Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118628
Ningjie Gao, R. Huo, Shuo Wang, Tao Huang
Blockchain has been widely used to solve data privacy and security issues in edge computing scenarios. However, the blockchain based on edge computing still has some performance problems, such as insufficient scalability, difficulty in balancing security and edge device power consumption, and inability to simultaneously meet low latency, high throughput, high security and privacy issues, etc. In order to solve these problems, this paper proposes a generally improved Byzantine consensus mechanism based on the K-medoids clustering algorithm - FIBFT. Considering the different performance characteristics of each node in the network, the node’s state is first abstracted into a multi-dimensional state space containing eigenvalues, and then the nodes are divided into subnets by the efficient K-medoids clustering algorithm. Each subnet uses a Byzantine consensus mechanism based on arbitration for consensus and data interaction, and the consensus data could be exchanged between the subnets without interfering with the consensus process. The research results show that FIBFT has better scalability and throughput while ensuring high security compared with the traditional Byzantine consensus algorithm.
{"title":"FIBFT: An Improved Byzantine Consensus Mechanism for Edge Computing","authors":"Ningjie Gao, R. Huo, Shuo Wang, Tao Huang","doi":"10.1109/WCNC55385.2023.10118628","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118628","url":null,"abstract":"Blockchain has been widely used to solve data privacy and security issues in edge computing scenarios. However, the blockchain based on edge computing still has some performance problems, such as insufficient scalability, difficulty in balancing security and edge device power consumption, and inability to simultaneously meet low latency, high throughput, high security and privacy issues, etc. In order to solve these problems, this paper proposes a generally improved Byzantine consensus mechanism based on the K-medoids clustering algorithm - FIBFT. Considering the different performance characteristics of each node in the network, the node’s state is first abstracted into a multi-dimensional state space containing eigenvalues, and then the nodes are divided into subnets by the efficient K-medoids clustering algorithm. Each subnet uses a Byzantine consensus mechanism based on arbitration for consensus and data interaction, and the consensus data could be exchanged between the subnets without interfering with the consensus process. The research results show that FIBFT has better scalability and throughput while ensuring high security compared with the traditional Byzantine consensus algorithm.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115404309","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118876
Xu Feng, Khuong An Nguyen, Zhiyuan Luo
Despite its high accuracy in the ideal condition where there is a direct line-of-sight between the Access Points and the user, most WiFi indoor positioning systems struggle under the non-line-of-sight scenario. Thus, we propose a novel feature selection algorithm leveraging Machine Learning based weighting methods and multi-scale selection, with WiFi RTT and RSS as the input signals. We evaluate the algorithm performance on a campus building floor. The results indicated an accuracy of 93% line-of-sight detection success with 13 Access Points, using only 3 seconds of test samples at any moment; and an accuracy of 98% for individual AP line-of-sight detection.
{"title":"A Multi-Scale Feature Selection Framework for WiFi Access Points Line-of-sight Identification","authors":"Xu Feng, Khuong An Nguyen, Zhiyuan Luo","doi":"10.1109/WCNC55385.2023.10118876","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118876","url":null,"abstract":"Despite its high accuracy in the ideal condition where there is a direct line-of-sight between the Access Points and the user, most WiFi indoor positioning systems struggle under the non-line-of-sight scenario. Thus, we propose a novel feature selection algorithm leveraging Machine Learning based weighting methods and multi-scale selection, with WiFi RTT and RSS as the input signals. We evaluate the algorithm performance on a campus building floor. The results indicated an accuracy of 93% line-of-sight detection success with 13 Access Points, using only 3 seconds of test samples at any moment; and an accuracy of 98% for individual AP line-of-sight detection.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115473448","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118779
Ahmet Burak Ozyurt, W. Popoola
This paper analyzes a new and accurate overthe-air time synchronization (TS) method for industrial LiFi networks which achieves synchronization via the exchange of timestamps between nodes. The necessity for accurate TS will become increasingly important with future networks due to its connection with industrial applications. Over-the-air TS vision is to achieve accuracy of the order of 1 µs. In this technique, timestamps are transmitted using optical signals which are used for the estimation of the time-of-arrival (TOA). To this end, the Cramer-Rao Lower Bound (CRLB) is computed as the theoretical limit on the performance and accuracy. In this way, the effects of distance, optical power, and semi-angle at half illuminance of the transmitter are investigated. Calculations and comparison show that the proposed technique can be efficiently used for TS in future wireless networks.
{"title":"Analysis of Over-the-Air Time Synchronization for Industrial LiFi Networks","authors":"Ahmet Burak Ozyurt, W. Popoola","doi":"10.1109/WCNC55385.2023.10118779","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118779","url":null,"abstract":"This paper analyzes a new and accurate overthe-air time synchronization (TS) method for industrial LiFi networks which achieves synchronization via the exchange of timestamps between nodes. The necessity for accurate TS will become increasingly important with future networks due to its connection with industrial applications. Over-the-air TS vision is to achieve accuracy of the order of 1 µs. In this technique, timestamps are transmitted using optical signals which are used for the estimation of the time-of-arrival (TOA). To this end, the Cramer-Rao Lower Bound (CRLB) is computed as the theoretical limit on the performance and accuracy. In this way, the effects of distance, optical power, and semi-angle at half illuminance of the transmitter are investigated. Calculations and comparison show that the proposed technique can be efficiently used for TS in future wireless networks.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115779597","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118760
Li Qiao, Anwen Liao, Zhen Gao, Hua Wang
Millimeter-wave (mmWave) extra-large scale multiple-input-multiple-output (XL-MIMO) is a promising technique for achieving high data rates in the upcoming sixth-generation communication networks. This paper considers an indoor massive Internet-of-Things (IoT) access scenario served by mmWave XL-MIMO, where the wireless channels exhibit spatial non-stationarity and the coexistence of far-field and near-field communication. By analyzing and exploiting such mmWave XL-MIMO channels, we propose a low-latency grant-free massive IoT access scheme based on joint active user detection (AUD) and channel estimation (CE). Specifically, by exploiting the common user activity in different pilot subcarriers and the block sparsity of the angular-domain XL-MIMO channels, we propose a low-complexity generalized multiple measurement vector-joint AUD and CE algorithm for efficient indoor massive access. Simulation results verify that the proposed solutions outperform the state-of-the-art greedy compressive sensing-based schemes in terms of AUD and CE performance.
{"title":"Indoor Massive IoT Access Relying on Millimeter-Wave Extra-Large-Scale MIMO","authors":"Li Qiao, Anwen Liao, Zhen Gao, Hua Wang","doi":"10.1109/WCNC55385.2023.10118760","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118760","url":null,"abstract":"Millimeter-wave (mmWave) extra-large scale multiple-input-multiple-output (XL-MIMO) is a promising technique for achieving high data rates in the upcoming sixth-generation communication networks. This paper considers an indoor massive Internet-of-Things (IoT) access scenario served by mmWave XL-MIMO, where the wireless channels exhibit spatial non-stationarity and the coexistence of far-field and near-field communication. By analyzing and exploiting such mmWave XL-MIMO channels, we propose a low-latency grant-free massive IoT access scheme based on joint active user detection (AUD) and channel estimation (CE). Specifically, by exploiting the common user activity in different pilot subcarriers and the block sparsity of the angular-domain XL-MIMO channels, we propose a low-complexity generalized multiple measurement vector-joint AUD and CE algorithm for efficient indoor massive access. Simulation results verify that the proposed solutions outperform the state-of-the-art greedy compressive sensing-based schemes in terms of AUD and CE performance.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115828088","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}
Low earth orbit (LEO) mega-constellations in future 6G have attracted the attention from both academia and industry. However, due to the high dynamic characteristic of the LEO satellite network topology and the limited on-board resources, existing approaches relying on high on-board processing capabilities and monitoring the global network state, result in intolerable packet loss rate and excessive signalling overhead. In this paper, a routing algorithm based on area segmentation for LEO mega-constellations is proposed according to the topological characteristics of the network. Specifically, we divide the LEO mega-constellations into multiple areas with four quadrant parts. In addition, the transmission cluster is defined consisted of two adjacent parts based on transmission direction. With the relative geographical location and transmission clusters, we joint intra-area and inter-area routing to realize multi-path routing and forwarding by periodically updating the link state, instead of globally calculating. Simulation results demonstrate that the proposed algorithm can achieve higher throughput, decrease the packet loss rate by 22% and reduce the signalling overhead significantly.
{"title":"LEO Mega-Constellations Routing Algorithm Based on Area Segmentation","authors":"Rui Li, Jiaxin Zhang, Shuang Zheng, Kaiwei Wang, Peng Wang, Xing Zhang","doi":"10.1109/WCNC55385.2023.10118676","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118676","url":null,"abstract":"Low earth orbit (LEO) mega-constellations in future 6G have attracted the attention from both academia and industry. However, due to the high dynamic characteristic of the LEO satellite network topology and the limited on-board resources, existing approaches relying on high on-board processing capabilities and monitoring the global network state, result in intolerable packet loss rate and excessive signalling overhead. In this paper, a routing algorithm based on area segmentation for LEO mega-constellations is proposed according to the topological characteristics of the network. Specifically, we divide the LEO mega-constellations into multiple areas with four quadrant parts. In addition, the transmission cluster is defined consisted of two adjacent parts based on transmission direction. With the relative geographical location and transmission clusters, we joint intra-area and inter-area routing to realize multi-path routing and forwarding by periodically updating the link state, instead of globally calculating. Simulation results demonstrate that the proposed algorithm can achieve higher throughput, decrease the packet loss rate by 22% and reduce the signalling overhead significantly.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124375250","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118937
Fan Jiang, Y. Li, Changyin Sun, Chaowei Wang
This paper investigates a computation offloading and resource allocation policy for multiple vehicle user equipments (VUEs) in the Internet of Vehicles (IoV). Aiming at balancing the delay and energy consumption during the offloading procedure, a Support Vector Machine (SVM) is initially adopted to classify the offloading tasks into two categories according to different delay and energy consumption requirements. Consequently, VUEs can choose to offload the tasks to the mobile edge computing (MEC) server or other VUEs for completion. In particular, to further decrease the task offloading time in the MEC processing mode, the non-orthogonal multiple access (NOMA) scheme is adopted, which makes it possible for the MEC server to serve two VUEs simultaneously on the same sub-channel. To minimize the total cost, a Dueling Double Deep Q-Network (D3QN) based resource allocation algorithm is proposed, which can allocate the corresponding radio or computing resources under different task processing modes. Simulation results demonstrate that the proposed scheme can effectively reduce the total offloading cost within the maximum delay tolerance compared with existing methods.
研究了车联网中多车辆用户设备(vue)的计算卸载和资源分配策略。为了平衡卸载过程中的延迟和能耗，初步采用支持向量机(SVM)根据不同的延迟和能耗要求将卸载任务分为两类。因此，vue可以选择将任务卸载到移动边缘计算(MEC)服务器或其他vue完成。特别是为了进一步减少MEC处理模式下的任务卸载时间，采用了非正交多址(NOMA)方案，使得MEC服务器可以在同一子信道上同时服务两个vue。为了使总成本最小化，提出了一种基于Dueling Double Deep Q-Network (D3QN)的资源分配算法，该算法可以在不同的任务处理模式下分配相应的无线电或计算资源。仿真结果表明，与现有方法相比，该方案能在最大延迟容限内有效降低总卸载成本。
{"title":"Dueling Double Deep Q-Network Based Computation Offloading and Resource Allocation Scheme for Internet of Vehicles","authors":"Fan Jiang, Y. Li, Changyin Sun, Chaowei Wang","doi":"10.1109/WCNC55385.2023.10118937","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118937","url":null,"abstract":"This paper investigates a computation offloading and resource allocation policy for multiple vehicle user equipments (VUEs) in the Internet of Vehicles (IoV). Aiming at balancing the delay and energy consumption during the offloading procedure, a Support Vector Machine (SVM) is initially adopted to classify the offloading tasks into two categories according to different delay and energy consumption requirements. Consequently, VUEs can choose to offload the tasks to the mobile edge computing (MEC) server or other VUEs for completion. In particular, to further decrease the task offloading time in the MEC processing mode, the non-orthogonal multiple access (NOMA) scheme is adopted, which makes it possible for the MEC server to serve two VUEs simultaneously on the same sub-channel. To minimize the total cost, a Dueling Double Deep Q-Network (D3QN) based resource allocation algorithm is proposed, which can allocate the corresponding radio or computing resources under different task processing modes. Simulation results demonstrate that the proposed scheme can effectively reduce the total offloading cost within the maximum delay tolerance compared with existing methods.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124394413","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118893
Yi Yue, Xiongyan Tang, W. Yang, Xuebei Zhang, Zhiyan Zhang, Chuyang Gao, Lexi Xu
Virtual Network Function (VNF) placement in NTNs is challenging because Non-Terrestrial Networks (NTNs), such as satellite networks, have limited resources regarding computational power and rate. However, existing solutions do not consider satellites’ resource constraints and the bandwidth constraints of links, which are essential metrics for designing VNF placement strategies in NTNs. Utilizing Network Function Virtualization (NFV) technology to deploy related network services on satellites in VNFs is a reasonable way. This paper focuses on delay-aware VNF placement in 6G NTNs to meet the ultra-low delay requirements of different applications. In addition, we also consider how to improve the resource utilization of servers to eliminate the resource bottlenecks of resource-constrained 6G NTN facilities. Then we formulate the VNF placement problem as a weighted graph-matching problem, aiming to maximize resource utilization. We propose the Linear Programming based algorithm and the Hungarian-based algorithm to solve the VNF placement problem. Evaluation results show that our proposed solutions outperform the benchmarks regarding resource utilization and execution time.
虚拟网络功能(VNF)在ntn中的放置具有挑战性，因为非地面网络(ntn)，如卫星网络，在计算能力和速率方面的资源有限。然而，现有的解决方案没有考虑卫星的资源约束和链路的带宽约束，这是设计ntn中VNF放置策略的基本指标。利用网络功能虚拟化(Network Function Virtualization, NFV)技术将相关网络业务部署在VNFs中的卫星上是一种合理的方式。本文重点研究了延迟感知VNF在6G ntn中的放置，以满足不同应用的超低延迟需求。此外，我们还考虑如何提高服务器的资源利用率，以消除资源受限的6G NTN设施的资源瓶颈。然后，我们将VNF放置问题表述为一个加权图匹配问题，以最大限度地提高资源利用率。我们提出了基于线性规划的算法和基于匈牙利的算法来解决VNF的放置问题。评估结果表明，我们提出的解决方案在资源利用率和执行时间方面优于基准测试。
{"title":"Delay-aware and Resource-efficient VNF placement in 6G Non-Terrestrial Networks","authors":"Yi Yue, Xiongyan Tang, W. Yang, Xuebei Zhang, Zhiyan Zhang, Chuyang Gao, Lexi Xu","doi":"10.1109/WCNC55385.2023.10118893","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118893","url":null,"abstract":"Virtual Network Function (VNF) placement in NTNs is challenging because Non-Terrestrial Networks (NTNs), such as satellite networks, have limited resources regarding computational power and rate. However, existing solutions do not consider satellites’ resource constraints and the bandwidth constraints of links, which are essential metrics for designing VNF placement strategies in NTNs. Utilizing Network Function Virtualization (NFV) technology to deploy related network services on satellites in VNFs is a reasonable way. This paper focuses on delay-aware VNF placement in 6G NTNs to meet the ultra-low delay requirements of different applications. In addition, we also consider how to improve the resource utilization of servers to eliminate the resource bottlenecks of resource-constrained 6G NTN facilities. Then we formulate the VNF placement problem as a weighted graph-matching problem, aiming to maximize resource utilization. We propose the Linear Programming based algorithm and the Hungarian-based algorithm to solve the VNF placement problem. Evaluation results show that our proposed solutions outperform the benchmarks regarding resource utilization and execution time.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"361 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124541825","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118916
Chuanhong Liu, Caili Guo, Siyi Wang, Yuze Li, Dingxing Hu
Task-oriented semantic communication has received growing interests, which can significantly reduce the amount of transmitted data without affecting task performance. In this paper, a novel semantic communication system based on semantic triplets (SCST) is proposed, in which the semantics is represented via the explainable semantic triplets. Specifically, we propose a semantic extraction method to convert the transmitted texts into semantic triplets, which can be further compressed via the designed semantic filtering method. The semantic triplets then will be encoded and transmitted via the wireless channel to complete intelligent tasks at the receiver. Moreover, we then apply the SCST to sentiment analysis task and question-answering task to verify the effectiveness, where the semantic encoder and decoder are designed respectively considering the final task. The experiment results show that the proposed SCST can obtain at least 43.5% and 52% accuracy gains, compared to the baselines using traditional communication method.
{"title":"Task-Oriented Semantic Communication Based on Semantic Triplets","authors":"Chuanhong Liu, Caili Guo, Siyi Wang, Yuze Li, Dingxing Hu","doi":"10.1109/WCNC55385.2023.10118916","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118916","url":null,"abstract":"Task-oriented semantic communication has received growing interests, which can significantly reduce the amount of transmitted data without affecting task performance. In this paper, a novel semantic communication system based on semantic triplets (SCST) is proposed, in which the semantics is represented via the explainable semantic triplets. Specifically, we propose a semantic extraction method to convert the transmitted texts into semantic triplets, which can be further compressed via the designed semantic filtering method. The semantic triplets then will be encoded and transmitted via the wireless channel to complete intelligent tasks at the receiver. Moreover, we then apply the SCST to sentiment analysis task and question-answering task to verify the effectiveness, where the semantic encoder and decoder are designed respectively considering the final task. The experiment results show that the proposed SCST can obtain at least 43.5% and 52% accuracy gains, compared to the baselines using traditional communication method.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114391901","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118848
Yaya Etiabi, Wafa Njima, El-Mehdi Amhoud
The proliferation of connected devices in indoor environments opens the floor to a myriad of indoor applications with positioning services as key enablers. However, as privacy issues and resource constraints arise, it becomes more challenging to design accurate positioning systems as required by most applications. To overcome the latter challenges, we present in this paper, a federated learning (FL) framework for hierarchical 3D indoor localization using a deep neural network. Indeed, we firstly shed light on the prominence of exploiting the hierarchy between floors and buildings in a multi-building and multi-floor indoor environment. Then, we propose an FL framework to train the designed hierarchical model. The performance evaluation shows that by adopting a hierarchical learning scheme, we can improve the localization accuracy by up to 24.06% compared to the non-hierarchical approach. We also obtain a building and floor prediction accuracy of 99.90% and 94.87% respectively. With the proposed FL framework, we can achieve a near-performance characteristic as of the central training with an increase of only 7.69% in the localization error. Moreover, the conducted scalability study reveals that the FL system accuracy is improved when more devices join the training.
{"title":"Federated Learning based Hierarchical 3D Indoor Localization","authors":"Yaya Etiabi, Wafa Njima, El-Mehdi Amhoud","doi":"10.1109/WCNC55385.2023.10118848","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118848","url":null,"abstract":"The proliferation of connected devices in indoor environments opens the floor to a myriad of indoor applications with positioning services as key enablers. However, as privacy issues and resource constraints arise, it becomes more challenging to design accurate positioning systems as required by most applications. To overcome the latter challenges, we present in this paper, a federated learning (FL) framework for hierarchical 3D indoor localization using a deep neural network. Indeed, we firstly shed light on the prominence of exploiting the hierarchy between floors and buildings in a multi-building and multi-floor indoor environment. Then, we propose an FL framework to train the designed hierarchical model. The performance evaluation shows that by adopting a hierarchical learning scheme, we can improve the localization accuracy by up to 24.06% compared to the non-hierarchical approach. We also obtain a building and floor prediction accuracy of 99.90% and 94.87% respectively. With the proposed FL framework, we can achieve a near-performance characteristic as of the central training with an increase of only 7.69% in the localization error. Moreover, the conducted scalability study reveals that the FL system accuracy is improved when more devices join the training.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"263 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114645725","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}
Pub Date : 2023-03-01DOI: 10.1109/WCNC55385.2023.10118823
Ning Li, Pingzhi Fan
In this paper, the Cell-Free (CF) massive MIMO (mMIMO) network with user equipment (UE) hardware impairments in spatially correlated channels is investigated. By using the established generic UE hardware impairment model, a minimum mean-squared error (MMSE) channel estimator is derived. Then, a lower bound on the uplink ergodic capacity is derived for CF mMIMO network with UE hardware impairments, and an optimal receive combining vector is also derived for obtaining maximum instantaneous signal-to-interference-and-noise ratio (SINR). Considering the computational complexity of the MMSE combining, the regularized zero-forcing (RZF) and maximum ratio (MR) combining schemes are presented as alternatives. Our results show that the RZF combining scheme has little loss in sum spectral efficiency (SE) compared to MMSE combining under different hardware impairments. Based on the use-and-then-forget (UatF) bound, a new closed-form uplink SE expression with MMSE estimator is derived for MR combining. Finally, we evaluate the tightness of the capacity bounds under different hardware impairments.
{"title":"Impact of UE Hardware Impairments on Uplink Spectral Efficiency of Cell-Free Massive MIMO Network","authors":"Ning Li, Pingzhi Fan","doi":"10.1109/WCNC55385.2023.10118823","DOIUrl":"https://doi.org/10.1109/WCNC55385.2023.10118823","url":null,"abstract":"In this paper, the Cell-Free (CF) massive MIMO (mMIMO) network with user equipment (UE) hardware impairments in spatially correlated channels is investigated. By using the established generic UE hardware impairment model, a minimum mean-squared error (MMSE) channel estimator is derived. Then, a lower bound on the uplink ergodic capacity is derived for CF mMIMO network with UE hardware impairments, and an optimal receive combining vector is also derived for obtaining maximum instantaneous signal-to-interference-and-noise ratio (SINR). Considering the computational complexity of the MMSE combining, the regularized zero-forcing (RZF) and maximum ratio (MR) combining schemes are presented as alternatives. Our results show that the RZF combining scheme has little loss in sum spectral efficiency (SE) compared to MMSE combining under different hardware impairments. Based on the use-and-then-forget (UatF) bound, a new closed-form uplink SE expression with MMSE estimator is derived for MR combining. Finally, we evaluate the tightness of the capacity bounds under different hardware impairments.","PeriodicalId":259116,"journal":{"name":"2023 IEEE Wireless Communications and Networking Conference (WCNC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114698639","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}
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