The core technology to construct large-scale quantum networks is to efficiently distribute remote entanglement among quantum nodes. Entanglement routing deals with the problem of discovering and selecting paths to perform the multi-hop entanglement swapping between two distant nodes. In order to succeed in end-to-end entanglement creation for a given path, entanglement distribution of all direct quantum links and Bell state measurement of all intermediate nodes along the path should be successful. Existing studies have addressed the entanglement routing problem based on path discovery algorithms considering the above characteristics and selecting the optimal path from the individual performance perspective of each path. In this article, we newly define the entanglement routing problem considering the joint-utilization between multiple paths to maximize the network resource efficiency, and present an optimal link-set configuration algorithm, named RELiC, as a solution for this. The performance of the proposed entanglement routing protocol is evaluated in small- and large-scale quantum networks through mathematical analysis and simulation study.

With photo-realistic image generation, Neural Radiance Field (NeRF) is widely used for large-scale dynamic scene reconstruction as autonomous driving simulator. However, large-scale scene reconstruction still suffers from extremely long training time and rendering time. Low-resolution (L-R) rendering combined with upsampling can alleviate this problem but it degrades image quality. In this paper, we design a lightweight reference decoder which exploits prior information from known views to improve image reconstruction quality of new views. In addition, to speed up prior information search, we propose an optical flow and structural similarity based prior information search method. Results on KITTI and VKITTI2 datasets show that our method significantly outperforms the baseline method in terms of training speed, rendering speed and rendering quality.

Large Language Models (LLMs) have displayed remarkable performances across various complex tasks by leveraging Chain-of-Thought (CoT) prompting.Recently, studies have proposed a Knowledge Distillation (KD) approach, reasoning distillation, which transfers such reasoning ability of LLMs through fine-tuning language models of multi-step rationales generated by LLM teachers.However, they have inadequately considered two challenges regarding insufficient distillation sets from the LLM teacher model, in terms of 1) data quality and 2) soft label provision.In this paper, we propose Mentor-KD, which effectively distills the multi-step reasoning capability of LLMs to smaller LMs while addressing the aforementioned challenges.Specifically, we exploit a mentor, intermediate-sized task-specific fine-tuned model, to augment additional CoT annotations and provide soft labels for the student model during reasoning distillation.We conduct extensive experiments and confirm Mentor-KD's effectiveness across various models and complex reasoning tasks 1 .

Recently, the demand for communication systems that utilize a high-frequency band and massive antennas has increased. These systems can generate very small beams, resulting in high communication capacity; however, beam search for initial access is difficult due to the numerous beams in a given region. In this paper, we propose fast beam scanning and tracking techniques based on frequency-gradient array antennas. The proposed system provides a faster beam search technique than the phase-gradient array antenna systems considered in conventional communication systems. In numerical analysis and simulation studies, the performance of fast beam scanning and tracking techniques is evaluated and compared.

In recent years, neural image compression emerges as a rapidly developing topic in computer vision, where the state-of-the-art approaches now exhibit superior compression performance than their conventional counterparts. Despite the great progress, current methods still have limitations in preserving fine spatial details for optimal reconstruction, especially at low compression rates. We make three contributions in tackling this issue. First, we develop a novel back projection method with attentional and multi-scale feature fusion for augmented representation power. Our back projection method recalibrates the current estimation by establishing feedback connections between high-level and low-level attributes in an attentional and discriminative manner. Second, we propose to decompose the input image and separately process the distinct frequency components, whose derived latents are recombined using a novel dual attention module, so that details inside regions of interest could be explicitly manipulated. Third, we propose a novel training scheme for reducing the latent rounding residual. Experimental results show that, when measured in PSNR, our model reduces BD-rate by 9.88% and 10.32% over the state-of-the-art method, and 4.12% and 4.32% over the latest coding standard Versatile Video Coding (VVC) on the Kodak and CLIC2020 Professional Validation dataset, respectively. Our approach also produces more visually pleasant images when optimized for MS-SSIM. The significant improvement upon existing methods shows the effectiveness of our method in preserving and remedying spatial information for enhanced compression quality.

In this paper, we propose a joint transmission and reception with phase control for beamforming in a multi-cell environment. For generated transmit weight vectors of multiple base stations (BSs), a mobile station (MS) calculates phases to maximize an achievable rate with low rate feedback. By using the phases, the multiple transmit weight vectors are coordinated to improve the signal to noise ratio (SNR). In order to find optimal phases, we present a phase control method for the effective channel via geometrical approach.

In this paper, we investigate efficient multi-cell cooperation based on CoMP-joint processing and transmission (CoMP-JPT) with orthogonal beamforming. Through the use of a combinatorial optimization algorithm, the optimal user scheduling for joint transmission using multiple transmitters is accomplished. The throughput of the CoMP-JPT can be significantly improved while maintaining fairness among users over a multi-cell environment.

Coordinated multiple point transmission (CoMP) technologies have recently been proposed to improve the performance of cell-edge users who suffer strong inter-cell interference. Nevertheless, as more transmitters get involved in cooperation, the complexity associated with the selection of multi-dimensional parameters increases exponentially. In this work, we investigate efficient multi-cell cooperation based on CoMP-joint processing and transmission (CoMP-JPT) with orthogonal beamforming using limited feedback. Through the utilization of combinatorial optimization, optimal user scheduling for joint transmission via multiple transmitters is accomplished, while the computational complexity is significantly reduced. In particular, a generalized beam assignment problem (GBAP) is formulated and solved using a combinatorial algorithm that is generalized in terms of the number of transmitters o\mathcal{B}o. The performance of the combinatorial orthogonal beamforming (COBF) scheme is mathematically analyzed so as to demonstrate its superiority and capability to maintain fairness among users in a multi-cell environment. In the simulation results, a performance gain of more than 50% for cell-edge users is obtained without a loss in the average throughput for the total number of users. In addition, the COBF method can reduce the complexity by more than 60% when compared to the conventional exhaustive search technique.

The signal to interference plus noise ratio (SINR) feedback has been utilized in random beamforming (RBF) to select users for multiple-input multiple-output (MIMO) systems where a large number of users are required to obtain a multi-user diversity gain. However, if the number of users is not large enough, it may be difficult to obtain the performance gain and be easy to have a performance degradation conversely. To resolve this problem, it is necessary to find orthogonal random beams close to the channel matrix of selected users. In this paper, we present RBF using a non-orthogonal beamforming matrix (RBF NOBM), demonstrating an efficient search of random beams correlated with the channel matrix of selected users over a small number of users. For single user RBF using NOBM (SU-RBF NOBM) with two transmit antennas, the beamforming matrix for user data service is obtained through closed form expressions, and SU-RBF NOBM is then expanded to more than two transmit antennas. We also propose user selection algorithms for SU-RBF NOBM and multi-user RBF using NOBM (MU-RBF NOBM). Via simulation results, we demonstrate that the performances of RBF NOBM represent significant improvements compared to conventional beamforming schemes.

The signal to interference plus noise ratio (SINR) feedback has been utilized in random beamforming (RBF) to select users for the provision of service in multiple-input multiple-output (MIMO) systems. A large number of users are required to obtain the gain of multi-user diversity for a downlink transmission. However, if the number is not large enough, it may be difficult to obtain multi-user diversity, leading to a rapid degradation in performance. To resolve this problem, it is necessary to find beamforming matrix close to the channels of the selected users. In this paper, a beamforming matrix transformation for RBF (BMT-RBF) is presented, demonstrating an efficient search of random beams correlated with user channels as opposed to conventional ones, over a small number of users.

In conventional cellular systems with a frequency reuse factor (FRF) of one, quality of service (QoS) may not be guaranteed for users located at the cell boundary due to inter cell interference (ICI). Even if relay nodes are adopted for cooperative communications, ICI and inter-relay interference (IRI) remain significant obstacles to reliable data transmission at the border region of each cell. This paper presents an efficient fixed relay station (FRS)-cooperative strategy termed interference cancelation based FRS-cooperation (ICFC) to improve the QoS. The proposed scheme enables to improve the performances of two users at cell-edge in each cell through cooperation and interference cancelation, simultaneously. In particular, the ICFC scheme is well applicable to practical systems because no CSIT is required.