Pub Date : 2020-03-01DOI: 10.1109/6GSUMMIT49458.2020.9083926
Po-Chen Chen, Yen-Chen Chen, Wei-Hsiang Huang, Chih-Wei Huang, O. Tirkkonen
The development of the fifth-generation (5G) system on capability and flexibility enables emerging applications with stringent requirements, such as ultra-high-resolution video streaming and online interactive virtual reality (VR) gaming. Hence, the resource management problem becomes more complicated than in the past, and machine learning can be a powerful tool to provide solutions. In this article, the Deep Deterministic Policy Gradient (DDPG) is used to schedule resources in an edge network environment. We integrate a 3D radio resource structure with componentized Markov decision process (MDP) actions to work on user interactivity-based groups. From the simulation results, we can see that more users are satisfied with DDPG-based radio resource management, especially in bandwidth and latency demanding situations.
{"title":"DDPG-Based Radio Resource Management for User Interactive Mobile Edge Networks","authors":"Po-Chen Chen, Yen-Chen Chen, Wei-Hsiang Huang, Chih-Wei Huang, O. Tirkkonen","doi":"10.1109/6GSUMMIT49458.2020.9083926","DOIUrl":"https://doi.org/10.1109/6GSUMMIT49458.2020.9083926","url":null,"abstract":"The development of the fifth-generation (5G) system on capability and flexibility enables emerging applications with stringent requirements, such as ultra-high-resolution video streaming and online interactive virtual reality (VR) gaming. Hence, the resource management problem becomes more complicated than in the past, and machine learning can be a powerful tool to provide solutions. In this article, the Deep Deterministic Policy Gradient (DDPG) is used to schedule resources in an edge network environment. We integrate a 3D radio resource structure with componentized Markov decision process (MDP) actions to work on user interactivity-based groups. From the simulation results, we can see that more users are satisfied with DDPG-based radio resource management, especially in bandwidth and latency demanding situations.","PeriodicalId":385212,"journal":{"name":"2020 2nd 6G Wireless Summit (6G SUMMIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130527065","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 : 2020-03-01DOI: 10.1109/6GSUMMIT49458.2020.9083745
Joel Reijonen, M. Opsenica, T. Kauppinen, M. Komu, Jimmy Kjällman, Tomas Mecklin, Eero Hiltunen, J. Arkko, Timo Simanainen, M. Elmusrati
We benchmark Q-learning methods, with various action selection strategies, in intelligent orchestration of the network edge. Q-learning is a reinforcement learning technique that aims to find optimal action policies by taking advantage of the experiences in the past without utilizing a model that describes the dynamics of the environment. With experiences, we refer to the observed causality between the action and the corresponding impact to the environment. In this paper, the environment for Q-learning is composed of virtualized networking resources along with their dynamics that are monitored with Spindump, an in-network latency measurement tool with support for QUIC and TCP. We optimize the orchestration of these networking resources by introducing Q-learning as part of the machine learning driven, intelligent orchestration that is applicable in the edge. Based on the benchmarking results, we identify which action selection strategies support network orchestration that provides low latency and packet loss by considering network resource allocation in the edge.
{"title":"Benchmarking Q-Learning Methods for Intelligent Network Orchestration in the Edge","authors":"Joel Reijonen, M. Opsenica, T. Kauppinen, M. Komu, Jimmy Kjällman, Tomas Mecklin, Eero Hiltunen, J. Arkko, Timo Simanainen, M. Elmusrati","doi":"10.1109/6GSUMMIT49458.2020.9083745","DOIUrl":"https://doi.org/10.1109/6GSUMMIT49458.2020.9083745","url":null,"abstract":"We benchmark Q-learning methods, with various action selection strategies, in intelligent orchestration of the network edge. Q-learning is a reinforcement learning technique that aims to find optimal action policies by taking advantage of the experiences in the past without utilizing a model that describes the dynamics of the environment. With experiences, we refer to the observed causality between the action and the corresponding impact to the environment. In this paper, the environment for Q-learning is composed of virtualized networking resources along with their dynamics that are monitored with Spindump, an in-network latency measurement tool with support for QUIC and TCP. We optimize the orchestration of these networking resources by introducing Q-learning as part of the machine learning driven, intelligent orchestration that is applicable in the edge. Based on the benchmarking results, we identify which action selection strategies support network orchestration that provides low latency and packet loss by considering network resource allocation in the edge.","PeriodicalId":385212,"journal":{"name":"2020 2nd 6G Wireless Summit (6G SUMMIT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133059903","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 : 2020-03-01DOI: 10.1109/6GSUMMIT49458.2020.9083831
V. Räisänen
We analyze drivers for evolution of 5G networks towards 6G and propose a high-level capability provisioning framework which covers both evolutionary aspects as well as service and enabler provisioning related aspects of 6G. The framework represents definition of a scope for provisioning capabilities and encompasses both Digital Service Provider (DSP) and dedicated networks such as private networks and neutral hosts. A key ingredient is introduction of capabilities as a generalization of enablers provided by current networks and traded on a marketplace. We outline implications of this approach for DSP networks.
{"title":"A framework for capability provisioning in B5G","authors":"V. Räisänen","doi":"10.1109/6GSUMMIT49458.2020.9083831","DOIUrl":"https://doi.org/10.1109/6GSUMMIT49458.2020.9083831","url":null,"abstract":"We analyze drivers for evolution of 5G networks towards 6G and propose a high-level capability provisioning framework which covers both evolutionary aspects as well as service and enabler provisioning related aspects of 6G. The framework represents definition of a scope for provisioning capabilities and encompasses both Digital Service Provider (DSP) and dedicated networks such as private networks and neutral hosts. A key ingredient is introduction of capabilities as a generalization of enablers provided by current networks and traded on a marketplace. We outline implications of this approach for DSP networks.","PeriodicalId":385212,"journal":{"name":"2020 2nd 6G Wireless Summit (6G SUMMIT)","volume":"425 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122865120","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 : 2020-03-01DOI: 10.1109/6GSUMMIT49458.2020.9083914
Nalin Jayaweera, Dileepa Marasinghe, Nandana Rajatheva, M. Latva-aho
Ultra-reliable and low-latency communications (URLLC) play a vital role in factory automation. To share the situational awareness data collected from the infrastructure as raw or processed data, the system should guarantee the URLLC capability since this is a safety-critical application. In this work, the resource allocation problem for an infrastructure-based communication architecture (Elevated LiDAR system/ELiD) has been considered which can support the autonomous driving in a factory floor. The decoder error probability and the number of channel uses parameterize the reliability and the latency in the considered optimization problems. A maximum decoder error probability minimization problem and a total energy minimization problem have been considered in this work to analytically evaluate the performance of the ELiD system under different vehicle densities.
{"title":"Factory Automation: Resource Allocation of an Elevated LiDAR System with URLLC Requirements","authors":"Nalin Jayaweera, Dileepa Marasinghe, Nandana Rajatheva, M. Latva-aho","doi":"10.1109/6GSUMMIT49458.2020.9083914","DOIUrl":"https://doi.org/10.1109/6GSUMMIT49458.2020.9083914","url":null,"abstract":"Ultra-reliable and low-latency communications (URLLC) play a vital role in factory automation. To share the situational awareness data collected from the infrastructure as raw or processed data, the system should guarantee the URLLC capability since this is a safety-critical application. In this work, the resource allocation problem for an infrastructure-based communication architecture (Elevated LiDAR system/ELiD) has been considered which can support the autonomous driving in a factory floor. The decoder error probability and the number of channel uses parameterize the reliability and the latency in the considered optimization problems. A maximum decoder error probability minimization problem and a total energy minimization problem have been considered in this work to analytically evaluate the performance of the ELiD system under different vehicle densities.","PeriodicalId":385212,"journal":{"name":"2020 2nd 6G Wireless Summit (6G SUMMIT)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130789470","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 : 2020-03-01DOI: 10.1109/6GSUMMIT49458.2020.9083877
Gilberto Berardinelli, P. Mogensen, Ramoni O. Adeogun
Short range low power 6th Generation (6G) wireless subnetworks can support life critical services like engine and break control in intra-vehicle scenarios, or intra-body heart-rate control. Such services may target communication cycles below 0.1 ms and a wired-like reliability, translating to a multi-GHz spectrum demand in case of dense deployments (e.g., up to 40000 subnetworks per km2). We foresee the possibility for 6G subnetworks to operate as an underlay system in the below 30 GHz spectrum given its advantageous propagation condition and its limited effective utilization. 6G subnetworks should be equipped with artificial intelligence (AI) capabilities for healthy mutual coexistence, as well as for coexistence with other systems active in the same bands.
{"title":"6G subnetworks for Life-Critical Communication","authors":"Gilberto Berardinelli, P. Mogensen, Ramoni O. Adeogun","doi":"10.1109/6GSUMMIT49458.2020.9083877","DOIUrl":"https://doi.org/10.1109/6GSUMMIT49458.2020.9083877","url":null,"abstract":"Short range low power 6th Generation (6G) wireless subnetworks can support life critical services like engine and break control in intra-vehicle scenarios, or intra-body heart-rate control. Such services may target communication cycles below 0.1 ms and a wired-like reliability, translating to a multi-GHz spectrum demand in case of dense deployments (e.g., up to 40000 subnetworks per km2). We foresee the possibility for 6G subnetworks to operate as an underlay system in the below 30 GHz spectrum given its advantageous propagation condition and its limited effective utilization. 6G subnetworks should be equipped with artificial intelligence (AI) capabilities for healthy mutual coexistence, as well as for coexistence with other systems active in the same bands.","PeriodicalId":385212,"journal":{"name":"2020 2nd 6G Wireless Summit (6G SUMMIT)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129281474","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 : 2020-03-01DOI: 10.1109/6GSUMMIT49458.2020.9083825
M. Kokkonen, S. Myllymäki, H. Jantunen
In telecommunications (5G/6G) lenses can be used to manipulate the electric field emitted by an antenna. In this paper different permittivity lenses were studied with 3×3 dipole array acting as antenna source. Iterative study to the lens eccentricity showed different lenses for different permittivity where a low permittivity lens with heavily eccentric shape increased antenna gain by 14.6 dB and high permittivity lens gain by 9.9 dB and the total gain was 32 dB for low permittivity lens and 27 dB for higher permittivity lenses. With high permittivity lenses the whole lens surface was not illuminated by the feeding antenna.
{"title":"3×3 Dipole lens antenna at 300 GHz with different permittivity lenses","authors":"M. Kokkonen, S. Myllymäki, H. Jantunen","doi":"10.1109/6GSUMMIT49458.2020.9083825","DOIUrl":"https://doi.org/10.1109/6GSUMMIT49458.2020.9083825","url":null,"abstract":"In telecommunications (5G/6G) lenses can be used to manipulate the electric field emitted by an antenna. In this paper different permittivity lenses were studied with 3×3 dipole array acting as antenna source. Iterative study to the lens eccentricity showed different lenses for different permittivity where a low permittivity lens with heavily eccentric shape increased antenna gain by 14.6 dB and high permittivity lens gain by 9.9 dB and the total gain was 32 dB for low permittivity lens and 27 dB for higher permittivity lenses. With high permittivity lenses the whole lens surface was not illuminated by the feeding antenna.","PeriodicalId":385212,"journal":{"name":"2020 2nd 6G Wireless Summit (6G SUMMIT)","volume":"29 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125865421","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 : 2020-02-26DOI: 10.1109/6GSUMMIT49458.2020.9083766
I. Moerman, D. Zeghlache, A. Shahid, João F. Santos, L. Dasilva, K. David, J. Farserotu, Ad de Ridder, Wei Liu, J. Hoebeke
The wireless industry is driven by key stakeholders that follow a holistic approach of “one-system-fits-all” that leads to moving network functionality of meeting stringent End-to-End (E2E) communication requirements towards the core and cloud infrastructures. This trend is limiting smaller and new players for bringing in new and novel solutions. For meeting these E2E requirements, tenants and end-users need to be active players for bringing their needs and innovations. Driving E2E communication not only in terms of quality of service (QoS) but also overall carbon footprint and spectrum efficiency from one specific community may lead to undesirable simplifications and a higher level of abstraction of other network segments may lead to sub-optimal operations. Based on this, the paper presents a paradigm shift that will enlarge the role of wireless innovation at academia, Small and Medium-sized Enterprises (SME)'s, industries and start-ups while taking into account decentralized mandate-driven intelligence in E2E communications.
{"title":"Mandate-driven Networking Eco-system: A Paradigm Shift in End-to-End Communications","authors":"I. Moerman, D. Zeghlache, A. Shahid, João F. Santos, L. Dasilva, K. David, J. Farserotu, Ad de Ridder, Wei Liu, J. Hoebeke","doi":"10.1109/6GSUMMIT49458.2020.9083766","DOIUrl":"https://doi.org/10.1109/6GSUMMIT49458.2020.9083766","url":null,"abstract":"The wireless industry is driven by key stakeholders that follow a holistic approach of “one-system-fits-all” that leads to moving network functionality of meeting stringent End-to-End (E2E) communication requirements towards the core and cloud infrastructures. This trend is limiting smaller and new players for bringing in new and novel solutions. For meeting these E2E requirements, tenants and end-users need to be active players for bringing their needs and innovations. Driving E2E communication not only in terms of quality of service (QoS) but also overall carbon footprint and spectrum efficiency from one specific community may lead to undesirable simplifications and a higher level of abstraction of other network segments may lead to sub-optimal operations. Based on this, the paper presents a paradigm shift that will enlarge the role of wireless innovation at academia, Small and Medium-sized Enterprises (SME)'s, industries and start-ups while taking into account decentralized mandate-driven intelligence in E2E communications.","PeriodicalId":385212,"journal":{"name":"2020 2nd 6G Wireless Summit (6G SUMMIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121319786","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 : 2020-02-19DOI: 10.1109/6GSUMMIT49458.2020.9083800
A. Hoeller, J. Sant’Ana, Juho Markkula, Konstantin Mikhaylov, R. Souza, H. Alves
In this paper, we deliver a discussion regarding the role of Low-Power Wide-Area Networks (LPWAN) in the cellular Internet-of-Things (IoT) infrastructure to support massive Machine-Type Communications (mMTC) in next-generation wireless systems beyond 5G. We commence by presenting a performance analysis of current LPWAN systems, specifically LoRaWAN, in terms of coverage and throughput. The results obtained using analytic methods and network simulations are combined in the paper for getting a more comprehensive vision. Next, we identify possible performance bottlenecks, speculate on the characteristics of coming IoT applications, and seek to identify potential enhancements to the current technologies that may overcome the identified shortcomings.
{"title":"Beyond 5G Low-Power Wide-Area Networks: A LoRaWAN Suitability Study","authors":"A. Hoeller, J. Sant’Ana, Juho Markkula, Konstantin Mikhaylov, R. Souza, H. Alves","doi":"10.1109/6GSUMMIT49458.2020.9083800","DOIUrl":"https://doi.org/10.1109/6GSUMMIT49458.2020.9083800","url":null,"abstract":"In this paper, we deliver a discussion regarding the role of Low-Power Wide-Area Networks (LPWAN) in the cellular Internet-of-Things (IoT) infrastructure to support massive Machine-Type Communications (mMTC) in next-generation wireless systems beyond 5G. We commence by presenting a performance analysis of current LPWAN systems, specifically LoRaWAN, in terms of coverage and throughput. The results obtained using analytic methods and network simulations are combined in the paper for getting a more comprehensive vision. Next, we identify possible performance bottlenecks, speculate on the characteristics of coming IoT applications, and seek to identify potential enhancements to the current technologies that may overcome the identified shortcomings.","PeriodicalId":385212,"journal":{"name":"2020 2nd 6G Wireless Summit (6G SUMMIT)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133543434","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 : 2020-02-18DOI: 10.1109/6GSUMMIT49458.2020.9083909
Dileepa Marasinghe, Nalin Jayaweera, Nandana Rajatheva, M. Latva-aho
Non-orthogonal multiple access (NOMA) and mmWave are two complementary technologies that can support the capacity demand that arises in 5G and beyond networks. The increasing number of users are served simultaneously while providing a solution for the scarcity of the bandwidth. In this paper we present a method for clustering the users in a mmWave-NOMA system with the objective of maximizing the sum-rate. An unsupervised machine learning technique, namely, hierarchical clustering is utilized which does the automatic identification of the optimal number of clusters. The simulations prove that the proposed method can maximize the sum-rate of the system while satisfying the minimum QoS for all users without the need of the number of clusters as a prerequisite when compared to other clustering methods such as k-means clustering.
{"title":"Hierarchical User Clustering for mmWave-NOMA Systems","authors":"Dileepa Marasinghe, Nalin Jayaweera, Nandana Rajatheva, M. Latva-aho","doi":"10.1109/6GSUMMIT49458.2020.9083909","DOIUrl":"https://doi.org/10.1109/6GSUMMIT49458.2020.9083909","url":null,"abstract":"Non-orthogonal multiple access (NOMA) and mmWave are two complementary technologies that can support the capacity demand that arises in 5G and beyond networks. The increasing number of users are served simultaneously while providing a solution for the scarcity of the bandwidth. In this paper we present a method for clustering the users in a mmWave-NOMA system with the objective of maximizing the sum-rate. An unsupervised machine learning technique, namely, hierarchical clustering is utilized which does the automatic identification of the optimal number of clusters. The simulations prove that the proposed method can maximize the sum-rate of the system while satisfying the minimum QoS for all users without the need of the number of clusters as a prerequisite when compared to other clustering methods such as k-means clustering.","PeriodicalId":385212,"journal":{"name":"2020 2nd 6G Wireless Summit (6G SUMMIT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121700548","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 : 2020-01-28DOI: 10.1109/6GSUMMIT49458.2020.9083812
Xi Zheng, Sheng Zhou, Z. Niu
Timely status updating is crucial for future applications that involve remote monitoring and control, such as autonomous driving and Industrial Internet of Things (IIoT). Age of Information (AoI) has been proposed to measure the freshness of status updates. However, it is incapable of capturing critical systematic context information that indicates the time-varying importance of status information, and the dynamic evolution of status. In this paper, we propose a context-based metric, namely the Urgency of Information (UoI), to evaluate the timeliness of status updates. Compared to AoI, the new metric incorporates both time-varying context information and dynamic status evolution, which enables the analysis on context-based adaptive status update schemes, as well as more effective remote monitoring and control. The minimization of average UoI for a status update terminal with an updating frequency constraint is investigated, and an update-index-based adaptive scheme is proposed. Simulation results show that the proposed scheme achieves a near-optimal performance with a low computational complexity.
及时更新状态对于涉及远程监控的未来应用至关重要，例如自动驾驶和工业物联网(IIoT)。信息时代(Age of Information, AoI)被用来衡量状态更新的新鲜度。然而，它无法捕获关键的系统上下文信息，这些信息表明状态信息的重要性随时间变化，以及状态的动态演变。在本文中，我们提出了一个基于上下文的度量，即信息紧迫性(UoI)，以评估状态更新的时效性。与AoI相比，新度量结合了时变上下文信息和动态状态演变，能够分析基于上下文的自适应状态更新方案，以及更有效的远程监测和控制。研究了具有更新频率约束的状态更新终端平均ui的最小化问题，提出了一种基于更新索引的自适应方案。仿真结果表明，该方案在较低的计算复杂度下获得了接近最优的性能。
{"title":"Beyond Age: Urgency of Information for Timeliness Guarantee in Status Update Systems","authors":"Xi Zheng, Sheng Zhou, Z. Niu","doi":"10.1109/6GSUMMIT49458.2020.9083812","DOIUrl":"https://doi.org/10.1109/6GSUMMIT49458.2020.9083812","url":null,"abstract":"Timely status updating is crucial for future applications that involve remote monitoring and control, such as autonomous driving and Industrial Internet of Things (IIoT). Age of Information (AoI) has been proposed to measure the freshness of status updates. However, it is incapable of capturing critical systematic context information that indicates the time-varying importance of status information, and the dynamic evolution of status. In this paper, we propose a context-based metric, namely the Urgency of Information (UoI), to evaluate the timeliness of status updates. Compared to AoI, the new metric incorporates both time-varying context information and dynamic status evolution, which enables the analysis on context-based adaptive status update schemes, as well as more effective remote monitoring and control. The minimization of average UoI for a status update terminal with an updating frequency constraint is investigated, and an update-index-based adaptive scheme is proposed. Simulation results show that the proposed scheme achieves a near-optimal performance with a low computational complexity.","PeriodicalId":385212,"journal":{"name":"2020 2nd 6G Wireless Summit (6G SUMMIT)","volume":" 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113950106","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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