pwc-archive/papers-with-abstracts huggingface dataset:
Semantic Communications for Wireless Sensing: RIS-aided Encoding and Self-supervised Decoding
Authors: Hongyang Du, Jiacheng Wang, Dusit Niyato, Jiawen Kang, Zehui Xiong, Junshan Zhang, Xuemin, Shen
Date: 2022-11-23 00:00:00
Tasks: Self-Supervised Learning, Semantic Communication

Semantic communications can reduce the resource consumption by transmitting task-related semantic information extracted from source messages. However, when the source messages are utilized for various tasks, e.g., wireless sensing data for localization and activities detection, semantic communication technique is difficult to be implemented because of the increased processing complexity. In this paper, we propose the inverse semantic communications as a new paradigm. Instead of extracting semantic information from messages, we aim to encode the task-related source messages into a hyper-source message for data transmission or storage. Following this paradigm, we design an inverse semantic-aware wireless sensing framework with three algorithms for data sampling, reconfigurable intelligent surface (RIS)-aided encoding, and self-supervised decoding, respectively. Specifically, on the one hand, we propose a novel RIS hardware design for encoding several signal spectrums into one MetaSpectrum. To select the task-related signal spectrums for achieving efficient encoding, a semantic hash sampling method is introduced. On the other hand, we propose a self-supervised learning method for decoding the MetaSpectrums to obtain the original signal spectrums. Using the sensing data collected from real-world, we show that our framework can reduce the data volume by 95% compared to that before encoding, without affecting the accomplishment of sensing tasks. Moreover, compared with the typically used uniform sampling scheme, the proposed semantic hash sampling scheme can achieve 67% lower mean squared error in recovering the sensing parameters. In addition, experiment results demonstrate that the amplitude response matrix of the RIS enables the encryption of the sensing data.
Wireless Sensing Data Collection and Processing for Metaverse Avatar Construction
Authors: Jiacheng Wang, Hongyang Du, Xiaolong Yang, Dusit Niyato, Jiawen Kang, Shiwen Mao
Date: 2022-11-23 00:00:00

Recent advances in emerging technologies such as artificial intelligence and extended reality have pushed the Metaverse, a virtual, shared space, into reality. In Metaverse, users can customize virtual avatars to experience a different life. While impressive, avatar construction requires a lot of data that manifest users in the physical world from various perspectives, and wireless sensing data is one of them. For example, machine learning (ML) and signal processing can help extract information about user behavior from sensing data, thereby facilitating avatar behavior construction in the Metaverse. This article presents a wireless sensing dataset to support the emerging research on Metaverse avatar construction. Rigorously, the existing data collection platforms and datasets are analyzed first. On this basis, we introduce the platform used in this paper, as well as the data collection method and scenario. We observe that the collected sensing data, i.e., channel state information (CSI), suffers from a phase shift problem, which negatively affects the extraction of user information such as behavior and heartbeat and further deteriorates the avatar construction. Therefore, we propose to detect and correct this phase shift by a sliding window and phase compensation, respectively, and then validate the proposed scheme with the collected data. Finally, several research directions related to the avatar construction are given from the perspective of datasets.
Joint Design of Power Control and Access Point Scheduling for Uplink Cell-Free Massive MIMO Networks
Authors: Hyeonsik Yeom, Junguk Park, Jinho Choi, Jeongseok Ha
Date: 2022-11-23 00:00:00
Tasks: CPU, Fairness, Scheduling

This work proposes a joint power control and access points (APs) scheduling algorithm for uplink cell-free massive multiple-input multiple-output (CF-mMIMO) networks without channel hardening assumption. Extensive studies have done on the joint optimization problem assuming the channel hardening. However, it has been reported that the channel hardening may not be validated in some CF-mMIMO environments. In particular, the existing Use-and-then-Forget (UatF) bound based on the channel hardening often seriously underestimates user rates in CF-mMIMO. Therefore, a new performance evaluation technique without resorting to the channel hardening is indispensable for accurate performance estimations. Motivated by this, we propose a new bound on the achievable rate of uplink CF-mMIMO. It is demonstrated that the proposed bound provides a more accurate performance estimate of CF-mMIMO than that of the existing UatF bound. The proposed bound also enables us to develop a joint power control and APs scheduling algorithm targeting at both improving fairness and reducing the resource between APs and a central processing unit (CPU). We conduct extensive performance evaluations and comparisons for systems designed with the proposed and existing algorithms. The comparisons show that a considerable performance improvement is achievable with the proposed algorithm even at reduced resource between APs and CPU.
3-D Positioning and Resource Allocation for Multi-UAV Base Stations Under Blockage-Aware Channel Model
Authors: Pengfei Yi, Lipeng Zhu, Zhenyu Xiao, Rui Zhang, Zhu Han, Xiang-Gen Xia
Date: 2022-11-23 00:00:00

In this paper, we propose to deploy multiple unmanned aerial vehicle (UAV) mounted base stations to serve ground users in outdoor environments with obstacles. In particular, the geographic information is employed to capture the blockage effects for air-to-ground (A2G) links caused by buildings, and a realistic blockage-aware A2G channel model is proposed to characterize the continuous variation of the channels at different locations. Based on the proposed channel model, we formulate the joint optimization problem of UAV three-dimensional (3-D) positioning and resource allocation, by power allocation, user association, and subcarrier allocation, to maximize the minimum achievable rate among users. To solve this non-convex combinatorial programming problem, we introduce a penalty term to relax it and develop a suboptimal solution via a penalty-based double-loop iterative optimization framework. The inner loop solves the penalized problem by employing the block successive convex approximation (BSCA) technique, where the UAV positioning and resource allocation are alternately optimized in each iteration. The outer loop aims to obtain proper penalty multipliers to ensure the solution of the penalized problem converges to that of the original problem. Simulation results demonstrate the superiority of the proposed algorithm over other benchmark schemes in terms of the minimum achievable rate.
Joint Secure Communication and Radar Beamforming: A Secrecy-Estimation Rate-Based Design
Authors: Rong Wen, Ying Zhang, Qiang Li, Youxi Tang
Date: 2022-11-23 00:00:00

This paper considers transmit beamforming in dual-function radar-communication (DFRC) system, where a DFRC transmitter simultaneously communicates with a communication user and detects a malicious target with the same waveform. Since the waveform is embedded with information, the information is risked to be intercepted by the target. To address this problem, physical-layer security technique is exploited. By using secrecy rate and estimation rate as performance measure for communication and radar, respectively, three secrecy rate maximization (SRM) problems are formulated, including the SRM with and without artificial noise (AN), and robust SRM. For the SRM beamforming, we prove that the optimal beamformer can be computed in closed form. For the AN-aided SRM, by leveraging alternating optimization similar closed-form solution is obtained for the beamformer and the AN covariance matrix. Finally, the imperfect CSI of the target is also considered under the premise of a moment-based random phase-error model on the direction of arrival at the target. Simulation results demonstrate the efficacy and robustness of the proposed designs.
A priori knowledge-free fast positioning approach for BeiDou receivers
Authors: Sihao Zhao, Xiaowei Cui, Mingquan Lu
Date: 2022-11-22 00:00:00
Tasks: Position

A Global Navigation Satellite System (GNSS) receiver usually needs a sufficient number of full pseudorange measurements to obtain a position solution. However, it is time-consuming to acquire full pseudorange information from only the satellite broadcast signals due to the navigation data features of GNSS. In order to realize fast positioning during a cold or warm start in a GNSS receiver, the existing approaches require an initial estimation of position and time or require a number of computational steps to recover the full pseudorange information from fractional pseudoranges and then compute the position solution. The BeiDou Navigation Satellite System (BDS) has a unique constellation distribution and a fast navigation data rate for geostationary earth orbit (GEO) satellites. Taking advantage of these features, we propose a fast positioning technique for BDS receivers. It simultaneously processes the full and fractional pseudorange measurements from the BDS GEOs and non-GEOs, respectively, which is faster than processing all full measurements. This method resolves the position solution and recovers the full pseudoranges for non-GEOs simultaneously within 1 s theoretically and does not need an estimate of the initial position. Simulation and real data experiments confirm that the proposed technique completes fast positioning without a priori position and time estimation, and the positioning accuracy is identical with the conventional single-point positioning approach using full pseudorange measurements from all available satellites.
Radar Range Deception with Time-Modulated Scatterers
Authors: V. Kozlov, D. Vovchuk, P. Ginzburg
Date: 2022-11-22 00:00:00

Modern radar systems are designed to have high Doppler tolerance to detect fast-moving targets. This means range and Doppler estimations are inevitably coupled, opening pathways to concealing objects by imprinting artificial Doppler signatures on the reflected echoes. Proper temporal control of the backscattered phase can cause the investigating radar to estimate wrong range and velocity, thus cloaking the real position and trajectory of the scatterer. This deception method is exploited here theoretically for arbitrary Doppler tolerant waveforms and then tested experimentally on an example of the linear frequency modulated radar, which is the most common waveform of that class used in practice. The method allows retaining radio silence with a semi passive (battery assisted) approach that can work well with time-dependent metasurfaces. Furthermore, as an insight into new capabilities, we demonstrate that temporally concealed objects could even be made to appear closer than they truly are without violating the laws of relativity.
Reconfigurable Intelligent Surface MIMO Simulation using Quasi Deterministic Radio Channel Model
Authors: Semyon Dorokhin, Pavel Lysov, Anastasia Aderkina, Vladimir Lyashev
Date: 2022-11-22 00:00:00

Reconfigurable Intelligent Surface (RIS) is a planar array that can control reflection and thus can implement the concept of partially controllable propagation environment. RIS received a lot of attention from industry and academia, but the majority of the researchers who study RIS-assisted systems use simple Rician model. Though it is suitable for theoretical analysis, stochastic Non Line-of-Sight (NLoS) component in Rician model does not account for the geometry of deployment. Furthermore, Rician model is not eligible to evaluate 3GPP standardization proposals. In this article we adapt the popular Quasi Deterministic Radio channel Generator (QuaDRiGa) for RIS-assisted systems and compare it against Rician model. The comparison shows that geometry-inconsistent NLoS Rician modeling results in higher estimated achievable rate. Our method, in contrast, inherits the advantages of QuaDRiGa: spatial consistency of Large Scale Fading, User Equipment mobility support as well as consistency between Large Scale and Small Scale Fading. Moreover, QuaDRiGa comes with calibrated scenario parameters that ensure 3GPP compatibility. Finally, the proposed method can be applied to any model or software originally designed for conventional MIMO, so every researcher can use it to build a simulation platform for RIS-assisted systems.
Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer
Authors: Jie Zhang, Yihui Zhao, Tianzhe Bao, Zhenhong Li, Kun Qian, Alejandro F. Frangi, Sheng Quan Xie, Zhi-Qiang Zhang
Date: 2022-11-22 00:00:00
Tasks: Transfer Learning

Data-driven methods have become increasingly more prominent for musculoskeletal modelling due to their conceptually intuitive simple and fast implementation. However, the performance of a pre-trained data-driven model using the data from specific subject(s) may be seriously degraded when validated using the data from a new subject, hindering the utility of the personalised musculoskeletal model in clinical applications. This paper develops an active physics-informed deep transfer learning framework to enhance the dynamic tracking capability of the musculoskeletal model on the unseen data. The salient advantages of the proposed framework are twofold: 1) For the generic model, physics-based domain knowledge is embedded into the loss function of the data-driven model as soft constraints to penalise/regularise the data-driven model. 2) For the personalised model, the parameters relating to the feature extraction will be directly inherited from the generic model, and only the parameters relating to the subject-specific inference will be finetuned by jointly minimising the conventional data prediction loss and the modified physics-based loss. In this paper, we use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Moreover, convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework, and the physics law between muscle forces and joint kinematics is utilised as the soft constraints. Results of comprehensive experiments on a self-collected dataset from eight healthy subjects indicate the effectiveness and great generalization of the proposed framework.
RIS-Assisted Self-Interference Mitigation for In-Band Full-Duplex Transceivers
Authors: Wei zhang, Yi Jiang, Bin Zhou
Date: 2022-11-22 00:00:00
Tasks: Quantization

The wireless in-band full-duplex (IBFD) technology can in theory double the system capacity over the conventional frequency division duplex (FDD) or time-division duplex (TDD) alternatives. But the strong self-interference of the IBFD can cause excessive quantization noise in the analog-to-digital converters (ADC), which represents the hurdle for its real implementation. In this paper, we consider employing a reconfigurable intelligent surface (RIS) for IBFD communications. While the BS transmits and receives the signals to and from the users simultaneously on the same frequency band, it can adjust the reflection coefficients of the RIS to configure the wireless channel so that the self-interference of the BS is sufficiently mitigated in the propagation domain. Taking the impact of the quantization noise into account, we propose to jointly design the downlink (DL) precoding matrix and the RIS coefficients to maximize the sum of uplink (UL) and DL rates. The effectiveness of the proposed RIS-assisted in-band full-duplex (RAIBFD) system is verified by simulation studies, even taking into considerations that the phases of the RIS have only finite resolution.
Vector Approximate Message Passing based Channel Estimation for MIMO-OFDM Underwater Acoustic Communications
Authors: Wenxuan Chen, Jun Tao, Lu Ma, Gang Qiao
Date: 2022-11-22 00:00:00

Accurate channel estimation is critical to the performance of orthogonal frequency-division multiplexing (OFDM) underwater acoustic (UWA) communications, especially under multiple-input multiple-output (MIMO) scenarios. In this paper, we explore Vector Approximate Message Passing (VAMP) coupled with Expected Maximum (EM) to obtain channel estimation (CE) for MIMO OFDM UWA communications. The EM-VAMP-CE scheme is developed by employing a Bernoulli-Gaussian (BG) prior distribution for the channel impulse response, and hyperparameters of the BG prior distribution are learned via the EM algorithm. Performance of the EM-VAMP-CE is evaluated through both synthesized data and real data collected in two at-sea UWA communication experiments. It is shown the EM-VAMP-CE achieves better performance-complexity tradeoff compared with existing channel estimation methods.
Implementation and Applications of a Ternary Threshold Logic Gate
Authors: Ahmet Unutulmaz, Cem Ünsalan
Date: 2022-11-22 00:00:00

Reducing delay, power consumption, and chip area of a logic circuit are the main targets of a designer. Most of the times, the designer sacrifices power consumption and chip area to improve delay for a given technology node. To overcome this problem, we propose a ternary threshold logic gate. We implement the proposed gate by combining threshold logic and ternary logic. Then, we construct basic building blocks of a ternary ALU (as logic gates, comparator, and arithmetic circuits) using the proposed gate. We show that the proposed ternary TLG improves delay, power consumption, and chip area of ternary circuits via simulations. Thus, the proposed gate can be used to improve delay, power consumption, and chip area of ternary circuits.
Device-Agnostic Millimeter Wave Beam Selection using Machine Learning
Authors: Sajad Rezaie, João Morais, Ahmed Alkhateeb, Carles Navarro Manchón
Date: 2022-11-22 00:00:00

Most research in the area of machine learning-based user beam selection considers a structure where the model proposes appropriate user beams. However, this design requires a specific model for each user-device beam codebook, where a model learned for a device with a particular codebook can not be reused for another device with a different codebook. Moreover, this design requires training and test samples for each antenna placement configuration/codebook. This paper proposes a device-agnostic beam selection framework that leverages context information to propose appropriate user beams using a generic model and a post processing unit. The generic neural network predicts the potential angles of arrival, and the post processing unit maps these directions to beams based on the specific device's codebook. The proposed beam selection framework works well for user devices with antenna configuration/codebook unseen in the training dataset. Also, the proposed generic network has the option to be trained with a dataset mixed of samples with different antenna configurations/codebooks, which significantly eases the burden of effective model training.
STILN: A Novel Spatial-Temporal Information Learning Network for EEG-based Emotion Recognition
Authors: Yiheng Tang, Yongxiong Wang, Xiaoli Zhang, Zhe Wang
Date: 2022-11-22 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Emotion Recognition

The spatial correlations and the temporal contexts are indispensable in Electroencephalogram (EEG)-based emotion recognition. However, the learning of complex spatial correlations among several channels is a challenging problem. Besides, the temporal contexts learning is beneficial to emphasize the critical EEG frames because the subjects only reach the prospective emotion during part of stimuli. Hence, we propose a novel Spatial-Temporal Information Learning Network (STILN) to extract the discriminative features by capturing the spatial correlations and temporal contexts. Specifically, the generated 2D power topographic maps capture the dependencies among electrodes, and they are fed to the CNN-based spatial feature extraction network. Furthermore, Convolutional Block Attention Module (CBAM) recalibrates the weights of power topographic maps to emphasize the crucial brain regions and frequency bands. Meanwhile, Batch Normalizations (BNs) and Instance Normalizations (INs) are appropriately combined to relieve the individual differences. In the temporal contexts learning, we adopt the Bidirectional Long Short-Term Memory Network (Bi-LSTM) network to capture the dependencies among the EEG frames. To validate the effectiveness of the proposed method, subject-independent experiments are conducted on the public DEAP dataset. The proposed method has achieved the outstanding performance, and the accuracies of arousal and valence classification have reached 0.6831 and 0.6752 respectively.
A CNN-Transformer Deep Learning Model for Real-time Sleep Stage Classification in an Energy-Constrained Wireless Device
Authors: Zongyan Yao, Xilin Liu
Date: 2022-11-20 00:00:00
Tasks: Automatic Sleep Stage Classification, EEG, Electroencephalogram (EEG)

This paper proposes a deep learning (DL) model for automatic sleep stage classification based on single-channel EEG data. The DL model features a convolutional neural network (CNN) and transformers. The model was designed to run on energy and memory-constrained devices for real-time operation with local processing. The Fpz-Cz EEG signals from a publicly available Sleep-EDF dataset are used to train and test the model. Four convolutional filter layers were used to extract features and reduce the data dimension. Then, transformers were utilized to learn the time-variant features of the data. To improve performance, we also implemented a subject specific training before the inference (i.e., prediction) stage. With the subject specific training, the F1 score was 0.91, 0.37, 0.84, 0.877, and 0.73 for wake, N1-N3, and rapid eye movement (REM) stages, respectively. The performance of the model was comparable to the state-of-the-art works with significantly greater computational costs. We tested a reduced-sized version of the proposed model on a low-cost Arduino Nano 33 BLE board and it was fully functional and accurate. In the future, a fully integrated wireless EEG sensor with edge DL will be developed for sleep research in pre-clinical and clinical experiments, such as real-time sleep modulation.
A Closed-loop Sleep Modulation System with FPGA-Accelerated Deep Learning
Authors: Mingzhe Sun, Aaron Zhou, Naize Yang, Yaqian Xu, Yuhan Hou, Xilin Liu
Date: 2022-11-19 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Quantization, Sleep Quality

Closed-loop sleep modulation is an emerging research paradigm to treat sleep disorders and enhance sleep benefits. However, two major barriers hinder the widespread application of this research paradigm. First, subjects often need to be wire-connected to rack-mount instrumentation for data acquisition, which negatively affects sleep quality. Second, conventional real-time sleep stage classification algorithms give limited performance. In this work, we conquer these two limitations by developing a sleep modulation system that supports closed-loop operations on the device. Sleep stage classification is performed using a lightweight deep learning (DL) model accelerated by a low-power field-programmable gate array (FPGA) device. The DL model uses a single channel electroencephalogram (EEG) as input. Two convolutional neural networks (CNNs) are used to capture general and detailed features, and a bidirectional long-short-term memory (LSTM) network is used to capture time-variant sequence features. An 8-bit quantization is used to reduce the computational cost without compromising performance. The DL model has been validated using a public sleep database containing 81 subjects, achieving a state-of-the-art classification accuracy of 85.8% and a F1-score of 79%. The developed model has also shown the potential to be generalized to different channels and input data lengths. Closed-loop in-phase auditory stimulation has been demonstrated on the test bench.