All-in-One: Transferring Vision Foundation Models into Stereo Matching

As a fundamental vision task, stereo matching has made remarkable progress. While recent iterative optimization-based methods have achieved promising performance, their feature extraction capabilities still have room for improvement. Inspired by the ability of vision foundation models (VFMs) to extract general representations, in this work, we propose AIO-Stereo which can flexibly select and transfer knowledge from multiple heterogeneous VFMs to a single stereo matching model. To better reconcile features between heterogeneous VFMs and the stereo matching model and fully exploit prior knowledge from VFMs, we proposed a dual-level feature utilization mechanism that aligns heterogeneous features and transfers multi-level knowledge. Based on the mechanism, a dual-level selective knowledge transfer module is designed to selectively transfer knowledge and integrate the advantages of multiple VFMs. Experimental results show that AIO-Stereo achieves start-of-the-art performance on multiple datasets and ranks $1^{st}$ on the Middlebury dataset and outperforms all the published work on the ETH3D benchmark.

Modeling and Design of RIS-Assisted Multi-cell Multi-band Networks with RSMA

Reconfigurable intelligent surface (RIS) has been identified as a promising technology for future wireless communication systems due to its ability to manipulate the propagation environment intelligently. RIS is a frequency-selective device, thus it can only effectively manipulate the propagation of signals within a specific frequency band. This frequency selective characteristic can make deploying RIS in wireless cellular networks more challenging, as adjacent base stations (BSs) operate on different frequency bands. In addition, rate-splitting multiple access (RSMA) scheme has been shown to enhance the performance of RIS-aided multi-user communication systems. Accordingly, this work considers a more practical reflection model for RIS-aided RSMA communication systems, which accounts for the responses of signals across different frequency bands. To that end, new analytical expressions for the ergodic sum-rate are derived using the moment generating function (MGF) and Jensen inequality. Based on these analytical sum-rate expressions, novel practical RIS reflection designs and power allocation strategies for the RSMA scheme are proposed and investigated to maximize the achievable sum-rate in RIS-assisted multi-cell, multi-band cellular networks. Simple sub-optimal designs are also introduced and discussed. The results validate the significant gains of our proposed reflection design algorithms with RSMA over conventional schemes in terms of achievable sum-rate. Additionally, the power allocation strategy for the RSMA scheme is shown to offer superior performance compared to conventional precoding schemes that do not rely on RSMA.

Token-Mol 1.0: Tokenized drug design with large language model

Significant interests have recently risen in leveraging sequence-based large language models (LLMs) for drug design. However, most current applications of LLMs in drug discovery lack the ability to comprehend three-dimensional (3D) structures, thereby limiting their effectiveness in tasks that explicitly involve molecular conformations. In this study, we introduced Token-Mol, a token-only 3D drug design model. This model encodes all molecular information, including 2D and 3D structures, as well as molecular property data, into tokens, which transforms classification and regression tasks in drug discovery into probabilistic prediction problems, thereby enabling learning through a unified paradigm. Token-Mol is built on the transformer decoder architecture and trained using random causal masking techniques. Additionally, we proposed the Gaussian cross-entropy (GCE) loss function to overcome the challenges in regression tasks, significantly enhancing the capacity of LLMs to learn continuous numerical values. Through a combination of fine-tuning and reinforcement learning (RL), Token-Mol achieves performance comparable to or surpassing existing task-specific methods across various downstream tasks, including pocket-based molecular generation, conformation generation, and molecular property prediction. Compared to existing molecular pre-trained models, Token-Mol exhibits superior proficiency in handling a wider range of downstream tasks essential for drug design. Notably, our approach improves regression task accuracy by approximately 30% compared to similar token-only methods. Token-Mol overcomes the precision limitations of token-only models and has the potential to integrate seamlessly with general models such as ChatGPT, paving the way for the development of a universal artificial intelligence drug design model that facilitates rapid and high-quality drug design by experts.

Coding-Enhanced Cooperative Jamming for Secret Communication in Fluid Antenna Systems

This letter investigates the secret communication problem for a fluid antenna system (FAS)-assisted wiretap channel, where the legitimate transmitter transmits an information-bearing signal to the legitimate receiver, and at the same time, transmits a jamming signal to interfere with the eavesdropper (Eve). Unlike the conventional jamming scheme, which usually transmits Gaussian noise that interferes not only with Eve but also with the legitimate receiver, in this letter, we consider that encoded codewords are transmitted to jam Eve. Then, by employing appropriate coding schemes, the legitimate receiver can successfully decode the jamming signal and then cancel the interference, while Eve cannot, even if it knows the codebooks. We aim to maximize the secrecy rate through port selection and power control. Although the problem is non-convex, we show that the optimal solution can be found. Simulation results show that by using the FAS technique and the proposed jamming scheme, the secrecy rate of the system can be significantly increased.

Design and Implementation of mmWave Surface Wave Enabled Fluid Antennas and Experimental Results for Fluid Antenna Multiple Access

While multiple-input multiple-output (MIMO) technologies continue to advance, concerns arise as to how MIMO can remain scalable if more users are to be accommodated with an increasing number of antennas at the base station (BS) in the upcoming sixth generation (6G). Recently, the concept of fluid antenna system (FAS) has emerged, which promotes position flexibility to enable transmitter channel state information (CSI) free spatial multiple access on one radio frequency (RF) chain. On the theoretical side, the fluid antenna multiple access (FAMA) approach offers a scalable alternative to massive MIMO spatial multiplexing. However, FAMA lacks experimental validation and the hardware implementation of FAS remains a mysterious approach. The aim of this paper is to provide a novel hardware design for FAS and evaluate the performance of FAMA using experimental data. Our FAS design is based on a dynamically reconfigurable "fluid" radiator which is capable of adjusting its position within a predefined space. One single-channel fluid antenna (SCFA) and one double-channel fluid antenna (DCFA) are designed, electromagnetically simulated, fabricated, and measured. The measured radiation patterns of prototypes are imported into channel and network models for evaluating their performance in FAMA. The experimental results demonstrate that in the 5G millimeter-wave (mmWave) bands (24-30 GHz), the FAS prototypes can vary their gain up to an averaged value of 11 dBi. In the case of 4-user FAMA, the double-channel FAS can significantly reduce outage probability by 57% and increases the multiplexing gain to 2.27 when compared to a static omnidirectional antenna.

Cache-Enabled Millimetre-Wave Fluid Antenna Systems: Modeling and Performance

This letter investigates the performance of content caching in a heterogeneous cellular network (HetNet) consisting of fluid antenna system (FAS)-equipped mobile users (MUs) and millimeter-wave (mm-wave) single-antenna small base stations (SBSs), distributed according to the independent homogeneous Poisson point processes (HPPP). In particular, it is assumed that the most popular contents are cached in the SBSs to serve the FAS-equipped MUs requests. To assess the system performance, we derive compact expressions for the successful content delivery probability (SCDP) and the content delivery delay (CDD) using the Gauss-Laguerre quadrature technique. Our numerical results show that the performance of cache-enabled mm-wave HetNets can be greatly improved, when the FAS is utilized at the MUs instead of traditional fixed-antenna system deployment.

On Performance of RIS-Aided Fluid Antenna Systems

This letter studies the performance of reconfigurable intelligent surface (RIS)-aided communications for a fluid antenna system (FAS) enabled receiver. Specifically, a fixed singleantenna base station (BS) transmits information through a RIS to a mobile user (MU) which is equipped with a planar fluid antenna in the absence of a direct link.We first analyze the spatial correlation structures among the positions (or ports) in the planar FAS, and then derive the joint distribution of the equivalent channel gain at the user by exploiting the central limit theorem. Furthermore, we obtain compact analytical expressions for the outage probability (OP) and delay outage rate (DOR). Numerical results illustrate that using FAS with only one activated port into the RIS-aided communication network can greatly enhance the performance, when compared to traditional antenna systems (TAS).

User Clustering for STAR-RIS Assisted Full-Duplex NOMA Communication Systems

In contrast to conventional reconfigurable intelligent surface (RIS), simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has been proposed recently to enlarge the serving area from 180o to 360o coverage. This work considers the performance of a STAR-RIS aided full-duplex (FD) non-orthogonal multiple access (NOMA) communication systems. The STAR-RIS is implemented at the cell-edge to assist the cell-edge users, while the cell-center users can communicate directly with a FD base station (BS). We first introduce new user clustering schemes for the downlink and uplink transmissions. Then, based on the proposed transmission schemes closed-form expressions of the ergodic rates in the downlink and uplink modes are derived taking into account the system impairments caused by the self interference at the FD-BS and the imperfect successive interference cancellation (SIC). Moreover, an optimization problem to maximize the total sum-rate is formulated and solved by optimizing the amplitudes and the phase-shifts of the STAR-RIS elements and allocating the transmit power efficiently. The performance of the proposed user clustering schemes and the optimal STAR-RIS design are investigated through numerical results

Channel Estimation for FAS-assisted Multiuser mmWave Systems

This letter investigates the challenge of channel estimation in a multiuser millimeter-wave (mmWave) time-division duplexing (TDD) system. In this system, the base station (BS) employs a multi-antenna uniform linear array (ULA), while each mobile user is equipped with a fluid antenna system (FAS). Accurate channel state information (CSI) plays a crucial role in the precise placement of antennas in FAS. Traditional channel estimation methods designed for fixed-antenna systems are inadequate due to the high dimensionality of FAS. To address this issue, we propose a low-sample-size sparse channel reconstruction (L3SCR) method, capitalizing on the sparse propagation paths characteristic of mmWave channels. In this approach, each fluid antenna only needs to switch and measure the channel at a few specific locations. By observing this reduced-dimensional data, we can effectively extract angular and gain information related to the sparse channel, enabling us to reconstruct the full CSI. Simulation results demonstrate that our proposed method allows us to obtain precise CSI with minimal hardware switching and pilot overhead. As a result, the system sum-rate approaches the upper bound achievable with perfect CSI.

STAR-RIS Assisted Full-Duplex Communication Networks

Different from conventional reconfigurable intelligent surfaces (RIS), a recent innovation called simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has emerged, aimed at achieving complete 360-degree coverage in communication networks. Additionally, fullduplex (FD) technology is recognized as a potent approach for enhancing spectral efficiency by enabling simultaneous transmission and reception within the same time and frequency resources. In this study, we investigate the performance of a STAR-RIS-assisted FD communication system. The STAR-RIS is strategically placed at the cell-edge to facilitate communication for users located in this challenging region, while cell-center users can communicate directly with the FD base station (BS). We employ a non-orthogonal multiple access (NOMA) pairing scheme and account for system impairments, such as self-interference at the BS and imperfect successive interference cancellation (SIC). We derive closed-form expressions for the ergodic rates in both the up-link and down-link communications and extend our analysis to bidirectional communication between cell-center and cell-edge users. Furthermore, we formulate an optimization problem aimed at maximizing the ergodic sum-rate. This optimization involves adjusting the amplitudes and phase-shifts of the STAR-RIS elements and allocating total transmit power efficiently. To gain deeper insights into the achievable rates of STAR-RIS-aided FD systems, we explore the impact of various system parameters through numerical results.