IEEE INFOCOM 2021 - IEEE Conference on Computer Communications最新文献_第10页

Multi-Agent Reinforcement Learning for Urban Crowd Sensing with For-Hire Vehicles 基于出租车辆的城市人群感知多智能体强化学习

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488713

Rong Ding, Zhaoxing Yang, Yifei Wei, Haiming Jin, Xinbing Wang

{"title":"Multi-Agent Reinforcement Learning for Urban Crowd Sensing with For-Hire Vehicles","authors":"Rong Ding, Zhaoxing Yang, Yifei Wei, Haiming Jin, Xinbing Wang","doi":"10.1109/INFOCOM42981.2021.9488713","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488713","url":null,"abstract":"Recently, vehicular crowd sensing (VCS) that leverages sensor-equipped urban vehicles to collect city-scale sensory data has emerged as a promising paradigm for urban sensing. Nowadays, a wide spectrum of VCS tasks are carried out by for-hire vehicles (FHVs) due to various hardware and software constraints that are difficult for private vehicles to satisfy. However, such FHV-enabled VCS systems face a fundamental yet unsolved problem of striking a balance between the order-serving and sensing outcomes. To address this problem, we propose a novel graph convolutional cooperative multi-agent reinforcement learning (GCC-MARL) framework, which helps FHVs make distributed routing decisions that cooperatively optimize the system-wide global objective. Specifically, GCC-MARL meticulously assigns credits to agents in the training process to effectively stimulate cooperation, represents agents’ actions by a carefully chosen statistics to cope with the variable agent scales, and integrates graph convolution to capture useful spatial features from complex large-scale urban road networks. We conduct extensive experiments with a real-world dataset collected in Shenzhen, China, containing around 1 million trajectories and 50 thousand orders of 553 taxis per-day from June 1st to 30th, 2017. Our experiment results show that GCC-MARL outperforms state-of-the-art baseline methods in order-serving revenue, as well as sensing coverage and quality.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132892912","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}

引用次数: 10

Jellyfish: Locality-Sensitive Subflow Sketching 水母:局部敏感子流素描

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488847

Yongquan Fu, Lun An, S. Shen, Kai Chen, P. Barlet-Ros

{"title":"Jellyfish: Locality-Sensitive Subflow Sketching","authors":"Yongquan Fu, Lun An, S. Shen, Kai Chen, P. Barlet-Ros","doi":"10.1109/INFOCOM42981.2021.9488847","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488847","url":null,"abstract":"To cope with increasing network rates and massive traffic volumes, sketch-based methods have been extensively studied to trade accuracy for memory scalability and storage cost. However, sketches are sensitive to hash collisions due to skewed keys in real world environment, and need complicated performance control for line-rate packet streams.We present Jellyfish, a locality-sensitive sketching framework to address these issues. Jellyfish goes beyond network flow-based sketching towards fragments of network flows called subflows. First, Jellyfish splits consecutive packets from each network flow to subflow records, which not only reduces the rate contention but also provides intermediate subflow representations in form of truncated counters. Next, Jellyfish maps similar subflow records to the same bucket array and merges those from the same network flow to reconstruct the network-flow level counters. Real-world trace-driven experiments show that Jellyfish reduces the average estimation errors by up to six orders of magnitude for per-flow queries, by six orders of magnitude for entropy queries, and up to ten times for heavy-hitter queries.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116795545","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}

引用次数: 1

Resource-Efficient Federated Learning with Hierarchical Aggregation in Edge Computing 边缘计算中具有层次聚合的资源高效联邦学习

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488756

Zhiyuan Wang, Hongli Xu, Jianchun Liu, He Huang, C. Qiao, Yangming Zhao

{"title":"Resource-Efficient Federated Learning with Hierarchical Aggregation in Edge Computing","authors":"Zhiyuan Wang, Hongli Xu, Jianchun Liu, He Huang, C. Qiao, Yangming Zhao","doi":"10.1109/INFOCOM42981.2021.9488756","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488756","url":null,"abstract":"Federated learning (FL) has emerged in edge computing to address limited bandwidth and privacy concerns of traditional cloud-based centralized training. However, the existing FL mechanisms may lead to long training time and consume a tremendous amount of communication resources. In this paper, we propose an efficient FL mechanism, which divides the edge nodes into K clusters by balanced clustering. The edge nodes in one cluster forward their local updates to cluster header for aggregation by synchronous method, called cluster aggregation, while all cluster headers perform the asynchronous method for global aggregation. This processing procedure is called hierarchical aggregation. Our analysis shows that the convergence bound depends on the number of clusters and the training epochs. We formally define the resource-efficient federated learning with hierarchical aggregation (RFL-HA) problem. We propose an efficient algorithm to determine the optimal cluster structure (i.e., the optimal value of K) with resource constraints and extend it to deal with the dynamic network conditions. Extensive simulation results obtained from our study for different models and datasets show that the proposed algorithms can reduce completion time by 34.8%-70% and the communication resource by 33.8%-56.5% while achieving a similar accuracy, compared with the well-known FL mechanisms.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123651318","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}

引用次数: 66

FedSens: A Federated Learning Approach for Smart Health Sensing with Class Imbalance in Resource Constrained Edge Computing FedSens:资源受限边缘计算中类不平衡智能健康感知的联邦学习方法

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488776

D. Zhang, Ziyi Kou, Dong Wang

{"title":"FedSens: A Federated Learning Approach for Smart Health Sensing with Class Imbalance in Resource Constrained Edge Computing","authors":"D. Zhang, Ziyi Kou, Dong Wang","doi":"10.1109/INFOCOM42981.2021.9488776","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488776","url":null,"abstract":"The advance of mobile sensing and edge computing has brought new opportunities for abnormal health detection (AHD) systems where edge devices such as smartphones and wearable sensors are used to collect people’s health information and provide early alerts for abnormal health conditions such as stroke and depression. The recent development of federated learning (FL) allows participants to collaboratively train powerful AHD models while keeping their health data private to local devices. This paper targets at addressing a critical challenge of adapting FL to train AHD models, where the participants’ health data is highly imbalanced and contains biased class distributions. Existing FL solutions fail to address the class imbalance issue due to the strict privacy requirements of participants as well as the heterogeneous resource constraints of their edge devices. In this work, we propose FedSens, a new FL framework dedicated to address the class imbalance problem in AHD applications with explicit considerations of participant privacy and device resource constraints. We evaluate FedSens using a real-world edge computing testbed on two real-world AHD applications. The results show that FedSens can significantly improve the accuracy of AHD models in the presence of severe class imbalance with low energy cost to the edge devices.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121804458","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}

引用次数: 22

AutoML for Video Analytics with Edge Computing 自动视频分析与边缘计算

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488704

Apostolos Galanopoulos, J. Ayala-Romero, D. Leith, G. Iosifidis

{"title":"AutoML for Video Analytics with Edge Computing","authors":"Apostolos Galanopoulos, J. Ayala-Romero, D. Leith, G. Iosifidis","doi":"10.1109/INFOCOM42981.2021.9488704","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488704","url":null,"abstract":"Video analytics constitute a core component of many wireless services that require processing of voluminous data streams emanating from handheld devices. Multi-Access Edge Computing (MEC) is a promising solution for supporting such resource-hungry services, but there is a plethora of configuration parameters affecting their performance in an unknown and possibly time-varying fashion. To overcome this obstacle, we propose an Automated Machine Learning (AutoML) framework for jointly configuring the service and wireless network parameters, towards maximizing the analytics’ accuracy subject to minimum frame rate constraints. Our experiments with a bespoke prototype reveal the volatile and system/data-dependent performance of the service, and motivate the development of a Bayesian online learning algorithm which optimizes on-the-fly the service performance. We prove that our solution is guaranteed to find a near-optimal configuration using safe exploration, i.e., without ever violating the set frame rate thresholds. We use our testbed to further evaluate this AutoML framework in a variety of scenarios, using real datasets.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126299258","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}

引用次数: 30

A Deep-Learning-based Link Adaptation Design for eMBB/URLLC Multiplexing in 5G NR 基于深度学习的5G NR eMBB/URLLC复用链路自适应设计

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488790

Yan Huang, Yiwei Thomas Hou, W. Lou

{"title":"A Deep-Learning-based Link Adaptation Design for eMBB/URLLC Multiplexing in 5G NR","authors":"Yan Huang, Yiwei Thomas Hou, W. Lou","doi":"10.1109/INFOCOM42981.2021.9488790","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488790","url":null,"abstract":"URLLC is an important use case in 5G NR that targets at 1-ms level delay-sensitive applications. For fast transmission of URLLC traffic, a promising mechanism is to multiplex URLLC traffic into a channel occupied by eMBB service through preemptive puncturing. Although preemptive puncturing can offer transmission resource to URLLC on demand, it will adversely affect throughput and link reliability performance of eMBB service. To mitigate such an adverse impact, a possible approach is to employ link adaptation (LA) through MCS selection for eMBB users. In this paper, we study the problem of maximizing eMBB throughput through MCS selection while ensuring link reliability requirement for eMBB users. We present DELUXE – the first successful design and implementation based on deep learning to address this problem. DELUXE involves a novel mapping method to compress high-dimensional eMBB transmission information into a low-dimensional representation with minimal information loss, a learning method to learn and predict the block-error rate (BLER) under each MCS, and a fast calibration method to compensate errors in BLER predictions. For proof of concept, we implement DELUXE through a link-level 5G NR simulator. Extensive experimental results show that DELUXE can successfully maintain the desired link reliability for eMBB while striving for spectral efficiency. In addition, our implementation can meet the real-time requirement (< 125 μs) in 5G NR.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"18 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129012847","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}

引用次数: 9

Minimizing Entropy for Crowdsourcing with Combinatorial Multi-Armed Bandit 组合多武装强盗众包的熵最小化

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488800

Yiwen Song, Haiming Jin

{"title":"Minimizing Entropy for Crowdsourcing with Combinatorial Multi-Armed Bandit","authors":"Yiwen Song, Haiming Jin","doi":"10.1109/INFOCOM42981.2021.9488800","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488800","url":null,"abstract":"Nowadays, crowdsourcing has become an increasingly popular paradigm for large-scale data collection, annotation, and classification. Today’s rapid growth of crowdsourcing platforms calls for effective worker selection mechanisms, which oftentimes have to operate with a priori unknown worker reliability. We discover that the empirical entropy of workers’ results, which measures the uncertainty in the final aggregated results, naturally becomes a suitable metric to evaluate the outcome of crowdsourcing tasks. Therefore, this paper designs a worker selection mechanism that minimizes the empirical entropy of the results submitted by participating workers. Specifically, we formulate worker selection under sequentially arriving tasks as a combinatorial multi-armed bandit problem, which treats each worker as an arm, and aims at learning the best combination of arms that minimize the cumulative empirical entropy. By information theoretic methods, we carefully derive an estimation of the upper confidence bound for empirical entropy minimization, and leverage it in our minimum entropy upper confidence bound (ME-UCB) algorithm to balance exploration and exploitation. Theoretically, we prove that ME-UCB has a regret upper bound of O(1), which surpasses existing submodular UCB algorithms. Our extensive experiments with both a synthetic and real-world dataset empirically demonstrate that our ME-UCB algorithm outperforms other state-of-the-art approaches.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121231344","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}

引用次数: 10

Coexistence of Wi-Fi 6E and 5G NR-U: Can We Do Better in the 6 GHz Bands? Wi-Fi 6E和5G NR-U共存:我们能在6 GHz频段做得更好吗?

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488780

G. Naik, J. Park

{"title":"Coexistence of Wi-Fi 6E and 5G NR-U: Can We Do Better in the 6 GHz Bands?","authors":"G. Naik, J. Park","doi":"10.1109/INFOCOM42981.2021.9488780","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488780","url":null,"abstract":"Regulators in the US and Europe have stepped up their efforts to open the 6 GHz bands for unlicensed access. The two unlicensed technologies likely to operate and coexist in these bands are Wi-Fi 6E and 5G New Radio Unlicensed (NR-U). The greenfield 6 GHz bands allow us to take a fresh look at the coexistence between Wi-Fi and 3GPP-based unlicensed technologies. In this paper, using tools from stochastic geometry, we study the impact of Multi User Orthogonal Frequency Division Multiple Access, i.e., MU OFDMA—a feature introduced in 802.11ax—on this coexistence issue. Our results reveal that by disabling the use of the legacy contention mechanism (and allowing only MU OFDMA) for uplink access in Wi-Fi 6E, the performance of both NR-U networks and uplink Wi-Fi 6E can be improved. This is indeed feasible in the 6 GHz bands, where there are no operational Wi-Fi or NR-U users. In so doing, we also highlight the importance of accurate channel sensing at the entity that schedules uplink transmissions in Wi-Fi 6E and NR-U. If the channel is incorrectly detected as idle, factors that improve the uplink performance of one technology contribute negatively to the performance of the other technology.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121560248","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}

引用次数: 17

Monitoring Cloud Service Unreachability at Scale 大规模监控云服务不可达性

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488778

Kapil Agrawal, Viral Mehta, Sundararajan Renganathan, Sreangsu Acharyya, V. Padmanabhan, Chakri Kotipalli, Liting Zhao

{"title":"Monitoring Cloud Service Unreachability at Scale","authors":"Kapil Agrawal, Viral Mehta, Sundararajan Renganathan, Sreangsu Acharyya, V. Padmanabhan, Chakri Kotipalli, Liting Zhao","doi":"10.1109/INFOCOM42981.2021.9488778","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488778","url":null,"abstract":"We consider the problem of network unreachability in a global-scale cloud-hosted service that caters to hundreds of millions of users. Even when the service itself is up, the \"last mile\" between where users are, and the cloud is often the weak link that could render the service unreachable.We present NetDetector, a tool for detecting network-unreachability based on measurements from a client-based HTTP-ping service. NetDetector employs two models. The first, GA (Gaussian Alerts) models temporally averaged raw success rate of the HTTP-pings as a Gaussian distribution and flags significant dips below the mean as unreachability episodes. The second, more sophisticated approach (BB, or Beta-Binomial) models the health of network connectivity as the probability of an access request succeeding, estimates health from noisy samples, and alerts based on dips in health below a client-network-specific SLO (service-level objective) derived from data. These algorithms are enhanced by a drill-down technique that identifies a more precise scope of the unreachability event. We present promising results from GA, which has been in deployment, and the experimental BB detector over a 4-month period. For instance, GA flags 49 country-level unreachability incidents, of which 42 were labelled true positives based on investigation by on-call engineers (OCEs).","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122568448","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}

引用次数: 1

LoFi: Enabling 2.4GHz LoRa and WiFi Coexistence by Detecting Extremely Weak Signals LoFi:通过检测极弱信号，实现2.4GHz LoRa和WiFi共存

IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Pub Date : 2021-05-10 DOI: 10.1109/INFOCOM42981.2021.9488891

Gonglong Chen, Wei Dong, Jiamei Lv

{"title":"LoFi: Enabling 2.4GHz LoRa and WiFi Coexistence by Detecting Extremely Weak Signals","authors":"Gonglong Chen, Wei Dong, Jiamei Lv","doi":"10.1109/INFOCOM42981.2021.9488891","DOIUrl":"https://doi.org/10.1109/INFOCOM42981.2021.9488891","url":null,"abstract":"Low-Power Wide Area Networks (LPWANs) emerges as attractive communication technologies to connect the Internet-of-Things. A new LoRa chip has been proposed to pro-vide long range and low power support on 2.4GHz. Comparing with previous LoRa radios operating on sub-gigahertz, the new one can transmit LoRa packets faster without strict channel duty cycle limitations and have attracted many attentions. Prior studies have shown that LoRa packets may suffer from severe corruptions with WiFi interference. However, there are many limitations in existing approaches such as too much signal processing overhead on weak devices or low detection accuracy. In this paper, we propose a novel weak signal detection approach, LoFi, to enable the coexistence of LoRa and WiFi. LoFi utilizes a typical physical phenomenon Stochastic Resonance (SR) to boost weak signals with a specific frequency by adding appropriate white noise. Based on the detected spectrum occupancy of LoRa signals, LoFi reserves the spectrum for LoRa transmissions. We implement LoFi on USRP N210 and conduct extensive experiments to evaluate its performance. Results show that LoFi can enable the coexistence of LoRa and WiFi in 2.4GHz. The packet reception ratio of LoRa achieves 98% over an occupied 20MHz WiFi channel, and the WiFi throughput loss is reduced by up to 13%.","PeriodicalId":293079,"journal":{"name":"IEEE INFOCOM 2021 - IEEE Conference on Computer Communications","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124013981","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}

引用次数: 10