Due to the multi-dimensional search in the near-field (NF), the excessive computational burden has become one of the major problems. To address this issue, this paper proposes a computationally efficient angle and distance estimation algorithm for extremely large uniform planar array (UPA) systems. To reduce computation, the proposed algorithm decouples 3D search into a series of 2D search and 1D search. The 2D search estimates the azimuth and elevation, followed by the 1D search that estimates the distance. While the proposed algorithm brings significant improvement in computational complexity, the estimation of the proposed algorithm is guaranteed to be accurate as long as the distance between the receiver and transmitter (or scatterer) exceeds a specific threshold. For UPAs, we establish that this threshold is around a quarter of the Rayleigh distance. The simulation results demonstrate that the proposed algorithm has a superior accuracy-complexity trade-off compared to existing works.

Reconfigurable intelligent surface (RIS) is an emerging technique for robust millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. In this paper, we study the channel estimation problem for extremely large-scale RIS (XL-RIS) assisted multi-user XL-MIMO systems with hybrid beamforming structures. In this system, we propose an unified channel estimation method that yields a notable estimation accuracy in the near-field BS-RIS and near-field RIS-User channels (in short, near-near field channels), far-near field channels, and far-far field channels. Our key idea is that the effective channels to be estimated can be each factorized as the product of low-rank matrices (i.e., the product of a common matrix and a user-specific coefficient matrix). The common matrix whose columns are the basis of the column space of the BS-RIS channel is efficiently estimated via a collaborative low-rank approximation (CLRA). Leveraging the hybrid beamforming structures, we develop an efficient iterative algorithm that jointly optimizes the user-specific coefficient matrices. Via experiments and complexity analysis, we verify the effectiveness of the proposed channel estimation method (named CLRA-JO) for the three categories of wireless channels.

This paper presents a robust near-field (NF) beam tracking algorithm for terahertz communications based on deep Q-network (DQN). Traditional NF beam tracking methods relying on mobility models are fatal in ultra-massive MIMO systems, where even the slightest error could result in beam tracking failures. Thus, the proposed algorithm aims to maintain a stable beamforming gain by tracking the mobile station through the analysis of received signals without requiring mobile dynamics. By utilizing DQN, the proposed algorithm strengthens its tracking capability from online experiences and updates the combining beam towards positions expected to maximize beamforming gain. Throughout simulations, we compare the proposed algorithm with the Bayesian filter-based NF beam tracking algorithm. The simulation results confirm the robustness of the proposed algorithm for NF beam tracking, especially for abrupt changes in mobile dynamics.

Radio simultaneous localization and mapping (SLAM) with active sensing, such as radar and LiDAR, faces difficulty in detecting mirror-like walls that cause specular reflection. To solve this problem, the proposed radio SLAM algorithm merges active and passive sensing. Passive sensing exploits low-frequency radio signals that are specularly reflected from objects. However, maps created by active and passive sensing have different characteristics. Thus, the proposed algorithm fuses heterogeneous maps using Dirichlet process-based clustering to create one integrated map and improve mapping accuracy. Simulation results demonstrate that the proposed radio SLAM algorithm outperforms the classical methods only with active or passive sensing in mixed reflection type environments.

This paper presents an efficient channel estimation algorithm for multi-user reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) systems. In this paper, the concept of low rank matrix completion (LRMC) is exploited to reduce beam training overhead for channel estimation. The proposed beam training samples part of each channel matrix in a special pattern that is suitable for LRMC with less beam training overhead. Then, the beam training is followed by multi-user channel estimation. For computationally efficient channel estimation, the proposed algorithm exploits the property that all the channel matrices share the same low-rank subspace in multi-user RIS-aided systems. The shared subspace is derived by combining candidate subspaces, which are estimated by fast alternating least squares (FALS) from partially observed channels. With the shared subspace, all the missing entries of channels are recovered via computationally efficient linear estimation. The simulations and complexity analysis demonstrate that the proposed algorithm shows a superior accuracy-complexity trade-off compared to existing works.

In this paper, we propose location-aware beam training and multi-dimensional atomic norm minimization (ANM)-based channel estimation for reconfigurable intelligent surface (RIS)-aided millimeter-wave systems. The use of both location information and RIS beamwidth adaptation allows a significant reduction of beam training overhead. However, considering a trade-off between accuracy and beam training overhead, this may induce inaccurate channel estimation. Nevertheless, superior channel estimation performance is achieved by multi-dimensional ANM techniques, which have been shown to be effective in capturing cascaded structures such as the channel in RIS-aided systems. In the proposed work, a cascade of BS-to-RIS channel and RIS-to-BS channel is represented as a linear combination of either steering vectors, 2D steering vectors, or 3D steering vectors, and ANM with appropriate dimension is applied to estimate the channel. From simulation results, it has been demonstrated that location-aware channel estimation via 2D ANM and 3D ANM achieves excellent estimation accuracy along with a reduced beam training overhead.

This paper proposes a two-stage beam training and a channel estimation based on fast alternating least squares (FALS) for reconfigurable intelligent surface (RIS)-aided millimeter-wave systems. To reduce the beam training overhead, only selected columns and rows of the channel matrix are observed by two-stage beam training. This beam training produces a partly observed channel matrix with low coherence, which enables the low rank matrix completion technique to recover unobserved entries. Unobserved entries are recovered by FALS, which alternatingly updates the left and the right singular vectors that comprise the channel. Simulation results and analysis show that the proposed algorithm is computationally efficient and has superior accuracy to existing algorithms.

This paper presents the first real-world demonstration of a vision-aided 28 GHz mmWave transmission with a joint Tx-Rx beam tracking for 5G communications. This demonstration employs the architecture of the joint Tx-Rx beam tracking which is designed to update Tx and Rx beams simultaneously based on computer vision with deep learning model. For the demonstration setup, we build a complete end-to-end real-time 5G 28 GHz system testbed. Through this demonstration, we showcase how the presented vision-aided mmWave transmission can effectively sustain a high-throughput link despite Tx-Rx misalignment induced by user mobility. A demo video can be found at https://youtu.be/Mu6pxEYuvbY.

Due to the millimeter wave's high path loss, beamforming is required to provide higher gain. Conventional beamformer is widely used for its simplicity in many other studies. However, it is vulnerable to unexpected interferences since it has high level of sidelobes. This paper proposes a new beamformer which can be used to make an interference-robust codebook for receivers. The idea of the new method is to null out sidelobes of conventional beamformer by using the linear constraint minimum variance (LCMV) beamformer algorithm. It modifies the original LCMV beamformer to be applicable to the analog beamformer. To compare its performance with conventional beamformer, an empirical millimeter wave channel model was used. Results show a significant increase in signal to interference plus noise ratio (SINR) on the general millimeter wave environment.