pwc-archive/papers-with-abstracts huggingface dataset:
Low Complexity Adaptation for Reconfigurable Intelligent Surface-Based MIMO Systems
Authors: Zehra Yigit, Ertugrul Basar, Ibrahim Altunbas
Date: 2020-06-05 00:00:00

Reconfigurable intelligent surface (RIS)-based transmission technology offers a promising solution to enhance wireless communication performance cost-effectively through properly adjusting the parameters of a large number of passive reflecting elements. This letter proposes a cosine similarity theorem-based low-complexity algorithm for adapting the phase shifts of an RIS that assists a multiple-input multiple-output (MIMO) transmission system. A semi-analytical probabilistic approach is developed to derive the theoretical average bit error probability (ABEP) of the system. Furthermore, the validity of the theoretical analysis is supported through extensive computer simulations.
Classification-Aided Multitarget Tracking Using the Sum-Product Algorithm
Authors: Domenico Gaglione, Giovanni Soldi, Paolo Braca, Giovanni De Magistris, Florian Meyer, Franz Hlawatsch
Date: 2020-08-04 00:00:00
Tasks: Classification, General Classification

Multitarget tracking (MTT) is a challenging task that aims at estimating the number of targets and their states from measurements of the target states provided by one or multiple sensors. Additional information, such as imperfect estimates of target classes provided by a classifier, can facilitate the target-measurement association and thus improve MTT performance. In this letter, we describe how a recently proposed MTT framework based on the sum-product algorithm can be extended to efficiently exploit class information. The effectiveness of the proposed approach is demonstrated by simulation results.
Performance Evaluation of OTFS Over Measured V2V Channels at 60 GHz
Authors: Thomas Blazek, Danilo Radovic
Date: 2020-08-04 00:00:00
Tasks: Diversity

This paper presents an analysis of the Orthogonal Time Frequency Space (OTFS) modulation scheme when applied to realistic vehicular channel situations. OTFS modulates symbols in delay-Doppler domain, hoping to exploit diversity in both. The penalty for doing this is the requirement of complex interference cancellation equalizers, as this domain incurs a strong amount of intercarrier and intersymbol interference. We conduct this analysis using measured millimeter wave vehicular channels, and we assume typical physical layer settings for a performance analysis. Our results show that there is a challenging trade-off between channel conditions that are easy to equalize and channel conditions that allow OFTS to exploit the two-dimensional diversity. In the first case we observe a good overall performance that is barely enhanced by employing OTFS. In the second case performance gain through OTFS is visible, yet with a bad overall performance.
Dynamic Resource Optimization for Decentralized Estimation in Energy Harvesting IoT Networks
Authors: C. Battiloro, P. Di Lorenzo, P. Banelli, S. Barbarossa
Date: 2020-08-04 00:00:00
Tasks: Stochastic Optimization

We study decentralized estimation of time-varying signals at a fusion center, when energy harvesting sensors transmit sampled data over rate-constrained links. We propose dynamic strategies to select radio parameters, sampling set, and harvested energy at each node, with the aim of estimating a time-varying signal while ensuring: i) accuracy of the recovery procedure, and ii) stability of the batteries around a prescribed operating level. The approach is based on stochastic optimization tools, which enable adaptive optimization without the need of apriori knowledge of the statistics of radio channels and energy arrivals processes. Numerical results validate the proposed approach for decentralized signal estimation under communication and energy constraints typical of Internet of Things (IoT) scenarios.
Terahertz Line-Of-Sight MIMO Communication: Theory and Practical Challenges
Authors: Heedong Do, Sungmin Cho, Jeonghun Park, Ho-Jin Song, Namyoon Lee, Angel Lozano
Date: 2020-08-04 00:00:00

A relentless trend in wireless communications is the hunger for bandwidth, and fresh bandwidth is only to be found at ever-higher frequencies. While 5G systems are seizing the mmWave band, the attention of researchers is shifting already to the terahertz range. In that distant land of tiny wavelengths, antenna arrays can serve for more than power-enhancing beamforming. Defying lower-frequency wisdom, spatial multiplexing becomes feasible even in line-of-sight conditions. This paper reviews the underpinnings of this phenomenon, and it surveys recent results on the ensuing information-theoretic capacity. Reconfigurable array architectures are put forth that can closely approach such capacity, practical challenges are discussed, and supporting experimental evidence is presented.
Reliability and Battery Lifetime Improvement for IoT Networks: Challenges and AI-powered solutions
Authors: Amin Azari, Mahmoud Abbasi
Date: 2020-08-04 00:00:00

Towards realizing an intelligent networked society, enabling low-cost low-energy connectivity for things, also known as Internet of Things (IoT), is of crucial importance. While the existing wireless access networks require centralized signaling for managing network resources, this approach is of less interest for future generations of wireless networks due to the energy consumption in such signaling and the expected increase in the number of IoT devices. Then, in this work we investigate leveraging machine learning for distributed control of IoT communications. Towards this end, first we investigate low-complex learning schemes which are applicable to resource-constrained IoT communications. Then, we propose a lightweight learning scheme which enables the IoT devices to adapt their communication parameters to the environment. Further, we investigate analytical expressions presenting performance of a centralized control scheme for adapting communication parameters of IoT devices, and compare the results with the results from the proposed distributed learning approach. The simulation results confirm that the reliability and energy efficiency of IoT communications could be significantly improved by leveraging the proposed learning approach.
Comparison of Source Coding Techniques for the Vehicle to Vehicle Communication
Authors: Varad Vinod Prabhu, Subrahmanya Gunaga, Rahul M. S., Akash Kulkarni, Nalini C. Iyer
Date: 2020-08-04 00:00:00
Tasks: Autonomous Driving

Autonomous driving is gaining its importance due to the advancements in technology. With the intention of safety during human driving and with the longer-term aim to act as a communication enabler for autonomous driving, vehicle to vehicle communication is gaining its importance. In this paper, we discuss and compare various source coding techniques that can be used for vehicle to vehicle communication. We propose abbreviation-based and probability-based source coding methods for the vehicle to vehicle communication. We compare the proposed application-specific source coding methods with other techniques like Huffman, Arithmetic, and Lempel-Ziv-Welch coding. Experimental results show that the proposed probability-based source coding has better values of the compression ratio to the time required for all the messages considered.
Sporadic Ultra-Time-Critical Crowd Messaging in V2X
Authors: Yulin Shao, Soung Chang Liew, Jiaxin Liang
Date: 2020-03-04 00:00:00

Life-critical warning message, abbreviated as warning message, is a special event-driven message that carries emergency warning information in Vehicle-to-Everything (V2X). Three important characteristics that distinguish warning messages from ordinary vehicular messages are sporadicity, crowding, and ultra-time-criticality. In other words, warning messages come only once in a while in a sporadic manner; however, when they come, they tend to come as a crowd and they need to be delivered in short order. This paper puts forth a medium-access control (MAC) protocol for warning messages. To circumvent potential inefficiency arising from sporadicity, we propose an override network architecture whereby warning messages are delivered on the spectrum of the ordinary vehicular messages. Specifically, a vehicle with a warning message first sends an interrupt signal to pre-empt the transmission of ordinary messages, so that the warning message can use the wireless spectrum originally allocated to ordinary messages. In this way, no exclusive spectrum resources need to be pre-allocated to the sporadic warning messages. To meet the crowding and ultra-time-criticality aspects, we use advanced channel access techniques to ensure highly reliable delivery of warning messages within an ultra-short time in the order of 10 ms. In short, the overall MAC protocol operates by means of interrupt-and-access.
UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization
Authors: Dinh-Hieu Tran, Van-Dinh Nguyen, Sumit Gautam, Symeon Chatzinotas, Thang X. Vu, Bjorn Ottersten
Date: 2020-08-01 00:00:00

Unmanned aerial vehicle (UAV) communication has emerged as a prominent technology for emergency communications (e.g., natural disaster) in the Internet of Things (IoT) networks to enhance the ability of disaster prediction, damage assessment, and rescue operations promptly. A UAV can be deployed as a flying base station (BS) to collect data from time-constrained IoT devices and then transfer it to a ground gateway (GW). In general, the latency constraint at IoT devices and UAV's limited storage capacity highly hinder practical applications of UAV-assisted IoT networks. In this paper, {full-duplex (FD) radio} is adopted at the UAV to overcome these challenges. In addition, half-duplex (HD) scheme for UAV-based relaying is also considered to provide a comparative study between two modes (viz., FD and HD). {Herein, a device is considered to be successfully served iff its data is collected by the UAV and conveyed to GW timely during flight time}. In this context, we aim to maximize the number of served IoT devices by jointly optimizing bandwidth, power allocation, and the UAV trajectory while satisfying each device's requirement and the UAV's limited storage capacity. The formulated optimization problem is troublesome to solve due to its non-convexity and combinatorial nature. {Towards appealing applications, we first relax binary variables into continuous ones and transform the original problem into a more computationally tractable form.} By leveraging inner approximation framework, we derive newly approximated functions for non-convex parts and then develop a simple yet efficient iterative algorithm for its solutions. Next, we attempt to maximize the total throughput subject to the number of served IoT devices. Finally, numerical results show that the proposed algorithms significantly outperform benchmark approaches in terms of the number of served IoT devices and system throughput.
Optimization-driven Hierarchical Learning Framework for Wireless Powered Backscatter-aided Relay Communications
Authors: Shimin Gong, Yuze Zou, Jing Xu, Dinh Thai Hoang, Bin Lyu, Dusit Niyato
Date: 2020-08-04 00:00:00

In this paper, we employ multiple wireless-powered relays to assist information transmission from a multi-antenna access point to a single-antenna receiver. The wireless relays can operate in either the passive mode via backscatter communications or the active mode via RF communications, depending on their channel conditions and energy states. We aim to maximize the overall throughput by jointly optimizing the access point's beamforming and the relays' radio modes and operating parameters. Due to the non-convex and combinatorial structure, we develop a novel optimization-driven hierarchical deep deterministic policy gradient (H-DDPG) approach to adapt the beamforming and relay strategies dynamically. The optimization-driven H-DDPG algorithm firstly decomposes the binary relay mode selection into the outer-loop deep Q-network (DQN) algorithm and then optimizes the continuous beamforming and relaying parameters by using the inner-loop DDPG algorithm. Secondly, to improve the learning efficiency, we integrate the model-based optimization into the DDPG framework by providing a better-informed target estimation for DNN training. Simulation results reveal that these two special designs ensure a more stable learning and achieve a higher reward performance, up to nearly 20%, compared to the conventional DDPG approach.
Identification and Correction of False Data Injection Attacks against AC State Estimation using Deep Learning
Authors: Fayha ALmutairy, Reem Shadid, Safwan Wshah
Date: 2020-08-04 00:00:00
Tasks: Denoising, State Estimation

recent literature has proposed various detection and identification methods for FDIAs, but few studies have focused on a solution that would prevent such attacks from occurring. However, great strides have been made using deep learning to detect attacks. Inspired by these advancements, we have developed a new methodology for not only identifying AC FDIAs but, more importantly, for correction as well. Our methodology utilizes a Long-Short Term Memory Denoising Autoencoder (LSTM-DAE) to correct attacked-estimated states based on the attacked measurements. The method was evaluated using the IEEE 30 system, and the experiments demonstrated that the proposed method was successfully able to identify the corrupted states and correct them with high accuracy.
Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology
Authors: Mark Cheung, John Shi, Oren Wright, Lavender Y. Jiang, Xujin Liu, José M. F. Moura
Date: 2020-08-04 00:00:00
Tasks: Deep Learning

Deep learning, particularly convolutional neural networks (CNNs), have yielded rapid, significant improvements in computer vision and related domains. But conventional deep learning architectures perform poorly when data have an underlying graph structure, as in social, biological, and many other domains. This paper explores 1)how graph signal processing (GSP) can be used to extend CNN components to graphs in order to improve model performance; and 2)how to design the graph CNN architecture based on the topology or structure of the data graph.
Short-Term Reliability Evaluation of Generating Systems Using Fixed-Effort Generalized Splitting
Authors: O. A. Ansari, S. M. Mazhari, Y. Gong, C. Y. Chung
Date: 2020-08-03 00:00:00
Tasks: Decision Making

The short-term reliability evaluation techniques provide a rational approach for risk-informed decision making during power system operation. The existing reliability assessment techniques involve large computational burden and therefore are not directly applicable for short-term reliability evaluation during system operation. To this end, this paper presents a computationally-efficient approach for short-term reliability evaluation of wind-integrated generating systems. The proposed approach makes use of the fixed-effort generalized splitting (FEGS) technique, which is a variant of importance splitting. To realize the implementation of FEGS, a discrete version of component-wise Metropolis-Hastings (MH) algorithm for Markov Chain Monte-Carlo (MCMC) is also presented. Besides, the proposed FEGS approach is extended to take the uncertainties of wind generation and load demand into account. The simulation results indicate that, in comparison to crude Monte-Carlo simulation (CMCS), the proposed approach is able to evaluate short-term reliability indices with a low computational burden. Moreover, further simulation results indicate the impacts of uncertainties of wind generation and load demand on short-term reliability indices.
Xilinx RF-SoC-based Digital Multi-Beam Array Processors for 28/60~GHz Wireless Testbeds
Authors: Sravan Pulipati, Viduneth Ariyarathna, Aditya Dhananjay, Mohammed E. Eltayeb, Marco Mezzavilla, Josep M. Jornet, Soumyajit Mandal, Shubhendu Bhardwaj, Arjuna Madanayake
Date: 2020-08-03 00:00:00

Emerging wireless applications such as 5G cellular, large intelligent surfaces (LIS), and holographic massive MIMO require antenna array processing at mm-wave frequencies with large numbers of independent digital transceivers. This paper summarizes the authors' recent progress on the design and testing of 28 GHz and 60 GHz fully-digital array processing platforms based on wideband reconfigurable FPGA-based software-defined radios (SDRs). The digital baseband and microwave interfacing aspects of the SDRs are implemented on single-chip RF system-on-chip (RF-SoC) processors from Xilinx. Two versions of the RF-SoC technology (ZCU-111 and ZCU-1275) were used to implement fully-digital real-time array processors at 28~GHz (realizing 4 parallel beams with 0.8 GHz bandwidth per beam) and 60~GHz (realizing 4 parallel beams with 1.8~GHz bandwidth per beam). Dielectric lenslet arrays fed by a digital phased-array feed (PAF) located on the focal plane are proposed for further increasing antenna array gain.
Effects of Turbulence Induced Scattering on Underwater Optical Wireless Communications
Authors: Callum T. Geldard, John Thompson, Wasiu O. Popoola
Date: 2020-08-03 00:00:00

This paper presents a comprehensive description of the relative effect of optical underwater turbulence in combination with absorption and scattering. Turbulence induced scattering is shown to cause and increase both spatial and temporal spreading at the receiver plane. It is also demonstrated that the relative impact of turbulence on a received signal is lower in a highly scattering channel. Received intensity distributions are presented confirming that fluctuations in received power from this method follow the commonly used Log-Normal fading model. The impact of turbulence induced scattering on maximum achievable data rate in the underwater channel is investigated.
Energy-Aware Graph Task Scheduling in Software-Defined Air-Ground Integrated Vehicular Networks
Authors: Minghui LiWang, Zhibin Gao, Xianbin Wang
Date: 2020-08-03 00:00:00
Tasks: Scheduling

The Software-Defined Air-Ground integrated Vehicular (SD-AGV) networks have emerged as a promising paradigm, which realize the flexible on-ground resource sharing to support innovative applications for UAVs with heavy computational overhead. In this paper, we investigate a vehicular cloud-assisted task scheduling problem in SD-AGV networks, where the computation-intensive tasks carried by UAVs, and the vehicular cloud are modeled via graph-based representation. To map each component of the graph tasks to a feasible vehicle, while achieving the trade-off among minimizing UAVs' task completion time, energy consumption, and the data exchange cost among moving vehicles, we formulate the problem as a mixed-integer non-linear programming problem, which is Np-hard. Moreover, the constraint associated with preserving task structures poses addressing the subgraph isomorphism problem over dynamic vehicular topology, that further complicates the algorithm design. Motivated by which, we propose an efficient decoupled approach by separating the template (feasible mappings between components and vehicles) searching from the transmission power allocation. For the former, we present an efficient algorithm of searching for all the isomorphic subgraphs with low computation complexity. For the latter, we introduce a power allocation algorithm by applying $p$-norm and convex optimization techniques. Extensive simulations demonstrate that the proposed approach outperforms the benchmark methods considering various problem sizes.
IEEE 802.11be-Wi-Fi 7: New Challenges and Opportunities
Authors: Cailian Deng, Xuming Fang, Xiao Han, Xianbin Wang, Li Yan, Rong He, Yan Long, Yuchen Guo
Date: 2020-07-27 00:00:00
Tasks: 4k, 8k, Survey

With the emergence of 4k/8k video, the throughput requirement of video delivery will keep grow to tens of Gbps. Other new high-throughput and low-latency video applications including augmented reality (AR), virtual reality (VR), and online gaming, are also proliferating. Due to the related stringent requirements, supporting these applications over wireless local area network (WLAN) is far beyond the capabilities of the new WLAN standard -- IEEE 802.11ax. To meet these emerging demands, the IEEE 802.11 will release a new amendment standard IEEE 802.11be -- Extremely High Throughput (EHT), also known as Wireless-Fidelity (Wi-Fi) 7. This article provides the comprehensive survey on the key medium access control (MAC) layer techniques and physical layer (PHY) techniques being discussed in the EHT task group, including the channelization and tone plan, multiple resource units (multi-RU) support, 4096 quadrature amplitude modulation (4096-QAM), preamble designs, multiple link operations (e.g., multi-link aggregation and channel access), multiple input multiple output (MIMO) enhancement, multiple access point (multi-AP) coordination (e.g., multi-AP joint transmission), enhanced link adaptation and retransmission protocols (e.g., hybrid automatic repeat request (HARQ)). This survey covers both the critical technologies being discussed in EHT standard and the related latest progresses from worldwide research. Besides, the potential developments beyond EHT are discussed to provide some possible future research directions for WLAN.
On the Resolution Probability of Conditional and Unconditional Maximum Likelihood DoA Estimation
Authors: Xavier Mestre, Pascal Vallet
Date: 2020-08-03 00:00:00

After decades of research in Direction of Arrival (DoA) estimation, today Maximum Likelihood (ML) algorithms still provide the best performance in terms of resolution capabilities. At the cost of a multidimensional search, ML algorithms achieve a significant reduction of the outlier production mechanism in the threshold region, where the number of snapshots per antenna and/or the signal to noise ratio (SNR) are low. The objective of this paper is to characterize the resolution capabilities of ML algorithms in the threshold region. Both conditional and unconditional versions of the ML algorithms are investigated in the asymptotic regime where both the number of antennas and the number of snapshots are large but comparable in magnitude. By using random matrix theory techniques, the finite dimensional distributions of both cost functions are shown to be Gaussian distributed in this asymptotic regime, and a closed form expression of the corresponding asymptotic covariance matrices is provided. These results allow to characterize the asymptotic behavior of the resolution probability, which is defined as the probability that the cost function evaluated at the true DoAs is smaller than the values that it takes at the positions of the other asymptotic local minima.