Reconfigurable intelligent surfaces (RISs) have emerged as a key enabling technology for next-generation (xG) Internet-of-Things (IoT) networks operating in high-frequency bands such as terahertz (THz). Due to the large number of reflecting elements, RIS-assisted communications predominantly occur in the near-field region, where signal propagation is governed by spherical wavefronts. This far-field to near-field transition induces a nonlinear coupling between the channel response and the user position, which significantly complicates channel estimation and localization in RIS-assisted THz systems. Existing approaches typically address these two tasks separately, leading to redundant processing and error propagation between channel and position domains. To overcome these limitations, we propose a unified framework, termed near-field unified channel estimation and localization (NF-UCL), that exploits the intrinsic bijective correspondence between the near-field channel and the user position. We introduce the concept of a near-field channel map and show that its image, termed the near-field channel manifold, exhibits a smooth Riemannian structure. This geometric characterization enables the application of manifold optimization techniques for efficient joint processing. Specifically, we develop an Riemannian conjugate gradient (RCG)-based algorithm that performs channel estimation and localization directly on the near-field channel manifold. Simulation results demonstrate that the proposed NF-UCL framework outperforms conventional methods in terms of channel normalized mean square error (NMSE) and user position root mean square error (RMSE).

The rapid advancement of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) has spurred growing interest in multi-UAV cooperative systems across various applications. However, effectively coordinating multiple UAVs for target point (TP) data processing, task offloading, and path planning remains challenging, particularly when aiming to minimize task completion time. To address these challenges, we propose a joint task assignment and resource allocation optimization (TARO) approach for efficient TP access in multi-UAV MEC systems. Specifically, we formulate an optimization problem that minimizes the task completion time while balancing task assignment through path planning, task offloading and bandwidth allocation. Given the mixed-integer nonlinear programming (MINLP) nature of the problem, we develop an efficient optimization algorithm by decomposing it into three subproblems. We propose an iterative algorithm to solve the challenging problem effectively. Simulation results demonstrate that the proposed TARO approach significantly reduces task completion time compared to traditional benchmarks.

Recently, large multimodal models (LMMs) have been successfully adopted in various fields due to their outstanding adaptability and reasoning abilities. Despite their potential to automate diverse tasks in communications systems, application to the physical layer remains underexplored. The primary reason is that the traditional physical layer relies on analytic channel measurements (e.g., pilot measurements), which capture only quantitative changes in transmitted signals and fail to characterize qualitative physical interactions (e.g., reflections and blockages) with the environment. In this paper, we propose an LMM-based environment-aware channel estimation framework that captures the contextual channel information by leveraging both perceptual sensor data and numerical pilot measurements. The main idea of the proposed scheme is to utilize visual channel parameters (VCPs), i.e., positions of user equipment (UE), reflection points, and scatterers. Since VCPs provide a direct visualization of the propagation environment, we can identify how signals propagate and physically interacts with the surrounding objects. To extract the VCPs and learn their probability distributions, we develop a reflection learning technique based on LMM. By incorporating these parameters, we establish a fundamental channel knowledge map (CKM) between the UE position and the channel. Simulation results demonstrate that the proposed scheme can effectively predict the channel throughout the wireless environments.

As a means to provide ubiquitous connectivity across the ground-air-space 3D network, low Earth orbit (LEO) satellite mega-constellation systems comprising thousands of LEO satellites have attracted significant interest from both academia and industry recently. One major issue of LEO mega-constellation systems is the frequent handovers between satellites and beams, causing an increase in communication latency and deterioration of quality of service (QoS). In this paper, we propose a user-centric cooperative communication framework for next generation (xG) LEO satellite mega-constellation systems. In the proposed framework, a group of LEO satellites simultaneously serve all the user equipments (UEs) using the same timefrequency resources. By dynamically organizing the clusters of serving satellites and coordinating their joint transmission based on statistical channel state information (CSI), the handover frequency and inter-satellite interference can be reduced effectively, thereby achieving significant enhancements in the spectral efficiency and coverage probability. From the achievable rate analysis and extensive simulations on realistic xG LEO satellite communication environments, we show that the proposed scheme substantially improves the spectral efficiency and coverage over the conventional beam-centric systems.

Reconfigurable intelligent surface (RIS)-assisted wideband terahertz (THz) communications are essential for achieving ultra-high data rates in sixth-generation (6G) networks. By adjusting the phase shifts of reflecting elements, RIS can effectively reshape wireless channels to enhance overall performance. However, two major challenges arise in RIS-assisted THz systems: 1) the dual beam split effect, where the large bandwidth causes subcarrier beam directions to diverge at both base station (BS) and RIS; and 2) the near-field effect, where the channel becomes a nonlinear function of both angle and distance. In this paper, we propose a novel beamforming technique, termed RIS-assisted wideband beamforming (RWB), to address these challenges and maximize data rates in RIS-assisted wideband THz systems. The RWB scheme leverages partially-connected true time delays (TTDs) and phase shifters (PSs) to generate frequency-dependent BS transmit beamforming vectors, while utilizing passive reflecting elements to control the frequency-invariant RIS reflect beamforming vector. By jointly optimizing the transmit and reflect beamforming vectors on the Riemannian manifold of unit-modulus phase shifts, RWB effectively mitigates both beam split and near-field effects. Numerical evaluations demonstrate that RWB achieves substantial data rate improvements over conventional wideband beamforming schemes.

To meet the ever-increasing data rate demand expected in 6G networks, terahertz (THz) ultra-massive (UM) multiple-input multiple-output (MIMO) systems have gained much attention recently. One notable aspect of these systems is that the deployment of an extremely large-scale antenna array and high transmission frequency result in an expansion of the near-field region where the electromagnetic (EM) radiation is modeled as a spherical wave. In the near-field region, the channel becomes a function of a position of a user equipment (UE) rather than the direction, giving rise to a beam focusing operation that focuses the signal power onto the specific position. However, the traditional approaches relying on the sweeping of discretized beam codewords cannot support this ultra-sharp beam focusing operation in THz UM-MIMO systems. This paper proposes a novel beam focusing technique based on sensing and computer vision (CV) technologies. The essence of the proposed scheme is to estimate the UE’s position from the vision information using the CV technique and then generates the beam heading towards the estimated position. By replacing the discretized and time-consuming beam sweeping operation with a highly precise CV-based positioning, the positioning accuracy as well as the beam focusing gain can be improved significantly. Numerical results show that the proposed scheme achieves significant positioning accuracy and data rate gains over the conventional codebook-based beam focusing schemes.

Recently, a reconfigurable intelligent surface (RIS) that controls the reflection characteristics of incident signals has received a great deal of attention. To make the most of the RIS-aided systems, an acquisition of RIS reflected channel information at the base station (BS) is crucial. However, this task is by no means easy due to the pilot overhead induced by the large number of reflecting elements. In this paper, we propose an efficient channel estimation and phase shift control technique reducing the pilot overhead of the RIS-aided mmWave systems. Key idea of the proposed scheme is to decompose the RIS reflected channel into three major components, i.e., static BS-RIS angles, quasi-static RIS-UE angles, and time-varying BS-RIS-UE path gains, and then estimate them in different time scales. By estimating the BS-RIS and RIS-UE angles occasionally and estimating only the path gains frequently, the proposed scheme achieves a significant reduction on the pilot overhead. Further, by optimizing the phase shifts using the channel components with relatively long coherence time, we can improve the channel estimation accuracy. From the performance analysis and numerical evaluations, we demonstrate that the proposed scheme achieves more than 60% pilot overhead reduction over the conventional techniques.

Terahertz (THz) multiple-input multiple-output (MIMO) beamforming is a key technology to support immersive mobile services in 6G communication systems. In the beamforming vector generation, the analog phase shifter which generates the phase invariant to the signal frequency have been widely used. However, since the spatial directions of the subcarrier channels are functions of the subcarrier frequency in the wideband THz systems, the beamforming techniques based on the analog phase shifters suffer from a severe beamforming gain loss. In this paper, we propose a novel beamforming technique that exploits the true time delay (TTD)-based phase shifters to generate multiple frequency-dependent beamforming vectors for the wideband THz systems. Intriguing feature of the proposed scheme is to exploit a deliberately designed TTD-based signal propagation network called calibrator to bridge the gap between the desired beamforming vectors and the frequency-dependent beamforming vectors. In doing so, the signal power is concentrated onto the mainlobe so that the generated beamforming vectors can achieve the maximum beamforming gain. From the numerical results, we demonstrate that the proposed scheme achieves more than 80% data rate gain over the conventional beamforming schemes.

To support extremely high data rates in 6G wireless networks, terahertz (THz) communication has attracted great interest in recent years. However, due to the strong directivity and severe signal attenuation of THz signal, the link quality is highly sensitive to obstacles, especially when there is only a line-of-sight (LoS) path. To enable proactive proactive handover to a transmitter with an alternative LoS link, accurate blockage prediction is essential. Unfortunately, existing methods focusing on outdoor environments often fail to predict the blockages in complicated indoor environments. In this paper, we propose a vision-aided blockage prediction framework that utilizes the sequences of historical RGB-depth (RGB-D) information and the beam index to detect and localize users and potential blockages, predict their trajectory, and foresee the blockages in dynamic indoor scenarios. Specifically, we first model the background and use a deep learning-based object detector to detect the users as well as potential blockages. We then predict the future locations of the users using an LSTM-based neural network. We demonstrate from numerical results that the proposed scheme outperforms conventional schemes in terms of blockage prediction accuracy and handover decision-making.

As a means to support extremely high data rates in 6G wireless networks, terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) systems have attracted great interest in recent years. Unfortunately, due to the strong directivity and severe attenuation of the THz signal, the number of propagation paths is at most a few in the THz band. In most cases, therefore, the THz channel matrix is a low-rank matrix, which dramatically limits the channel capacity of THz systems. To increase the channel capacity of THz systems, the array-of-subarray (AoSA) technique that exploits a group of widely-spaced antenna subarrays has been proposed. A major issue of the AoSA scheme is that the base station (BS) has to employ a large number of subarrays along with the radio frequency (RF) chains connected to the subarrays so that the power consumption is considerable. In this paper, we propose an efficient THz UM-MIMO subarray architecture maximizing the channel capacity while reducing the power consumption. Key idea of the proposed scheme referred to as distance-aware subarray selection (DSS), is to choose a small number of subarrays maximizing the channel capacity, and then activate only the RF chains connected to the chosen subarrays. From the simulation results, we demonstrate that the proposed DSS scheme achieves a significant channel capacity gain over the conventional schemes.

To support extremely high data rates in 6G wireless networks, reconfigurable intelligent surface (RIS)-assisted terahertz (THz) communications have gained much attention. By controlling the phase shifts of reflecting elements, RIS can proactively modify the wireless THz channel, thereby enhancing the achievable data rate significantly. One major challenge of the wideband THz communication is the severe array gain loss caused by the beam split effect that the path components split into different spatial directions at different subcarrier frequencies. Therefore, the conventional phase shift control and beamforming techniques cannot be directly applied to wideband THz systems. In this paper, we propose a RIS-assisted frequency-dependent beamforming (R-FDB) technique maximizing the average data rate of the RIS-assisted wideband THz systems. Key idea of R-FDB is to alternately optimize the analog beamforming vector and the RIS phase shift vector by properly designing the parameters of the R-FDB network such that the average data rate of the wideband THz system is maximized. We demonstrate from the numerical evaluations that R-FDB achieves a significant data rate gain over the conventional schemes.

The main purpose of this paper is to propose an effective precoding technique for the frequency-division-duplexing (FDD)-based massive MIMO systems under the common scattering effect. Key idea of the proposed path-selective precoding (PSP) scheme is to choose a small paths maximizing the data rate and then exploit only the angle information of the chosen paths for the downlink data precoding. To efficiently select the paths for each mobile, we use the notion of leakage, a metric of how much signal power leaks into other mobiles. While the interference is a joint function of precoding vectors of different mobiles, the leakage is solely a function of the precoding vector of corresponding mobile so that the signal-to-leakage-and-noise-ratio (SLNR) maximization problem can be decoupled into the sub-problems for each mobile. To find out a near-optimal solution of the decoupled SLNR maximization problem, we propose a greedy algorithm that iteratively removes the index of shared paths from the candidate index set until the SLNR does not increase. We demonstrate from the simulation results that the proposed PSP scheme achieves the significant data rate gains over the conventional angular-domain precoding schemes.

To support extremely high data rates in 6G wireless networks, reconfigurable intelligent surface (RIS)-assisted terahertz (THz) communications have gained much attention in recent years. By manipulating the phase shifts of reflecting elements, the RIS can proactively adjust the wireless propagation environment of THz systems, thereby enhancing the overall throughput significantly. To realize the full potential of RIS-assisted THz systems, an acquisition of accurate channel information is of great importance. However, since the wavefront of the THz electromagnetic signal is spherical, the conventional channel estimation techniques using the planar wavefront assumption suffer from severe performance degradation in the near-field RIS-assisted THz systems. An aim of this work is to propose an efficient channel estimation technique for near-field RIS-assisted wideband THz systems. Key idea of the proposed polar-domain frequency-dependent RIS-assisted channel estimation (PF-RCE) scheme is to estimate the sparse multipath components (i.e., angles, distances, and path gains) of the near-field THz channel by exploiting the polar-domain sparsity and common support properties. We demonstrate from the numerical evaluations that PF-RCE achieves a significant performance gain over the conventional THz channel estimation schemes in terms of the normalized mean square error (NMSE).

In this paper, a new version of time-frequency (T-F) ESPRIT algorithm with reduced computational complexity is proposed. The key idea of proposed covariance-based T-F ESPRIT (CB T-F ESPRIT) algorithm is to use the covariance-based DoA (CB-DoA) approach for the signal subspace construction. Specifically, the proposed CB T-F ESPRIT algorithm first constructs the time-frequency data model and then exploits the STFD matrix for the estimation of signal subspace. In particular, instead of directly performing EVD on the covariance matrix obtained from the averaged STFD matrix, the proposed scheme employs the CB-DoA approach which provides a lower computational complexity while maintaining the performance gain of T-F ESPRIT algorithm over the conventional ESPRIT algorithm. From the computational complexity analysis and the numerical evaluations, we demonstrate that CB T-F ESPRIT algorithm outperforms the conventional DoA estimation schemes with reduced computational complexity.

Terahertz (THz) communication has been widely considered as a key enabler for future wireless systems. However, due to the strong directivity and severe attenuation of THz signals, the communication performance relies heavily on the existence of a line-of-sight (LoS) link. To deal with this problem, a reconfigurable intelligent surface (RIS) that modifies the wireless channel through intelligent signal reflection has gained much attention recently. In this paper, we propose an efficient near-field RIS-assisted wideband THz channel estimation scheme. Key idea of the proposed scheme, referred to as the polar-domain frequency-dependent RIS-assisted channel estimation (PF -RCE), is to exploit the polar-domain sparsity of the near-field channel and common support property of the wideband THz channel. To the best of our knowledge, this is the first work that investigates the characteristics of RIS-assisted wideband THz channel in the near- filed region and provides an efficient channel estimation scheme. From the numerical evaluations, we demonstrate that the proposed PF-RCE scheme achieves a significant performance gain over the conventional THz channel estimation schemes in terms of the normalized mean square error (NMSE).

© 2002-2012 IEEE.Recently, reconfigurable intelligent surface (RIS), a planar metasurface consisting of a large number of low-cost reflecting elements, has received much attention due to its ability to improve both the spectrum and energy efficiencies by reconfiguring the wireless propagation environment. In this paper, we propose an RIS phase shift and BS beamforming optimization technique that minimizes the uplink transmit power of the RIS-aided IoT network. Key idea of the proposed scheme, referred to as Riemannian conjugate gradient-based joint optimization (RCG-JO), is to jointly optimize the RIS phase shifts and the BS beamforming vectors using the Riemannian conjugate gradient technique. By exploiting the product Riemannian manifold structure of the sets of unit-modulus phase shifts and unit-norm beamforming vectors, we convert the nonconvex uplink power minimization problem into the unconstrained problem and then find out the optimal solution over the product Riemannian manifold. From the performance analysis and numerical evaluations, we demonstrate that the proposed RCG-JO technique achieves 94% reduction of the uplink transmit power over the conventional scheme without RIS.

Intelligent refiecting surface (IRS), a planar meta- surface consisting of a large number of reflecting elements, is a promising solution for improving the spectral efficiency of future wireless systems. In order to maximize the throughput gain of IRS, the base station (BS) needs to acquire not only the conventional direct channel between the BS and user equipment (UE) but also the IRS reflected channel. Since the dimension of the IRS reflected channel is proportional to the number of reflecting elements, the pilot overhead as well as the channel estimation error are tremendous, resulting in a significant data rate degradation. In this paper, we propose a deep learning (DL)-based approach to find out the IRS phase shift maximizing the data rate of IRS-aided communication systems. To achieve this goal, we express the relationship between the noisy estimated channel and the IRS phase shifts using the deep neural network. We then train the network parameters in the direction of maximizing the data rate formulated with the ideal channel. From the simulation results, we demonstrate that the proposed scheme outperforms the benchmark schemes by a large margin.

Employing intelligent reflecting surfaces (IRSs) is emerging as a green alternative to massive antenna systems for improving signal quality and suppressing interference. Specifically, IRS is a planar surface consisting of a large number of low-cost and passive elements each being able to reflect the incident signal independently with an adjustable phase shift, thus the three-dimension (3D) passive beamforming can be collaboratively achieved without the need of any transmit radio-frequency (RF) chains. In this paper, we study the uplink power control of an IRS-aided Internet of Things (IoT) network under the quality of service (QoS) constraints at each user. Our goal is to minimize the total user power by jointly optimizing the phase shifts of IRS reflecting elements and the receiving beamforming at the BS, subject to each user's individual signal-to-interference-plus-noise ratio (SINR) constraint which characterizes its QoS. To solve the formulated non-convex optimization problem, we develop an efficient scheme, called the Riemannian manifold-based alternating optimization (RM-AO). Simulation results demonstrate that the proposed RM-AO algorithm saves the uplink transmit power significantly.

Employing intelligent reflecting surfaces (IRSs) is emerging as a green alternative to improve the signal quality and suppress interference for massive antenna systems. Specifically, IRS is a planar surface consisting of a large number of low-cost and passive elements each being able to reflect the incident signal independently with an adjustable phase shift. In this paper, we study the power control problem at the user for an IRS-aided uplink system under the quality of service (QoS) constraints. Our goal is to minimize the total transmit power at the user by jointly optimizing the phase shifts of passive elements at the IRS and the receiving beamforming at the BS, subject to the signal-to-noise ratio (SNR) constraint at the user. To solve the resulting non-convex optimization problem, we develop an efficient algorithm, called the manifold-based alternating optimization (M-AO). Simulation results show that the proposed algorithm significantly saved the transmit power.