Reconfigurable intelligent surface (RIS) emerges as an efficient and promising technology for the next wireless generation networks and has attracted a lot of attention owing to the capability of extending wireless coverage by reflecting signals toward targeted receivers. In this paper, we consider a RIS-assisted high-speed train (HST) communication system to enhance wireless coverage and improve coverage probability. First, coverage performance of the downlink single-input-single-output system is investigated, and the closed-form expression of coverage probability is derived. Moreover, travel distance maximization problem is formulated to facilitate RIS discrete phase design and RIS placement optimization, which is subject to coverage probability constraint. Simulation results validate that better coverage performance and higher travel distance can be achieved with deployment of RIS. The impacts of some key system parameters including transmission power, signal-to-noise ratio threshold, number of RIS elements, number of RIS quantization bits, horizontal distance between base station and RIS, and speed of HST on system performance are investigated. In addition, it is found that RIS can well improve coverage probability with limited power consumption for HST communications.
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Knowledge distillation (KD) emerges as a promising yet challenging technique for compressing deep neural networks, aiming to transfer extensive learning representations from proficient and computationally intensive teacher models to compact student models. However, current KD methods for super-resolution (SR) models have limited performance and restricted applications, since the characteristics of SR tasks are overlooked. In this paper, we put forth an approach from the perspective of effective data utilization, namely, the Data Upcycling Knowledge Distillation (DUKD), which facilitates the student model by the prior knowledge the teacher provided through the upcycled in-domain data derived from the input images. Besides, for the first time, we realize the label consistency regularization in KD for SR models, which is implemented by the paired invertible data augmentations. It constrains the training process of KD and leads to better generalization capability of the student model. The DUKD, due to its versatility, can be applied across a broad spectrum of teacher-student architectures (e.g., CNN and Transformer models) and SR tasks, such as single image SR, real-world SR, and SR quantization, and is in parallel with other compression techniques. Comprehensive experiments on diverse benchmarks demonstrate that the DUKD method significantly outperforms previous art.
Deploying unmanned aerial vehicle (UAV) networks to provide coverage for outdoor users has attracted great attention during the last decade. However, outdoor coverage is challenging due to the high mobility of crowds and the diverse terrain configurations causing building blockage. Most studies use stochastic channel models to characterize the impact of building blockage on user performance and do not take into account terrain information. On the other hand, real-time search methods use terrain information, but they are only practical when a single UAV serves a single user.In this paper, we put forward two methods to avoid building blockage in a multi-user system by collecting prior terrain information and using real-time search.We proposed four algorithms related to the combinations of the above methods and their performances are evaluated and compared in different scenarios.By adjusting the height of the UAV based on terrain information collected before networking, the performance is significantly enhanced compared to the one when no terrain information is available.The algorithm based on real-time search further improves the coverage performance by avoiding the shadow of buildings. During the execution of the real-time search algorithm, the search distance is reduced using the collected terrain information.
We present a novel, power- & hardware-efficient, multiuser, multibeam RIS (Reflective Intelligent Surface) architecture for multiuser MIMO, especially for very high frequency bands (e.g., high mmWave and sub-THz), where channels are typically sparse in the beamspace and LOS is the dominant component. The key module is formed by an active multiantenna feeder (AMAF) with a small number of active antennas, placed in the near field of a RIS with a much larger number of passive controllable reflecting elements. We propose a pragmatic approach to obtain a steerable beam with high gain and very low sidelobes. Then K independently controlled beams can be achieved by closely stacking K such AMAF-RIS modules. Our analysis includes the mutual interference between the modules and the fact that, due to the delay difference of propagation through the AMAF-RIS structure, the resulting channel matrix is frequency selective even for pure LOS propagation. We consider a 3D geometry and show that "beam focusing" is in fact possible (and much more effective in terms of coverage) also in the far-field, by creating spotbeams with limited footprint both in angle and in range. Our results show that: 1) simple RF beamforming (BF) without computationally expensive baseband multiuser precoding is sufficient to practically eliminate multiuser interference when the users are chosen with sufficient angular/range separation, thanks to the extremely low sidelobe beams; 2) the impact of beam pointing errors with standard deviation as large as 2.5 deg and RIS quantized phase-shifters with quantization bits > 2 is essentially negligible; 3) The proposed architecture is more power efficient & much simpler from a hardware implementation viewpoint than standard active arrays with the same BF performance. We show also that the array gain of the proposed AMAF-RIS structure is linear with the RIS aperture.
Approximate nearest neighbor (ANN) search is a widely applied technique in modern intelligent applications, such as recommendation systems and vector databases. Therefore, efficient and high-throughput execution of ANN search has become increasingly important. In this paper, we first characterize the state-of-the-art product quantization-based method of ANN search and identify a significant source of inefficiency in the form of unnecessary pairwise distance calculations and accumulations. To improve efficiency, we propose JUNO, an end-to-end ANN search system that adopts a carefully designed sparsity- and locality-aware search algorithm. We also present an efficient hardware mapping that utilizes ray tracing cores in modern GPUs with pipelined execution on tensor cores to execute our sparsity-aware ANN search algorithm. Our evaluations on four datasets ranging in size from 1 to 100 million search points demonstrate 2.2x-8.5x improvements in search throughput. Moreover, our algorithmic enhancements alone achieve a maximal 2.6x improvement on the hardware without the acceleration of the RT core.
Artificial intelligence (AI) has been clearly established as a technology with the potential to revolutionize fields from healthcare to finance - if developed and deployed responsibly. This is the topic of responsible AI, which emphasizes the need to develop trustworthy AI systems that minimize bias, protect privacy, support security, and enhance transparency and accountability. Explainable AI (XAI) has been broadly considered as a building block for responsible AI (RAI), with most of the literature considering it as a solution for improved transparency. This work proposes that XAI and responsible AI are significantly more deeply entwined. In this work, we explore state-of-the-art literature on RAI and XAI technologies. Based on our findings, we demonstrate that XAI can be utilized to ensure fairness, robustness, privacy, security, and transparency in a wide range of contexts. Our findings lead us to conclude that XAI is an essential foundation for every pillar of RAI.
In the traditional cellular-based mobile edge computing (MEC), users at the edge of the cell are prone to suffer severe inter-cell interference and signal attenuation, leading to low throughput even transmission interruptions. Such edge effect severely obstructs offloading of tasks to MEC servers. To address this issue, we propose user-centric mobile edge computing (UCMEC), a novel MEC architecture integrating user-centric transmission, which can ensure high throughput and reliable communication for task offloading. Then, we formulate an optimization problem with joint consideration of task offloading, power control, and computing resource allocation in UCMEC, aiming at obtaining the optimal performance in terms of long-term average total delay. To solve the intractable problem, we propose two decentralized joint optimization schemes based on multi-agent deep reinforcement learning (MADRL) and convex optimization, which consider both cooperation and non-cooperation among network nodes. Simulation results demonstrate that the proposed schemes in UCMEC can significantly improve the uplink transmission rate by at most 343.56% and reduce the long-term average total delay by at most 45.57% compared to traditional cellular-based MEC.
The surge in real-time data collection across various industries has underscored the need for advanced anomaly detection in both univariate and multivariate time series data. Traditional methods, while comprehensive, often struggle to capture the complex interdependencies in such data. This paper introduces TransNAS-TSAD, a novel framework that synergizes transformer architecture with neural architecture search (NAS), enhanced through NSGA-II algorithm optimization. This innovative approach effectively tackles the complexities of both univariate and multivariate time series, balancing computational efficiency with detection accuracy. Our evaluation reveals that TransNAS-TSAD surpasses conventional anomaly detection models, demonstrating marked improvements in diverse data scenarios. We also propose the Efficiency-Accuracy-Complexity Score (EACS) as a new metric for assessing model performance, emphasizing the crucial balance between accuracy and computational resources. TransNAS-TSAD sets a new benchmark in time series anomaly detection, offering a versatile, efficient solution for complex real-world applications. This research paves the way for future developments in the field, highlighting its potential in a wide range of industry applications.
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity is readily available nowadays, which renders researchers an opportunity to tackle current geoscience applications in a fresh way. With the joint utilization of EO data, much research on multimodal RS data fusion has made tremendous progress in recent years, yet these developed traditional algorithms inevitably meet the performance bottleneck due to the lack of the ability to comprehensively analyse and interpret these strongly heterogeneous data. Hence, this non-negligible limitation further arouses an intense demand for an alternative tool with powerful processing competence. Deep learning (DL), as a cutting-edge technology, has witnessed remarkable breakthroughs in numerous computer vision tasks owing to its impressive ability in data representation and reconstruction. Naturally, it has been successfully applied to the field of multimodal RS data fusion, yielding great improvement compared with traditional methods. This survey aims to present a systematic overview in DL-based multimodal RS data fusion. More specifically, some essential knowledge about this topic is first given. Subsequently, a literature survey is conducted to analyse the trends of this field. Some prevalent sub-fields in the multimodal RS data fusion are then reviewed in terms of the to-be-fused data modalities, i.e., spatiospectral, spatiotemporal, light detection and ranging-optical, synthetic aperture radar-optical, and RS-Geospatial Big Data fusion. Furthermore, We collect and summarize some valuable resources for the sake of the development in multimodal RS data fusion. Finally, the remaining challenges and potential future directions are highlighted.
With the advent of 5G commercialization, the need for more reliable, faster, and intelligent telecommunication systems are envisaged for the next generation beyond 5G (B5G) radio access technologies. Artificial Intelligence (AI) and Machine Learning (ML) are not just immensely popular in the service layer applications but also have been proposed as essential enablers in many aspects of B5G networks, from IoT devices and edge computing to cloud-based infrastructures. However, most of the existing surveys in B5G security focus on the performance of AI/ML models and their accuracy, but they often overlook the accountability and trustworthiness of the models' decisions. Explainable AI (XAI) methods are promising techniques that would allow system developers to identify the internal workings of AI/ML black-box models. The goal of using XAI in the security domain of B5G is to allow the decision-making processes of the security of systems to be transparent and comprehensible to stakeholders making the systems accountable for automated actions. In every facet of the forthcoming B5G era, including B5G technologies such as RAN, zero-touch network management, E2E slicing, this survey emphasizes the role of XAI in them and the use cases that the general users would ultimately enjoy. Furthermore, we presented the lessons learned from recent efforts and future research directions on top of the currently conducted projects involving XAI.
Unmanned aerial vehicle (UAV) swarm enabled edge computing is envisioned to be promising in the sixth generation wireless communication networks due to their wide application sensories and flexible deployment. However, most of the existing works focus on edge computing enabled by a single or a small scale UAVs, which are very different from UAV swarm-enabled edge computing. In order to facilitate the practical applications of UAV swarm-enabled edge computing, the state of the art research is presented in this article. The potential applications, architectures and implementation considerations are illustrated. Moreover, the promising enabling technologies for UAV swarm-enabled edge computing are discussed. Furthermore, we outline challenges and open issues in order to shed light on the future research directions.
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