Pub Date : 2019-09-01DOI: 10.1109/VTCFall.2019.8891539
Weijia Dai, Haichao Wei, Jiaxi Zhou, Wuyang Zhou
In 5G wireless communication network, massive machine type communication (mMTC) is an emerging research topic. For mMTC, non-orthogonal multiple access (NOMA) has been proposed to support its large-scale connectivity. Due to the sparsity of mMTC, compressed sensing based algorithms can be used to identify the active users and recover the sparse channel state information (CSI) vector. In this paper, we propose a Bayesian message passing algorithm based on expectation propagation (EP) for joint active user detection (AUD) and channel estimation (CE) in NOMA. The proposed method approximates the complicated target distribution with a Gaussian distribution to achieve linear complexity. Simulations demonstrate that the EP-based algorithm achieves better performance in joint AUD and CE than the exiting algorithms, especially in the low SNR regime.
{"title":"An Efficient Message Passing Algorithm for Active User Detection and Channel Estimation in NOMA","authors":"Weijia Dai, Haichao Wei, Jiaxi Zhou, Wuyang Zhou","doi":"10.1109/VTCFall.2019.8891539","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891539","url":null,"abstract":"In 5G wireless communication network, massive machine type communication (mMTC) is an emerging research topic. For mMTC, non-orthogonal multiple access (NOMA) has been proposed to support its large-scale connectivity. Due to the sparsity of mMTC, compressed sensing based algorithms can be used to identify the active users and recover the sparse channel state information (CSI) vector. In this paper, we propose a Bayesian message passing algorithm based on expectation propagation (EP) for joint active user detection (AUD) and channel estimation (CE) in NOMA. The proposed method approximates the complicated target distribution with a Gaussian distribution to achieve linear complexity. Simulations demonstrate that the EP-based algorithm achieves better performance in joint AUD and CE than the exiting algorithms, especially in the low SNR regime.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":"31 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87371407","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 : 2019-09-01DOI: 10.1109/VTCFall.2019.8890974
Yiqun Liu, Y. G. Liao, Ming-Chia Lai
The performance of the lithium-ion battery is highly dependent on the operating temperature. In order to keep the operating temperature within the optimal range, a thermal management system (TMS) is used to cool down or warm up the battery. Understanding the heat generation characteristics and temperature distribution of the lithium-ion batteries is essential to design an effective TMS. In this paper, the surface temperature distribution over a 20Ah lithium-ion polymer battery cell is measured under different charging and discharging conditions. A cell thermal model is then built using the ANSYS Fluent. The simulation results are correlated and validated well with the experimental data. The validated cell thermal model provides a design guideline to thermal management system in the level of battery module and pack.
{"title":"Temperature Distribution on Lithium-Ion Polymer Battery Cell: Experiment and Modeling","authors":"Yiqun Liu, Y. G. Liao, Ming-Chia Lai","doi":"10.1109/VTCFall.2019.8890974","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8890974","url":null,"abstract":"The performance of the lithium-ion battery is highly dependent on the operating temperature. In order to keep the operating temperature within the optimal range, a thermal management system (TMS) is used to cool down or warm up the battery. Understanding the heat generation characteristics and temperature distribution of the lithium-ion batteries is essential to design an effective TMS. In this paper, the surface temperature distribution over a 20Ah lithium-ion polymer battery cell is measured under different charging and discharging conditions. A cell thermal model is then built using the ANSYS Fluent. The simulation results are correlated and validated well with the experimental data. The validated cell thermal model provides a design guideline to thermal management system in the level of battery module and pack.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90567590","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 : 2019-09-01DOI: 10.1109/VTCFall.2019.8891150
Dileep Bapatla, S. Prakriya
In this paper, we analyze the performance of a two-hop cooperative communication network in which both source and the relay are energy buffer-aided energy harvesting nodes. We consider fixed-rate signalling at both the nodes. The source and relay are assumed to harvest energy from ambient sources and store it in energy buffers. In this paper we use discrete-time continuous-state space Markov chain to model the energy stored in the buffers. We consider two different energy management policies at the source - best-effort policy (BEP) and on-off policy (OOP) while only OOP is considered at the relay. Using these policies, two different cooperative transmission schemes are pre- sented. We also compare performance with direct transmission schemes. Simulation results are presented to validate the derived analytical expressions and bring out useful insights.
{"title":"Performance of a Cooperative Network with Energy Harvesting Source and Relay","authors":"Dileep Bapatla, S. Prakriya","doi":"10.1109/VTCFall.2019.8891150","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891150","url":null,"abstract":"In this paper, we analyze the performance of a two-hop cooperative communication network in which both source and the relay are energy buffer-aided energy harvesting nodes. We consider fixed-rate signalling at both the nodes. The source and relay are assumed to harvest energy from ambient sources and store it in energy buffers. In this paper we use discrete-time continuous-state space Markov chain to model the energy stored in the buffers. We consider two different energy management policies at the source - best-effort policy (BEP) and on-off policy (OOP) while only OOP is considered at the relay. Using these policies, two different cooperative transmission schemes are pre- sented. We also compare performance with direct transmission schemes. Simulation results are presented to validate the derived analytical expressions and bring out useful insights.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":"36 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90623390","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 : 2019-09-01DOI: 10.1109/VTCFall.2019.8891430
Yanlin Yue, Wen Sun, Jiajia Liu, Yuanhe Jiang
Crowdsourcing, as an essential part in Internet of Vehicles (IoV), can provide vehicles with various functions such as road condition monitoring and path planning. The prevalence and heterogeneity of crowdsourcing devices, although enabling various emerging applications in IoV, makes it challenging to yield intelligent and flexible incentive and management framework, while ensuring optimal choice for all entities. Note that artificial intelligence (AI) algorithms could automatically select the significant features in the underlying data and globally find optimal solutions even for non-convex object functions. In this paper, we propose an AI-driven incentive scheme using a deep learning based reverse auction scheme, in order to achieve revenue-optimal, dominant-strategy incentive compatible objectives. The effectiveness of the proposed framework has been verified through extensive simulations.
{"title":"Ai-Enhanced Incentive Design for Crowdsourcing in Internet of Vehicles","authors":"Yanlin Yue, Wen Sun, Jiajia Liu, Yuanhe Jiang","doi":"10.1109/VTCFall.2019.8891430","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891430","url":null,"abstract":"Crowdsourcing, as an essential part in Internet of Vehicles (IoV), can provide vehicles with various functions such as road condition monitoring and path planning. The prevalence and heterogeneity of crowdsourcing devices, although enabling various emerging applications in IoV, makes it challenging to yield intelligent and flexible incentive and management framework, while ensuring optimal choice for all entities. Note that artificial intelligence (AI) algorithms could automatically select the significant features in the underlying data and globally find optimal solutions even for non-convex object functions. In this paper, we propose an AI-driven incentive scheme using a deep learning based reverse auction scheme, in order to achieve revenue-optimal, dominant-strategy incentive compatible objectives. The effectiveness of the proposed framework has been verified through extensive simulations.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":"12 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86056768","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 : 2019-09-01DOI: 10.1109/VTCFall.2019.8891132
Teruaki Shikuma, Y. Yuda, K. Higuchi
We propose a novel non-orthogonal multiple access (NOMA)-based optimal multiplexing method for multiple downlink service channels to maximize the integrated system throughput. In the fifth generation (5G) mobile communication system, the support of various wireless communication services such as massive machine-type communications (mMTC), ultra-reliable low latency communications (URLLC), and enhanced mobile broadband (eMBB) is expected. These services will serve different numbers of terminals and have different requirements regarding the spectrum efficiency and fairness among terminals. Furthermore, different operators may have different policies regarding the overall spectrum efficiency and fairness among services. Therefore, efficient radio resource allocation is essential during the multiplexing of multiple downlink service channels considering these requirements. The proposed method achieves better system performance than the conventional orthogonal multiple access (OMA)-based multiplexing method thanks to the wider transmission bandwidth per terminal and inter-terminal interference cancellation using the successive interference canceller (SIC). Computer simulation results reveal that the effectiveness of the proposed method is especially significant when the system prioritizes the fairness among terminals (including fairness among services).
{"title":"NOMA-Based Optimal Multiplexing Method for Downlink Service Channels to Maximize Integrated System Throughput","authors":"Teruaki Shikuma, Y. Yuda, K. Higuchi","doi":"10.1109/VTCFall.2019.8891132","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891132","url":null,"abstract":"We propose a novel non-orthogonal multiple access (NOMA)-based optimal multiplexing method for multiple downlink service channels to maximize the integrated system throughput. In the fifth generation (5G) mobile communication system, the support of various wireless communication services such as massive machine-type communications (mMTC), ultra-reliable low latency communications (URLLC), and enhanced mobile broadband (eMBB) is expected. These services will serve different numbers of terminals and have different requirements regarding the spectrum efficiency and fairness among terminals. Furthermore, different operators may have different policies regarding the overall spectrum efficiency and fairness among services. Therefore, efficient radio resource allocation is essential during the multiplexing of multiple downlink service channels considering these requirements. The proposed method achieves better system performance than the conventional orthogonal multiple access (OMA)-based multiplexing method thanks to the wider transmission bandwidth per terminal and inter-terminal interference cancellation using the successive interference canceller (SIC). Computer simulation results reveal that the effectiveness of the proposed method is especially significant when the system prioritizes the fairness among terminals (including fairness among services).","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":"21 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91167234","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 : 2019-09-01DOI: 10.1109/VTCFall.2019.8891560
Cheonsol Lee, Soochang Park, Taehun Yang, Sang-Hoon Lee
Parking at an affordable place is the precedent task for all activities of the everyday life in urban environments such as shopping, working, exercising, etc. So, it is the most common and essential requirement of all users in a car park to fast search a preferred parking spot closely associated their current intent. Although modern parking lots have installed the sensing and display systems to inform drivers on the availability of parking areas, such systems are unable to tell drivers exact parking spots and make any recommendation to improve the traffic conditions and driver experiences. In this paper, a novel analytic- based smart parking system clustering Internet of Things, smart mobile devices and edge computing is proposed. This novel parking system aims at providing customized parking experience to users through highly accurate positioning and user status detection which are achieved by joint mobile sensing-machine learning based analytics as the edge intelligence. Based on the proof-of-concept implementation, the proposed scheme can achieve 99.1% positioning accuracy of a parking spot; in terms of user status sensing, especially getting in a car and out of a car, detection accuracy shows 96%; finally, it shows much shorter service consumption time of 15.6 times than the legacy approach.
{"title":"Smart Parking with Fine-Grained Localization and User Status Sensing Based on Edge Computing","authors":"Cheonsol Lee, Soochang Park, Taehun Yang, Sang-Hoon Lee","doi":"10.1109/VTCFall.2019.8891560","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891560","url":null,"abstract":"Parking at an affordable place is the precedent task for all activities of the everyday life in urban environments such as shopping, working, exercising, etc. So, it is the most common and essential requirement of all users in a car park to fast search a preferred parking spot closely associated their current intent. Although modern parking lots have installed the sensing and display systems to inform drivers on the availability of parking areas, such systems are unable to tell drivers exact parking spots and make any recommendation to improve the traffic conditions and driver experiences. In this paper, a novel analytic- based smart parking system clustering Internet of Things, smart mobile devices and edge computing is proposed. This novel parking system aims at providing customized parking experience to users through highly accurate positioning and user status detection which are achieved by joint mobile sensing-machine learning based analytics as the edge intelligence. Based on the proof-of-concept implementation, the proposed scheme can achieve 99.1% positioning accuracy of a parking spot; in terms of user status sensing, especially getting in a car and out of a car, detection accuracy shows 96%; finally, it shows much shorter service consumption time of 15.6 times than the legacy approach.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":"2 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72940325","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}
This paper investigates the AP deployment optimization for wireless local area networks (WLANs) within non- uniform service areas. Specifically, this paper proposes a genetic algorithm (GA) based AP deployment scheme to jointly optimize the location and the transmit power of each AP under the constraint of full coverage. Different from most existing works, the proposed scheme takes the non-uniform service area where there are various obstacles within it into fully consideration. Sufficient simulations have been done to evaluate the performance of the GA- based deployment scheme by comparing with the uniform deployment scheme. Simulation results indicate that the total transmit power can be significantly reduced by leveraging the proposed scheme while guaranteeing the full coverage of the desired service area. At the same time, the overlap rate of the WLAN can also be significantly reduced by the proposed scheme.
{"title":"AP Deployment Optimization in Non-Uniform Service Areas: A Genetic Algorithm Approach","authors":"Zicong Zhi, Jianghong Wu, Xin Meng, Mengqian Yao, Qian Hu, Zhenzhou Tang","doi":"10.1109/VTCFall.2019.8891308","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891308","url":null,"abstract":"This paper investigates the AP deployment optimization for wireless local area networks (WLANs) within non- uniform service areas. Specifically, this paper proposes a genetic algorithm (GA) based AP deployment scheme to jointly optimize the location and the transmit power of each AP under the constraint of full coverage. Different from most existing works, the proposed scheme takes the non-uniform service area where there are various obstacles within it into fully consideration. Sufficient simulations have been done to evaluate the performance of the GA- based deployment scheme by comparing with the uniform deployment scheme. Simulation results indicate that the total transmit power can be significantly reduced by leveraging the proposed scheme while guaranteeing the full coverage of the desired service area. At the same time, the overlap rate of the WLAN can also be significantly reduced by the proposed scheme.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":"58 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73096702","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 : 2019-09-01DOI: 10.1109/VTCFall.2019.8891120
Peter Wolf, Alvin Chin, B. Bäker
The majority of data-driven fault detection approaches for automotive diagnostics are trained in a supervised manner. However, gathering diagnostics datasets to train supervised fault detection models is still challenging. Precise correlation of input data and defect events is non-trivial and requires extensive support of domain experts. Additionally, a strong imbalance caused by rare faulty events results in models which are biased towards non-faulty events. Hence, in this work, we propose a fully unsupervised data- driven diagnostics approach to detect faults in high frequency in-vehicle data. We transfer the concept of deep embedded clustering for static data to multivariate in- vehicle time series. We extend the approach by modifying the neural network architecture and comparing three similarity measures in the clustering layer, i.e., soft dynamic time warping, complexity invariant distance, and Euclidean distance. We further introduce an adapted target distribution to tackle imbalanced datasets. Our approach is evaluated on multivariate high frequency electronic control unit data of a test vehicle to detect pre-ignitions in high pressure turbocharged petrol engines. Current state-of-the-art time series clustering approaches are used as baselines for performance comparison. The results show that our approach is able to identify pre-ignitions without labels and outperforms the baselines by 10 percent in terms of accuracy.
{"title":"Unsupervised Data-Driven Automotive Diagnostics with Improved Deep Temporal Clustering","authors":"Peter Wolf, Alvin Chin, B. Bäker","doi":"10.1109/VTCFall.2019.8891120","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891120","url":null,"abstract":"The majority of data-driven fault detection approaches for automotive diagnostics are trained in a supervised manner. However, gathering diagnostics datasets to train supervised fault detection models is still challenging. Precise correlation of input data and defect events is non-trivial and requires extensive support of domain experts. Additionally, a strong imbalance caused by rare faulty events results in models which are biased towards non-faulty events. Hence, in this work, we propose a fully unsupervised data- driven diagnostics approach to detect faults in high frequency in-vehicle data. We transfer the concept of deep embedded clustering for static data to multivariate in- vehicle time series. We extend the approach by modifying the neural network architecture and comparing three similarity measures in the clustering layer, i.e., soft dynamic time warping, complexity invariant distance, and Euclidean distance. We further introduce an adapted target distribution to tackle imbalanced datasets. Our approach is evaluated on multivariate high frequency electronic control unit data of a test vehicle to detect pre-ignitions in high pressure turbocharged petrol engines. Current state-of-the-art time series clustering approaches are used as baselines for performance comparison. The results show that our approach is able to identify pre-ignitions without labels and outperforms the baselines by 10 percent in terms of accuracy.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":"1 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73121587","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 : 2019-09-01DOI: 10.1109/VTCFall.2019.8891118
Brice Leblanc, H. Fouchal, Cyril de Runz
C-ITS (Cooperative Intelligent Transport Systems) are growing very quickly in many parts over the world. Their benefits are of importance for fuel consumption, traffic management and road safety. Their deployments are in advanced steps in many countries. Their impacts on human life are not clearly known. For this reason, we propose to analyze a large set of data collected during real tests on open roads with many connected vehicles. This analysis allows us to focus on relevant information like driver profiles, abnormal driving behaviours, etc. In this paper, we present a methodology to analyze data provided by a real experimentation of C-ITS mobile stations. We mainly analyze the headings of each driver when approaching some Points of Interest (POI). We use unsupervised machine learning approaches to detect driver profiles. The interesting features about driver profiles obtained need to be enhanced and confirmed for larger data-sets.
{"title":"Driver Profile Detection Using Points of Interest Neighbourhood","authors":"Brice Leblanc, H. Fouchal, Cyril de Runz","doi":"10.1109/VTCFall.2019.8891118","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891118","url":null,"abstract":"C-ITS (Cooperative Intelligent Transport Systems) are growing very quickly in many parts over the world. Their benefits are of importance for fuel consumption, traffic management and road safety. Their deployments are in advanced steps in many countries. Their impacts on human life are not clearly known. For this reason, we propose to analyze a large set of data collected during real tests on open roads with many connected vehicles. This analysis allows us to focus on relevant information like driver profiles, abnormal driving behaviours, etc. In this paper, we present a methodology to analyze data provided by a real experimentation of C-ITS mobile stations. We mainly analyze the headings of each driver when approaching some Points of Interest (POI). We use unsupervised machine learning approaches to detect driver profiles. The interesting features about driver profiles obtained need to be enhanced and confirmed for larger data-sets.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":"35 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73384781","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 : 2019-09-01DOI: 10.1109/VTCFall.2019.8891575
Sharan Naribole, Wookbong Lee, A. Ranganath
To achieve increased radio utilization in dense WLAN deployments, IEEE 802.11ax amendment introduces Orthogonal Frequency Division Multiple Access (OFDMA) to the Wi-Fi standard. As the Access Point (AP) initiates the uplink OFDMA operation via a trigger, this operation provides an additional means for the STAs to gain medium access. Consequently, to retain fairness in the network and reduce contention, a novel MU EDCA channel access mode has been introduced in 802.11ax standard to temporarily deprioritize medium access for STAs participating in uplink OFDMA operation. In this paper, we implement the key components of 802.11ax OFDMA operation in custom ns-3 network simulator and evaluate the impact of MU EDCA channel access mode on 802.11ax dense WLAN performance under various network and traffic conditions. Our results show that (a) temporarily switching to MU EDCA channel access mode significantly improves network performance in terms of throughput and real-time application latency (b) the duration of temporary switch to MU EDCA channel access mode has negligible impact on network performance and (c) 200 ms of MU EDCA channel access mode can potentially support a dense network of almost 2000 STAs.
{"title":"Impact of MU EDCA Channel Access on IEEE 802.11ax WLANs","authors":"Sharan Naribole, Wookbong Lee, A. Ranganath","doi":"10.1109/VTCFall.2019.8891575","DOIUrl":"https://doi.org/10.1109/VTCFall.2019.8891575","url":null,"abstract":"To achieve increased radio utilization in dense WLAN deployments, IEEE 802.11ax amendment introduces Orthogonal Frequency Division Multiple Access (OFDMA) to the Wi-Fi standard. As the Access Point (AP) initiates the uplink OFDMA operation via a trigger, this operation provides an additional means for the STAs to gain medium access. Consequently, to retain fairness in the network and reduce contention, a novel MU EDCA channel access mode has been introduced in 802.11ax standard to temporarily deprioritize medium access for STAs participating in uplink OFDMA operation. In this paper, we implement the key components of 802.11ax OFDMA operation in custom ns-3 network simulator and evaluate the impact of MU EDCA channel access mode on 802.11ax dense WLAN performance under various network and traffic conditions. Our results show that (a) temporarily switching to MU EDCA channel access mode significantly improves network performance in terms of throughput and real-time application latency (b) the duration of temporary switch to MU EDCA channel access mode has negligible impact on network performance and (c) 200 ms of MU EDCA channel access mode can potentially support a dense network of almost 2000 STAs.","PeriodicalId":6713,"journal":{"name":"2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73785523","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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