pwc-archive/papers-with-abstracts huggingface dataset:
Smart City Enabled by 5G/6G Networks: An Intelligent Hybrid Random Access Scheme
Authors: Huimei Han, Wenchao Zhai, Jun Zhao
Date: 2021-01-16 00:00:00

The Internet of Things (IoT) is the enabler for smart city to achieve the envision of the "Internet of Everything" by intelligently connecting devices without human interventions. The explosive growth of IoT devices makes the amount of business data generated by machine-type communications (MTC) account for a great proportion in all communication services. The fifth-generation (5G) specification for cellular networks defines two types of application scenarios for MTC: One is massive machine type communications (mMTC) requiring massive connections, while the other is ultra-reliable low latency communications (URLLC) requiring high reliability and low latency communications. 6G, as the next generation beyond 5G, will have even stronger scales of mMTC and URLLC. mMTC and URLLC will co-exist in MTC networks for 5G 6G-enabled smart city. To enable massive and reliable LLC access to such heterogeneous MTC networks where mMTC and URLLC co-exist, in this article, we introduce the network architecture of heterogeneous MTC networks, and propose an intelligent hybrid random access scheme for 5G/6G-enabled smart city. Numerical results show that, compared to the benchmark schemes, the proposed scheme significantly improves the successful access probability, and satisfies the diverse quality of services requirements of URLLC and mMTC devices.
Resource Allocation in NOMA-based Self-Organizing Networks using Stochastic Multi-Armed Bandits
Authors: Marie Josepha Youssef, Venugopal V. Veeravalli, Joumana Farah, Charbel Abdel Nour, Catherine Douillard
Date: 2021-01-16 00:00:00
Tasks: Management, Multi-Armed Bandits

To achieve high data rates and better connectivity in future communication networks, the deployment of different types of access points (APs) is underway. In order to limit human intervention and reduce costs, the APs are expected to be equipped with self-organizing capabilities. Moreover, due to the spectrum crunch, frequency reuse among the deployed APs is inevitable, aggravating the problem of inter-cell interference (ICI). Therefore, ICI mitigation in self-organizing networks (SONs) is commonly identified as a key radio resource management mechanism to enhance performance in future communication networks. With the aim of reducing ICI in a SON, this paper proposes a novel solution for the uncoordinated channel and power allocation problems. Based on the multi-player multi-armed bandit (MAB) framework, the proposed technique does not require any communication or coordination between the APs. The case of varying channel rewards across APs is considered. In contrast to previous work on channel allocation using the MAB framework, APs are permitted to choose multiple channels for transmission. Moreover, non-orthogonal multiple access (NOMA) is used to allow multiple APs to access each channel simultaneously. This results in an MAB model with varying channel rewards, multiple plays and non-zero reward on collision. The proposed algorithm has an expected regret in the order of O(log^2 T ), which is validated by simulation results. Extensive numerical results also reveal that the proposed technique significantly outperforms the well-known upper confidence bound (UCB) algorithm, by achieving more than a twofold increase in the energy efficiency.
Kalman filter based MIMO CSI phase recovery for COTS WiFi devices
Authors: Chu Li, Jeremy Brauer, Aydin Sezgin, Christian Zenger
Date: 2021-01-15 00:00:00

Recently channel state information (CSI) measurements from commercial multi input multi output (MIMO) WiFi systems have been ubiquitously used for different wireless sensing applications. However, the phase of the CSI realizations is usually distorted severely by phase errors due to the hardware impairments, which significantly reduce the sensing performance. In this paper, we directly utilize the modeling of the phase distortions caused by the hardware impairments and propose an adaptive CSI estimation approach based on Kalman filter (KF) with maximum a posteriori (MAP) estimation that considers the CSI from the previous time. The performance of the proposed algorithm is compared against the Cramer Rao lower bound (CRLB). Simulation and experimental results demonstrate that our approach can track the channel variations while eliminating the phase errors accurately.
Spoofing attack detection in dynamic channels with imperfect CSI
Authors: Chu Li, Aydin Sezgin
Date: 2021-01-15 00:00:00

Recently, channel state information (CSI) at the physical-layer has been utilized to detect spoofing attacks in wireless communications. However, due to hardware impairments and communication noise, the CSI cannot be estimated accurately, which significantly degrades the attack detection performance. Besides, the reliability of CSI based detection schemes is challenged by time-varying scenarios. To address these issues, we propose an adaptive Kalman based detection scheme. By utilizing the knowledge of the predicted channel we eliminate the channel estimation error, especially the random phase error which occurs due to the lack of synchronization between transmitter and receiver. Furthermore, we define a Kalman residual based test statistic for attack detection. Simulation results show that our proposed scheme makes the detection more robust at low signal-to-noise ratio (SNR) and in dynamic scenarios.
Impact of Phase Noise and Oscillator Stability on Ultra-Narrow-Band-IoT Waveforms for Satellite
Authors: Christian A Hofmann, Kai-Uwe Storek, Andreas Knopp
Date: 2021-01-15 00:00:00

It has been shown that ultra-narrow-band (uNB) massive machine type communication using very compact devices with direct access to satellites is possible at ultra low rate. This enables global ubiquitous coverage for terminals without terrestrial service in the Internet of Remote Things and provides access to any satellite up to the the geostationary earth orbit. The lower data rate for waveforms providing uNB communication is set by the stability and the phase noise of the applied oscillators. In this paper we analyze the physical layer of two candidate waveforms, which are LoRa and Unipolar Coded Chirp-Spread Spectrum (UCSS) with respect to phase noise and oscillator frequency drifts. It is figured out that UCSS is more robust against linear frequency drifts, which is the main source of error for uNB transmissions.
Joint Positioning and Tracking via NR Sidelink in 5G-Empowered Industrial IoT: Releasing the Potential of V2X Technology
Authors: Yi Lu, Mike Koivisto, Jukka Talvitie, Elizaveta Rastorgueva-Foi, Toni Levanen, Elena Simona Lohan, Mikko Valkama
Date: 2021-01-15 00:00:00

The fifth generation (5G) mobile networks with enhanced connectivity and positioning capabilities play an increasingly important role in the development of automated vehicle-to-everything (V2X) and other advanced industrial Internet of Things (IoT) systems. In this article, we address the prospects of 5G New Radio (NR) sidelink based V2X networks and their applicability for increasing the situational awareness, in terms of continuous tracking of moving connected machines and vehicles, in industrial systems. For increased system flexibility and fast deployments, we assume that the locations of the so-called anchor nodes are unknown, and describe an extended Kalman filter-based joint positioning and tracking framework in which the locations of both the anchor nodes and the target nodes can be estimated simultaneously. We assess and demonstrate the achievable 3D positioning and tracking performance in the context of a realistic industrial warehouse facility, through extensive ray-tracing based evaluations at the 26 GHz NR band. Our findings show that when both angle-based and time-based measurements are utilized, reaching sub-1 meter accuracy is realistic and that the system is also relatively robust against different node geometries. Finally, several research challenges towards achieving robust, high-performance and cost-efficient positioning solutions are outlined and discussed, identifying various potential directions for future work.
Integration of IRS in Indoor VLC Systems: Challenges, Potential and Promising Solutions
Authors: Mohamed Amine Arfaoui, Ali Ghrayeb, Chadi Assi
Date: 2021-01-15 00:00:00

Visible light communication (VLC) is an optical wireless communication technology that is considered a promising solution for high-speed indoor connectivity. Unlike the case in conventional radio-frequency wireless systems, the VLC channel is not isotropic, meaning that the device orientation affects the channel gain significantly. In addition, due to the use of optical frequency bands, the presence of different obstacles (e.g., walls, human bodies, furniture) may easily block the VLC links. One solution to overcome these issues is the integration of the intelligent reflective surface (IRS), which is a new and revolutionizing technology that has the potential to significantly improve the performance of wireless networks. IRS is capable of smartly reconfiguring the wireless propagation environment with the use of massive low-cost passive reflecting elements integrated on a planar surface. In this paper, a framework for integrating IRS in indoor VLC systems is presented. We give an overview of IRS, including its advantages, different types and main applications in VLC systems, where we demonstrate the potential of IRS in overcoming the effects of random device orientation and links blockages. We discuss key factors pertaining to the design and integration of IRS in VLC systems, namely, the deployment of IRSs, the channel state information acquisition, the optimization of IRS configuration and the real-time IRS control. We also lay out a number of promising research directions that center around the integration of IRS in indoor VLC systems.
GPU Acceleration for Synthetic Aperture Sonar Image Reconstruction
Authors: Isaac D. Gerg, Daniel C. Brown, Stephen G. Wagner, Daniel Cook, Brian N. O'Donnell, Thomas Benson, Thomas C. Montgomery
Date: 2021-01-14 00:00:00
Tasks: GPU, Image Reconstruction

Synthetic aperture sonar (SAS) image reconstruction, or beamforming as it is often referred to within the SAS community, comprises a class of computationally intensive algorithms for creating coherent high-resolution imagery from successive spatially varying sonar pings. Image reconstruction is usually performed topside because of the large compute burden necessitated by the procedure. Historically, image reconstruction required significant assumptions in order to produce real-time imagery within an unmanned underwater vehicle's (UUV's) size, weight, and power (SWaP) constraints. However, these assumptions result in reduced image quality. In this work, we describe ASASIN, the Advanced Synthetic Aperture Sonar Imagining eNgine. ASASIN is a time domain backprojection image reconstruction suite utilizing graphics processing units (GPUs) allowing real-time operation on UUVs without sacrificing image quality. We describe several speedups employed in ASASIN allowing us to achieve this objective. Furthermore, ASASIN's signal processing chain is capable of producing 2D and 3D SAS imagery as we will demonstrate. Finally, we measure ASASIN's performance on a variety of GPUs and create a model capable of predicting performance. We demonstrate our model's usefulness in predicting run-time performance on desktop and embedded GPU hardware.
Non-Parametric Quickest Detection of a Change in the Mean of an Observation Sequence
Authors: Yuchen Liang, Venugopal V. Veeravalli
Date: 2021-01-14 00:00:00

We study the problem of quickest detection of a change in the mean of an observation sequence, under the assumption that both the pre- and post-change distributions have bounded support. We first study the case where the pre-change distribution is known, and then study the extension where only the mean and variance of the pre-change distribution are known. In both cases, no knowledge of the post-change distribution is assumed other than that it has bounded support. For the case where the pre-change distribution is known, we derive a test that asymptotically minimizes the worst-case detection delay over all post-change distributions, as the false alarm rate goes to zero. We then study the limiting form of the optimal test as the gap between the pre- and post-change means goes to zero, which we call the Mean-Change Test (MCT). We show that the MCT can be designed with only knowledge of the mean and variance of the pre-change distribution. We validate our analysis through numerical results for detecting a change in the mean of a beta distribution. We also demonstrate the use of the MCT for pandemic monitoring.
On the SIR Meta Distribution in Massive MTCNetworks with Scheduling and Data Aggregation
Authors: Nelson J. Mayedo Rodríguez, Onel L. Alcaraz López, Hirley Alves, Matti Latva-aho
Date: 2021-01-13 00:00:00
Tasks: Scheduling

Data aggregation is an efficient approach to handle the congestion introduced by a massive number of machine type devices (MTDs). The aggregators not only collect data but also implement scheduling mechanisms to cope with scarce network resources. We use the concept of meta distribution (MD) of the signal-to-interference ratio (SIR) to gain a complete understanding of the per-link reliability and describe the performance of two scheduling methods for data aggregation of machine type communication (MTC): random resource scheduling (RRS) and channel-aware resource scheduling (CRS). The results show the fraction of users in the network that achieves a target reliability, which is an important aspect to consider when designing wireless systems with stringent service requirements.
Network Slicing for eMBB and mMTC with NOMA and Space Diversity Reception
Authors: Eduardo Noboro Tominaga, Hirley Alves, Onel Luiz Alcaraz López, Richard Demo Souza, João Luiz Rebelatto, Matti Latva-aho
Date: 2021-01-13 00:00:00
Tasks: Diversity

In this work we study the coexistence in the same Radio Access Network (RAN) of two generic services present in the Fifth Generation (5G) of wireless communication systems: enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC). eMBB services are requested for applications that demand extremely high data rates and moderate requirements on latency and reliability, whereas mMTC enables applications for connecting a massive number of low-power and low-complexity devices. The coexistence of both services is enabled by means of network slicing and Non-Orthogonal Multiple Access (NOMA) with Successive Interference Cancellation (SIC) decoding. Under the orthogonal slicing, the radio resources are exclusively allocated to each service, while in the non-orthogonal slicing the traffics from both services overlap in the same radio resources. We evaluate the uplink performance of both services in a scenario with a multi-antenna Base Station (BS). Our simulation results show that the performance gains obtained through multiple receive antennas are more accentuated for the non-orthogonal slicing than for the orthogonal allocation of resources, such that the non-orthogonal slicing outperforms its orthogonal counterpart in terms of achievable data rates or number of connected devices as the number of receive antennas increases.
Deep learning based prediction of Alzheimer's disease from magnetic resonance images
Authors: Manu Subramoniam, Aparna T. R., Anurenjan P. R., Sreeni K. G
Date: 2021-01-13 00:00:00
Tasks: image-classification, Image Classification, Transfer Learning

Alzheimer's disease (AD) is an irreversible, progressive neuro degenerative disorder that slowly destroys memory and thinking skills and eventually, the ability to carry out the simplest tasks. In this paper, a deep neural network based prediction of AD from magnetic resonance images (MRI) is proposed. The state of the art image classification networks like VGG, residual networks (ResNet) etc. with transfer learning shows promising results. Performance of pre-trained versions of these networks are improved by transfer learning. ResNet based architecture with large number of layers is found to give the best result in terms of predicting different stages of the disease. The experiments are conducted on Kaggle dataset.
Non-Bayesian Parametric Missing-Mass Estimation
Authors: Shir Cohen, Tirza Routtenberg, Lang Tong
Date: 2021-01-12 00:00:00

We consider the classical problem of missing-mass estimation, which deals with estimating the total probability of unseen elements in a sample. The missing-mass estimation problem has various applications in machine learning, statistics, language processing, ecology, sensor networks, and others. The naive, constrained maximum likelihood (CML) estimator is inappropriate for this problem since it tends to overestimate the probability of the observed elements. Similarly, the conventional constrained Cramer-Rao bound (CCRB), which is a lower bound on the mean-squared-error (MSE) of unbiased estimators, does not provide a relevant bound on the performance for this problem. In this paper, we introduce a frequentist, non-Bayesian parametric model of the problem of missing-mass estimation. We introduce the concept of missing-mass unbiasedness by using the Lehmann unbiasedness definition. We derive a non-Bayesian CCRB-type lower bound on the missing-mass MSE (mmMSE), named the missing-mass CCRB (mmCCRB), based on the missing-mass unbiasedness. The missing-mass unbiasedness and the proposed mmCCRB can be used to evaluate the performance of existing estimators. Based on the new mmCCRB, we propose a new method to improve existing estimators by an iterative missing-mass Fisher scoring method. Finally, we demonstrate via numerical simulations that the proposed mmCCRB is a valid and informative lower bound on the mmMSE of state-of-the-art estimators for this problem: the CML, the Good-Turing, and Laplace estimators. We also show that the performance of the Laplace estimator is improved by using the new Fisher-scoring method.
Two beams are better than one: Enabling reliable and high throughput mmWave links
Authors: Ish Kumar Jain, Raghav Subbaraman, Dinesh Bharadia
Date: 2021-01-12 00:00:00

Millimeter-wave communication with high throughput and high reliability is poised to be a gamechanger for V2X and VR applications. However, mmWave links are notorious for low reliability since they suffer from frequent outages due to blockage and user mobility. We build mmReliable, a reliable mmWave system that implements multi-beamforming and user tracking to handle environmental vulnerabilities. It creates constructive multi-beam patterns and optimizes their angle, phase, and amplitude to maximize the signal strength at the receiver. Multi-beam links are reliable since they are resilient to occasional blockages of few constituent beams compared to a single-beam system. We implement mmReliable on a 28 GHz testbed with 400 MHz bandwidth, and a 64 element phased array supporting 5G NR waveforms. Rigorous indoor and outdoor experiments demonstrate that mmReliable achieves close to 100\% reliability providing 2.3x improvement in the throughput-reliability product than single-beam systems. This is an extended version of a paper published in Sigcomm'21.
Implementation of OpenAirInterface-based real-world channel measurement for evaluating wireless transmission algorithms
Authors: Qiuheng Zhou, Wei Jiang
Date: 2021-01-11 00:00:00

The fourth-generation Wireless Technology (4G) has been adopted by all major operators in the world and has already ruled the cellular landscape for around a decade. A lot of researches and new technologies are being considered as potential elements contributing to the next generation wireless communication (5G). The lack of realistic and flexible experimentation platforms for collecting real communication data has limited and slowed the landing of new approaches. Software Defined Radio (SDR) can provide flexible, upgradable, and long lifetime radio equipment for the wireless communications infrastructure, which can also provide more flexible and possibly cheaper multi-standard-terminals for end users. By altering the open-source code individually, we can freely perform the real value measurement. This paper provides a real Long Term Evolution (LTE) channel measurement method based on the OpenAirInterface (OAI) for the evaluation of the channel prediction algorithm. Firstly, the experimentation platform will be established by using OAI, Universal Software Radio Peripheral (USRP), and commercial User Equipment (UE). Then, some source codes of OAI are analyzed and changed, so that the real-time over-the-air channel measurement can be achieved. The results from the measurement are then trained and tested on the channel prediction algorithm. The results of the test illustrate that the implemented channel measurement method can meet the need for algorithms' verification and can be further extended for more development of algorithms.
Fast Randomized-MUSIC for mm-Wave Massive MIMO Radars
Authors: Li Bin, Wang Shuseng, Zhang Jun, Cao Xianbin, Zhao Chenglin
Date: 2021-01-11 00:00:00
Tasks: subspace methods, Super-Resolution

Subspace methods are essential to high-resolution environment sensing in the emerging unmanned systems, if further combined with the millimeter-wave (mm-Wave) massive multi-input multi-output (MIMO) technique. The estimation of signal/noise subspace, as one critical step, is yet computationally complex and presents a particular challenge when developing high-resolution yet low-complexity automotive radars. In this work, we develop a fast randomized-MUSIC (R-MUSIC) algorithm, which exploits the random matrix sketching to estimate the signal subspace via approximated computation. Our new approach substantially reduces the time complexity in acquiring a high-quality signal subspace. Moreover, the accuracy of R-MUSIC suffers no degradation unlike others low-complexity counterparts, i.e. the high-resolution angle of arrival (AoA) estimation is attained. Numerical simulations are provided to validate the performance of our R-MUSIC method. As shown, it resolves the long-standing contradiction in complexity and accuracy of MIMO radar signal processing, which hence have great potentials in real-time super-resolution automotive sensing.
Optimized Multi-Level Prime Array Configurations
Authors: Saleh A. Alawsh, Ali H. Muqaibel
Date: 2021-01-11 00:00:00

Antenna arrays have many applications in direction-of-arrival (DOA) estimation. Sparse arrays such as nested arrays, super nested arrays, and coprime arrays have large degrees of freedom (DOFs). They can estimate large number of sources greater than the number of elements. They also have closed form expressions for antenna locations and the achievable DOFs. The multi-level prime array (MLPA) uses multiple uniform linear subarrays where the number of elements in the subarrays are pairwise coprime integers. The array achieves large DOFs and it has closed form expressions for the antenna locations and the required aperture size. For a given number of subarrays and total number of elements, there are different design alternatives. This paper finds the optimum number of elements within each subarray and the optimized ordered inter-element spacing. In almost all cases, we have found that a unique configuration jointly realizes the maximum number of unique lags and the maximum number of consecutive lags.