2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)最新文献

{"title":"Experimental Evaluation of End-to-end Flow Latency Reduction in Softwarized Cellular Networks through Dynamic Multi-Access Edge Computing","authors":"Pablo Fondo-Ferreiro, David Candal-Ventureira, F. Gil-Castiñeira, F. González-Castaño, Diarmuid Collins","doi":"10.1109/PIMRC50174.2021.9569590","DOIUrl":"https://doi.org/10.1109/PIMRC50174.2021.9569590","url":null,"abstract":"Over the last few years the Multi-Access Edge Computing (MEC) paradigm has been gaining attention as a key enabler for low latency applications in cellular networks. In this paper we analyze a solution based on Software-Defined Networking (SDN) for supporting dynamic and transparent relocation of the endpoint of a communication from the core to an edge infrastructure in current cellular networks. We also provide results of real-world experiments utilising a Network Function Virtualization (NFV)-based testbed for evaluating session continuity and latency reduction when the gateway or anchor point used by two end User Equipments (UEs) is relocated to edge resources. Our experimental results show that the communication can be dynamically relocated from the core to the edge while guaranteeing session continuity during the whole process. We demonstrate that the mechanism is able to reduce latency considerably when the core network is congested.","PeriodicalId":283606,"journal":{"name":"2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127434736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of Full-Duplex Cooperative NOMA Network with Nonlinear Energy Harvesting","authors":"Ujjawal Makhanpuri, Kamal Agrawal, Anand Jee, S. Prakriya","doi":"10.1109/pimrc50174.2021.9569313","DOIUrl":"https://doi.org/10.1109/pimrc50174.2021.9569313","url":null,"abstract":"This paper investigates a cooperative non-orthogonal multiple access (C-NOMA) network consisting of a base station (BS), a far user (FU) and a full-duplex (FD) near user (NU). The NU does not use its own battery energy to relay to FU, and harvests energy using SWIPT principles from the BS. In this paper we consider a more practical nonlinear energy harvesting model for the first time in such a framework. We show that use of the idealized linear EH model leads to gross over-estimation of FU performance. Considering the time-splitting (TS) energy harvesting (EH) protocol, and modelling the self-interference at the FD NU, expressions are derived for the FU and NU outage probabilities in closed-form. Further, we show that by optimal selection of the TS parameter performance of FU can be further enhanced. Computer simulations confirm the accuracy of the derived analytical expressions.","PeriodicalId":283606,"journal":{"name":"2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127556343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Data Replication for URLLC in Cooperative 4G/5G Networks","authors":"Faten Bou Dihn, Amal Abdel Razzac, A. Falou, S. Elayoubi","doi":"10.1109/pimrc50174.2021.9569368","DOIUrl":"https://doi.org/10.1109/pimrc50174.2021.9569368","url":null,"abstract":"We design a multi-connectivity scheme based on the cooperation between the networks of the fourth generation (4G) and the fifth generation (5G) of mobile technologies, to improve the reliability of Ultra-Reliable Low-Latency Communication (URLLC) services. While 5G system is characterized by a short Transmission Time Interval (TTI) and fast retransmissions within the delay budget, 4G system has a large TTI so that only blind retransmissions are possible. We develop an optimization problem that couples this multi-connectivity with an adapted number of replications, scheduled by exploiting Power Domain Non-Orthogonal Multiple Access. The resulting optimal scheme is evaluated in an outdoor urban macro scenario for different reliability targets and user locations in the cell. Results show that the scheme minimizes the number of replicas while achieving the performance targets for both URLLC and eMBB users.","PeriodicalId":283606,"journal":{"name":"2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125417730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-Effort Deep Learning Method Trained through Virtual Trajectories for Indoor Tracking","authors":"Aisha Javed, N. Hassan","doi":"10.1109/PIMRC50174.2021.9569701","DOIUrl":"https://doi.org/10.1109/PIMRC50174.2021.9569701","url":null,"abstract":"We develop a novel low-effort Wi-Fi fingerprinting method to generate a fully labeled dataset of received signal strength indicator (RSSI) values to train a deep learning model for indoor localization and trajectory estimation. The positioning accuracy of Wi-Fi fingerprinting approach can be improved by collecting large amount of labeled data with diverse set of hardware devices, orientation angles, and environmental conditions. However, the cost of data collection becomes prohibitive. In crowdsourcing method, volunteers who frequently visit the indoor space, contribute unlabeled but diverse trajectories. However, despite the potential to collect large quantities of data, unlabeled trajectories produce large positioning errors. In this paper, we propose a novel method where we only collect a base-fingerprinting set with a limited number of devices, orientation angles, and environmental conditions. Then, we develop RSSI model which allows us to simulate device & orientation angle and environmental conditions diversity as noises that are added on top of RSSI values in the base-fingerprinting set. We then generate a very large set of fully labeled diverse virtual trajectories to train appropriate deep learning models for use in the online-phase of Wi-Fi fingerprinting method. In this paper, we train 1-D convolutional neural network (1-D CNN) models called base- and diverse-models. Base-model is trained on the virtual trajectories obtained exclusively from the base-fingerprinting set. On the other hand, diverse-model is trained on the so-called noisy trajectories which are created with the help of RSSI model. To validate the effectiveness of our approach, we perform experiments in our campus library. When the online-phase trajectory is coming from hardware & orientation angles and environmental conditions not used for data collection, the diverse-model achieves an average mean square error of 1.24m as compared to 19.25m for the base-model. These results demonstrate the effectiveness and simplicity of our proposed approach.","PeriodicalId":283606,"journal":{"name":"2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125458076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards FDD Massive MIMO: Downlink Channel Covariance Matrix Estimation Using Conditional Generative Adversarial Networks","authors":"Bitan Banerjee, R. Elliott, W. Krzymień, H. Farmanbar","doi":"10.1109/PIMRC50174.2021.9569379","DOIUrl":"https://doi.org/10.1109/PIMRC50174.2021.9569379","url":null,"abstract":"Estimating or predicting the downlink channel state information (CSI) is extremely important for practical implementation of frequency division duplex (FDD) massive MIMO. Estimation of downlink CSI from uplink CSI using second order channel statistics, namely the channel covariance matrix (CCM), is a promising approach. However, published work so far has rarely applied machine learning techniques to solve this problem using CCMs, most probably due to the unavailability of a direct mapping function or parametric model for supervised learning to convert from uplink to downlink CCMs. In this paper, we develop a conditional generative adversarial network (CGAN) method for uplink-to-downlink CCM conversion. To apply the CGAN-based method, we convert the uplink and downlink CCMs to images and use image translation techniques for CGANs. The normalized mean square error performance of the proposed CGAN is evaluated for several antenna array sizes and with both perfect and imperfect knowledge of the CCMs. Our results demonstrate performance improvement over existing algorithms.","PeriodicalId":283606,"journal":{"name":"2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125628455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low Overhead Codebook Design for mmWave Roadside Units Placed at Smart Intersections","authors":"Bryse Flowers, Xinyu Zhang, S. Dey","doi":"10.1109/PIMRC50174.2021.9569638","DOIUrl":"https://doi.org/10.1109/PIMRC50174.2021.9569638","url":null,"abstract":"In order to meet the high data rate requirements of emerging roadway use cases, mmWave vehicular communications will be needed. This work studies the ability of vehicles to communicate with a Roadside Unit (RSU) placed at an intersection. Practical mmWave radios utilize a codebook, a discrete set of analog beams, that is periodically searched during runtime to find the optimal beam to use for each receiver. This search creates overhead as the wireless channel is not used for communication while this beam search is happening. This work focuses on reducing the overhead of beam training by optimizing the site-specific codebook design of a RSU. Owing to the sparsity of the mmWave channel and the user distribution for vehicles, it is found that 85% of beams can be removed from the codebook with zero-impact. By carefully selecting the usage of wide beams the codebook size can be further reduced to just 64 beams while still providing omni-directional coverage for an intersection. Other research thrusts have focused on attempting to augment or remove beam training entirely; however, this necessitates a change to the PHY layer. Codebook optimization achieves approximately 80% of the communications performance that would be achieved if beam training overhead could be completely removed while only requiring a radio configuration update. Thus, this work finds that today’s commercial mmWave radios are sufficient for deployments in RSUs. To validate the proposed codebook optimization algorithm, a detailed mmWave ray tracing framework that encompasses 3D environmental information and material properties of reflectors is developed.","PeriodicalId":283606,"journal":{"name":"2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127496378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconfigurable Intelligent Surface Aided Secure UAV Communications","authors":"Wen Wang, Hui Tian, Wanli Ni, Meihui Hua","doi":"10.1109/pimrc50174.2021.9569667","DOIUrl":"https://doi.org/10.1109/pimrc50174.2021.9569667","url":null,"abstract":"This paper investigates the problem of secure communication in the unmanned aerial vehicle (UAV) enabled net-works aided by a reconfigurable intelligent surface (RIS) from the physical layer security perspective. Specifically, the RIS is deployed to assist the wireless transmission from the UAV to the ground user in the presence of an eavesdropper. The objective of this work is to maximize the secrecy rate by jointly optimizing the phase shifts at the RIS as well as the transmit beamforming vector and location of the UAV. However, the formulated problem is difficult to solve directly due to the non-linear and non-convex objective function and constraints. By invoking the successive convex approximation and fractional programming techniques, the intractable original problem is transformed into convex ones, then an alternating algorithm is proposed to solve the challenging problem effectively. Simulations results demonstrate that the designed algorithm for RIS-aided UAV communications can achieve higher secrecy rate than benchmarks.","PeriodicalId":283606,"journal":{"name":"2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","volume":"250 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124263990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Time-Of-Arrival Estimation Approach with Channel Frequency Response Reconstruction in OFDM systems","authors":"Ziming He, F. Tong","doi":"10.1109/pimrc50174.2021.9569726","DOIUrl":"https://doi.org/10.1109/pimrc50174.2021.9569726","url":null,"abstract":"This paper presents a novel TOA estimation approach by utilizing multipath parameter estimation algorithm to reconstruct channel frequency response on null subcarriers in OFDM systems. The proposed approach is evaluated using legacy long training field defined in IEEE 802.11ac standard, and is shown by simulations to outperform the state-of-the-arts when signal bandwidth is 20-40 MHz in both 802.11 channel model B and WINNER A1 LOS channel.","PeriodicalId":283606,"journal":{"name":"2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121547748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Step Optimization of Indoor Localization Accuracy Using Commodity WiFi","authors":"Shuyu Li, Sherif Welsen, V. Brusic","doi":"10.1109/PIMRC50174.2021.9569286","DOIUrl":"https://doi.org/10.1109/PIMRC50174.2021.9569286","url":null,"abstract":"We present a Multi-step optimization Localization Algorithm (MoLA) for improved accuracy of indoor localization by WiFi. Using consumer-grade WiFi devices, we can determine the location of an object carrying WiFi device with higher accuracy than the comparable popular systems. MoLA removes the phase error by using calibration, then estimates the angle-of-arrival (AoA) of the signal by combining I-MUSIC algorithm and the minimum description length (MDL) equation. The line-of-sight (LoS) path is identified using our novel estimator function. In the last step, the object location is estimated. Extensive measurement experiments have shown that MoLA can achieve improvement in both median localization and point localization error in a 290 m2 environment having adequate LoS with a single receiver.","PeriodicalId":283606,"journal":{"name":"2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122104927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Privacy-Preserving Pedestrian Dead Reckoning Framework Based on Differential Privacy","authors":"Tianyi Feng, Zhixiang Zhang, L. W. Wong, Sumei Sun, B. Sikdar","doi":"10.1109/pimrc50174.2021.9569650","DOIUrl":"https://doi.org/10.1109/pimrc50174.2021.9569650","url":null,"abstract":"Pedestrian dead reckoning (PDR) is a widely used approach to estimate locations and trajectories. Accessing location-based services with trajectory data can bring convenience to people, but may also raise privacy concerns that need to be addressed. In this paper, a privacy-preserving pedestrian dead reckoning framework is proposed to protect a user’s trajectory privacy based on differential privacy. We introduce two metrics to quantify trajectory privacy and data utility. Our proposed privacy-preserving trajectory extraction algorithm consists of three mechanisms for the initial locations, stride lengths and directions. In addition, we design an adversary model based on particle filtering to evaluate the performance and demonstrate the effectiveness of our proposed framework with our collected sensor reading dataset.","PeriodicalId":283606,"journal":{"name":"2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123464322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}