Intelligent Reflecting Surface Aided MIMO with Cascaded Line-of-Sight Links: Channel Modelling and Capacity Analysis

In this paper, we build up a new intelligent reflecting surface (IRS) aided multiple-input multiple-output (MIMO) channel model, named the cascaded LoS MIMO channel. The proposed channel model consists of a transmitter (Tx) and a receiver (Rx) both equipped with uniform linear arrays (ULAs), and an IRS used to enable communications between the transmitter and the receiver through the line-of-sight (LoS) links seen by the IRS. To model the reflection of electromagnetic waves at the IRS, we take into account the curvature of the wavefront on different reflecting elements (REs), which is distinct from most existing works that take the plane-wave assumption. Based on the established model, we study the spatial multiplexing capability and input-output mutual information (MI) of the cascaded LoS MIMO system. We generalize the notion of Rayleigh distance originally coined for the single-hop MIMO channel to the full multiplexing region (FMR) for the cascaded LoS MIMO channel, where the FMR is, roughly speaking, the union of Tx-IRS and IRS-Rx distance pairs that enable full multiplexing communication between the Tx and the Rx. We propose a new passive beamforming (PB) strategy named reflective focusing, which aims to coherently superimpose the waves originating from a transmit antenna, reflected by the IRS, and focused on a receive antenna. With reflective focusing, we derive an inner bound of the FMR, and provide the corresponding orientation settings of the antenna arrays that enable full multiplexing. We further employ the MI to measure the quality of the cascaded LoS MIMO channel, and formulate an optimization problem to maximize the MI over PB and antenna array orientations. We give analytical solutions to the problem under asymptotic conditions such as high or low signal-to-noise ratio (SNR) regimes. For general cases, we propose an alternating optimization method to solve the problem.

Elevation Angle-Dependent Trajectory Design for Aerial RIS-aided Communication

This paper investigates an aerial reconfigurable intelligent surface (RIS)-aided communication system under the probabilistic line-of-sight (LoS) channel, where an unmanned aerial vehicle (UAV) equipped with an RIS is deployed to assist two ground nodes in their information exchange. An optimization problem with the objective of maximizing the minimum average achievable rate is formulated to design the communication scheduling, the RIS's phase, and the UAV trajectory. To solve such a non-convex problem, we propose an efficient iterative algorithm to obtain its suboptimal solution. Simulation results show that our proposed design significantly outperforms the existing schemes and provides new insights into the elevation angle and distance trade-off for the UAV-borne RIS communication system.

Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learning

Federated edge learning (FEEL) has emerged as a revolutionary paradigm to develop AI services at the edge of 6G wireless networks as it supports collaborative model training at a massive number of mobile devices. However, model communication over wireless channels, especially in uplink model uploading of FEEL, has been widely recognized as a bottleneck that critically limits the efficiency of FEEL. Although over-the-air computation can alleviate the excessive cost of radio resources in FEEL model uploading, practical implementations of over-the-air FEEL still suffer from several challenges, including strong straggler issues, large communication overheads, and potential privacy leakage. In this article, we study these challenges in over-the-air FEEL and leverage reconfigurable intelligent surface (RIS), a key enabler of future wireless systems, to address these challenges. We study the state-of-the-art solutions on RIS-empowered FEEL and explore the promising research opportunities for adopting RIS to enhance FEEL performance.

Two-Way Reflecting Communication with UAV-Borne Reconfigurable Intelligent Surfaces

This paper investigates the unmanned aerial vehicle (UAV)-aided two-way reflecting (TWR) communication system under the probabilistic line-of-sight (LoS) channel, where a UAV equipped with an RIS is deployed to assist two ground nodes in their information exchange. An optimization problem with the objective of maximizing the minimum average (expected) achievable rate is formulated to design the communication scheduling, the RIS's phase, and the UAV trajectory. To solve such a non-convex problem, we propose an efficient iterative algorithm to obtain its suboptimal solution. Simulation results show that our proposed design provides new insights into the elevation angle-distance trade-off for the UAV-aided TWR communication system, and improves the rate by 28% compared to the scheme under the conventional LoS channel.

Multi-task Over-the-Air Federated Learning: A Non-Orthogonal Transmission Approach

In this letter, we propose a multi-task over-theair federated learning (MOAFL) framework, where multiple learning tasks share edge devices for data collection and learning models under the coordination of a edge server (ES). Specially, the model updates for all the tasks are transmitted and superpositioned concurrently over a non-orthogonal uplink channel via over-the-air computation, and the aggregation results of all the tasks are reconstructed at the ES through an extended version of the turbo compressed sensing algorithm. Both the convergence analysis and numerical results demonstrate that the MOAFL framework can significantly reduce the uplink bandwidth consumption of multiple tasks without causing substantial learning performance degradation.

Full-Dimensional Rate Enhancement for UAV-Enabled Communications via Intelligent Omni-Surface

This paper investigates the achievable rate maximization problem of a downlink unmanned aerial vehicle (UAV)-enabled communication system aided by an intelligent omni-surface (IOS). Different from the state-of-the-art reconfigurable intelligent surface (RIS) that only reflects incident signals, the IOS can simultaneously reflect and transmit the signals, thereby providing full-dimensional rate enhancement. To tackle such a problem, we formulate it by jointly optimizing the IOS's phase shift and the UAV trajectory. Although it is difficult to solve it optimally due to its non-convexity, we propose an efficient iterative algorithm to obtain a high-quality suboptimal solution. Simulation results show that the IOS-assisted UAV communications can achieve more significant improvement in achievable rates than other benchmark schemes.

Fully Convolutional Line Parsing

We present a one-stage Fully Convolutional Line Parsing network (F-Clip) that detects line segments from images. The proposed network is very simple and flexible with variations that gracefully trade off between speed and accuracy for different applications. F-Clip detects line segments in an end-to-end fashion by predicting them with each line's center position, length, and angle. Based on empirical observation of the distribution of line angles in real image datasets, we further customize the design of convolution kernels of our fully convolutional network to effectively exploit such statistical priors. We conduct extensive experiments and show that our method achieves a significantly better trade-off between efficiency and accuracy, resulting in a real-time line detector at up to 73 FPS on a single GPU. Such inference speed makes our method readily applicable to real-time tasks without compromising any accuracy of previous methods. Moreover, when equipped with a performance-improving backbone network, F-Clip is able to significantly outperform all state-of-the-art line detectors on accuracy at a similar or even higher frame rate. Source code https://github.com/Delay-Xili/F-Clip.

Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image

Single-image room layout reconstruction aims to reconstruct the enclosed 3D structure of a room from a single image. Most previous work relies on the cuboid-shape prior. This paper considers a more general indoor assumption, i.e., the room layout consists of a single ceiling, a single floor, and several vertical walls. To this end, we first employ Convolutional Neural Networks to detect planes and vertical lines between adjacent walls. Meanwhile, estimating the 3D parameters for each plane. Then, a simple yet effective geometric reasoning method is adopted to achieve room layout reconstruction. Furthermore, we optimize the 3D plane parameters to reconstruct a geometrically consistent room layout between planes and lines. The experimental results on public datasets validate the effectiveness and efficiency of our method.

Sparse Kronecker-Product Coding for Unsourced Multiple Access

In this paper, a sparse Kronecker-product (SKP) coding scheme is proposed for unsourced multiple access. Specifically, the data of each active user is encoded as the Kronecker product of two component codewords with one being sparse and the other being forward-error-correction (FEC) coded. At the receiver, an iterative decoding algorithm is developed, consisting of matrix factorization for the decomposition of the Kronecker product and soft-in soft-out decoding for the component sparse code and the FEC code. The cyclic redundancy check (CRC) aided interference cancelation technique is further incorporated for performance improvement. Numerical results show that the proposed scheme outperforms the state-of-the-art counterparts, and approaches the random coding bound within a gap of only 0.1 dB at the code length of 30000 when the number of active users is less than 75, and the error rate can be made much lower than the existing schemes, especially when the number of active users is relatively large.