Workshops and Tutorials

Intelligent IoT Connectivity, Automation and Applications (ICA)

Session FWS7-S1

Invited Keynote Presentation

1:00 PM — 1:55 PM KST
May 25 Mon, 12:00 AM — 12:55 AM EDT

Keynote: Challenges for Advanced 5G - Realizing Ultra-dense RAN

Fumiyuki Adachi (Tohoku University)

This talk does not have an abstract.

Session Chair

Jihong Park (Deakin University, Australia)

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Session FWS7-S2

Distributed Sensing and Localization

2:00 PM — 3:30 PM KST
May 25 Mon, 1:00 AM — 2:30 AM EDT

Energy-Balanced and Distributed Clustering Protocol for IoT Wireless Sensors

Bahaa Al-Musawi (Faculty of Engineering & University of Kufa, Iraq); Mohammed Falih Hassan (University of Kufa, Iraq); Shiva Raj Pokhrel (Deakin University & Nepal Telecom, Australia)

With the ensuing massive communication demands for IoT Wireless Sensor Networks (WSNs), seminal routing protocols in wireless networks are not applicable for the massive IoT sensors networking. Consequently, a new type of routing protocol called energy-aware routing protocols has been proposed. Major design goals of such protocols are to uniformly distribute energy consumption among the IoT nodes and minimize the energy dissipation optimally. In this paper, we develop a novel Energy Balanced Distributed Clustering protocol (EBDC) to minimize the energy consumption among sensor nodes uniformly. Moreover, the proposed EBDC is based on an adaptive clustering and re-clustering process. We evaluate the proposed protocol on different energy-based the WSNs protocols. Our evaluation shows EBDC achieves a notable enhancement in terms of balanced energy consumption and extended network lifetime compared to the other existing protocols. In addition, we propose a new metric for the evaluation of protocols.

Coalition Game-based Beamwidth Selection for D2D Users Underlying Ultra Dense mmWave Networks

Jinxi Zhang, Gang Chuai, Weidong Gao, Saidiwaerdi Maimaiti and Zhiwei Si (Beijing University of Posts and Telecommunications, China)

With the advent of 5G mobile communication era, ultra-dense millimeter wave (mmWave) networks have become an important architecture to improve network performance. However, the susceptibility to attenuation becomes a bottleneck for long-distance transmission of mmWave links. D2D communication can alleviate the bottleneck utilizing its short-range direct communication and its ability to offload traffic for base stations. The adoption of high-directivity signal beams at cellular users (CUs) and D2D users (DUs) reusing the full mmWave frequency band can greatly improve the spectrum efficiency, but at the same time, it will also cause unexpected co-channel interference. This paper proposes a beamwidth selection scheme for D2D pairs underlying mmWave network, which automatically selects the optimal beamwidth for DUs via non-cooperative coalition game, thereby reducing the interference between DUs and CUs and increasing network capacity. In the proposed algorithm, D2D users continuously update their beamwidth strategies to explore and evaluate the potential coalition structure in the direction of improving system capacity. Finally, a stable partition structure with the optimal network utility is reached. Simulation results show that the proposed algorithm can achieve the close performance compared with particle swarm optimization (PSO) algorithm, but the complexity is sharply reduced.

Adaptive Beamforming Design for mmWave RIS-Aided Joint Localization and Communication

Jiguang He and Tachporn Sanguanpuak (University of Oulu, Finland); Henk Wymeersch (Chalmers University of Technology, Sweden); Olli Silvén and Markku Juntti (University of Oulu, Finland)

The concept of reconfigurable intelligent surface (RIS) has been proposed to change the propagation of electromagnetic waves, e.g., reflection, diffraction, and refraction. To accomplish this goal, the phase values of the discrete RIS units need to be optimized. In this paper, we consider RIS-aided millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems for both accurate positioning and high data-rate transmission. We propose an adaptive phase shifter design based on hierarchical codebooks and feedback from the mobile station (MS). The benefit of the scheme lies in that the RIS does not require deployment of any active sensors and baseband processing units. During the update process of phase shifters, the combining vector at the MS is sequentially refined. Simulation results show the performance improvement of the proposed algorithm over the random phase design scheme, in terms of both positioning accuracy and data rate. Moreover, the performance converges to that of the exhaustive search scheme even in the low signal-to-noise ratio regime.

A Decentralized Federated Learning Approach For Connected Autonomous Vehicles

Shiva Raj Pokhrel (Deakin University & Nepal Telecom, Australia); Jinho Choi (Deakin University, Australia)

In this paper, we propose an autonomous blockchain-based federated learning (BFL) design for privacy-aware and efficient vehicular communication networking, where local on vehicle machine learning (oVML) model updates are exchanged and verified in a distributed fashion. BFL enables on-vehicle machine-learning without any centralized training data or coordination by utilizing the consensus mechanism of the blockchain. Relying on a renewal reward approach, we develop a mathematical framework that features the controllable network and BFL parameters, such as the retransmission limit, block size, block arrival rate, and the frame sizes, so as to capture their impact on system-level performance. More importantly, our rigorous analysis of oVML system dynamics quantifies the end to- end delay with BFL, which provides important insights into deriving optimal block arrival rate by considering communication and consensus delays. We present a variety of numerical and simulation results highlighting various non-trivial findings and insights for adaptive BFL design. In particular, we provide insights into minimizing system delay by exploiting the channel dynamics and demonstrate that the proposed idea of tuning block arrival rate is capable of driving the system dynamics to the desired operating point. It also identifies the improved dependency on other blockchain parameters for any given set of channel conditions, retransmission limits, and frame sizes.

Wireless Electrocardiograph Monitoring Based on Wavelet Convolutional Neural Network

Xucun Yan, Zihuai Lin and Peng Wang (University of Sydney, Australia)

Recently, Cardiovascular diseases (CVD) have drawn high concerns from diversity of disciplines due to acute and fatal characteristics. Rapidly accumulating evidence suggests that CVD threats human health without any symptom. Finding a way to forestall it or notice ongoing warning becomes significant. Electrocardiograph (ECG) monitoring ECG monitoring is one of the commonly used techniques to address this problem. The aim of this project is to explore various deep learning models with data collected from our developed wearable ECG patch: IREALCARE. We apply new dataset and test the performance of existing CNN and RNN models. However, current models are not able to identify two main classes (class S and A), resulting in 58.0\% mean accuracy. To overcome this drawback, two Wavelet Based Convolutional Neural Networks (WBCNN) are proposed and the final mean accuracy reach to 73.8\%.

LoRa Signal Monitoring System of Multi-Node Software Define Radio

Hailang Zhao (Xidian University, China)

LoRa is a widely deployed LPWAN technology and is considered by a large number of industries as a base for their IoT applications. However, if the IoT growth forecasts are confirmed, the collisions between packets will multiply and thus degrade the system spectral and transer efficiencies. Detecting abnormal
behavior in wireless spectrum is a arduous task because of the complexity of the use of the electromagnetic spectrum. The LoRa signal of internet of things needs to be blindly detected and parameter estimation. A multi-node LoRa signal detection system based on software radio is proposed. The system includes multiple
nodes, data fusion center, and user application. The LoRa modulation method is identified by the neural network, and combined with the traditional energy detection method to complete the LoRa signal detection and parameter estimation. The data fusion center process the data of multi-node make use of the
improvement of k-means clustering. The experimental results show that the system improving the probability of LoRa signal detection and reducing the error of parameters estimation.

Session Chair

Jihong Park (Deakin University, Australia)

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Session FWS7-S3

IoT Applications

3:40 PM — 5:15 PM KST
May 25 Mon, 2:40 AM — 4:15 AM EDT

Analysis of Automotive Radar Interference among Multiple Vehicles

Zixi Fang, Zhiqing Wei, Hao Ma, Xu Chen and Zhiyong Feng (Beijing University of Posts and Telecommunications, China)

With the popularity of millimeter-wave radar technology in modern vehicles, the problem of mutual interference due to the sharing spectrum scheme is gaining attention. This paper explores the radar mutual interference among multiple
vehicles using stochastic geometry. In particular, we consider the fluctuation of the target radar cross section (RCS) using Chi-square model. Based on this model, we derive the closed-form expressions for successful ranging probability including incident interference and reflected interference. The impact of vehicle intensity on the lane, as well as the impact of signal-to-interference-noise ratio, are analyzed. Our analysis shows that the RCS characteristics have a great impact on successful ranging probability. Besides, the reflected interference can incur a substantial decline in radar performance. We verify the correctness
of theoretical analysis by Monte Carlo simulations.

Joint Optimization of Resource Allocation and Multi-UAV Trajectory in Space-Air-Ground IoRT Networks

Man Liu, Ying Wang, Zhendong Li, Xinpeng Lyu and Yuanbin Chen (Beijing University of Posts and Telecommunications, China)

Given suburban and rural areas with limited ground infrastructure, the Internet of Remote Things (IoRT) is considered as a promising way to provide services for smart devices that have low computing capability and wide coverage. In this paper, we present a multiple unmanned aerial vehicle (UAV) space-air-ground (SAG) IoRT computing offloading network, which provides IoRT devices powerful edge and cloud computing services. Then, the resource allocation scheme under partial computing offloading mode is studied, which jointly optimizes device scheduling, resource partitioning, bit allocation and UAV trajectory to minimize the weighted total system energy consumption with considering the constraints of UAV mobility and obstacle avoidance. To solve this non-convex problem with coupled variables, we decompose the problem into three sub-problems, then the Lagrange dual decomposition method and the successive convex optimization (SCO) technique are adopted. Simulation results demonstrate the effectiveness of the proposed algorithm in terms of saving energy.

Resource Allocation in Relay-Assisted Mission-Critical Industrial Internet of Things

Weichen Ning, Ying Wang, Yuanbin Chen and Man Liu (Beijing University of Posts and Telecommunications, China)

As one of the main scenarios of 5G, Ultra Reliable Low Latency Communication (URLLC) plays an important role in Industrial Internet of Things (IIoT) for smart factories. At present, most researches on URLLC use Shannon capacity for infinite blocklength, which is not suitable for short packet transmission adopted by URLLC. In this paper, we consider the resource allocation of mission-critical service in the smart factory, where the IIoT devices receive the control information from both base station (BS) and relay. The purpose of this paper is to jointly optimize the power allocation, the position of relay, and the blocklength with finite blocklength information theory to minimize the decoding error rate of the device. This paper proposes a low-complexity algorithm that effectively solves this optimization problem. Simulation results show that relay-assisted transmission and optimized resource allocation can significantly reduce the error rate.

Access Control for Machine-type Communication Assisted by D2D in Heterogeneous Networks

Qijun Han, Gang Feng, Shuang Qin and Qianyi Zhang (University of Electronic Science and Technology of China, China)

The forthcoming mobile cellular networks (5G) and beyond need to address the challenges of performance requirements in diverse technical scenarios, such as seamless wide-area coverage, hot-spot high-capacity, and low power massive connections, etc. It is widely recognized that Heterogeneous Network (HetNet) is an effective network architecture for addressing these challenges, especially dealing with the access control issue of massive machine-type communication (mMTC). In this paper, we propose a device-to-device (D2D) communication-assisted MTC access mechanism with aim of maximizing the number of admitted MTCDs (MTCDs) in the system while ensuring the quality of service (QoS) requirements of MTCDs. In our proposed scheme, we first determine the access mode for the devices that fail in directly accessing the base station. Specifically, another MTCD which has been successfully connected to the base station (BS) is selected as the relay for a specific MTCD via device-to-device (D2D) communications. We first formulate the access control problem of maximizing the number of admitted MTCDs and prove its NP-hardness. We then resort to genetic algorithm (GA) to solve the optimal relay MTCD selection problem. We conduct simulation experiments to evaluate the performance of our proposed algorithm and numerical results show that the proposed algorithm outperforms the traditional algorithms in terms of the number of successful access MTCDs, transmission rate, and system throughput.

Joint Active Device and Data Detection for Massive MTC Relying on Spatial Modulation

Li Qiao and Zhen Gao (Beijing Institute of Technology, China)

The Internet of Things promises the massive connectivity of everything ubiquitously, whereas enabling massive machine-type communications (mMTC) is one of the paramount hurdles. To this end, a new paradigm is conceived for mMTC by employing spatial modulation at the devices for enhancing throughput and massive multi-input multi-output at the base station (BS) with improved detection performance. However, the associated massive access poses the intractable active device and data detection challenge. In this paper, we formulate the massive access problem as sparse signal recovery problem by exploiting the sporadic traffic of mMTC. To solve this problem, we propose a joint structured approximate message passing (JS-AMP) algorithm for joint active device and data detection. Specifically, we use AMP to decouple the superimposed received signal into uncoupled scalar elements. Furthermore, to achieve enhanced data detection performance, we compute the posterior estimation of each scalar element by using the structured sparsity of spatial modulated symbols. Moreover, we estimate the device activity by exploiting expectation maximization, where the block sparsity of successive time slots is considered for improving performance. Finally, simulation results demonstrate that the proposed solution exhibits a significant performance gain over the state-of-the-art solutions.

Data-aided Sensing where Communication and Sensing Meet: An Introduction

Jinho Choi (Deakin University, Australia)

Since there are a number of Internet-of-Things (IoT) applications that need to collect data sets from a large number of sensors or devices in real-time, sensing and communication need to be integrated for efficient uploading from devices. In this paper, we introduce the notion of data-aided sensing (DAS) where a base station (BS) utilizes a subset of data that is already uploaded and available to select the next device for efficient data collection or sensing. Thus, using DAS, certain tasks in IoT applications, including federated learning, can be completed by uploading from a small number of selected devices. Two different types of DAS are considered: one is centralized DAS and the other is distributed DAS. In centralized DAS, the BS decides the uploading order, while each device can decide when to upload its own local data set among multiple uploading rounds in distributed DAS. In distributed DAS, random access is employed where the access probability of each device is decided according to its local measurement for efficient uploading.

Session Chair

Jihong Park (Deakin University, Australia)

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