pwc-archive/papers-with-abstracts huggingface dataset:
Joint User Identification, Channel Estimation, and Signal Detection for Grant-Free NOMA
Authors: Shuchao Jiang, Xiaojun Yuan, Xin Wang, Chongbin Xu, Wei Yu
Date: 2020-01-12 00:00:00
Tasks: User Identification

For massive machine-type communications, centralized control may incur a prohibitively high overhead. Grant-free non-orthogonal multiple access (NOMA) provides possible solutions, yet poses new challenges for efficient receiver design. In this paper, we develop a joint user identification, channel estimation, and signal detection (JUICESD) algorithm. We divide the whole detection scheme into two modules: slot-wise multi-user detection (SMD) and combined signal and channel estimation (CSCE). SMD is designed to decouple the transmissions of different users by leveraging the approximate message passing (AMP) algorithms, and CSCE is designed to deal with the nonlinear coupling of activity state, channel coefficient and transmit signal of each user separately. To address the problem that the exact calculation of the messages exchanged within CSCE and between the two modules is complicated due to phase ambiguity issues, this paper proposes a rotationally invariant Gaussian mixture (RIGM) model, and develops an efficient JUICESD-RIGM algorithm. JUICESD-RIGM achieves a performance close to JUICESD with a much lower complexity. Capitalizing on the feature of RIGM, we further analyze the performance of JUICESD-RIGM with state evolution techniques. Numerical results demonstrate that the proposed algorithms achieve a significant performance improvement over the existing alternatives, and the derived state evolution method predicts the system performance accurately.
Resource Allocation for SWIPT in Multi-Service Wireless Networks
Authors: Jalal Jalali
Date: 2020-07-04 00:00:00

The novel resource allocation for simultaneous wireless information and power transfer (SWIPT) is presented as a means of not only helping to communicate and access information with increasing efficiency in the next generation of mobile data networks but also contributing to minimizing a network's overall power consumption by providing a green energy source. First, a unique architecture is proposed that harvests energy from an access point (AP) without the receiver needing a splitter. In the proposed system model, a portion of the spectrum is used for information decoding (ID) while the remaining portion is exploited for energy harvesting (EH) in an orthogonal frequency division multiple access (OFDMA) network. To investigate the performance gain, an optimization problem is formulated that maximizes the harvested energy of a multi-user single-cell OFDMA downlink (DL) network with SWIPT and also satisfies a minimum data-rate requirement for all users. A locally optimal solution for the underlying problem, which is essentially non-convex due to the coupling of the integer variable, is obtained by using optimization tools. Second, the proposed system model is improved in order to investigate the resource allocation problem of needing to maximize throughput based on the separated receiver architecture in an OFDMA multi-user multi-cell system that uses SWIPT. The resulting problem, which jointly optimizes the subcarrier assignment and power allocation, is a mixed-integer non-linear problem (MINLP) that is difficult to solve. Third, a state-of-the-art harvesting technique at the receiver that is based on receiver antenna selection with a co-located architecture is explored to optimize the energy efficiency (EE) of a SWIPT-enabled multi-cell multi-user OFDMA network. This is referred to as a Generalized Antenna-Switching Technique.
Monitoring Depression in Bipolar Disorder using Circadian Measures from Smartphone Accelerometers
Authors: Oliver Carr, Fernando Andreotti, Kate E. A. Saunders, Niclas Palmius, Guy M. Goodwin, Maarten De Vos
Date: 2020-07-04 00:00:00
Tasks: Management

Current management of bipolar disorder relies on self-reported questionnaires and interviews with clinicians. The development of objective measures of deteriorating mood may also allow for early interventions to take place to avoid transitions into depressive states. The objective of this study was to use acceleration data recorded from smartphones to predict levels of depression in a population of participants diagnosed with bipolar disorder. Data were collected from 52 participants, with a mean of 37 weeks of acceleration data with a corresponding depression score recorded per participant. Time varying hidden Markov models were used to extract weekly features of activity, sleep and circadian rhythms. Personalised regression achieved mean absolute errors of 1.00(0.57) from a possible scale of 0 to 27 and was able to classify depression with an accuracy of 0.84(0.16). The results demonstrate features derived from smartphone accelerometers are able to provide objective markers of depression. Low barriers for uptake exist due to the widespread use of smartphones, with personalised models able to account for differences in the behaviour of individuals and provide accurate predictions of depression.
Multi-mode OAM Radio Waves: Generation, Angle of Arrival Estimation and Reception With UCAs
Authors: Rui Chen, Wen-Xuan Long, Xiaodong Wang, Jiandong Li
Date: 2020-07-02 00:00:00

Orbital angular momentum (OAM) at radio frequency (RF) provides a novel approach of multiplexing a set of orthogonal modes on the same frequency channel to achieve high spectrum efficiencies. However, there are still big challenges in the multi-mode OAM generation, OAM antenna alignment and OAM signal reception. To solve these problems, we propose an overall scheme of the line-of-sight multi-carrier and multi-mode OAM (LoS MCMM-OAM) communication based on uniform circular arrays (UCAs). First, we verify that UCA can generate multi-mode OAM radio beam with both the RF analog synthesis method and the baseband digital synthesis method. Then, for the considered UCA-based LoS MCMM-OAM communication system, a distance and AoA estimation method is proposed based on the two-dimensional ESPRIT (2-D ESPRIT) algorithm. A salient feature of the proposed LoS MCMM-OAM and LoS MCMM-OAM-MIMO systems is that the channel matrices are completely characterized by three parameters, namely, the azimuth angle, the elevation angle and the distance, independent of the numbers of subcarriers and antennas, which significantly reduces the burden by avoiding estimating large channel matrices, as traditional MIMO-OFDM systems. After that, we propose an OAM reception scheme including the beam steering with the estimated AoA and the amplitude detection with the estimated distance. At last, the proposed methods are extended to the LoS MCMM-OAM-MIMO system equipped with uniform concentric circular arrays (UCCAs). Both mathematical analysis and simulation results validate that the proposed OAM reception scheme can eliminate the effect of the misalignment error of a practical OAM channel and approaches the performance of an ideally aligned OAM channel.
On the use of higher-order tensors to model muscle synergies
Authors: Ahmed Ebied, Loukianos Spyrou, Eli Kinney-Lang, Javier Escudero
Date: 2020-07-03 00:00:00
Tasks: Electromyography (EMG), Tensor Decomposition

The muscle synergy concept provides the best framework to understand motor control and it has been recently utilised in many applications such as prosthesis control. The current muscle synergy model relies on decomposing multi-channel surface Electromyography (EMG) signals into a synergy matrix (spatial mode) and its weighting function (temporal mode). This is done using several matrix factorisation techniques, with Non-negative matrix factorisation (NMF) being the most prominent method. Here, we introduce a 4th-order tensor muscle synergy model that extends the current state of the art by taking spectral information and repetitions (movements) into account. This adds more depth to the model and provides more synergistic information. In particular, we illustrate a proof-of-concept study where the Tucker3 tensor decomposition model was applied to a subset of wrist movements from the Ninapro database. The results showed the potential of Tucker3 tensor factorisation in finding patterns of muscle synergies with information about the movements and highlights the differences between the current and proposed model.
Consistency of Muscle Synergies Extracted via Higher-Order Tensor Decomposition Towards Myoelectric Control
Authors: Ahmed Ebied, Eli Kinney-Lang, Javier Escudero
Date: 2020-07-03 00:00:00
Tasks: Tensor Decomposition

In recent years, muscle synergies have been pro-posed for proportional myoelectric control. Synergies were extracted using matrix factorisation techniques (mainly non-negative matrix factorisation, NMF), which requires identification of synergies to tasks or movements. In addition, NMF methods were viable only with a task dimension of 2 degrees of freedoms(DoFs). Here, the potential use of a higher-order tensor model for myoelectric control is explored. We assess the ability of a constrained Tucker tensor decomposition to estimate consistent synergies when the task dimensionality is increased up to 3-DoFs. Synergies extracted from 3rd-order tensor of 1 and 3 DoFs were compared. Results showed that muscle synergies extracted via constrained Tucker decomposition were consistent with the increase of task-dimension. Hence, these results support the consideration of proportional 3-DoF myoelectric control based on tensor decompositions.
A model-free tuning method for proportional-multi-resonant controllers
Authors: Charles Lorenzini, Luís Fernando Alves Pereira, Alexandre Sanfelice Bazanella, Gustavo R. Gonçalves da Silva
Date: 2020-07-03 00:00:00

Resonant controllers are widely used in applications involving reference tracking and disturbance rejection of periodic signals. The controller design is typically performed by a trial-and-error approach or by means of time and resource-consuming analytic methods that require an accurate plant model, intricated mathematics and sophisticated tools. In this paper, we propose an easily implementable, model-free method for tuning a proportional-multi-resonant controller applicable to general linear time-invariant causal plants. Just like the Ziegler-Nichols methods, the proposed methodology consist in identifying one specific point of the plant's frequency response -- which is easily obtained in a relay with adjustable phase experiment -- and then designing the controller with simple tuning formulas and tables. The method is analyzed in detail for three examples, showing its practical appeal and wide applicability.
Adaptive Background Compensation of FI-DACs with Application to Coherent Optical Transceivers
Authors: Agustín C. Galetto, Benjamín T. Reyes, Damián A. Morero, Mario R. Hueda
Date: 2020-07-03 00:00:00

This work proposes a novel adaptive background compensation scheme for frequency interleaved digital-to-analog converters (FI-DACs). The technique is applicable to high speed digital transceivers such as those used in coherent optical communications. Although compensation of FI-DACs has been discussed before in the technical literature, adaptive background techniques have not yet been reported. The importance of the latter lies in their capability to automatically compensate errors caused by process, voltage, and temperature variations in the echnology (e.g., CMOS, SiGe, etc.) implementations of the data converters, and therefore ensure high manufacturing yield. The key ingredients of the proposed technique are a multiple-input multiple-output (MIMO) equalizer and the backpropagation algorithm used to adapt the coefficients of the aforementioned equalizer. Simulations show that the impairments of the analog signal path are accurately compensated and their effect is essentially eliminated, resulting in a high performance transmitter system.
Symbiotic Radio: Cognitive Backscattering Communications for Future Wireless Networks
Authors: Ying-Chang Liang, Qianqian Zhang, Erik G. Larsson, Geoffrey Ye Li
Date: 2020-07-03 00:00:00
Tasks: Diversity

The heterogenous wireless services and exponentially growing traffic call for novel spectrum- and energy-efficient wireless communication technologies. In this paper, a new technique, called symbiotic radio (SR), is proposed to exploit the benefits and address the drawbacks of cognitive radio (CR) and ambient backscattering communications(AmBC), leading to mutualism spectrum sharing and highly reliable backscattering communications. In particular, the secondary transmitter (STx) in SR transmits messages to the secondary receiver (SRx) over the RF signals originating from the primary transmitter (PTx) based on cognitive backscattering communications, thus the secondary system shares not only the radio spectrum, but also the power, and infrastructure with the primary system. In return, the secondary transmission provides beneficial multipath diversity to the primary system, therefore the two systems form mutualism spectrum sharing. More importantly, joint decoding is exploited at SRx to achieve highly reliable backscattering communications. To exploit the full potential of SR, in this paper, we address three fundamental tasks in SR: (1) enhancing the backscattering link via active load; (2) achieving highly reliable communications through joint decoding; and (3) capturing PTx's RF signals using reconfigurable intelligent surfaces. Emerging applications, design challenges and open research problems will also be discussed.
Real-time Intersection Optimization for Signal Phasing, Timing, and Automated Vehicles' Trajectories
Authors: Mahmoud Pourmehrab, Lily Elefteriadou, Sanjay Ranka
Date: 2020-07-03 00:00:00

This study aims to develop a real-time intersection optimization (RIO) control algorithm to efficiently serve traffic of Connected and Automated Vehicles (CAVs) and conventional vehicles (CNVs). This paper extends previous work to consider demand over capacity conditions and trajectory deviations by re-optimizing decisions. To jointly optimize Signal Phase and Timing (SPaT) and departure time of CAVs, we formulated a joint optimization model which is reduced to and solved as a Minimum Cost Flow (MCF) problem. The MCF-based optimization models is embedded into the RIO algorithm to operate the signal controller and to plan the movement of CAVs. Simulation experiments showed 18-22% travel time decrease and up to 12% capacity improvement compared to the base scenario.
An Implementation of Partial Transmit Sequences to Design Energy Efficient Underwater Acoustic OFDM Communication System
Authors: Waleed Raza, Xuefei Ma, Amir Ali, Zubair Ali Shah, Ghazanfar Mehdi
Date: 2020-07-02 00:00:00

In this article we research about underwater acoustics transceivers. As Underwater acoustic transceivers consume more power than Radio frequency transceivers. The techniques which are being utilized in radio frequency cannot be implemented directly in underwater acoustic system it needs to be re investigated to design new methods. To achieve reliable acoustic data transmission new techniques should be achieved or the traditional Orthogonal frequency divisional multiplexing techniques should be revised. The power consumption also relies upon underwater acoustic signal propagation and transmission distances. Several underwater acoustic applications require long-term monitoring of the sea. For the battery powered modems, it becomes very serious problem. By designing an Energy efficient OFDM Communication system we can solve this problem. We study about peak to average power ratio in an Orthogonal frequency divisional multiplexing system by reducing the major draw-back of OFDM system. The PAPR reduction utilized in this paper is Partial Transmit Sequences for underwater acoustic OFDM communication system which has lesser complexity. The results have provided better performance in underwater acoustic OFDM communication system.
Gradient-Adaptive Spline-Interpolated LUT Methods for Low-Complexity Digital Predistortion
Authors: Pablo Pascual Campo, Alberto Brihuega, Lauri Anttila, Matias Turunen, Dani Korpi, Markus Allén, Mikko Valkama
Date: 2019-07-04 00:00:00

In this paper, new digital predistortion (DPD) solutions for power amplifier (PA) linearization are proposed, with particular emphasis on reduced processing complexity in future 5G and beyond wideband radio systems. The first proposed method, referred to as the spline-based Hammerstein (SPH) approach, builds on complex spline-interpolated lookup table (LUT) followed by a linear finite impulse response (FIR) filter. The second proposed method, the spline-based memory polynomial (SMP) approach, contains multiple parallel complex spline-interpolated LUTs together with an input delay line such that more versatile memory modeling can be achieved. For both structures, gradient-based learning algorithms are derived to efficiently estimate the LUT control points and other related DPD parameters. Large set of experimental results are provided, with specific focus on 5G New Radio (NR) systems, showing successful linearization of multiple sub-6 GHz PA samples as well as a 28 GHz active antenna array, incorporating channel bandwidths up to 200 MHz. Explicit performance-complexity comparisons are also reported between the SPH and SMP DPD systems and the widely-applied ordinary memory-polynomial (MP) DPD solution. The results show that the linearization capabilities of the proposed methods are very close to that of the ordinary MP DPD, particularly with the proposed SMP approach, while having substantially lower processing complexity.
Energy Efficiency Optimization in IRS-Enhanced mmWave Systems with Lens Antenna Array
Authors: Yazheng Wang, Hancheng Lu, Dan Zhao, Huan Sun
Date: 2020-07-02 00:00:00
Tasks: Blocking

In millimeter wave (mmWave) systems, the advanced lens antenna array can effectively reduce the radio frequency chains cost. However, the mmWave signal is still vulnerable to blocking obstacles and suffers from severe path loss. To address this problem, we propose an intelligent reflect surface (IRS) enhanced multi-user mmWave communication system with lens antenna array. Moreover, we attempt to optimize energy efficiency in the proposed system. An energy efficiency maximization problem is formulated where the transmit beamforming at base station and the reflect beamforming at IRSs are jointly considered. To solve this non-convex problem, we propose an algorithm based on the alternating optimization technique. In the proposed algorithm, the transmit beamforming is handled by the sequential convex approximation method and the reflect beamforming is optimized based on the quadratic transform method. Our simulation results show that the proposed algorithm can achieve significant energy efficiency improvement under various scenarios.
Non-Gaussianity Detection of EEG Signals Based on a Multivariate Scale Mixture Model for Diagnosis of Epileptic Seizures
Authors: Akira Furui, Ryota Onishi, Akihito Takeuchi, Tomoyuki Akiyama, Toshio Tsuji
Date: 2020-07-02 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Seizure Detection

Objective: The detection of epileptic seizures from scalp electroencephalogram (EEG) signals can facilitate early diagnosis and treatment. Previous studies suggested that the Gaussianity of EEG distributions changes depending on the presence or absence of seizures; however, no general EEG signal models can explain such changes in distributions within a unified scheme. Methods: This paper describes the formulation of a stochastic EEG model based on a multivariate scale mixture distribution that can represent changes in non-Gaussianity caused by stochastic fluctuations in EEG. In addition, we propose an EEG analysis method by combining the model with a filter bank and introduce a feature representing the non-Gaussianity latent in each EEG frequency band. Results: We applied the proposed method to multichannel EEG data from twenty patients with focal epilepsy. The results showed a significant increase in the proposed feature during epileptic seizures, particularly in the high-frequency band. The feature calculated in the high-frequency band allowed highly accurate classification of seizure and non-seizure segments [area under the receiver operating characteristic curve (AUC) = 0.881] using only a simple threshold. Conclusion: This paper proposed a multivariate scale mixture distribution-based stochastic EEG model capable of representing non-Gaussianity associated with epileptic seizures. Experiments using simulated and real EEG data demonstrated the validity of the model and its applicability to epileptic seizure detection. Significance: The stochastic fluctuations of EEG quantified by the proposed model can help detect epileptic seizures with high accuracy.
Kalman Filter Meets Subjective Logic: A Self-Assessing Kalman Filter Using Subjective Logic
Authors: Thomas Griebel, Johannes Müller, Michael Buchholz, Klaus Dietmayer
Date: 2020-07-01 00:00:00

Self-assessment is a key to safety and robustness in automated driving. In order to design safer and more robust automated driving functions, the goal is to self-assess the performance of each module in a whole automated driving system. One crucial component in automated driving systems is the tracking of surrounding objects, where the Kalman filter is the most fundamental tracking algorithm. For Kalman filters, some classical online consistency measures exist for self-assessment, which are based on classical probability theory. However, these classical approaches lack the ability to measure the explicit statistical uncertainty within the self-assessment, which is an important quality measure, particularly, if only a small number of samples is available for the self-assessment. In this work, we propose a novel online self-assessment method using subjective logic, which is a modern extension of probabilistic logic that explicitly models the statistical uncertainty. Thus, by embedding classical Kalman filtering into subjective logic, our method additionally features an explicit measure for statistical uncertainty in the self-assessment.
Performance Evaluation of UAV-enabled Cellular Networks with Battery-limited Drones
Authors: Yujie Qin, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2020-07-01 00:00:00

Unmanned aerial vehicles (UAVs) can be used as flying base stations (BSs) to offload Macro-BSs in hotspots. However, due to the limited battery on-board, UAVs can typically stay in operation for less than 1.5 hours. Afterward, the UAV has to fly back to a dedicated charging station that recharges/replaces the UAV's battery. In this paper, we study the performance of a UAV-enabled cellular network while capturing the influence of the spatial distribution of the charging stations. In particular, we use tools from stochastic geometry to derive the coverage probability of a UAV-enabled cellular network as a function of the battery size, the density of the charging stations, and the time required for recharging/replacing the battery.