pwc-archive/papers-with-abstracts huggingface dataset:
Viewport Prediction, Bitrate Selection, and Beamforming Design for THz-Enabled 360° Video Streaming
Authors: Mehdi Setayesh, Vincent W. S. Wong
Date: 2024-01-23 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Federated Learning, Personalized Federated Learning

360{\deg} videos require significant bandwidth to provide an immersive viewing experience. Wireless systems using terahertz (THz) frequency band can meet this high data rate demand. However, self-blockage is a challenge in such systems. To ensure reliable transmission, this paper explores THz-enabled 360{\deg} video streaming through multiple multi-antenna access points (APs). Guaranteeing users' quality of experience (QoE) requires accurate viewport prediction to determine which video tiles to send, followed by asynchronous bitrate selection for those tiles and beamforming design at the APs. To address users' privacy and data heterogeneity, we propose a content-based viewport prediction framework, wherein users' head movement prediction models are trained using a personalized federated learning (PFL) algorithm. To address asynchronous decision-making for tile bitrates and dynamic THz link connections, we formulate the optimization of bitrate selection and beamforming as a macro-action decentralized partially observable Markov decision process (MacDec-POMDP) problem. To efficiently tackle this problem for multiple users, we develop two deep reinforcement learning (DRL) algorithms based on multi-agent actor-critic methods and propose a hierarchical learning framework to train the actor and critic networks. Experimental results show that our proposed approach provides a higher QoE when compared with three benchmark algorithms.
Pilot Distributions for Phase Noise Estimation in Electro-Optic Frequency Comb Systems
Authors: Mohammad Farsi, Magnus Karlsson, Erik Agrell
Date: 2024-01-25 00:00:00
Tasks: Noise Estimation

We explore the optimal pilot positioning for phase tracking in electro-optic frequency comb setups. We show that, in contrast to previous results for regular multichannel systems, allocating the first and the last channels for pilots is optimal given a fixed pilot overhead
Modelling Micro-Doppler Signature of Drone Propellers in Distributed ISAC
Authors: Heraldo Cesar Alves Costa, Saw James Myint, Carsten Andrich, Sebastian W. Giehl, Christian Schneider, Reiner S. Thomä
Date: 2024-01-25 00:00:00
Tasks: Integrated sensing and communication, ISAC

Integrated Sensing and Communication (ISAC) comprises detection and analysis of non-cooperative targets by exploiting the resources of the mobile radio system. In this context, micro-Doppler is of great importance for target classification, in order to distinguish objects with local movements. For developing algorithms for target classification, it is necessary to have a large amount of target signatures. Aiming to generate these data, this paper proposes a mathematical model for the micro-Doppler of drone rotating propellers, and validate the proposed model by comparing it to measured micro-Doppler. Results show that the proposed mathematical model can generate micro-Doppler data very similar to those from measurement data.
Energy-Efficient Power Allocation in Cell-Free Massive MIMO via Graph Neural Networks
Authors: Ramprasad Raghunath, Bile Peng, Eduard A. Jorswieck
Date: 2024-01-25 00:00:00

CF-mMIMO systems are a promising solution to enhance the performance in 6G wireless networks. Its distributed nature of the architecture makes it highly reliable, provides sufficient coverage and allows higher performance than cellular networks. EE is an important metric that reduces the operating costs and also better for the environment. In this work, we optimize the downlink EE performance with MRT precoding and power allocation. Our aim is to achieve a less complex, distributed and scalable solution. To achieve this, we apply unsupervised ML with permutation equivariant architecture and use a non-convex objective function with multiple local optima. We compare the performance with the centralized and computationally expensive SCA. The results indicate that the proposed approach can outperform the baseline with significantly less computation time.
Active Simultaneously Transmitting and Reflecting Surface Assisted NOMA Networks
Authors: Xinwei Yue, Jin Xie, Chongjun Ouyang, Yuanwei Liu, Xia Shen, Zhiguo Ding
Date: 2024-01-25 00:00:00

The novel active simultaneously transmitting and reflecting surface (ASTARS) has recently received a lot of attention due to its capability to conquer the multiplicative fading loss and achieve full-space smart radio environments. This paper introduces the ASTARS to assist non-orthogonal multiple access (NOMA) communications, where the stochastic geometry theory is used to model the spatial positions of pairing users. We design the independent reflection/transmission phase-shift controllers of ASTARS to align the phases of cascaded channels at pairing users. We derive new closed-form and asymptotic expressions of the outage probability and ergodic data rate for ASTARS-NOMA networks in the presence of perfect/imperfect successive interference cancellation (pSIC). The diversity orders and multiplexing gains for ASTARS-NOMA are derived to provide more insights. Furthermore, the system throughputs of ASTARS-NOMA are investigated in both delay-tolerant and delay-limited transmission modes. The numerical results are presented and show that: 1) ASTARS-NOMA with pSIC outperforms ASTARS assisted-orthogonal multiple access (ASTARS-OMA) in terms of outage probability and ergodic data rate; 2) The outage probability of ASTARS-NOMA can be further reduced within a certain range by increasing the power amplification factors; 3) The system throughputs of ASTARS-NOMA are superior to that of ASTARS-OMA in both delay-limited and delay-tolerant transmission modes.
Knowledge Graph Driven UAV Cognitive Semantic Communication Systems for Efficient Object Detection
Authors: Xi Song, Lu Yuan, Zhibo Qu, Fuhui Zhou, Qihui Wu, Tony Q. S. Quek, Rose Qingyang Hu
Date: 2024-01-25 00:00:00
Tasks: Object, object-detection, Object Detection, Semantic Communication, Semantic Compression

Unmanned aerial vehicles (UAVs) are widely used for object detection. However, the existing UAV-based object detection systems are subject to the serious challenge, namely, the finite computation, energy and communication resources, which limits the achievable detection performance. In order to overcome this challenge, a UAV cognitive semantic communication system is proposed by exploiting knowledge graph. Moreover, a multi-scale compression network is designed for semantic compression to reduce data transmission volume while guaranteeing the detection performance. Furthermore, an object detection scheme is proposed by using the knowledge graph to overcome channel noise interference and compression distortion. Simulation results conducted on the practical aerial image dataset demonstrate that compared to the benchmark systems, our proposed system has superior detection accuracy, communication robustness and computation efficiency even under high compression rates and low signal-to-noise ratio (SNR) conditions.
Analog Beamforming for In-Band Full-Duplex Phased Arrays with Quantized Phase Shifters under a Per-Antenna Received Power Constraint
Authors: Ao Liu, Ian P. Roberts, Taneli Riihonen, Weixing Sheng
Date: 2024-01-25 00:00:00

This letter develops a novel transmit beamforming (BF) design for canceling self-interference (SI) in analog in-band full-duplex phased arrays. Our design maximizes transmit BF gain in a desired direction while simultaneously reducing SI power to below a specified threshold on per-antenna basis to avoid saturating receive-chain components, such as LNAs. Core to our approach is that it accounts for real-world phase shifters used in analog phased array systems, whose limited resolution imposes non-convex constraints on BF design. We overcome this by transforming these non-convex constraints into convex polygon constraints, which we then solve through semidefinite relaxation and a rank refinement procedure. Numerical results show that our proposed BF scheme reliably cancels SI to the target power threshold at each receive antenna while sacrificing little in transmit BF gain, even with modest phase shifter resolution.
A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges
Authors: Zhiyu Zhu, Yang Yang, Mingzhe Chen, Caili Guo, Julian Cheng, Shuguang Cui
Date: 2024-01-25 00:00:00
Tasks: Indoor Localization, Survey

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses.
Synthetic Waveform Generation for Satellite, HAPS, and 5G Base Station Positioning Reference Signal Using QuaDRiGa
Authors: Hongzhao Zheng, Mohamed Atia, Halim Yanikomeroglu, Paulo S. R. Diniz
Date: 2024-01-24 00:00:00

Waveform generation is essential for studying signal propagation and channel characteristics, particularly for objects that are conceptualized but still need to be operational. We introduce a comprehensive guide on creating synthetic signals using channel and delay coefficients derived from the Quasi-Deterministic Radio Channel Generator (QuaDRiGa), which is recognized as a 3GPP-3D and 3GPP 38.901 reference implementation. The effectiveness of the proposed synthetic waveform generation method is validated through accurate estimation of code delay and Doppler shift. This validation is achieved using both the parallel code phase search technique and the conventional tracking method applied to satellites. As the method of integrating channel and delay coefficients to create synthetic waveforms is the same for satellite, HAPS, and gNB PRS, validating this method on synthetic satellite signals could potentially be extended to HAPS and gNB PRS as well. This study could significantly contribute to the field of heterogeneous navigation systems.
Towards Optimal Pilot Spacing and Power Control in Multi-Antenna Systems Operating Over Non-Stationary Rician Aging Channels
Authors: Sajad Daei, Gabor Fodor, Mikael Skoglund, Miklos Telek
Date: 2024-01-24 00:00:00

Several previous works have addressed the inherent trade-off between allocating resources in the power and time domains to pilot and data signals in multiple input multiple output systems over block-fading channels. In particular, when the channel changes rapidly in time, channel aging degrades the performance in terms of spectral efficiency without proper pilot spacing and power control. Despite recognizing non-stationary stochastic processes as more accurate models for time-varying wireless channels, the problem of pilot spacing and power control in multi-antenna systems operating over non-stationary channels is not addressed in the literature. In this paper, we address this gap by introducing a refined first-order autoregressive model that exploits the inherent temporal correlations over non-stationary Rician aging channels. We design a multi-frame structure for data transmission that better reflects the non-stationary fading environment than previously developed single-frame structures. Subsequently, to determine optimal pilot spacing and power control within this multi-frame structure, we develop an optimization framework and an efficient algorithm based on maximizing a deterministic equivalent expression for the spectral efficiency, demonstrating its generality by encompassing previous channel aging results. Our numerical results indicate the efficacy of the proposed method in terms of spectral efficiency gains over the single frame structure.
LightSleepNet: Design of a Personalized Portable Sleep Staging System Based on Single-Channel EEG
Authors: Yiqiao Liao, Chao Zhang, Milin Zhang, Zhihua Wang, Xiang Xie
Date: 2024-01-24 00:00:00
Tasks: Domain Adaptation, EEG, Sleep Staging, Unsupervised Domain Adaptation

This paper proposed LightSleepNet - a light-weight, 1-d Convolutional Neural Network (CNN) based personalized architecture for real-time sleep staging, which can be implemented on various mobile platforms with limited hardware resources. The proposed architecture only requires an input of 30s single-channel EEG signal for the classification. Two residual blocks consisting of group 1-d convolution are used instead of the traditional convolution layers to remove the redundancy in the CNN. Channel shuffles are inserted into each convolution layer to improve the accuracy. In order to avoid over-fitting to the training set, a Global Average Pooling (GAP) layer is used to replace the fully connected layer, which further reduces the total number of the model parameters significantly. A personalized algorithm combining Adaptive Batch Normalization (AdaBN) and gradient re-weighting is proposed for unsupervised domain adaptation. A higher priority is given to examples that are easy to transfer to the new subject, and the algorithm could be personalized for new subjects without re-training. Experimental results show a state-of-the-art overall accuracy of 83.8% with only 45.76 Million Floating-point Operations per Second (MFLOPs) computation and 43.08 K parameters.
A novel wavelet-based filtering strategy to remove powerline interference from electrocardiograms with atrial fibrillation
Authors: Manuel Garcia, Miguel Martinez-Iniesta, Juan Rodenas, Jose J Rieta, Raul Alcaraz
Date: 2024-01-23 00:00:00

"Objective: The electrocardiogram (ECG) is currently the most widely used recording to diagnose cardiac disorders, including the most common supraventricular arrhythmia, such as atrial fibrillation (AF). However, different types of electrical disturbances, in which power-line interference (PLI) is a major problem, can mask and distort the original ECG morphology. This is a significant issue in the context of AF, because accurate characterization of fibrillatory waves (f-waves) is unavoidably required to improve current knowledge about its mechanisms. This work introduces a new algorithm able to reduce high levels of PLI and preserve, simultaneously, the original ECG morphology. Approach: The method is based on stationary wavelet transform shrinking and makes use of a new thresholding function designed to work successfully in a wide variety of scenarios. In fact, it has been validated in a general context with 48 ECG recordings obtained from pathological and non-pathological conditions, as well as in the particular context of AF, where 380 synthesized and 20 long-term real ECG recordings were analyzed. Main results: In both situations, the algorithm has reported a notably better performance than common methods designed for the same purpose. Moreover, its effectiveness has proven to be optimal for dealing with ECG recordings affected by AF, since f-waves remained almost intact after removing very high levels of noise. Significance: The proposed algorithm may facilitate a reliable characterization of the f-waves, preventing them from not being masked by the PLI nor distorted by an unsuitable filtering applied to ECG recordings with AF."
A BFF-Based Attention Mechanism for Trajectory Estimation in mmWave MIMO Communications
Authors: Mohammad Shamsesalehi, Mahmoud Ahmadian Attari, Mohammad Amin Maleki Sadr, Benoit Champagne, Marwa Qaraqe
Date: 2024-01-23 00:00:00

This paper explores a novel Neural Network (NN) architecture suitable for Beamformed Fingerprint (BFF) localization in a millimeter-wave (mmWave) multiple-input multiple-output (MIMO) outdoor system. The mmWave frequency bands have attracted significant attention due to their precise timing measurements, making them appealing for applications demanding accurate device localization and trajectory estimation. The proposed NN architecture captures BFF sequences originating from various user paths, and through the application of learning mechanisms, subsequently estimates these trajectories. Specifically, we propose a method for trajectory estimation, employing a transformer network (TN) that relies on attention mechanisms. This TN-based approach estimates wireless device trajectories using BFF sequences recorded within a mmWave MIMO outdoor system. To validate the efficacy of our proposed approach, numerical experiments are conducted using a comprehensive dataset of radio measurements in an outdoor setting, complemented with ray tracing to simulate wireless signal propagation at 28 GHz. The results illustrate that the TN-based trajectory estimator outperforms other methods from the existing literature and possesses the ability to generalize effectively to new trajectories outside the training dataset.
Perfect taxon sampling and fixing taxon traceability: Introducing a class of phylogenetically decisive collections of taxon sets
Authors: Mareike Fischer, Janne Pott
Date: 2012-06-15 00:00:00

Phylogenetically decisive collections of taxon sets have the property that if trees are chosen for each of their elements, as long as these trees are compatible, the resulting supertree is unique. This means that as long as the trees describing the phylogenetic relationships of the (input) species sets are compatible, they can only be combined into a common supertree in precisely one way. This setting is sometimes also referred to as \enquote{perfect taxon sampling}. While for rooted trees, the decision if a given set of input taxon sets is phylogenetically decisive can be made in polynomial time, the decision problem to determine whether a collection of taxon sets is phylogenetically decisive concerning \emph{unrooted} trees is unfortunately coNP-complete and therefore in practice hard to solve for large instances. This shows that recognizing such sets is often difficult. In this paper, we explain phylogenetic decisiveness and introduce a class of input taxon sets, namely so-called \emph{fixing taxon traceable} sets, which are guaranteed to be phylogenetically decisive and which can be recognized in polynomial time. Using both combinatorial approaches as well as simulations, we compare properties of fixing taxon traceability and phylogenetic decisiveness, e.g., by deriving lower and upper bounds for the number of quadruple sets (i.e., sets of 4-tuples) needed in the input set for each of these properties. In particular, we correct an erroneous lower bound concerning phylogenetic decisiveness from the literature. We have implemented the algorithm to determine if a given collection of taxon sets is fixing taxon traceable in \textsf{R} and made our software package \verb+FixingTaxonTraceR+ publicly available.
Health Digital Twins Supported by Artificial Intelligence-based Algorithms and Extended Reality in Cardiology
Authors: Zofia Rudnicka, Klaudia Proniewska, Mark Perkins, Agnieszka Pregowska
Date: 2024-01-25 00:00:00
Tasks: Anatomy, Ethics, Image Segmentation, Segmentation, Semantic Segmentation

Recently, significant efforts have been made to create Health Digital Twins (HDTs), digital twins for clinical applications. Heart modeling is one of the fastest-growing fields, which favors the effective application of HDTs. The clinical application of HDTs will be increasingly widespread in the future of healthcare services and has a huge potential to form part of the mainstream in medicine. However, it requires the development of both models and algorithms for the analysis of medical data, and advances in Artificial Intelligence (AI) based algorithms have already revolutionized image segmentation processes. Precise segmentation of lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapy. In this paper, a brief overview of recent achievements in HDT technologies in the field of cardiology, including interventional cardiology was conducted. HDTs were studied taking into account the application of Extended Reality (XR) and AI, as well as data security, technical risks, and ethics-related issues. Special emphasis was put on automatic segmentation issues. It appears that improvements in data processing will focus on automatic segmentation of medical imaging in addition to three-dimensional (3D) pictures to reconstruct the anatomy of the heart and torso that can be displayed in XR-based devices. This will contribute to the development of effective heart diagnostics. The combination of AI, XR, and an HDT-based solution will help to avoid technical errors and serve as a universal methodology in the development of personalized cardiology. Additionally, we describe potential applications, limitations, and further research directions.
Radical Realism
Authors: Nicolás Hinrichs, Noah Guzmán
Date: 2024-01-25 00:00:00

The ontogeny of cognitive neuroscience has emerged within the hegemony of substance ontology. Persistent physicalist influences are described through three developmental hallmarks that yielded epistemic attractors - promoters and perpetuators of material-discursive practices oriented toward reification and self-vindication across the interdisciplinary spectrum which, as a whole, has been driven away from its pretensions to scientific realism. In virtue of a desire for a radical return thereto, we adopt a metaphysic stance akin to pragmatism, and briefly make the case that such concerns have sociopolitical implications extending far beyond the realm of mere philosophical interest.