caused by the melting of the Campbell Glacier in the East Antarctic and the 2023 Turkey-Syria earthquake. This marks the first instance of large-scale surface displacement observations using KOMPSAT-5 SAR imagery, affirming the effectiveness of the SAR offset tracking technique for precise land surface displacement observations with KOMPSAT-5 SAR imagery.

We consider reconfigurable intelligent surface (RIS) aided sixth-generation (6G) terahertz (THz) communications for indoor environment in which a base station (BS) wishes to send independent messages to its serving users with the help of multiple RISs. For indoor environment, various obstacles such as pillars, walls, and other objects can result in no line-of-sight signal path between the BS and a user, which can significantly degrade performance. To overcome such limitation of indoor THz communication, we firstly optimize the placement of RISs to maximize the coverage area. Under the optimized RIS placement, we propose 3D hybrid beamforming at the BS and phase adjustment at RISs, which are jointly performed at the BS and RISs via codebook-based 3D beam scanning with low complexity. Numerical simulations demonstrate that the proposed scheme significantly improves the average sum rate compared to the cases of no RIS and randomly deployed RISs. It is further shown that the proposed codebook-based 3D beam scanning efficiently aligns analog beams between BS--user links or BS--RIS--user links and, as a consequence, achieves the average sum rate close to that of coherent beam alignment requiring global channel state information.

We study a new rate splitting (RS)-based hybrid beamforming scheme for multi-user downlink cellular networks in which the base station having a hybrid beamforming structure serves multiple users each having conventional multiple antennas. To maximize the potential of RS, we propose a general RS-enabled hybrid beamforming framework that can be applied to both fully-connected and sub-array hybrid beamforming structures, allowing for the assignment of a flexible number of streams for each user. Our proposed RS method divides each stream into an arbitrary number of common and private sub-streams, where private sub-streams are only recoverable by a dedicated user, whereas common streams can be recovered by all users. We propose a low-complexity analog and digital beamforming design suitable for the proposed RS and optimize the number of allocated common and private sub-streams for all users through a low-complexity genetic algorithm to maximize the achievable sum rate or minimum rate over multiple users. Numerical results demonstrate that the proposed scheme achieves a near-optimal sum rate close to that of dirty paper coding with low computational complexity and outperforms the benchmark approaches without considering RS.

Sundhnukur volcano on the Reykjanes Peninsula, Iceland, erupted on May 29, 2024, spewing a large amount of lava, forming a volcanic plateau and causing surface changes. In this study, the Coherence Change Detection (CCD) technique was applied to Sentinel-1 satellite images to observe surface changes caused by the initial volcanic eruption. The Support Vector Machine (SVM) algorithm was applied to Landsat-9 satellite images to derive the threshold for the CCD technique, and based on this, a damage proxy map and change area data were generated to evaluate the area changed by the volcanic eruption. The combined analysis using the CCD technique and the SVM algorithm showed that 7,536,000 m2 of volcanic plateau was formed during the initial period of the volcanic eruption, which showed an error of approximately 18% from the actual measurement result. In addition, it was found that the volcanic eruption caused surface changes in an area of 9,698,500 m(2). In this study, it was confirmed that the CCD technique can effectively detect surface changes that are difficult to detect with optical satellite images. This approach will contribute to identifying the characteristics of surface changes in the early stages of volcanic eruptions, and it is expected that it can be used as basic data for surface change detection and monitoring when the KOMPSAT-6 satellite is operated in the future.

The frequency and impact of major geological disasters are increasing worldwide, highlighting the growing importance of prompt and efficient damage analyses and response strategy development. Satellite imagery plays a crucial role in determining the extent and impact of disasters on the Earth’s surface, offering significant advantages such as extensive real-time data collection and enabling time-series analysis. This facilitates the immediate detection and response to surface changes due to earthquakes, landslides, and volcanic activity. Remote sensing data are used both domestically and internationally to develop and operate real-time monitoring systems for disaster and hazard responses. In particular, South Korea operates over seven low Earth and geostationary orbit satellites. Their use has expanded to include geological disaster surveillance and oceanic and atmospheric monitoring. This study focuses on the analysis of significant geological disasters in South Korea and investigates how satellite data can contribute to disaster response and prevention. Specifically, regions in South Korea prone to three major geological hazards—earthquakes, landslides, and volcanic activities—were selected for analysis based on disaster occurrences or potential occurrences. The data collection status of low Earth orbit satellites operated by South Korea under consistent conditions from 2015 to 2023 was examined. For optical imagery, the analysis of cloud cover during the study period was conducted to assess response possibilities during disaster events. Additionally, response strategies for Synthetic Aperture Radar (SAR) were explored to complement optical imagery. Furthermore, an analysis of satellite data collection trends over time provided fundamental insights for detecting and analyzing destructive geological disasters in the future. This paper is expected to assist South Korea in effectively responding to and managing such disasters, ultimately aiding in the development of comprehensive disaster response strategies.

Mine operational safety is an important aspect of maintaining the operational continuity of a mining area. In this study, we used the InSAR time series to analyze land surface changes using the ICOPS (improved combined scatterers with optimized point scatters) method. This ICOPS method combines persistent scatterers (PS) with distributed scatterers (DS) to increase surface deformation analysis’s spatial coverage and quality. One of the improvements of this study is the use of machine learning in postprocessing, based on convolutional neural networks, to increase the reliability of results. This study used data from the Sentinel-1 SAR C-band satellite during the 2016–2022 observation period at the Musan mine, North Korea. In the InSAR surface deformation time analysis, the maximum average rate of land subsidence was approximately > 15.00 cm per year, with total surface deformation of 170 cm and 70 cm for the eastern dumping area and the western dumping area, respectively. Analyzing the mechanism of land surface changes also involved evaluating the geological conditions in the Musan mining area. Our research findings show that combining machine learning and statistical methods has great potential to enhance the understanding of mine surface deformation.

In this study, we present KARI-MT-InSAR tool and analyze its efficiency for surface deformation monitoring. Resultingly, KARI-MT-InSAR tool considerably reduced the computation time for the estimation of crustal movements with high accuracy and precision. In addition, the convenient GUI was very efficient for understanding and interpretation for the results of each processing steps.

In this study, we utilize swarm-based optimization (SBO) techniques to retrieve the forest height (FH) in polarimetric synthetic aperture radar interferometry (PolInSAR) inversion. Two physical models, which are the random-volume-overground (RVoG) and the simplified version of random-motion-over-ground (RMoG) models are used to relate the quadpolarimetric SAR observations to the FH. In the results obtained using both the simulated and real data, the SBO method exhibit the better FH estimation results than the conventional method.

In this paper, we proposed a method based on statistically homogeneous pixel (SHP) and coherence information for time series change detection of urban areas using synthetic aperture radar (SAR) images. The proposed framework was applied to KOMPSAT-5 SAR time series data to conduct time series change detection experiments on container yards in port areas. If the algorithm is verified with more SAR images in the future, the algorithm for detecting time series changes in areas of interest using domestic KOMPSAT SAR satellite images will become more generalized and reliable.

In the upcoming 6G era, multiple access (MA) will play an essential role in achieving high throughput performances required in a wide range of wireless applications. Since MA and interference management are closely related issues, the conventional MA techniques are limited in that they cannot provide near-optimal performance in universal interference regimes. Recently, rate-splitting multiple access (RSMA) has been gaining much attention. RSMA splits an individual message into two parts: a common part, decodable by every user, and a private part, decodable only by the intended user. Each user first decodes the common message and then decodes its private message by applying successive interference cancellation (SIC). By doing so, RSMA not only embraces the existing MA techniques as special cases but also provides significant performance gains by efficiently mitigating inter-user interference in a broad range of interference regimes. In this article, we first present the theoretical foundation of RSMA. Subsequently, we put forth four key benefits of RSMA: spectral efficiency, robustness, scalability, and flexibility. Upon this, we describe how RSMA can enable ten promising scenarios and applications along with future research directions to pave the way for 6G.

A new multiple-input multiple-output (MIMO) receiver scheme for practical binary codes is proposed that provides consistent gains over conventional linear receivers. We first develop a practical successive integer forcing (IF) scheme based on practical binary codes rather than lattice codes. We then present the successive cancellation integer forcing (SC-IF) scheme, which combines and enhances successive IF and minimum mean squared error successive interference cancellation (MMSE-SIC). In this scheme, the receiver first decides whether individual decoding or IF sum decoding is appropriate for each data stream, and then conducts successive IF sum decoding only for selected streams while decoding the remaining streams using MMSE-SIC. The proposed SC-IF methodology mitigates the performance loss caused by mismatched IF filtering in fading channels, while attenuating the noise amplification caused by MMSE filtering. Extensive link-level simulations demonstrate that the proposed successive IF significantly improves the basic IF, and the SC-IF improves both the successive IF and MMSE-SIC, offering uniform improvements over conventional linear receivers for most channel correlation and variation parameters and modulation orders at comparable computational costs. These results illustrate the viability of SC-IF as a fundamental building block for high-performance MIMO receivers in 5G-Advanced and/or subsequent-generation communication systems.

We study cooperative communication with an active reconfigurable intelligent surface (RIS) for the $K$ -user rank-deficient multiple-input multiple-output (MIMO) interference channel. Specifically, we assume that transmitters and receivers use $M$ antennas each, and the channel matrix between each transmitter and each receiver has rank $L_{2}$ whereas the channel matrix between each transmitter or each receiver and the RIS has rank $L_{1}$ , where $L_{1}, L_{2}\leq M$ . In the absence of the RIS, the multiplexing gain from MIMO is severely limited when $L_{2}$ is small. We develop a novel cooperative transmission technique utilizing the RIS to overcome the channel rank deficiency and manage inter-user interference, and the key idea is to neutralize interfering links using signals reflected from the RIS or to reflect incident waves at the RIS to increase the rank of effective channel matrices, depending on the system configuration parameters. We analyze the achievable sum degrees of freedom (DoF) and sum rate, and derive an upper bound on the sum DoF, which is tight under certain conditions. The results show that using an active RIS can significantly improve both the sum rate and sum DoF compared to the network without the RIS, especially when $L_{1}$ is small and/or $L_{2}$ is large.

We consider reconfigurable intelligent surface (RIS) aided sixth-generation (6G) terahertz (THz) communications for indoor environment in which a base station (BS) wishes to send independent messages to its serving users with the help of multiple RISs. For indoor environment, various obstacles such as pillars, walls, and other objects can result in no line-of-sight signal path between the BS and a user, which can significantly degrade performance. To overcome such limitation of indoor THz communication, we firstly optimize the placement of RISs to maximize the coverage area. Under the optimized RIS placement, we propose 3D hybrid beamforming at the BS and phase adjustment at RISs, which are jointly performed at the BS and RISs via codebook-based 3D beam scanning with low complexity. Numerical simulations demonstrate that the proposed scheme significantly improves the average sum rate compared to the cases of no RIS and randomly deployed RISs and also achieves the average sum rate close to that of coherent beam alignment requiring global channel state information.

A new interference management scheme based on integer forcing (IF) receivers is studied for the two-user multiple-input and multiple-output (MIMO) interference channel. The proposed scheme employs a message splitting method that divides each data stream into common and private sub-streams, in which the private stream is recovered by the dedicated receiver only while the common stream is required to be recovered by both receivers. Specifically, to enable IF sum decoding at the receiver side, all streams are encoded using the same lattice code. Additionally, the number of common and private streams of each user is carefully determined by considering the number of antennas at transmitters and receivers, the channel matrices, and the effective signal-to-noise ratio (SNR) at each receiver to maximize the achievable rate. Furthermore, we consider various assumptions of channel state information at the transmitter side (CSIT) and propose low-complexity linear transmit beamforming suitable for each CSIT assumption. The achievable sum rate and rate region are analytically derived and extensively evaluated by simulation for various environments, demonstrating that the proposed interference management scheme strictly outperforms the previous benchmark schemes in a wide range of channel parameters due to the gain from IF sum decoding.